A daily bite-size selection of top business content.

PM edition. Issue number 1343

Latest 10 stories. Click the button for more.

Quote: Kristalina Georgieva - International Monetary Fund (IMF) Managing Director

"We collectively, including the fund, did not appreciate the backlash against globalisation that came from the fact that, yes, the world economy is doing better as a whole, but many communities were hollowed out because their jobs disappeared and there was not enough attention to them. I'll tell you what I'm very keen not to see repeated is the same with artificial intelligence." - Kristalina Georgieva - International Monetary Fund (IMF) Managing Director

The central issue is not whether a new technology makes economies more productive, but whether the gains arrive faster and more visibly than the losses. When job destruction is concentrated in particular towns, sectors, and skill groups, aggregate growth can look healthy while the social fabric in affected places weakens, and that imbalance has become a defining political risk around artificial intelligence. Kristalina Georgieva, who has served as Managing Director of the International Monetary Fund since October 1, 2019 and began a second term on October 1, 2024, has made that warning from a position of institutional authority that was shaped by the IMF's experience of multiple global shocks.

The remark reflects a lesson that global institutions learned, often slowly, from the era of rapid trade integration. The world economy can be better off on paper even as specific communities lose stable work, local spending power, and a sense of economic purpose. That distinction matters because politics is rarely organised around the global average. It is organised around visible closures, wage stagnation, and the feeling that national and international leaders celebrated efficiency while leaving the costs of adjustment to be absorbed locally. Georgieva's concern is that artificial intelligence could repeat that pattern on a faster clock, with the benefits accruing to firms, capital owners, and highly adaptable workers while the disruption lands on those whose tasks are easiest to automate.

From globalisation's backlash to AI's distributional shock

The comparison with globalisation is not rhetorical flourish; it is an argument about political economy. In her interview, Georgieva said that the IMF and others had not sufficiently appreciated the backlash against globalisation because they focused on the fact that the world economy was doing better as a whole, while many communities were hollowed out when jobs disappeared. That description captures the core failure of technocratic optimism: it can measure aggregate welfare precisely while underweighting the geography of decline. A region that loses a factory, a port function, a back-office cluster, or a processing plant does not experience the economy as a statistical average. It experiences it as closure, migration, and social churn.

Artificial intelligence creates a similar tension because it is best understood as a general-purpose technology whose economic effect is broad, uneven, and delayed. The IMF has estimated that almost 40% of global employment is exposed to AI, rising to about 60% in advanced economies. Exposure does not mean every exposed job vanishes, but it does mean that a substantial share of routine cognitive work, administrative handling, analysis, and content production may be altered, compressed, or partially automated. The IMF also noted that in advanced economies roughly half of the exposed jobs may benefit from AI integration, while the other half may see lower labour demand, lower wages, or in some cases disappearance.

This is why the social question is not merely about total output. If AI raises productivity by making firms leaner and faster, the headline number can be positive even when bargaining power shifts away from labour. Goldman Sachs has argued that generative AI could raise global GDP by 7% and lift productivity growth by 1,5 percentage points over 10 years, while also exposing the equivalent of 300 million full-time jobs to automation. Those figures are not incompatible. They describe a world in which technology expands the economic pie while simultaneously changing who gets the slices and who is left waiting outside the bakery.

The IMF's warning is also a warning about timing

One reason AI is politically delicate is that its benefits may be diffused over time, while its costs are immediate and local. Productivity gains can take years to appear in national accounts because firms need to adapt workflows, train staff, redesign products, and learn how to trust new systems. By contrast, a call centre that reduces headcount, a law office that automates first-draft work, or a media business that cuts junior roles can do so quickly. The result is a familiar asymmetry: the burden of adjustment arrives before the compensation mechanisms are ready.

This timing problem helps explain why economists disagree so sharply on the size of the prize. Optimistic estimates stress economy-wide efficiency gains, new products, and the value of complementary tasks. More restrained work emphasises that only a fraction of tasks can be profitably automated once implementation costs, error rates, oversight, regulation, and customer preferences are included. Daron Acemoglu has argued that the medium-term productivity effect may be far smaller than the largest headline estimates, with a much more modest uplift in output once only economically viable uses are counted. The disagreement matters because policy should not be built on the most dramatic forecast, nor should it ignore the possibility that adoption will be slower and less comprehensive than enthusiasts predict.

Georgieva's intervention sits between those poles. She is not denying that AI can boost growth. Indeed, the IMF itself has argued that AI is on the brink of a technological revolution that could jumpstart productivity, boost global growth, and raise incomes around the world. The warning is that the distributional consequences could still be severe enough to deepen inequality and social tension if governments assume that aggregate gains will automatically trickle down. In other words, the productivity story and the social story are not rivals. They are two halves of the same policy problem.

Why global institutions are especially sensitive to this pattern

The IMF's interest in this issue is not accidental. A multilateral lender and surveillance institution sees macroeconomic stability through the lens of crises, capital flows, unemployment, and political backlash. If a major technology wave deepens inequality inside countries, it can also change fiscal politics, trade politics, and attitudes towards international cooperation. Francine Lacqua, the interviewer in the podcast series, is a Bloomberg anchor who regularly speaks with central bankers, finance ministers, and senior officials, which makes the conversation part of a broader public debate about how economic power is being reorganised.

Georgieva's own background reinforces the institutional seriousness of the warning. Since the IMF has already had to manage the economic consequences of the pandemic and other global disruptions, it has become increasingly alert to the fact that resilience cannot be treated as an abstract ideal. It must be built in advance through labour-market policy, social protection, training, competition rules, and investment in digital capacity. That is especially true because AI does not affect all countries equally. The IMF has said exposure is highest in advanced economies, while emerging markets and low-income countries face lower but still significant exposure. That means the immediate labour-market shock may be concentrated in wealthier countries, but the longer-term diffusion of AI capabilities could widen the gap between economies that can adopt, regulate, and complement the technology, and those that cannot.

What was missed during globalisation

The phrase about communities being hollowed out points to a specific historical failure. Policymakers often treated trade and integration as a sum-of-parts problem: if the nation as a whole is richer, then the policy is successful. But local economies do not adjust frictionlessly. Workers in declining industries are not instantly reallocated to new sectors. Skills are not perfectly transferable. Housing markets are sticky, family ties matter, and the social meaning of work is not captured by GDP. When those frictions are ignored, resentment accumulates and eventually seeks political expression.

That experience is directly relevant to AI because the technology may hollow out different kinds of places. Globalisation often hit manufacturing towns, logistics hubs, and regions dependent on tradable goods. AI may instead pressure administrative centres, shared-service locations, media organisations, some professional services, and entry-level white-collar pathways. The political consequence may therefore be different in detail but similar in structure: whole ladders of advancement can be shortened before replacements are fully visible. For younger workers, especially, the problem is not just displacement but the erosion of the first rung of a career ladder.

There is also a deeper ideological parallel. During the globalisation era, many advocates implicitly assumed that efficiency was self-justifying. If something was cheaper, faster, and better for consumers, the distributional pain was treated as secondary. AI could repeat that error if firms and governments measure success by adoption rates alone. But broad adoption is not the same as broad benefit. A technology can be commercially successful, strategically important, and still socially destabilising if the gains are narrowly held.

The strategic debate: productivity engine or inequality accelerator?

The strongest argument in favour of AI is that it can raise productivity in economies that have struggled with weak growth, labour shortages, and ageing populations. Goldman Sachs' estimate of a 7% lift in global GDP captures the scale of ambition that surrounds the technology, while the IMF has stressed that AI could improve incomes and support growth if it is deployed well. In sectors from healthcare to education to finance, AI systems can reduce routine workload, accelerate analysis, and improve service quality. The promise is not only cost cutting but the creation of new products and business models.

The strongest argument against complacency is that AI may amplify existing inequalities in capital, data, and skill. Firms with the best models, the most data, and the strongest distribution channels will capture disproportionate value. Workers with high complementary skills may see their productivity rise, while workers in modular, repeatable tasks face stagnation or displacement. Countries with advanced digital infrastructure may use AI to widen their advantage, while countries with weaker institutional capacity struggle to keep up. Even when the overall effect on employment is positive in the long run, the short run may still bring a wave of churn that outpaces retraining and policy response.

This is why the debate is not really about whether AI is good or bad. It is about whether societies will manage transition costs with enough seriousness. The IMF has argued that policymakers should proactively address inequality to prevent AI from further stoking social tensions. That implies practical choices: stronger safety nets, wage insurance, mobility support, lifelong learning, and public investment in digital skills. It also implies a regulatory stance that encourages adoption while checking abuses, such as excessive market concentration or labour substitution without offsetting investment in human capability.

Why the warning matters now

Georgieva's message matters because it shifts the debate from hype to governance. It is easy to celebrate a technology when its promised benefits are still theoretical. It is harder to govern it when its disruptions are already visible. The IMF chief's insistence that the world should not repeat the mistakes of globalisation is a reminder that economic success measured at the top can coexist with social fracture at the base. If AI is allowed to proceed as a private efficiency project with public consequences ignored until later, then the backlash will not be surprising; it will be predictable.

That is the practical consequence buried inside the warning. AI can make economies richer, but it can also make societies less stable if the transition is unmanaged. The policy challenge is to ensure that productivity gains are not treated as an excuse to forget the communities and career paths that bear the cost of change. If that lesson is missed again, the political response may be harsher than the technology itself.

Quote: Demis Hassabis - Google Deepmind CEO

"When we look back at this time, I think we will realise that we were standing in the foothills of the singularity. It will be a profound moment for humanity." - Demis Hassabis - Google Deepmind CEO - 2026 Google I/O technology developer conference

The underlying issue is no longer whether machine intelligence will transform human affairs, but whether our political, economic and ethical systems can adapt at the same speed as the underlying technology that is now compounding year on year. The friction lies in a widening gap: frontier AI systems are moving from tools that wait for instructions to entities that can act, plan, and coordinate with minimal human supervision, while institutions, laws and norms still assume a world of slower, more legible change. When a leading AI scientist asserts that this transition marks the early stage of a new historical regime, he is naming a tension that is already visible in boardrooms, laboratories and legislatures.

From static tools to agentic systems

For several decades, AI systems were framed as narrow tools: chess engines, recommendation algorithms, translation services and search ranking models. They were powerful, but fundamentally reactive. They did not initiate projects, hold long-term goals or orchestrate complex workflows without an engineer in the loop. The recent shift to so-called "agentic" systems is qualitatively different. These models can decompose a user objective into sub-tasks, call tools such as browsers or code interpreters, write and debug software, and loop over their own outputs until a performance criterion is met. In effect, they act like junior colleagues rather than software menus.

At Google I/O, this shift was made concrete through demonstrations of AI systems that design operating systems, draft and execute multi-step research plans, and coordinate across products from search to productivity suites. One showcase involved an autonomous system that could construct a functional operating system for under USD 1 000 in compute and overhead, a task that would historically require teams of engineers working for months. The key is not that such feats are possible in principle; it is that they are rapidly becoming cheap, repeatable and integrated into mainstream platforms.

This transition matters because it changes the leverage a small group of people or organisations can exert. A single developer equipped with powerful agents can now build, test and deploy complex services that once demanded a mid-sized company. In security terms, the same leverage can enhance defensive capabilities but also lower the barrier for sophisticated cyberattacks, automated social engineering, or automated discovery of software vulnerabilities. The trajectory is towards a world where much more can be done by far fewer humans.

Why "singularity" entered the AI mainstream

The term "singularity" was originally borrowed from physics and mathematics, where it describes points such as the centre of a black hole, at which descriptive equations break down and conventional intuitions fail. In the early 1990s, computer scientist Vernor Vinge repurposed the idea for AI, suggesting that once systems exceed human cognitive capabilities and can improve themselves, the resulting feedback loop would produce change so rapid that it would be difficult to model with existing social or economic theories.

For years, such visions were largely confined to science fiction, futurist circles and a subset of AI safety researchers. Large technology companies tended to avoid the language, preferring incremental narratives about productivity and assistance. The decision by a major AI lab leader to adopt the singularity framing publicly signals a deliberate shift: it acknowledges that the slope of capability is steepening and that the transition from experimental systems to world-shaping infrastructure is well under way. It also functions as a warning that the timelines to serious disruption are short enough that preparation cannot be deferred.

Hassabis has suggested that artificial general intelligence, often defined as systems with performance roughly comparable to an expert human across a wide range of tasks, could emerge by around 2030, with uncertainty measured in only a few years. If those estimates are even approximately correct, then organisations that plan on decade-long cycles, from regulators to universities to defence ministries, face a planning problem they have rarely confronted: they must hedge against both the possibility of very rapid transformation and the possibility that the curve flattens.

The factual context: a platform company bets on autonomy

The backdrop to this language is a strategic reorientation of one of the world's largest technology companies around AI. At Google I/O 2026, Google and DeepMind unveiled an array of products and research initiatives: new frontier models, multimodal assistants integrated into search and productivity tools, autonomous coding systems, AI-augmented video generation tools, and bespoke hardware for training and serving models at scale. Rather than being siloed experiments, these systems are presented as a coherent platform spanning consumer, enterprise and developer ecosystems.

In this environment, Hassabis's statement is not an isolated philosophical remark. It sits alongside concrete decisions: allocating large capital budgets to specialised AI accelerators, restructuring products around AI agents, and articulating timelines that compress the expected arrival of broadly capable systems into the span of a single strategic planning horizon. The narrative is that humanity is entering a phase where each iteration of capability builds directly on the previous one, leading to compounding returns rather than linear gains.

In effect, the company is arguing that today's chatbots and coding assistants represent only the earliest stage of a broader transition. These are the first footholds, not the peak. As agents are networked, endowed with memory, and embedded in physical systems such as robots, vehicles and infrastructure, their actions will increasingly manifest in the material economy rather than just digital text and images. This is where concerns about labour markets, safety and governance become more immediate.

Acceleration, compounding and feedback

The strategic tension revolves around feedback loops. If AI systems can help design better versions of themselves, build more efficient hardware, discover new materials and streamline research, then progress in AI becomes entangled with progress in the rest of science and engineering. Hassabis has argued that AI may prove several times more transformative than past industrial revolutions because it targets the bottleneck that constrained earlier eras: the pace at which new ideas can be generated, tested and implemented.

Historically, improvements in productivity depended on larger workforces, more capital or incremental process optimisation. A significant share of that optimisation was done by human experts. If AI can augment or partially automate the role of these experts, the rate of innovation itself could accelerate. In economic terms, this raises the prospect that growth models based on a roughly constant rate of technological improvement could be replaced by regimes in which the effective innovation rate increases as AI improves.

For example, consider a stylised research process where the time required to complete a project is . If AI tools cut by a factor of , with , then the number of projects completed per year increases by . If AI is itself improved by the outputs of these projects, then can shrink over time, leading to a feedback loop in which the pace of progress itself accelerates. In more formal endogenous growth models, AI would augment the "effective" number of researchers, increasing the term governing idea production and pushing economies onto steeper growth trajectories.

In practice, such models are crude and highly uncertain, but they capture the intuition behind singularity language: beyond a certain level of capability, the interactions between AI, science and industry may generate dynamics qualitatively different from previous technological shifts. This is both the lure and the anxiety of the current moment.

Promise: scientific discovery and problem-solving

Hassabis has consistently emphasised the constructive side of this transition, particularly in science and healthcare. DeepMind's work on protein folding, through its AlphaFold system, offers an early indication of how AI can contribute to core scientific challenges. Where traditional approaches required painstaking experiments to infer the three-dimensional structure of proteins from their amino acid sequences, AI systems can now predict many such structures computationally, vastly expanding the available dataset for drug discovery and basic biology. Similar methods are being developed for material science, climate modelling and mathematics.

As models become more capable at exploring hypothesis spaces, designing experiments and interpreting complex datasets, the hope is that they will help unlock treatments for diseases, design low-carbon materials and optimise energy systems more rapidly than human research alone could achieve. This is part of why some AI leaders argue that the net impact of advanced AI could dwarf earlier industrial transformations: it does not only automate existing tasks but also amplifies the process by which new capabilities are created.

In a world facing climate change, ageing populations and geopolitical instability, such accelerations are understandably attractive. They offer a narrative in which AI is not primarily about efficiency or consumer convenience but about expanding the frontier of what is technically possible in domains that matter directly to human survival and flourishing.

Risk: misalignment, misuse and concentration of power

The same features that make advanced AI attractive also generate serious risks. Systems capable of autonomous planning and self-improvement raise questions about alignment: ensuring that their objectives, when pursued at scale, remain compatible with human values and legal constraints. Even if one is sceptical of scenarios involving fully superhuman intelligence, there are near-term concerns about AI systems that are merely very capable and deployed widely without sufficient safeguards.

One class of risk involves misuse. Autonomous coding agents can assist in writing malware, identifying vulnerabilities, or orchestrating coordinated attacks. Large-scale language models can generate persuasive disinformation tailored to specific demographics, potentially amplifying existing social fractures. As these systems become better at modelling human psychology and adapting in real time, the cost of high-quality manipulation could fall, with implications for elections, public health campaigns and social cohesion.

Another involves structural power. If the resources required to train frontier models remain concentrated in a handful of companies and states, control over the most capable systems will be highly centralised. Those actors could, intentionally or not, shape everything from labour markets to information flows. The singularity framing draws attention to a moment where artificial systems may hold more de facto power than any single human institution can easily check, not because they are sentient or malicious, but because they are embedded in so many layers of critical infrastructure.

There is also the possibility of accidents and emergent behaviour. As models grow larger and are coupled with external tools and other agents, predicting their behaviour in novel situations becomes more difficult. Aligning such systems may require new formal methods, rigorous evaluation regimes and international norms that do not yet exist at scale. Here, the concern is less a sudden catastrophic failure and more a series of cascading incidents-financial flash crashes, infrastructure outages, or uncontrolled propagation of flawed code-arising from tightly coupled automated systems.

The strategic and technological tension

At the heart of current debates is a tension between speed and control. On one side, there is the argument that rapid deployment is necessary to capture economic value, to stay ahead of competitors and to make beneficial applications widely available. On the other, there is the view that racing ahead without robust safety measures, regulatory frameworks and democratic oversight is irresponsible, particularly as systems approach or exceed human-level competence across many domains.

Hassabis's public positioning seeks to occupy a middle ground. He emphasises both the proximity of general-purpose AI and the need for society to prepare within a relatively short time window. This implicitly calls for a dual strategy: accelerate the development of beneficial uses while simultaneously investing in safety research, governance structures and public engagement. The challenge is that market incentives, geopolitical rivalry and the sheer pace of technical progress make coordinated restraint difficult.

Governments are only beginning to respond with AI acts, executive orders and voluntary code commitments. These instruments tend to lag technical frontier capabilities by several years. By the time a regulation is in place to address one generation of models, the next generation-with qualitatively different properties-may already be under development. This regulatory lag is familiar from other technologies but is amplified when the paradigm itself is in flux.

Debates and objections

Not all researchers or policymakers accept the singularity framing or the specific timelines associated with it. Critics raise several objections. One is empirical: past predictions of AI breakthroughs, including earlier waves of optimism in the 1960s and 1980s, were often overconfident. They argue that current systems, impressive as they are, still rely heavily on pattern recognition rather than deep understanding, struggle with long-term reasoning and lack robust grounding in the physical world.

From this perspective, equating progress in large language models and agents with an imminent singularity risks obscuring unresolved problems such as brittleness, hallucination and vulnerability to adversarial inputs. Some suggest that claims about timelines to AGI are influenced by competitive pressures and investor expectations, and that more humility is warranted. They also worry that dramatic narratives about near-term singularity could crowd out attention to mundane but urgent issues like labour displacement, privacy and market concentration.

Another objection targets the metaphor itself. The term "singularity" implies a sharp discontinuity, a moment after which extrapolating from previous trends becomes meaningless. Some economists and sociologists argue that a more accurate picture is one of uneven, domain-specific adoption. In this view, certain sectors-software, digital marketing, some scientific fields-may experience extremely rapid change, while others-construction, caregiving, public administration-move more slowly, constrained by physical, legal or cultural factors.

Accordingly, they suggest focusing less on hypothetical points of infinite change and more on concrete decisions about where and how AI is deployed, who benefits, and how costs are distributed. For them, the danger of singularity language is that it can induce either complacent fatalism-"nothing we do matters"-or reckless acceleration-"we must move as fast as possible to reach the promised land"-neither of which encourages careful stewardship.

Why the framing matters now

Regardless of whether one accepts the metaphor or the timelines, the choice by a central figure in AI to characterise the current era as the beginning of a singularity has practical consequences. It signals to engineers, investors and policymakers that they should treat AI not as a marginal upgrade to existing tools, but as a transformational general-purpose technology. That shift in perception can influence everything from research priorities to education policy.

In research, the framing encourages work on foundational capabilities and long-term safety rather than solely on narrow applications. Teams may prioritise interpretability, robustness and alignment techniques in anticipation of systems whose influence extends across critical infrastructures. In industry, the expectation of accelerating capability may drive aggressive investment in AI-native products, workforce retraining and new business models that assume AI will be a core component of almost every workflow.

In public policy, acknowledging that we might be in the "foothills" of a major transformation sharpens the urgency of questions about accountability, global coordination and equitable access. If advanced AI is likely to amplify existing inequalities unless actively governed, then social choices made in the next few years-about data rights, model access, liability regimes and international cooperation-will have outsized effects. The metaphor thus serves as a prompt: if we are indeed at an early stage of a steep climb, the route we choose now will determine which groups bear the risks and reap the rewards.

Finally, there is a psychological dimension. Seeing one's era as a hinge point in history can be both motivating and destabilising. For researchers and entrepreneurs, it provides a sense of purpose: their work may have consequences far beyond quarterly metrics. For citizens and policymakers, it can induce anxiety about loss of control. Navigating between these reactions requires a form of collective maturity: the ability to recognise that transformative capability is emerging, to take its risks seriously without succumbing to paralysis, and to articulate positive, plural visions of futures in which powerful AI is integrated into human institutions rather than simply unleashed.

Whether or not historians ultimately agree that this period marked the true "foothills" of a singularity, the underlying reality is that AI systems are already reshaping knowledge work, scientific research and digital infrastructure. The choice now is not whether to enter this terrain, but how to do so deliberately, with as much foresight as a rapidly changing technological landscape will allow.

Term: Trade marketing - FMCG / CPG

"Trade marketing in FMCG/CPG is a business-to-business (B2B) marketing strategy focused on selling products to supply chain partners, such as retailers, wholesalers, and distributors, rather than directly to the end shopper. Its primary objective is to maximize immediate sales volume by ensuring products are widely available, competitively priced for the channel, and highly visible on the retail shelf." - Trade marketing - FMCG / CPG

The struggle for share in grocery, convenience, pharmacy, and e-commerce is won or lost long before shoppers walk down the aisle. The decisive battleground is the relationship between manufacturers and their channel partners: which brands distributors push, which lines retailers list, where they sit on the shelf, how they are priced, and which promotions receive scarce in-store support. This upstream negotiation of attention, space, and effort is where trade marketing becomes economically critical for fast-moving consumer goods and consumer packaged goods.

From consumer pull to channel push

Manufacturers in everyday categories like food, beverages, personal care, and household cleaners operate in markets defined by high purchase frequency, low unit prices, and intense competition. Consumers typically decide within seconds, often on autopilot, with limited engagement or product research. In that setting, even strong brands cannot rely solely on consumer advertising and digital campaigns to secure volume. If a product is out of stock, relegated to a low-traffic shelf, obscured by competitor displays, or uncompetitive in channel pricing, consumer pull cannot convert into sales.

Trade marketing addresses this gap by focusing on the economic and operational incentives of wholesalers, distributors, and retailers themselves. The objective is to shape the assortment, placement, pricing, and promotional calendar at the point of sale so that channel partners actively prioritise one brand over alternatives. It transforms the manufacturer-retailer relationship from transactional listing to ongoing joint business planning, anchored in shared volume and margin goals.

Substantive meaning of trade marketing in FMCG / CPG

In substantive terms, trade marketing in these categories is a cluster of business-to-business practices aimed at making a product the easiest and most profitable choice for the channel. It is concerned less with brand positioning in the mind of the end consumer, and more with how the product competes for scarce shelf space, warehouse capacity, and promotional slots.

Key features include:

- Channel-first targeting. Activities are directed at distributors, wholesalers, and retailers rather than directly at shoppers. The immediate customer is the buyer at a supermarket chain, the procurement manager at a wholesale club, or the owner of a convenience store network.

- Volume orientation. Given that CPG and FMCG rely on thin per-unit margins and high turnover, profitability depends on large, repeat orders. Trade marketing tactics are therefore tuned to drive case volumes and share of shelf across outlets, not just brand awareness.

- Shelf and display optimisation. Because products are chosen quickly and often with low involvement, visibility at the point of sale materially affects demand. Securing eye-level shelf positions, secondary placements, end caps, and special displays is a central concern.

- Channel-specific pricing and promotion. Retailers need sufficient margins and promotional funding to justify prioritising a brand. Trade marketing negotiates wholesale pricing, discounts, rebates, and co-funded promotions that work economically for both sides.

- Joint execution and support. Successful programmes include training, point-of-sale materials, data sharing, and operational support to help retailers execute agreed plans effectively.

In practice, this means that much of the marketing budget in a large consumer goods company is channel-directed: trade discounts, promotional allowances, co-operative advertising, in-store activation, and the staff and systems needed to design and monitor these levers.

Core activities and mechanisms

Trade marketing in FMCG / CPG spans a wide set of operational and strategic activities, each tied to a specific mechanism for generating incremental sales.

1. Retailer relationship and joint planning

Relationship management with key accounts is foundational. Teams work with retailers to understand category strategies, margin targets, and shopper segments, then co-develop plans covering assortment, shelf layout, and promotion. This often involves:

- Annual or seasonal joint business plans specifying volume targets, investment levels, and marketing calendars.

- Category advice based on market and shopper data, positioning the manufacturer as a partner rather than just a supplier.

- Negotiation of listing fees, exclusive deals, and long-term contracts for distribution or shelf space where appropriate.

The underlying mechanism is alignment of economic incentives: the manufacturer offers value in the form of insights, investment, and reliable supply; the retailer responds with space, visibility, and featured promotion.

2. Product display, placement, and merchandising

Where and how a product appears in store strongly influences its share of purchases, especially in categories with many near-substitutable brands. Trade marketing teams design planograms, propose shelf adjacencies, and deploy merchandising resources to secure favourable locations such as eye-level shelves and aisle ends.

They also manage temporary and semi-permanent displays: floor stands, dump bins, pallet stacks, chillers, and branded fixtures. These serve dual purposes: meeting retailer objectives for revenue per square metre, and boosting spontaneous purchases on top of planned shopping lists.

3. Promotions, incentives, and trade deals

Promotional mechanics sit at the heart of trade marketing because retail partners must decide which deals to feature at any given time. Typical levers include:

- Volume-based discounts and tiered pricing for larger orders.

- Off-invoice discounts or rebates tied to sell-out performance.

- Multi-buy offers and value packs co-funded by manufacturers to encourage shoppers to buy more units at once.

- Seasonal or event-based promotions aligned to peak consumption periods.

These mechanics are designed to satisfy three constraints simultaneously: maintain adequate retailer margin, protect manufacturer profitability, and provide compelling value for shoppers. Misaligned deals may drive short-term volume at the expense of long-term price integrity or brand positioning, which is why careful planning and post-event evaluation are increasingly important.

4. Training, point-of-sale materials, and retail execution

Beyond financial incentives, FMCG suppliers often invest in retailer capability. This can include staff training on product benefits, usage occasions, and cross-selling opportunities, as well as the provision of brochures, shelf talkers, wobblers, and digital screens to communicate with shoppers.

Many companies also deploy field teams or outsourced agencies to audit compliance with agreed displays, check stock levels, and correct execution gaps. Retail execution technology helps track whether promotions and planograms are implemented as intended, closing the loop between head office agreements and in-store reality.

5. Market research, analytics, and data-driven targeting

Because FMCG categories are characterised by frequent repurchase and high volumes, point-of-sale data is rich. Trade marketing functions increasingly rely on analytics to understand which promotions drive incremental volume, which outlets under-perform, and how different channels respond to specific tactics.

Retailer loyalty data, syndicated market panels, and internal shipment records feed models that estimate baseline sales and promotional uplifts. This evidence base allows the more scientific allocation of trade spend and sharper negotiation with retailers about which programmes deliver genuine category growth versus simple brand switching.

Simple mathematical framing of trade marketing impact

Although most decisions are commercial and strategic, it is useful to express the mechanics in a simple quantitative framework. Consider a single product in a specific retail chain. Weekly sales volume can be viewed as a function of three key factors:

where is unit sales, represents numeric and weighted distribution (how widely and in which store formats the product is available), captures visibility and in-store activation (shelf position, number of facings, displays, promotions), and is the effective price to the shopper, net of discounts and deals.

Trade marketing interventions mainly act on and . Listing a product in more stores or more branches increases ; securing better shelf locations and additional displays raises . The combined effect can be conceptualised with elasticities:

where , , and are the elasticities of sales with respect to availability, visibility, and price. In many FMCG categories, the absolute value of and is high, because out-of-stock or poor placement directly suppress sales. Trade marketing focuses on improving and in a cost-effective way, while coordinating with pricing teams responsible for .

At a portfolio level, trade spend itself can be modelled as an input where manufacturers seek to maximise profit:

Here denotes profit, is volume for brand , is revenue, is cost, and is total trade investment. The optimisation challenge is to allocate across brands, channels, and mechanics to generate the largest incremental , taking into account retailer reactions and competitive responses.

Key parameters and levers in practice

While such equations are simplifications, they highlight the main parameters trade marketers manage in real organisations:

- Distribution breadth and depth. Numeric distribution (percentage of outlets stocking the brand) and weighted distribution (share of category sales represented by those outlets) are primary levers. Gaining entry into high-volume retailers or formats such as large supermarkets yields disproportionate impact.

- Shelf share and facings. The proportion of category shelf space devoted to a brand, and the number of facings at eye-level, directly influence visibility and availability under real-world conditions.

- Promotional intensity. Frequency, depth, and type of in-store promotions determine how strongly trade marketing influences trial and stock-up behaviours.

- Trade margin structure. The split of value between manufacturer and channel partners is a key determinant of retailer support.

- Compliance rates. The percentage of stores that actually implement agreed displays and promotions influences realised uplift versus planned uplift.

These parameters are increasingly monitored via digital tools, from electronic shelf labels and POS data feeds to in-store image recognition that tracks facings and compliance. This creates feedback loops that allow continuous optimisation of trade marketing plans.

Major schools of thought and strategic approaches

Different companies and consultants frame trade marketing in somewhat different ways, leading to several schools of thought within FMCG and CPG organisations.

Category management-centric view

One approach embeds trade marketing within category management, emphasising that manufacturers should help retailers grow entire categories, not just their own brands. Under this paradigm, trade initiatives are evaluated on their ability to increase total category sales and shopper satisfaction, on the assumption that retailers will reward genuine category growth with stronger long-term partnerships.

This view tends to favour data-rich, insight-driven interventions: shelf re-sets based on shopper missions, rationalisation of low-velocity SKUs, and promotions that recruit new shoppers rather than trigger subsidised switching between similar products.

Promotion-driven and sales-led view

A second school of thought sees trade marketing primarily as the engine of short-term volume, closely tied to sales quotas and quarterly targets. Here, success is measured in immediate sell-in and sell-out spikes around promotional windows, with heavy reliance on price cuts, multi-buy deals, and aggressive display activity.

This approach can deliver rapid results, especially in mature categories where consumer demand is responsive to price. However, it risks eroding perceived value and conditioning shoppers to buy only on deal, as well as fostering a cycle of promotion wars among competing brands.

Customer marketing and joint value creation

A more recent perspective frames trade marketing as customer marketing, underlining that retailers are themselves customers with distinct needs and brand equities. From this standpoint, the goal is to create tailored programmes for each key account that enhance the retailer's proposition as well as the manufacturer's.

Examples include exclusive SKUs or flavours for a specific chain, co-developed digital campaigns that run in the retailer's app or website, and shared sustainability initiatives that support both corporate strategies. This view aligns with the broader trend towards collaborative planning and integrated shopper marketing across online and offline channels.

Tensions, debates, and long-running challenges

Because trade marketing sits at the intersection of marketing, sales, finance, and supply chain, several persistent tensions shape practice and strategy.

Short-term volume vs long-term brand health

One recurring debate concerns the balance between driving immediate volume through deep discounts and protecting long-term brand equity. Frequent, steep promotions can train shoppers to perceive the regular price as inflated and to delay purchase until the next deal. At the same time, retailers often push for more trade funding as a condition for space and visibility.

Resolving this tension requires close coordination between brand marketing and trade marketing teams. Decisions about promotion frequency and depth must be aligned with the brand's positioning and consumer price expectations, not driven solely by short-term sales targets.

Transparency and complexity of trade spend

Trade marketing budgets are often among the largest line items in an FMCG P&L, yet historically they have been less transparent than above-the-line advertising spend. Different types of discounts, rebates, and allowances can make it difficult to measure true net prices and returns on investment.

As competition intensifies and margins remain thin in CPG industries, finance and revenue management functions are demanding more rigorous measurement of trade promotion effectiveness. This has led to growing use of trade promotion management and optimisation tools, which attempt to quantify incremental volume and profit from each activity rather than treating trade spend as a cost of doing business.

Channel conflict and omnichannel complexity

The growth of e-commerce and quick-commerce has added new layers of complexity. Manufacturers must manage relationships with traditional brick-and-mortar retailers alongside online marketplaces, direct-to-consumer sites, and delivery platforms. Trade terms negotiated with one channel can influence those in others, creating the risk of channel conflict over pricing, assortment, or exclusivity.

Trade marketing in this environment must adapt from a store-centric mindset to an omnichannel one, integrating digital shelf visibility (search ranking, sponsored placements, product detail page content) with physical merchandising and in-store activation. The basic principles of availability, pricing, and visibility still apply, but the execution space is now broader and more data-intensive.

Why trade marketing in FMCG / CPG still matters

Despite shifts towards digital media, influencer partnerships, and sophisticated brand storytelling, trade marketing remains crucial in fast-moving consumer categories. Several structural reasons explain why.

First, the underlying economics of FMCG and CPG are unchanged: products are often low cost with thin margins, sold in high volumes, and replenished frequently. Profitability still hinges on efficient distribution, reliable in-stock rates, and capturing as large a share of shopper baskets as possible. Trade marketing is the managerial function designed to secure these conditions.

Second, retailers continue to wield substantial power in deciding which brands appear where and with what support. Even as direct-to-consumer models grow, most everyday purchases still flow through supermarkets, convenience stores, pharmacies, and mass merchants. Winning in these channels requires understanding and influencing retailer economics, not just consumer preferences.

Third, the rise of data, AI, and retail media has, if anything, increased the sophistication and potential impact of trade marketing rather than rendering it obsolete. Manufacturers can now target spend more precisely, test and learn across banners and regions, and quantify payback with more rigour than before. Retailers, in turn, monetise their digital properties through sponsored listings and on-site advertising, blurring boundaries between trade investment and advertising spend.

Finally, macro trends such as sustainability, responsible sourcing, and health consciousness create new arenas for manufacturer-retailer collaboration. Joint initiatives on packaging reduction, ingredient reformulation, or community programmes often rely on the same relationship infrastructure and negotiation skills built through trade marketing. The conversation may extend beyond price and promotion to include shared values and long-term differentiation for both parties.

For practitioners in FMCG and CPG, mastering trade marketing means moving fluently between commercial negotiation, shopper insight, operational execution, and analytical evaluation. It is not simply a set of discounts or a department adjacent to sales; it is the discipline through which brands convert their consumer promise into real-world presence on the shelf, in the basket, and on the bottom line.

Quote: "Where is AI in GDP statistics?"

"The AI economy in the United States has been growing at an unprecedented rate, but this extraordinary growth is largely invisible in conventional GDP statistics. Treating the AI sector as a coherent economic entity yields preliminary estimates of nominal AI GDP at approximately $250 billion in 2025, growing at roughly 2 600 percent per year in quality-adjusted real terms." - "Where is AI in GDP statistics?" - May 2026 - Anton Korinek (PIIE) and Patrick McKelvey (Bank of Canada)

Economic statistics are struggling to keep pace with a technology whose productive capacity is compounding across hardware, data centres and algorithms faster than the measurement systems designed in the mid-20th century can register. The result is a widening gulf between what is happening inside the AI ecosystem and what appears in national accounts, complicating debates about productivity, inequality and policy at exactly the moment when artificial intelligence is beginning to reshape production methods and business models.

The invisible boom behind a modest spending line

On the surface, current US spending on AI looks like a sizeable but manageable line item in GDP: on one influential estimate, nominal AI compute outlays amount to about USD 250 billion a year by 2025, covering both inference and model training activities. That figure encompasses the purchase of specialised chips, cloud compute services, and associated infrastructure that is straightforwardly counted as investment or intermediate consumption in the national accounts. In conventional terms, this is the visible part of the AI economy: money changing hands for hardware, data centre capacity and access to models, all recorded using existing categories.

Yet behind that nominal spending path lies an extraordinary explosion in effective AI capacity. When the same researchers treat AI as a coherent production sector and apply quality adjustments for both hardware improvements and algorithmic progress, they find that real AI output is growing not by 140 percent a year but by more than 2 000 percent annually in 2024 and 2025. In other words, each year's spending is buying orders of magnitude more capability than the previous year's, even though the nominal cash flows entering GDP aggregates rise only modestly by comparison. The boom is hidden not because it is economically trivial, but because the price-quality relationship is collapsing too quickly for unadjusted statistics to capture.

Three compounding engines of AI output

The backstory to such eye-catching growth rates lies in three distinct but reinforcing processes that reshape what a given dollar of AI spending delivers. The first is the physical build-out of data centre capacity. AI-optimised facilities packed with accelerator chips and high-bandwidth networking are being deployed at an accelerating pace, so the raw compute available for training and running models is growing well above 200 percent a year. This expansion is visible in investment data, but only in a blunt way: the accounts see larger structures and more equipment, not the combinatorial increase in tasks those resources can perform.

The second process is hardware efficiency. Successive generations of AI chips deliver substantially more floating-point operations per second for each dollar of cost and each unit of energy. Measured in H100-equivalent units, US AI computing capacity is estimated to grow at more than 200 percent per year, significantly outpacing nominal spending growth because each chip class is more powerful than its predecessor at roughly comparable prices. This dynamic echoes the semiconductor industry's long history of rapid quality improvements that kept its GDP share modest even as real output exploded, but AI accelerators are doing so in a context where demand for compute is sky-high and model sizes are scaling aggressively.

The third, and most potent, engine is algorithmic progress. Advances in model architectures, training techniques, optimisation and data curation mean that the amount of compute required to achieve a given performance benchmark has been falling sharply, in some estimates by around two-thirds per year. Put differently, a fixed quantity of chips running for a given period now delivers far more useful task performance than it did a few years ago. When quality adjustments account simultaneously for (a) more data centre capacity, (b) more powerful hardware, and (c) more efficient algorithms, the implied growth in effective AI output jumps from high triple-digit percentages to the 2 000-2 600 percent range cited in recent work.

Formally, one can think of quality-adjusted AI output as the product of three components: physical compute capacity , hardware efficiency and algorithmic efficiency . A simple multiplicative representation is . If each term grows at an annual rate , and , then the growth rate of is approximately for moderate rates; with the extreme compounding observed in AI, the exact expression yields multi-thousand-percent annual increases when all three drivers are simultaneously large.

Why GDP barely moves

National accounts, by design, focus on market transactions valued at current prices, and only selectively adjust for quality improvements. When the price of a service falls as its quality rises, the accounts record a combination of higher quantities and lower prices, but the frameworks and data pipelines required to track both accurately for a new technology often arrive late. For AI, the issue is pronounced because per-unit prices for a given capability are dropping almost as fast as underlying capacity is rising. If API access to a model that can perform a given benchmark task becomes ten times cheaper in a year, and enterprises spend only modestly more on AI in total, then nominal AI revenues will show only a small increase, even though the effective quantity of AI services they purchase has surged.

This pattern is familiar from historical episodes. The semiconductor sector experienced decades of rapid performance improvements, yet its share of GDP remained modest because each new generation of chips delivered more performance for similar or lower nominal prices. Hedonic pricing methods allow statistical agencies to adjust for this by re-expressing prices in terms of constant performance metrics, but applying such methods requires stable benchmarks and extensive data that typically emerge only after technologies have matured. The AI wave is arriving too quickly for existing statistical routines to keep up, leaving much of the quality-adjusted output uncounted in official real GDP figures.

Defining an "AI GDP" and its boundary

Korinek and McKelvey therefore propose treating AI as a distinct economic entity with its own satellite accounts, yielding an "AI GDP" that complements, rather than replaces, standard aggregates. The idea is to delineate the production boundary of AI: which activities belong in the AI sector, how their output should be measured, and how to avoid double-counting when AI is embedded in other goods and services. They focus on the production side, aggregating spending on compute for model training and inference, as well as AI-related research and development, and then applying quality adjustments based on API pricing and estimates of algorithmic progress.

This boundary is necessarily provisional. Including only core compute and API-based access captures the narrow AI industry, but much of the economic value from AI will emerge in applying models to domains like healthcare, education, logistics and creative industries. If those downstream uses are counted as ordinary sectoral output without explicit attribution to AI, then AI's contribution to growth will remain partly hidden even if the upstream AI GDP is measured perfectly. Conversely, if the AI boundary is drawn too broadly, there is a risk of attributing to AI productivity improvements that are jointly driven by complementary investments in human capital, organisational change or non-AI software.

One emerging response in the measurement literature is to distinguish between AI's productive capacity and its realised utilisation. Capacity can be proxied by compute resources and model capabilities, while utilisation depends on demand, adoption and complementary changes in firms' processes. This motivates a conceptual gap between potential AI GDP, based on what the technology could deliver if fully deployed, and actual AI-enabled output that shows up in sectoral productivity data. The unusually high quality-adjusted growth rates identified in the AI sector look more like capacity expansion than like realised welfare gains; the satellite account framework is a way to track this capacity before it fully diffuses through the economy.

The strategic tension: capacity versus productivity

The divergence between internal AI growth and GDP statistics matters because it shapes how policymakers, firms and the public interpret the technology's macroeconomic role. On one view, the rapid expansion of AI capacity with limited reflection in aggregate productivity suggests a familiar pattern of lagging diffusion: general-purpose technologies often require time-consuming organisational and human-capital investments before they translate into economy-wide gains. The classic comparison is with electrification, where factories needed decades to reorganise around distributed motors instead of central shafts, during which time headline productivity growth remained subdued.

On another view, the mismatch raises questions about whether current measurement practices are still adequate. If AI is enabling substantial quality improvements in services that are poorly captured by prices or quantities, such as personalised education or medical diagnostics, then real welfare may already be rising faster than official statistics suggest. However, because GDP is anchored to transactions, not to subjective well-being or consumer surplus, an AI-augmented service that remains priced similarly to its predecessor will not add much to measured output even if users derive more value from it. This reinforces the importance of clarity about what GDP can and cannot represent.

Strategically, governments face a tension between under-reacting to AI because it is invisible in official numbers and over-hyping it based on internal metrics from the AI industry. An AI sector that is expanding at 2 600 percent per year in quality-adjusted terms looks like a revolution from the perspective of data centre operators and model developers. From the perspective of macroeconomic analysts focused on trend productivity growth of perhaps 1-2 percent per year, the effects so far look modest. Calibrating regulation, infrastructure policy and workforce programmes in this context is difficult: the technology's future impact is potentially enormous, but the statistical evidence of current gains is thin.

Debates and objections

The very concept of an AI GDP has sparked several lines of debate. One concern is that quality-adjusted growth rates on the order of thousands of percent may be more a reflection of the chosen metrics than of genuine economic output. Measuring AI in terms of benchmark tasks or API performance might overstate economically relevant progress if those benchmarks do not map cleanly to productivity in real-world workflows. Critics argue that the ability of models to score higher on academic tests or synthetic tasks does not automatically translate into major cost savings or revenue gains for firms.

Another line of criticism questions whether focusing on production-side compute spending misses the demand side of the story. GDP aggregates are meant to reconcile production with income and expenditure; a satellite account that captures only the production capacity of AI might be analytically useful but risks being misinterpreted as a measure of realised welfare. The authors themselves are cautious on this point, emphasising that their quality-adjusted AI GDP should be read as a signal of productive capacity rather than as a replacement for standard welfare concepts. From this perspective, the headline growth rates highlight how quickly the technological frontier is moving, not how much better off households currently are.

A third objection is practical. Statistical agencies operate under resource constraints and must prioritise improvements that deliver the greatest benefit for overall data quality. Some observers worry that building specialised AI satellite accounts could divert attention from more pressing tasks, such as better measuring services, intangibles and household production. In response, proponents of AI-focused measurement argue that because AI is highly input-intensive and potentially general-purpose, an early investment in dedicated tracking can prevent larger measurement problems later, particularly if AI-enabled services blur sector boundaries and reconfigure value chains.

Why it matters for policy and strategy

Despite the conceptual disputes, the attempt to quantify an AI GDP has several concrete implications. For macroeconomic policy, understanding the scale and trajectory of AI investment and capacity is essential for forecasting productivity, inflation and labour market dynamics. If AI capacity is growing far faster than utilisation, there may be a period in which capital deepening outpaces labour adaptation, affecting wage structures and sectoral employment even before aggregate productivity accelerates. Conversely, if AI adoption triggers rapid efficiency gains in certain tasks, it could exert disinflationary pressures in specific service categories, complicating monetary policy calibration.

For innovation and industrial policy, AI satellite accounts can inform decisions about infrastructure, regulation and public R&D support. Knowing whether AI investment is concentrated in a handful of large firms or spread across a wider ecosystem affects concerns about competition and resilience. Tracking the balance between training expenditure and inference-related spending sheds light on whether the frontier is shifting primarily through ever-larger models or through more efficient deployment of existing capabilities. These are questions that conventional sectoral classifications and investment data are not well suited to answer.

For firms, the backstory behind the AI GDP figures highlights the importance of complementarity. The mere existence of rapidly expanding AI capacity does not automatically translate into competitive advantage; what matters is the ability to integrate models into production processes, redesign workflows and manage data effectively. Businesses that treat AI as a drop-in technology may find that the gains visible in benchmark tests do not materialise in their own operations. Those that invest in organisational learning, experimentation and human-machine collaboration are more likely to convert the sector's quality-adjusted output growth into genuine productivity improvements.

Towards a more nuanced statistical architecture

Ultimately, the story behind the headline estimate of a 250-billion-dollar AI economy growing at thousands of percent per year is about the need for a richer statistical architecture. Traditional GDP will remain the workhorse indicator for macroeconomic analysis, but its design assumptions are being stretched by technologies that deliver rapid quality improvements at falling prices and that diffuse across sectors in ways that blur the boundaries between producers and users. Satellite accounts for AI, structured around clear production boundaries and transparent quality-adjustment methods, offer a way to track the technology's evolution without over-claiming about its current welfare impact.

The challenge for researchers and policymakers is to keep the conceptual distinctions clear. AI capacity is not the same as AI usage; AI usage is not the same as productivity; and productivity is not the same as welfare. Yet all four are linked, and their trajectories over the coming decade will shape living standards, industrial structures and geopolitical balances. An analytical framework that isolates AI's contribution to production, while acknowledging the limits of current data and the uncertainties around mapping benchmarks to economic value, is a crucial step in making sense of a transformation that standard GDP statistics barely register today.

Term: Category Management - FMCG / CPG

"In FMCG (Fast-Moving Consumer Goods) and CPG (Consumer Packaged Goods), Category Management is a collaborative business process between manufacturers and retailers. It treats a related group of products (e.g., carbonated soft drinks, laundry detergents) as a single "strategic business unit" to maximise sales, profitability, and shopper satisfaction." - Category Management - FMCG / CPG

Competitive pressure, proliferation of stock-keeping units, and increasingly fragmented shopper preferences make it almost impossible to manage products one by one. The commercial battleground has shifted from individual brands to the way whole aisles perform: how a retailer curates, prices, promotes, and presents an entire cluster of products that solve a single shopper need. This shift changes incentives for manufacturers too, because growth now depends as much on strengthening the performance of the total shelf as on pushing any single label.

From product-by-product trading to category-level strategy

Traditional buying focused on negotiating each item in isolation: unit cost, case discounts, and short-term deals. That approach can inflate assortment complexity, confuse shoppers, and erode margins. Category-level thinking replaces this with a view of the aisle as an integrated system: the combination of items, prices, space, and promotions that maximises penetration, frequency, and basket value for a given shopper mission. In practice, this means asking whether an additional flavour, size, or pack format contributes incremental sales or simply cannibalises existing lines, and whether promotional pressure grows the entire category or just subsidises volume that would have sold anyway.

In this system, the category is treated as a standalone commercial unit with its own demand dynamics, P&L characteristics, and strategic role within the store. Rather than optimising each brand independently, retailer and manufacturer focus on optimising category health, then competing for their share within that healthier environment. This changes the dialogue in joint business planning: from "how many extra facings can this brand secure?" to "how can the category deliver more value to shoppers, and which brands are best placed to drive that outcome?"

What constitutes a category in FMCG and CPG?

In practice, a category is a distinct cluster of products that shoppers perceive as interchangeable or complementary ways of meeting a single need. The exact definition is highly context-dependent. A grocery retailer might define soft drinks as a single category, splitting into subcategories such as cola, flavoured carbonates, water, and energy. A pharmacy may define skin care with subcategories for facial care, body care, and sun protection. The crucial criterion is alignment with shopper decision-making: the grouping must reflect how people actually shop the shelf, not just internal supply or brand structures.

Category definitions should therefore arise from behavioural and attitudinal research, panel data, and store observation. Shopper interviews can reveal whether consumers substitute across brands and formats within one bay, or whether they view items as distinct missions. Panel or loyalty-card data can show cross-purchase patterns and switching behaviour. If shoppers frequently substitute between two segments, they likely belong in a single broader category. If they appear in different missions, at different times, and with different price sensitivities, they may require separate strategies even if they sit near each other in the store.

Substance of category management in retail

Commercial teams use the category construct to make integrated decisions across four main levers: assortment, pricing, promotion, and space or merchandising.

- Assortment planning: deciding which SKUs to list, in which pack sizes and variants, and with how many facings per store. The objective is to balance breadth of choice against simplicity, ensuring shoppers can find what they need quickly without overloading them with near-duplicates.

- Price management: setting everyday prices, price ladders, and value architecture that align with category role (e.g. value traffic-driver vs premium margin engine) while maintaining competitiveness and profitability.

- Promotion strategy: determining depth, frequency, and mechanics of promotions in ways that incrementally grow the category rather than merely subsidising existing volume or shifting sales between brands.

- Shelf-space optimisation: allocating total space between subcategories and brands, deciding vertical and horizontal product placement, and shaping planograms to guide shopper flow and maximise visibility of high-potential lines.

The power of the discipline lies in managing all four together. Lowering prices without adjusting assortment can erode margin without materially improving perceived value. Increasing promotional pressure without aligning space and in-store execution can create short-lived spikes that train shoppers to buy only on deal. Effective category management instead builds coherent strategies that define the role of each segment and lever, then uses data to test and refine those strategies.

The collaborative manufacturer-retailer process

In FMCG and CPG, category decisions sit at the intersection of two organisations with distinct objectives and information. Retailers own the shelf, the shopper relationship, and the detailed point-of-sale and loyalty data. Manufacturers hold deep knowledge about product technology, brand equity, usage occasions, and consumer trends. Category management is designed as a collaborative process to combine these perspectives into a joint plan focused on the shopper.

Leading practitioners follow a structured multi-step process, often articulated in eight stages. While terminology varies, the logic is broadly consistent:

- Alignment and category definition: retailer and manufacturer agree the scope of the category and its subsegments, grounded in shopper behaviour.

- Role assignment: the retailer clarifies the strategic purpose of the category in the overall store proposition: destination, routine, seasonal, or convenience. This determines acceptable margin levels, space allocation, and promotional intensity.

- Performance assessment: both parties analyse sales, margin, traffic, conversion, and shopper metrics to understand current performance and identify issues or opportunities.

- Objective setting: they prioritise specific goals such as increasing penetration, premiumising the mix, reducing out-of-stocks, or improving basket attachment.

- Strategy development: broad directions are chosen, for instance expanding healthier options, rationalising tail SKUs, or repositioning price architecture.

- Tactical planning: concrete changes are defined across assortment, pricing, merchandising, and promotions.

- Implementation: plans are translated into planograms, listings, promotional calendars, and execution guidance for field teams.

- Review and optimisation: performance is tracked against KPIs, with periodic reviews to refine assumptions and adapt to new information.

This joint process depends on trust, transparency, and a shared commitment to category growth rather than narrow share grabs. Retailers often expect suppliers to provide fact-based recommendations supported by credible data sources, not simply arguments favouring their brands.

Practical meaning for roles and organisations

Within manufacturers, category management reshapes how commercial teams work. Dedicated category managers become responsible for synthesising data, shopper insights, and competitive intelligence into recommendations for assortment, merchandising, and promotional activity. They collaborate with sales to build retailer-specific proposals and with marketing to ensure brand plans align with retailer category strategies.

On the retail side, category managers or buyers hold P&L responsibility for the category and must balance supplier input with their own view of the total store. They decide which proposals to accept, how to allocate limited shelf space, and which categories deserve incremental support. Their performance is judged not just on margin rate but on contribution to traffic, loyalty, and overall store positioning.

Because decisions are highly data-driven, both parties invest in tools and skills. Syndicated data, retailer portals, and loyalty information enable fine-grained analysis of performance by store, segment, and shopper group. Planogramming and space-optimisation software allow teams to model different layouts and quantify the impact of moving facings between SKUs. These capabilities turn what was once an art into a more rigorous discipline, even though human judgement remains essential.

Analytical and mathematical backbone

Although the discipline is commercial rather than academic, the underlying logic can be expressed with simple quantitative relationships. A category can be evaluated through basic decompositions of volume and value. Suppose category value sales over a given period are the product of unit volume and average unit price : . Category managers then decompose into shopper penetration (number of buyers), average purchase frequency (trips per buyer), and average units per trip : . Targeted strategies can then aim to increase any of these components: bring more shoppers into the category, encourage them to buy more often, or increase units purchased per occasion.

At a more granular level, SKU-level contribution is evaluated through measures such as gross margin and rate of sale. If is the volume of SKU and is its unit margin, then its gross profit contribution is . Aggregate category profitability is . Category managers compare against space usage to estimate productivity per facing or per metre, guiding decisions to expand, shrink, or delist items. Advanced practitioners incorporate own- and cross-price elasticities to understand how changes in the price of one SKU affect demand for others, though estimation of full elasticity matrices can be data-intensive and complex.

Promotional evaluation likewise uses basic incrementality calculations. If baseline weekly sales for a SKU are and observed sales during promotion are , the incremental volume is . Analysts then adjust for pantry loading and post-promo dips to infer true incremental category volume versus simple timing shifts. These analyses feed back into promotional strategy: which mechanics create genuine category expansion and which mainly cannibalise future purchases or competing brands.

Major schools of thought and strategic postures

Within FMCG and CPG, different organisations emphasise distinct aspects of the discipline.

- Shopper-first school: This view treats the category primarily as a vehicle to satisfy shopper missions. Proponents invest heavily in shopper research and behavioural data, tailoring assortment and merchandising to specific missions such as "top-up shop", "weekly main shop", or "on-the-go snacking". They prioritise ease of navigation, clear signposting, and logical product adjacencies, even if this means rationalising SKUs or trading short-term margin for long-term loyalty.

- Margin-and-efficiency school: Others focus more on SKU productivity, supply chain efficiency, and working-capital optimisation. They aggressively prune long tails, consolidate volumes with fewer suppliers, and engineer packs to hit margin and price thresholds. Shopper needs still matter, but decisions are anchored in contribution per metre and supply economics.

- Innovation-and-growth school: A third stance views the category as an innovation platform. Here, the role of the discipline is to identify unmet needs, white spaces, and emerging trends such as health, sustainability, or premiumisation, then allocate space and promotional support to new concepts that can re-energise the aisle. Fail-fast experimentation is encouraged, with rapid test-and-learn cycles in limited store sets.

In practice, sophisticated retailers and manufacturers blend these perspectives. They define category strategies that clarify when to prioritise traffic and shopper delight, when to focus on efficiency and margin, and where to place innovation bets. The art lies in sequencing: for example, cleaning up the assortment to remove duplicative SKUs, then using freed space to introduce differentiated innovation aligned with shopper insights.

Tensions and debates in real-world practice

Despite its widespread adoption, the discipline remains contested in several areas.

Brand vs category primacy. Suppliers naturally want to grow their own brands, while retailers care about total category performance. The discipline formally prioritises the latter, but incentives such as volume-based bonuses can pull in the opposite direction. Debates persist over whether suppliers acting as "category captains" risk biasing decisions in their favour, despite processes designed to keep recommendations objective.

Assortment breadth vs simplicity. One argument holds that extensive choice is essential to satisfy fragmented tastes and niche needs. Another points to behavioural research suggesting that too much choice reduces conversion and satisfaction. Category managers must balance these views, often using data to identify high-overlap SKUs whose removal would not materially affect shopper satisfaction but would simplify operations and clarity.

EDLP vs high-low pricing. Retailers disagree on the right mix of everyday low prices versus high-low promotional strategies in specific categories. Empirical evidence shows that heavy discounting can erode perceived value and train shoppers to delay purchase until offers appear, but in some segments aggressive promotions remain a key driver of share. Category teams test different price architectures and promotional loads by segment and shopper group rather than applying one philosophy universally.

Central planograms vs local tailoring. Head-office teams often design standard layouts to ensure consistency and exploit scale in analysis. Yet shopper demographics and store missions differ sharply between locations. The growing availability of store-level data and more advanced software encourages a move toward more localised assortments and planograms, raising questions about how much autonomy individual stores should have.

Why the concept still matters in modern retail

Several structural trends in FMCG and CPG underline the continuing relevance of the discipline.

First, channel fragmentation means shoppers now split their baskets across supermarkets, discounters, convenience, online, and specialist formats. Each channel has distinct missions, price perceptions, and trip structures. A category framework allows firms to tailor strategies for each channel while keeping a coherent view of category roles and brand portfolios across the total market.

Second, digital transformation increases the volume and granularity of available data. Retailer portals, loyalty schemes, e-commerce clickstream information, and third-party panels provide near real-time insight into demand shifts, response to promotions, and regional variation. Category management provides the organisational mechanism for converting this data into concrete changes in assortment, pricing, and merchandising.

Third, sustainability, health, and regulatory pressures are reshaping what a "good" category looks like. Retailers introduce guidelines on sugar content, packaging recyclability, or responsible sourcing. Category managers must integrate these non-financial objectives alongside sales and margin metrics, sometimes re-engineering ranges to meet new standards while maintaining shopper appeal.

Finally, emerging brands and private labels continue to challenge established incumbents. For smaller manufacturers, understanding the retailer's category strategy is often the gateway to securing listings and shelf space. They must demonstrate how their proposition grows the total category or fills a genuine gap, not just how it competes on taste or price. For retailers, category discipline is the lens through which they decide which challengers to back and how to position private labels relative to brands.

In this environment, treating categories as strategic business units, managed collaboratively and analytically, remains central to commercial performance. The discipline links shopper understanding, brand strategy, and retail execution into a single decision-making framework. As data becomes richer and shopper expectations more demanding, the organisations that apply this framework most rigorously and adaptively are likely to set the standard in FMCG and CPG.

Quote: "Where is AI in GDP statistics?"

"The cost of using AI models has declined at about 94 percent per year, reflecting falling prices for given AI capability levels." - "Where is AI in GDP statistics?" - May 2026 - Anton Korinek (PIIE) and Patrick McKelvey (Bank of Canada)

Economic transformations rarely begin with booming revenues; they begin when the cost of a critical input collapses. In digital industries, that input is often computation, and over the past few years the price of delivering a fixed level of frontier AI capability has fallen at a pace that is extreme even by the standards of information technology. The resulting disconnect between the scale of technological change and the apparent modesty of measured output growth exposes structural blind spots in national accounts and complicates how policymakers, firms, and workers interpret what is happening in the economy.

From compute to capability: what is actually getting cheaper?

The headline figure that the cost of using AI models has declined at roughly 94 percent per year refers not to a vague notion of "AI" becoming cheaper, but to an explicitly defined unit of capability. Korinek and McKelvey treat AI as a production sector whose output is task performance, such as benchmark scores or specific application-level competencies, and then ask: how much must a user spend in a given year to buy a fixed quantum of that performance, delivered via current models and infrastructure?

This cost falls because three forces compound on one another.

- First, data centre capacity devoted to AI is expanding rapidly, so fixed costs are spread across more inference and training tasks.

- Second, hardware improves: newer accelerators deliver more raw compute per dollar, continuing a variant of the long run efficiency gains associated with semiconductors.

- Third, and most powerfully, algorithms become more efficient. For a given performance level, the amount of compute required falls sharply, so the same hardware can deliver much more capability.

When combined, these factors imply that the effective price of a given performance level falls far faster than nominal AI spending grows. Korinek and McKelvey estimate that nominal AI compute spending in the United States grew at more than 140 percent per year in 2024 and 2025, while raw compute capacity used for AI grew at over 200 percent. Yet after adjusting for algorithmic progress and falling API prices at fixed performance, the quality-adjusted output of the AI sector rises at more than 2 000 percent per year. A 94 percent annual price decline is simply the mirror image of that explosive quantity growth: if the quality-adjusted quantity of AI services rises by a factor of roughly 20, but total spending rises far more modestly, the implied price per unit must fall towards zero.

Invisible boom: why GDP barely moves

Such dynamics create a statistical paradox. On one hand, AI production, understood as capability delivered, is exploding. On the other hand, conventional GDP statistics register only a relatively small sector with nominal revenues around USD 250 billion in 2025 in the United States, a figure that, while large, remains modest relative to the overall economy. Measured in this way, the AI sector looks like a fast growing but not dominant niche.

The paradox arises because GDP records market transactions, not physical or functional quantities. A national accountant records how much was spent on cloud services, AI chips, and model access, weighted by prevailing prices. When those prices fall by nearly 94 percent per year for a given capability, revenue growth is capped even if the real quantity of AI services explodes. The situation echoes the history of semiconductors, where each generation of chips became dramatically cheaper per unit of performance, so the sector's GDP share remained modest while the underlying computational capacity grew exponentially.

This has two implications. First, the AI sector's footprint in GDP will systematically understate its contribution to productive capacity when prices fall faster than quantities rise in monetary terms. Second, any attempt to infer AI's real economic weight from observed revenues will be misleading if quality-adjusted output is not explicitly constructed, as Korinek and McKelvey attempt to do through an "AI GDP" satellite account.

Hedonics, quality adjustment, and their limits

Economic statisticians have tools to deal with products whose quality improves rapidly while prices decline, most notably hedonic price indices. In such approaches, statisticians regress prices on measurable characteristics of goods, effectively asking what portion of a price change is due to quality improvements rather than pure inflation or deflation. For AI, the relevant "characteristic" is model capability, such as benchmark scores or error rates on defined tasks. By comparing the cost of delivering a fixed performance bundle over time, one can infer the true price index for AI services.

Korinek and McKelvey's work can be read as a sectoral application of this logic: they construct quality-adjusted AI output by combining data on spending, compute, and algorithmic progress, effectively backing out an AI-specific price index that falls at the extraordinary rate implied by their 94 percent figure. However, as commentators have noted, such hedonic adjustments, while powerful for describing production-side changes, do not automatically capture the value users derive from those improvements.

GDP is built around market transactions. Even if the price of a unit of AI capability falls close to zero, a firm might use it to create new products, automate internal processes, or enable services that were previously impossible. The welfare gain from such applications can be vast, but unless it translates into higher measured revenues or explicit quality adjustments in downstream sectors, the national accounts will register only incremental changes. Hedonic indices for AI chip performance or API capability therefore bridge only part of the gap between technical progress and measured welfare.

Inside the 94 percent: compounding forces and simple formalisation

To understand the backstory more formally, consider a simplified relationship among spending, capability, and price. Let denote the quality-adjusted quantity of AI services delivered in period , such as a standardised unit of benchmark-equivalent inference. Let be the price per unit of this quality-adjusted output, and the total expenditure on AI services in that period. In a standard national accounts identity, one can write .

Suppose quality-adjusted output grows at a gross factor each year, while expenditure grows at . Then the implied gross factor for prices satisfies . If rises by more than 2 000 percent per year, which corresponds to a factor of around 21, and grows by roughly 2,4 (consistent with 140 percent spending growth), then , implying a price decline of about 89 percent in this stylised example. Korinek and McKelvey's more granular estimates, incorporating detailed compute and algorithmic data, yield an even steeper effective decline, around 94 percent per year.

Crucially, this decline is not driven by a single mechanism. Hardware efficiency enters mainly through more operations per joule and per dollar, while algorithmic progress reduces the operations needed for a given performance. If denotes hardware efficiency and algorithmic efficiency, the total effective cost per unit capability can be conceptualised as proportional to . Rapid growth in both and yields multiplicative declines in unit cost, helping explain how price reductions of this magnitude are possible within a few years.

The AI sector as a "producer of producers"

In their policy brief and working paper, Korinek and McKelvey argue that AI should be treated as a distinct production sector whose main output is an intermediate input into other activities, somewhat analogous to electricity or cloud infrastructure. The falling price of AI capability therefore works its way through the economy not primarily via consumer purchases, but via firms embedding AI into their internal processes and products.

This framing underscores the strategic significance of the 94 percent figure. For a firm, AI capability is a cost line item: the expense of integrating a model into a workflow, running inference at scale, or fine-tuning for specific tasks. When that cost collapses year on year, projects that previously failed a cost-benefit test suddenly become feasible. A workflow that was uneconomic to automate at last year's prices becomes attractive today; a product feature that once required a human-intensive process becomes a near-zero marginal cost AI service.

The competitive dynamics that follow resemble those of earlier general-purpose technologies. Early adopters may pay high prices for frontier models and custom integration, effectively subsidising the development and scaling that will later be available much more cheaply to others. Over time, as per-unit costs fall steeply, the barrier to adoption shifts from capital expense towards organisational capability: data readiness, process redesign, and capability to oversee AI systems become the binding constraints, not access to the technology itself.

Why national accounts struggle with nearly-free capability

The more rapidly AI capability becomes cheap, the more it undermines certain implicit assumptions in national accounts. Much of the national accounts architecture was built for an economy where major productivity-enhancing inputs were physical capital or labour time, both of which have relatively stable per-unit costs and clear ownership. When a firm invests in a new factory, that spending appears directly as gross fixed capital formation. When it hires workers, the resulting wage bill enters compensation of employees.

By contrast, a large share of AI's impact may come from non-market uses of near-zero-price tools within organisations. An employee who previously spent hours on routine drafting can now use an internal AI system to complete the same work in minutes. The value of the time freed up may be high, but if no explicit price is charged internally for those AI interactions, the national accounts see only unchanged wages and modest cloud spending. The dramatic effective price decline for AI capability therefore amplifies an existing challenge in measuring intangible capital and internal process improvements, pushing more of the productivity action into statistical shadows.

Korinek and McKelvey's proposed AI satellite accounts reflect a recognition that, in such a setting, standard aggregates like GDP need complementary measures that explicitly track the production and diffusion of AI capability. By treating AI as a distinct sector and constructing quality-adjusted output measures, their work provides an alternative lens through which the 94 percent price decline is not an oddity, but a central organising fact about the emerging AI economy.

Debates and objections: is the price decline overstated?

Despite the methodological care in the underlying work, several lines of debate arise around such extreme figures. One concern is that quality measurement for AI is inherently difficult. Benchmark performance may not map neatly onto real-world usefulness; models can be highly capable on standard tests while performing unevenly in complex, context-rich tasks. If the chosen capability metric overstates the functional improvement users actually experience, then the implied price decline for "true" capability may be overstated.

Another debate concerns the representativeness of the data. API prices for leading providers and benchmarked model performance may describe the frontier of commercially accessible AI, but not the entire distribution of tools used by firms and individuals. Smaller models, on-premises deployments, and open-source systems may follow different cost and capability trajectories. If most economic activity uses non-frontier systems, then the average price decline experienced in practice might be less dramatic than the headline 94 percent.

There is also a conceptual objection grounded in welfare economics. Even if the cost of achieving a given level of benchmarked performance falls dramatically, the welfare value of moving from one performance level to another may be highly non-linear. Many tasks exhibit threshold behaviours: a model that fails 20 percent of the time may be unusable, while reducing the failure rate to 3 percent suddenly unlocks a wide range of applications. In such cases, the relationship between price per benchmark unit and welfare per benchmark unit is complex. Critics may argue that focusing on cost per benchmark ignores the relationship between capability and the set of economically meaningful tasks that the model can undertake.

Korinek and McKelvey's framework does not claim to solve these normative issues; rather, it aims to construct a consistent, production-side measure of AI output and prices. For that purpose, using observable prices for defined bundles of API access at fixed performance, together with estimates of algorithmic efficiency, is a defensible approach, even if it cannot fully capture user surplus or task threshold effects.

The strategic tension: deflationary technology, inflationary narratives

Beyond statistical debates, the notion of a 94 percent annual decline in AI costs exposes a strategic tension. Public and policy discourse often frames AI as a booming sector commanding enormous valuations, attracting massive investment, and concentrating economic power. On this view, AI appears as a source of "inflation" in technological rents: a handful of firms supplying indispensable capabilities to the rest of the economy at high mark-ups.

Yet the production-side arithmetic tells a different story. If prices for a fixed capability bundle fall almost completely each year, then any static snapshot of mark-ups risks being misleading. The surplus is shared not only via producer profits but also via rapidly improving terms of trade for AI users. A firm that signs a long-term contract for model access today may find, within a year or two, that the same or better capability is available at a fraction of the cost, forcing providers into continuous price cutting or capability upgrades merely to sustain revenue.

This dynamic complicates policy responses. On one hand, concerns about market power, data advantages, and lock-in remain salient, especially where switching costs and proprietary models create barriers to entry. On the other hand, the technology's internal deflationary pressure is intense, pushing prices downward independent of regulatory intervention. Policymakers must therefore distinguish between static concentration in market structure and dynamic competition driven by rapid cost declines and algorithmic innovation.

Why it matters for productivity, labour, and policy

For productivity analysis, the core implication is that GDP-based measures will lag and understate AI's true impact if they fail to incorporate quality-adjusted AI output or downstream quality improvements in AI-intensive sectors. If a firm maintains constant prices but uses cheaper AI capability to improve product quality or reduce internal costs, standard productivity metrics may capture only a fraction of the gains. This echoes earlier episodes with digital technologies, but the speed and magnitude of AI price changes make the divergence more acute.

For labour markets, the falling cost of capability increases the economic incentive to substitute or augment labour with AI across more tasks. As unit costs fall, it becomes profitable to apply AI to lower-value tasks that previously did not justify automation. This may accelerate the reallocation of tasks within occupations: routine cognitive work is increasingly shifted onto models, while human workers focus on supervision, exception handling, and tasks that remain resistant to automation. The distributional consequences of such a shift depend heavily on institutions, bargaining power, and the pace at which new task categories and roles emerge.

For macroeconomic policy, the presence of a rapidly deflating technology sector complicates inflation measurement and interpretation. If AI services become dramatically cheaper while their use spreads through the economy, properly constructed price indices would show strong deflationary pressure in AI-intensive categories. Without explicit quality adjustments, much of this effect may be misclassified or missed, leading policymakers to misread the balance between demand-driven inflation and supply-driven cost reductions.

Finally, for industrial strategy, acknowledging the 94 percent annual price decline is crucial. It suggests that national comparative advantage may hinge less on temporary access to a scarce, expensive technology and more on the ability to build complementary assets: high-quality data, organisational know-how, regulatory frameworks that permit experimentation, and human capital capable of reconfiguring processes around abundant AI capability. Governments that focus narrowly on subsidising AI infrastructure risk chasing a moving target, while those that invest in absorptive capacity and measurement infrastructure may be better positioned to harness AI's true economic potential.

In this light, the dramatic fall in the cost of AI capability is not just a technical statistic. It is a sign that a general-purpose technology is approaching a regime of near-ubiquity, where the marginal cost of deploying intelligence-like services in software approaches that of bandwidth or storage. How societies measure, govern, and adapt to that shift will shape the extent to which AI's productive possibilities become visible not only in GDP but in lived economic outcomes.

Term: Channel advocacy - FMCG / CPG

"In FMCG (Fast-Moving Consumer Goods) and CPG (Consumer Packaged Goods), channel advocacy (also referred to as trade advocacy) is the strategy of educating and incentivizing supply chain intermediaries-such as distributors, wholesalers, dealers, and retail staff-so they actively promote and recommend your products to shoppers." - Channel advocacy - FMCG / CPG

Purchase decisions for everyday products are often made in seconds, in crowded aisles or on small mobile screens, with limited consumer attention and minimal deliberate comparison. In that environment, whoever shapes the recommendation at the point of choice - whether a merchandiser, pharmacist, store associate, category manager, marketplace algorithm, or retailer-owned media - can quietly redirect substantial demand without ever touching consumer advertising budgets. This leverage over the moment of choice is where channel advocacy becomes commercially decisive.

Why intermediaries matter more than most brand teams admit

FMCG and CPG markets are characterised by intense competition, high SKU density, and relatively low consumer involvement for many categories. Price promotions, habit, and convenience dominate behaviour, but distribution and in-store execution often determine which specific brand captures the basket. As manufacturers expanded into modern trade, e-commerce, quick commerce, and marketplace channels, they traded some direct control over shopper contact for scale and reach. Retailers, wholesalers, distributors, and their staff emerged as powerful gatekeepers not just of physical availability, but of mental availability at the shelf.

In practice, assortment decisions, facings, shelf location, secondary displays, in-app recommendations, and informal staff suggestions all act as filters before the shopper sees the full competitive set. When a pharmacist or beauty adviser reaches for a particular brand first, or a grocer grants a secondary endcap to a preferred supplier, they are exercising channel advocacy. The difference between neutral execution and active advocacy can translate into large swings in share in categories where consumers are willing to switch within a repertoire of acceptable brands.

Substance and practical meaning of channel advocacy

In operational terms, channel advocacy is about three linked behaviours by intermediaries:

- They give a product advantaged visibility - more facings, better shelf position, or preferential search and recommendation treatment online.

- They provide favourable framing - positive explanations, comparisons, or reassurance to shoppers for that brand rather than alternatives.

- They show discretionary effort - choosing to replenish it first, include it in displays, push its coupons, or feature it in retailer communications when they are not contractually obliged to do so.

To elicit those behaviours, manufacturers deploy a mix of education, incentives, tools, and relationship-building along the route to market. Field forces train retailer staff on product benefits, assortment roles, and solution-selling narratives. Trade marketing teams fund displays, in-store activations, and retailer media designed to make it easy and rewarding for the intermediary to spotlight the brand. Joint business planning with key accounts aligns promotions and space allocation with mutual financial objectives.

The practical difference between channel advocacy and generic trade spend lies in intentionality. Rather than simply paying for volume through discounts, advocacy programmes aim to change how intermediaries think about, prioritise, and recommend the brand. Price may still be part of the toolkit, but the focus is on durable preference in the channel's decisions, not just short-lived spikes in throughput.

A simple behavioural model of channel advocacy

Although trade advocacy is often discussed qualitatively, it can be framed with a behavioural response function linking manufacturer actions to intermediary support. Consider an intermediary's advocacy level represented by a score , combining factors such as share of shelf, recommendation rate, and inclusion in promotions. A stylised representation might be:

where:

- captures education inputs (training hours, quality of product information, availability of selling tools).

- represents incentives (trade margins, bonuses tied to distribution or display, contest mechanics).

- reflects relationship capital (joint planning intensity, account service levels, perceived fairness and reliability).

- is a baseline reflecting structural factors (category role, retailer strategy).

- captures noise, including competitor activity and exogenous shocks.

Manufacturers effectively choose combinations of , , and under budget and organisational constraints to maximise , subject to diminishing returns and retailer-specific response patterns. In practice, these parameters are estimated indirectly via experiments and econometric models that relate changes in advocacy measures to sales outcomes at store or account level.

At the next layer, advocacy contributes to sales through executional levers. For a given store and category, a simplified decomposition of brand sales could be:

with:

- = numeric and weighted distribution (availability across outlets and space share).

- = price and promotion index (relative price, discount depth, promotional presence).

- = conversion rate among shoppers who encounter the product.

Channel advocacy typically influences all three: supportive intermediaries are more inclined to list the full range and maintain availability (), to accept and execute promotions effectively (), and to recommend or signpost the brand in a way that increases conversion (). The measurable uplift in then provides an empirical basis for valuing advocacy investments versus alternative marketing uses of funds.

Mechanics across different intermediaries

The mechanisms through which advocacy is built vary materially by type of intermediary.

Distributors and wholesalers

For upstream partners, the central question is which brands they prioritise with their own field forces and route planning. Higher advocacy can mean more frequent calls, better stock holding, and active pitching to retail customers. Manufacturers typically influence this through margin structures, co-funded sales programmes, exclusive territories, and practical enablement such as sales apps, demo materials, and supply reliability.

Education at this level is less about consumer benefits and more about category economics, route efficiency, and the distributor's own profitability. When intermediaries see clear economic value from pushing one supplier over another, advocacy follows.

Retailers and buying offices

At retail head-office level, advocacy manifests in assortment decisions, planograms, promotional calendars, and digital shelf treatment. Here, data-sharing and joint analytics become critical. Manufacturers that provide credible shopper insights, category growth strategies, and tailored promotions aligned to retailer missions earn a reputation as category captains or trusted advisors. In turn, they are more likely to receive preferential space, participation in hero events, and integration into retailer media.

In this arena, sophisticated CPGs increasingly combine transactional metrics with softer relationship indicators, scoring accounts on collaboration depth and using structured engagement plans. The trade-off is often between investing more per key partner to deepen advocacy versus spreading budgets to maintain parity across multiple customers.

Store staff and local influencers

In pharmacies, beauty counters, specialist pet or baby outlets, and some grocery formats, individual staff can directly shape shopper decisions through personal recommendations. Training, sampling, and simple incentive schemes - from sales contests to recognition programmes - are common tools. The goal is to move these staff from basic product familiarity to confident advocacy, equipped with quick, credible reasons to reach for one brand first.

Digital channels create a parallel universe of intermediaries: personal shoppers on quick commerce platforms, marketplace reviewers, independent creators, and community moderators. Many of the same levers apply - education, access to product, and recognition - but programmes must respect platform rules and aim for authentic rather than scripted advocacy.

Incentives and the economics of trade advocacy

Financial mechanics underpin much of channel advocacy. Trade budgets in FMCG commonly absorb a large share of marketing spend, often exceeding brand media budgets in mature markets. Within that envelope, funds must be apportioned between volume-driving discounts and advocacy-building investments.

Manufacturers therefore ask three questions:

- What incremental gross profit is generated by a given level of advocacy versus a similar spend on consumer media or price promotion?

- How persistent is the advocacy effect after incentives or programmes end?

- How much of the benefit is captured by the intermediary rather than the manufacturer, through margin expansion or fee extraction?

To answer these, leading companies build response curves that link changes in shelf share, compliance, and recommendation metrics to scanned sales, then model the net present value of advocacy programmes. Threshold effects are common: below a certain spend, conditions may not meaningfully shift retailer behaviour; beyond another point, incremental dollars mainly improve the intermediary's economics without further changing execution.

The economics also vary by channel maturity. In markets where modern trade is highly consolidated, large retailers can exert strong bargaining power, turning some advocacy investments into quasi-mandatory listing or media fees. In more fragmented environments, smaller retailers and wholesalers may be more responsive to relatively modest support, and soft factors such as service reliability and credit terms can weigh heavily.

Schools of thought: relationship-led vs. performance-led approaches

Practitioners in FMCG / CPG channel management tend to cluster into two broad philosophies, even if most organisations combine elements of both.

Relationship-led advocacy

This school views long-term relational capital as the primary driver of preferential treatment. It emphasises trust, transparency, and joint value creation over transactional deals. Proponents invest heavily in:

- Category leadership capabilities and sophisticated shopper insight sharing.

- Senior-to-senior engagement and joint business planning frameworks.

- Operational excellence: dependable service levels, issue resolution, and supply chain collaboration.

The implicit assumption is that when a partner believes you grow their category profitably and reliably, they will naturally advocate your brands in decisions from assortment to in-store execution. This approach can be especially powerful in complex categories where category management expertise materially affects retailer performance.

Performance-led advocacy

The alternative school is more transactional and data-driven. It treats advocacy as a measurable output purchased through well-specified incentives and evaluated through granular metrics. Programmes feature:

- Pay-for-performance schemes linked to distribution, display, and share-of-shelf KPIs.

- Digital trade promotions and retail media packages optimised to short-term ROI.

- Test-and-learn pilots to calibrate which levers deliver the best response.

Advocacy here is less about personal relationships and more about objective results: if a retailer or distributor responds to an incentive by shifting space, listings, or recommendation rates, the programme is extended; if not, it is restructured or withdrawn. This philosophy aligns with the broader trend towards data-driven marketing and measurable performance in CPG.

Most leading companies try to reconcile these positions: they cultivate deep strategic partnerships with key customers while also enforcing rigorous measurement of investments, thus blending relational and performance logic.

Tensions, risks, and debates

Despite its benefits, channel advocacy raises several strategic and ethical questions.

Channel conflict and fairness

Heavy advocacy investments in a subset of partners can create perceptions of unfairness or neglect among others. For example, favouring modern trade chains with exclusive packs, promotions, or data may alienate traditional trade or e-commerce partners, especially when they see consumers diverted through those advantaged channels. Manufacturers must balance focus with coverage, often using differentiated propositions and clear communication to manage expectations.

Short-term volume vs. brand equity

There is constant pressure to convert advocacy budgets into immediate volume through price-led promotions. Yet repeated deep discounting can erode long-term brand value and condition both shoppers and intermediaries to wait for deals. The debate centres on whether advocacy should primarily support premium positioning and category value growth, or whether it is just another lever for price competition disguised as partnership-building.

Independence of advice and consumer welfare

When staff in pharmacies, baby stores, or specialist outlets recommend products, consumers often assume advice is unbiased and anchored in efficacy or safety rather than commercial incentives. Aggressive or opaque trade advocacy programmes risk undermining trust if recommendations are perceived as bought. Regulatory scrutiny in health-related and sensitive categories adds complexity. Many manufacturers respond by embedding evidence-based training, clear guidelines, and responsible incentive structures to ensure that any advocacy aligns with legitimate consumer benefits.

Data, retail media, and algorithmic advocacy

The rise of retailer-owned digital channels and retail media networks has shifted part of channel advocacy into algorithmic domains. Search rankings, recommendation widgets, loyalty app placements, and programmatic on-site ads can all be influenced by paid investments and data-sharing. The debate here concerns transparency and control: manufacturers must navigate between over-dependence on retailer data ecosystems and the need to build their own direct-to-consumer and first-party data assets.

Why trade advocacy still matters in a digital and DTC world

With the growth of direct-to-consumer models and brand-owned e-commerce, one might assume the importance of channel advocacy would decline as manufacturers build more direct relationships with end users. In practice, several trends reinforce its relevance.

- Even with DTC growth, the majority of FMCG volume continues to flow through third-party retail channels in most markets. Intermediaries remain gatekeepers of scale.

- Retailers are evolving into media owners and data partners, not just logistical intermediaries. Their ability to influence shopper journeys through digital touchpoints has increased, not diminished.

- Omnichannel purchasing journeys often involve a mix of online research, marketplace browsing, and offline purchase. Advocacy at any of these intermediated touchpoints can redirect the sale.

- In emerging markets, traditional trade and informal channels still play a dominant role, with trust in local retailers and wholesalers heavily shaping choices.

At the same time, the meaning of advocacy is being extended. It no longer concerns only human staff and physical facings; it includes algorithms, recommendation systems, and platform policies. Building advocacy in this context involves data collaboration, content optimisation, retail media investments, and active participation in marketplace ecosystems alongside classic field execution and training.

Capabilities required to excel

To operationalise channel advocacy effectively, FMCG and CPG companies need a blend of commercial, analytical, and behavioural capabilities:

- Advanced customer and channel segmentation, distinguishing accounts by influence on shoppers, strategic fit, and responsiveness to advocacy levers.

- Integrated trade investment management, with clear visibility of spend across discounts, media, and advocacy programmes, and robust ROI measurement.

- Shopper and execution analytics, linking store-level or digital execution (space, price, compliance, search position) with sales outcomes.

- Field and key-account excellence, including the ability to conduct joint planning, co-create category strategies, and manage multi-level relationships.

- Digital and retail media expertise, so that advocacy extends into retailer apps, marketplaces, and loyalty ecosystems coherently.

Ultimately, channel advocacy matters because a large portion of demand for everyday products is intermediated. Between the brand and the basket stand a series of actors and systems whose choices about what to list, display, recommend, and promote can amplify or mute even the strongest consumer campaigns. Treating those intermediaries as passive conduits leaves value on the table; engaging them as informed, incentivised advocates transforms the economics of growth in fast-moving categories.

Term: Overall Equipment Effectiveness (OEE) - Manufacturing

"Overall Equipment Effectiveness (OEE) is a standard manufacturing metric that measures the percentage of planned production time that is truly productive. It multiplies three core factors: Availability (tracking downtime losses), Performance (tracking speed losses), and Quality (tracking product defects)." - Overall Equipment Effectiveness (OEE) - Manufacturing

Overall Equipment Effectiveness addresses this by forcing every minute of planned production time to be accounted for as either genuinely productive or as a specific loss. Rather than asking only how many units were produced, it asks whether the equipment ran when it should, whether it ran as fast as it could, and whether it produced saleable product. This shift from counting output to examining how time is used is what makes the metric so influential in modern lean and digital manufacturing practices.

The structure of OEE: from time to effectiveness

The starting point is planned production time: the period in which the line, cell, or machine is scheduled to run, after excluding breaks, meetings, and other intervals when production is not intended. That planned window is progressively reduced by different categories of loss until only fully productive time remains. The ratio between fully productive time and planned production time is the overall effectiveness score.

Formally, OEE is built as the product of three factors: availability, performance, and quality. Each factor corresponds to a distinct loss family and can itself be defined through observable shop-floor data.

- Availability captures the share of planned production time during which the equipment is actually running. It is reduced by unplanned stops, such as breakdowns, and by planned stops that still erode capacity, such as changeovers or cleaning operations.

- Performance measures how close the running process stays to its designed or ideal speed. Micro-stops, minor jams, speed reductions, and other slow cycles all show up here.

- Quality reflects the fraction of output that meets specification without rework. Scrap, rework, and start-up rejects all reduce this component.

By construction, if any one factor collapses, the overall score collapses with it. A line that runs continuously but slowly, or one that runs fast but produces high scrap levels, will both show poor OEE despite apparently strong performance on a narrower metric.

Mathematical specification and parameter meanings

In quantitative terms, the classic shop-floor formulation defines availability as the ratio of run time to planned production time. If we denote planned production time by , stop time by , and run time by , then run time is defined as . Availability is then

Performance is expressed in terms of cycle times and counts. Let be the ideal cycle time per part, the total quantity started, and run time. The net run time at ideal speed is , and performance is defined as

Because rate is the inverse of cycle time, an equivalent formulation uses an ideal run rate and actual average rate :

Quality is often the simplest component: with the number of good units and the total units started,

The overall metric combines all three:

Substituting the definitions of availability, performance, and quality yields a more compact expression:

showing directly that OEE is the ratio of fully productive time () to planned production time . In practical terms, this is the share of scheduled time spent producing good parts at ideal speed without interruption.

Practical meaning on the shop floor

On a typical line, OEE is tracked at the level of a constraint machine, a cell, or an entire value stream. Because it is time-based, it connects operational details to financial outcomes. Improving OEE by, say, 5 percentage points allows a plant to ship more product without new capital investment, lowering unit costs and often avoiding or deferring major capacity projects.

Its practical power lies in the decomposition. A single composite number is useful for benchmarking, but continuous improvement requires tracing the score back to specific, observed events. To that end, manufacturers classify stoppages into categories such as breakdowns, changeovers, material shortages, and operator absence; speed losses into micro-stops and reduced speed periods; and quality losses into start-up scrap, process defects, and packaging damage. Each category can then be tied to root-cause analyses and countermeasures.

For example, a packaging line may exhibit strong availability but weak performance due to numerous small jams that operators clear within seconds. Traditional downtime accounting might ignore these micro-stops as insignificant, but in aggregate they can consume substantial net run time. Because OEE performance is based on ideal cycle time across all units, these small inefficiencies are visible in the metric where they would otherwise be hidden.

Relationship to the Six Big Losses and lean manufacturing

OEE is closely aligned with the lean manufacturing notion of the Six Big Losses, which group common efficiency drains into categories of breakdowns, setup and adjustments, small stops, reduced speed, start-up rejects, and production rejects. Each of these aligns with one of the three OEE factors: breakdowns and setups with availability, small stops and reduced speed with performance, and start-up and production rejects with quality.

This mapping makes the metric compatible with Total Productive Maintenance (TPM) and other lean frameworks. Autonomous maintenance routines, quick-changeover projects, and error-proofing initiatives are all partly justified and monitored through their impact on OEE. Plants can set target ranges, such as striving for scores above 85 % on critical assets, and then use the loss breakdown to cascade responsibilities and tasks to maintenance, engineering, and operations teams.

Data requirements and digital implementation

To calculate meaningful OEE, three basic data streams are needed: time, counts, and classifications. Time stamps demarcate planned production windows, downtime events, and run periods. Counters track total units and good units. Classifications describe the causes of downtime, speed loss, and quality loss. Historically, many factories collected this information manually using spreadsheets or paper forms, but such methods are prone to error and often too coarse to capture small yet frequent losses.

Modern digital manufacturing platforms connect machine sensors, programmable logic controllers, and quality systems to automatically gather run/stop signals, cycle times, and counts. Operators add contextual labels for ambiguous events, such as changeover delays or material waiting time. Real-time dashboards then compute availability, performance, quality, and OEE for each machine or line and present them alongside shift targets.

Advanced analytics can go a step further, using time-series data to segment downtime patterns, correlate speed losses with specific products or settings, and prioritise issues by their contribution to lost fully productive time. By embedding OEE into a broader Industrial Internet of Things architecture, companies gain a more precise, dynamic view of how equipment performance interacts with scheduling, maintenance, and quality decisions.

Schools of thought and variant formulations

Despite a common core, there are several schools of thought around how strictly to define and apply OEE. One axis of debate is scope. Some practitioners argue that the metric should be applied only to bottleneck equipment, where marginal improvements directly translate into higher throughput. Others advocate broader application across all assets, using OEE mainly as a loss-visibility and behavioural tool rather than as a formal capacity measure.

Another debate concerns how to treat planned versus unplanned downtime. Classical definitions include both breakdowns and changeovers as stop time within availability, on the grounds that both represent lost productive potential. Some organisations, however, track a variant sometimes called OOE (Overall Operations Effectiveness), which includes planned downtime in the denominator and focuses OEE more narrowly on unplanned losses. This can be useful for maintenance-focused diagnostics, but it risks underplaying the impact of long setups and frequent changeovers on overall capacity.

There is also discussion around the choice of ideal cycle time . A strict interpretation uses the theoretical fastest cycle time achievable by the equipment, based on design specifications or demonstrated best performance. A more pragmatic approach sometimes uses a sustainable best-known rate, discounting extreme conditions that might compromise quality or asset health. The former maximises ambition, while the latter may align better with safety and reliability constraints.

Finally, some commentators critique the tendency to chase a single headline figure. Because OEE multiplies three ratios, substantial effort may be required to shift the composite number once it is already moderate to high. A plant moving from 70 % to 75 % could be achieving this through meaningful reductions in downtime, or merely by tightening quality thresholds and scrapping borderline product less aggressively. Disaggregated loss analysis remains essential to avoid gaming the metric.

Interpretation benchmarks and limitations

In many manufacturing contexts, perfect OEE of 100 % would mean manufacturing only good parts, at maximum speed, with no stops of any kind. In practice this is unattainable, and industry guidelines sometimes suggest that scores around 85 % are world-class for discrete manufacturing, with figures in the 60-70 % range common in typical plants. However, absolute benchmarks must be interpreted with caution. Process industries with continuous operations, and highly automated plants with long production runs, usually achieve higher baseline scores than high-mix, low-volume environments with frequent changeovers.

Several limitations of the metric are worth keeping in mind. First, OEE is agnostic to demand; a line can show excellent effectiveness while producing stock that is not needed, leading to inventory build-up. Second, the metric does not consider labour utilisation or energy efficiency directly, although these may correlate with equipment effectiveness. Third, an exclusive focus on maximising OEE can conflict with flexibility and responsiveness; for example, reducing changeover frequency to boost availability might worsen lead times or service levels for smaller customers.

There is also measurement risk. Poorly calibrated sensors, inconsistent scrap recording, and ambiguous classification of stoppages all degrade the reliability of the metric. To avoid misleading results, organisations need clear definitions, operator training, and periodic audits of data integrity.

Linking OEE to financial performance and strategy

Despite these caveats, OEE remains a powerful bridge between technical performance and financial outcomes. Higher availability expands the time during which the plant can generate revenue; higher performance raises the volume of units produced per hour of labour and overhead; better quality reduces rework, scrap costs, and warranty claims. Because all three factors are multiplicative, improvements in each area compound.

From a strategic perspective, management can use OEE profiles to support make-versus-buy decisions, capital budgeting, and footprint optimisation. A plant operating with low OEE on critical assets may prefer to invest in debottlenecking projects rather than new lines, whereas another with consistently high scores and strong demand may be justified in adding capacity. At network level, comparing OEE across sites helps identify best practices and structural differences in product mix, maintenance maturity, and workforce skills.

In digital transformation programmes, OEE often acts as a central outcome metric. Predictive maintenance technologies are evaluated by their ability to reduce unplanned downtime and hence raise availability. Advanced process control and optimisation aim to boost performance by tightening operating windows and minimising slow cycles. Machine vision and inline inspection systems target the quality component by catching defects earlier and stabilising processes.

Why the concept still matters

As manufacturing becomes more connected and data-rich, there is a temptation to abandon composite metrics in favour of highly granular dashboards. Yet the simplicity and universality of availability, performance, and quality still make OEE a valuable organising concept. It provides a common language for operators, engineers, and executives; it encourages disciplined categorisation of losses; and it highlights the trade-offs between different improvement initiatives.

Moreover, the metric adapts well to modern constraints. Decarbonisation initiatives, for instance, frequently start with improving utilisation of existing assets to avoid emissions embedded in new equipment. Better OEE can support sustainability goals by reducing energy wasted in scrap production and in frequent start-ups and shutdowns. Likewise, in environments with volatile demand, maintaining high effectiveness on flexible assets can be a competitive differentiator, enabling rapid response without excessive capital buffers.

The continuing relevance of OEE does not lie in the headline percentage alone, but in the operational discipline that comes with making every minute of planned production time visible, classified, and contestable. When used thoughtfully and supported by robust data, it remains one of the most practical and actionable tools for understanding and improving manufacturing performance.

Quote: James Brocklebank - Co-chair of Advent International

"This is an AI that has been trained on all of our investment committee papers over the last 13 years. So, it's ingested everything - all the deals that we've shown the committee. And interestingly, it has the perspective on what happened to the deals that we did, but also the deals that we didn't do." - James Brocklebank - Co-chair of Advent International

The central vulnerability in institutional investing is not a lack of information but a failure to remember, interrogate and learn from past decisions at the moment new capital is committed. Files are archived, partners move on, and narratives about past wins and misses harden into anecdotes rather than data. In private equity, where each fund may back only a few dozen companies over a decade, this amnesia is particularly costly: every decision implicitly rests on a tiny sample of lived experience, filtered through selective memory and shifting market regimes. The attempt to encode an entire firm's investment history into a working artificial intelligence is, at heart, an attempt to eliminate that amnesia and to systematise learning from the full record of what was done - and what was passed over.

Private equity investment committees are designed as collective safeguards against error. Senior partners gather around a table, review thick papers laying out a thesis, risks, scenarios, and proposed structures, and interrogate the deal team until they are satisfied that the risk-reward trade-off justifies action. Formally, this process is meant to be rigorous and dispassionate. In practice, it is constrained by time, cognitive bandwidth and the tacit hierarchies of a partnership. Only a subset of past cases can be recalled in detail, and those that come to mind are often the most spectacular successes or failures, not the quiet, representative middle. An AI that has ingested every investment committee paper over more than a decade intervenes directly in this mechanism: it sits, figuratively, at the same table, but with an eidetic memory of every prior deal.

The factual context is highly specific. Over 13 years, a large global private equity firm will typically generate hundreds of investment committee papers spanning different sectors, geographies and economic cycles. Each paper encapsulates the state of knowledge and conviction at a point in time: management projections, competitive dynamics, due diligence findings, capital structures, exit scenarios and downside cases. Some of these deals were approved and executed, producing a realised track record of internal rates of return, multiples of invested capital, write-offs or restructurings. Others were rejected and never funded, but their subsequent trajectories can still be observed via public information, market data or later approaches. Training an AI on this full corpus turns the investment committee's hidden archive into a structured dataset of assumptions and outcomes.

What gives this approach its distinctive bite is the claim that the AI holds a perspective on both the deals that were done and those that were rejected. Conventional performance analysis focuses on the realised portfolio, dissecting which transactions created or destroyed value and why. The opportunity cost of deals passed over is usually discussed qualitatively, if at all. With a model that has systematically absorbed the records of rejected deals, it becomes possible to ask: when the committee declined an opportunity, how did that company actually perform in the market? Were there patterns in the kinds of deals that were reflexively dismissed but later succeeded under different owners? Conversely, did the committee correctly avoid a set of structurally flawed situations that later underperformed? The AI's value lies less in clever pattern recognition and more in reconstructing this counterfactual history that human participants cannot reliably hold in their heads.

From a technical standpoint, building such a system resembles constructing a domain-specific large language model or retrieval-augmented assistant specialised on deal memos. The raw material is unstructured text: multi-page internal papers, perhaps augmented by spreadsheets and slide decks. These need to be cleaned, anonymised where appropriate, and linked to outcome data: entry valuations, revenue and EBITDA paths, leverage levels, realised returns and holding periods. A typical pipeline might convert each paper into text embeddings and index them, enabling the model to retrieve the most relevant precedents when a new memo is submitted. On top of this, supervised or reinforcement learning could be used to align the model's outputs with the kind of questioning and scepticism the committee wants - highlighting, for example, where current assumptions about margin expansion or multiple arbitrage resemble past cases that disappointed.

Although this configuration rarely requires explicit equations, the conceptual structure is similar to mapping each historical deal to a feature vector (sector, geography, leverage, growth assumptions, sponsor plan) and outcome (return metrics, qualitative success or failure). The new deal is another point , and the AI's task is to estimate properties of by learning from the joint distribution of past . Unlike a traditional predictive model, however, a language-based system can expose not just a numeric forecast but a narrative: "this assumption resembles deal A, B and C, which underperformed for these reasons". That narrative interface is precisely what makes the tool usable in a committee that operates through argument and persuasion rather than pure quantitative scoring.

Strategic tension: judgement versus automation

Deploying an AI in this way exposes a powerful tension at the heart of private equity. The business sells itself on human judgement: the ability of partners to see around corners, interpret nuance in management teams, and navigate complex regulatory or technological change. At the same time, investors demand repeatability and process discipline, especially for firms managing tens of billions in commitments. Codifying 13 years of decisions into a model that interrogates new deals is a step towards industrialising judgement - turning what were once informal mental checklists into a systematised set of questions and warnings.

That shift cuts both ways. On the one hand, an AI grounded in the firm's own history can act as a powerful guardian against overconfidence and fad-chasing. When enthusiasm builds for a hot subsector that has already burned the firm in previous cycles, the model can surface those scars instantly: pointing to prior memos with similar narratives that ended badly. It can also challenge cognitive biases such as confirmation bias, by recalling contrary evidence the team has quietly de-emphasised. On the other hand, there is a risk that partners begin to treat the AI's prompts as an authoritative view rather than a stimulus for debate. If the tool repeatedly flags risks around duration, leverage or customer concentration, committees might mechanically shy away from any deal that looks remotely similar to a past failure, even when the factual circumstances differ.

This tension is particularly acute because the dataset itself encodes past biases. If, for example, the firm historically underweighted technology investments and overindexed to business services, the AI's training material will reflect that skew. It may implicitly learn that certain sectors are "not for us" simply because internal memos framed them more sceptically or lacked later outcome data. The model is also only as good as the analysis that went into past papers; if bad assumptions were never corrected in the documentation, the AI may take them at face value. The danger is that, without careful design and governance, the system could become a sophisticated echo chamber - amplifying, rather than interrogating, the partnership's existing worldview.

Why private equity is a fertile testbed

Private equity's structural features make it unusually well-suited to this form of AI augmentation. First, the ratio of documentation to decisions is high: a single investment committee paper can run to dozens of pages, capturing granular thinking about a company at a moment in time. That means the training data are rich, domain-specific and already curated around a common template, which improves both model performance and comparability across deals. Second, the feedback horizon, while long, is ultimately discrete: each deal produces a realised return profile and qualitative assessment. Over 13 years, a global firm will have observed multiple full cycles of entry, ownership and exit, providing the model with completed outcome labels rather than perpetually open-ended forecasts.

Third, the environment is competitive and information-sensitive but still human-scale. A firm might review hundreds of potential investments in a year, but only a fraction make it to a full committee, and fewer still are executed. This creates a natural filter: the AI is trained not on every pitch deck that crosses a junior associate's desk, but on the subset that senior leadership deemed worthy of intensive scrutiny. Finally, the governance structure of an investment committee - with clear accountability, minutes and voting records - makes it feasible to embed a tool that formally "speaks" during deliberations, providing a traceable record of what it raised and how the committee responded.

The specific personality and track record of the individual championing such a system also matters. James Brocklebank has been associated with a strategy that embraces complexity: targeting situations where operational improvement, carve-outs and regulatory nuance create barriers to entry for more formulaic capital. In that context, an AI trained on the firm's own intellectual history is not a substitute for this complexity-driven edge but an amplifier. It can scan across hundreds of prior carve-outs, regulatory approvals or multi-jurisdictional integrations to highlight patterns that a single partner, however experienced, might miss. At the same time, someone whose philosophy treats complexity as an opportunity rather than a threat is more likely to view the AI as a challenging partner - a source of discomfort as well as reassurance - than as a box-ticking device.

Debates and objections

Despite the appeal, there are serious objections to this approach, both technical and philosophical. One objection is that the past 13 years may be a misleading guide to the next decade, especially in a world reshaped by digital platforms, geopolitical risk and monetary regime shifts. Training an AI on this period risks baking in patterns that were contingent on cheap capital, benign inflation and a particular form of globalisation. Critics would argue that by systematising these patterns, the firm could become less, not more, adaptive to regime change. Defenders might respond that the AI's value lies less in predicting returns mechanically and more in surfacing how the firm reasoned under different conditions - including moments when earlier assumptions broke down. The model can be tuned to highlight where underlying macro regimes differed, prompting committees to ask whether current conditions are analogous or structurally novel.

A second objection concerns explainability and responsibility. If an AI trained on internal memos expresses scepticism about a deal, to what extent should that sway the committee? If the deal later fails and the AI had raised red flags, limited partners might ask why its warnings were not heeded. Conversely, if the AI favoured a deal that underperforms, was there a failure in model governance? The more central the tool becomes, the more it becomes part of the fiduciary chain. That raises questions about validation, monitoring and documentation familiar from algorithmic trading and credit underwriting, but less tested in discretionary private equity. Some observers fear a creeping "automation of blame", in which partners seek refuge in the model's outputs to justify conservative decisions, reducing entrepreneurial risk-taking.

Third, there are concerns about confidentiality and security. Investment committee papers contain highly sensitive information: detailed financials, customer lists, trade secrets and personal data about executives. Training an AI on this corpus implies that all of these details now live inside a system that needs to be carefully isolated, audited and controlled. Even if built entirely on-premise or within a tightly ring-fenced environment, questions remain about who can query it, whether their prompts are logged, and how to prevent unintended leakage between deals or to external counterparties. The technical challenge is not just to make the model smart but to embed it within a robust operating framework that satisfies legal, regulatory and reputational constraints.

Finally, there is a cultural objection. Investment partnerships are built around human apprenticeship: junior professionals learn by watching how seniors debate, where they push hardest, and how they navigate ambiguity. Introducing an AI "voice" into that process might skew attention towards what the machine highlights rather than what the most experienced partner finds troubling. Some fear that future generations could grow up over-reliant on prompts generated by the system, losing the hard-won intuition that comes from wrestling personally with messy, incomplete information. Others counter that younger professionals are already fluent in using sophisticated tools and that the real risk is leaving them with inferior, ad hoc aids while pretending that the old ways of doing committee work remain fit for a more complex, data-rich environment.

Why this matters beyond one firm

The broader significance of training an AI on 13 years of investment committee papers extends well beyond a single private equity house. It offers a template for how other long-horizon, high-consequence decision-makers could institutionalise learning. Any organisation that makes episodic, complex decisions - central banks setting policy, boards approving major acquisitions, regulators ruling on systemic cases - generates a similar trail of internal documents. Most of that trail is effectively inert: stored for compliance, occasionally resurfaced during crises or post-mortems, but not actively mined in real time when new decisions are taken. Turning that archive into a live analytical layer could profoundly change how institutions remember and reason.

In finance specifically, the approach draws a sharp line between generic AI assistance and firm-specific "memory machines". Off-the-shelf models trained on public data can summarise earnings calls or generate draft memos, but they cannot know how a particular partnership thinks about risk, what trade-offs it has historically accepted, or which blind spots have been most costly. By contrast, a model that has ingested all of a firm's past decisions becomes a reflection of its internal culture, with the power both to reinforce and to challenge that culture. Competitively, firms that succeed in building such tools may enjoy a compound advantage: every new deal and outcome improves the dataset, which in turn sharpens the model's ability to interrogate the next wave of opportunities.

There is also a subtle shift in the nature of due diligence itself. Traditionally, diligence focuses outward: on the target company, its market and its risks. An AI trained on internal committee papers shifts part of the exercise inward, forcing the sponsor to perform due diligence on its own prior judgement. Instead of asking only "what could go wrong in this company?", the team must also ask "when we last saw a situation that felt like this, how did we misjudge it?". This reflexivity is uncomfortable but healthy, especially in an industry where success can breed complacency and narratives of inevitability around past wins.

Over time, such systems could also reshape stakeholder expectations. Limited partners might begin to ask not only for track records and team biographies but for descriptions of how AI-augmented governance works in practice. Questions about model bias, validation and override policies could become part of standard due diligence questionnaires. Regulators might inquire how algorithmic inputs are documented in investment decisions, particularly where pension savers or retail investors are indirectly exposed. Meanwhile, portfolio companies might gain or lose confidence depending on whether they perceive that critical decisions about their future are influenced by a tool trained on past deals in other sectors or regions.

Ultimately, training an AI on the investment committee's full history forces a confrontation with a deeper question: how much of what a successful private equity firm does can be codified? Some aspects - statistical patterns in leverage tolerance, sector rotation, or sensitivity to macro variables - are clearly amenable to analysis and automation. Others - the chemistry between a deal partner and a founder, the instinct that a management team will rise to a challenge, the significance of subtle shifts in regulatory tone - resist formalisation. The power of the described system lies not in collapsing this distinction but in making it visible. Each time the AI raises a pattern and the committee chooses to override or reinterpret it, the partnership is forced to articulate why this situation is different.

In that sense, the long-run impact of such a tool may be less about the specific deals it nudges towards acceptance or rejection, and more about the discipline it imposes on how a firm explains itself to itself. By preserving a rich, interrogable memory of both executed and declined deals, including their eventual outcomes, the AI becomes a standing invitation to revisit comfortable narratives, to test folk wisdom against evidence, and to re-examine how appetite for risk has shifted over the cycles. For an industry built on the promise of superior judgement, the willingness to subject that judgement to systematic, machine-assisted scrutiny may prove to be the real competitive differentiator.

Term: Revenue Growth Management (RGM) - FMCG / CPG

"Revenue Growth Management (RGM) is a data-driven strategy used by FMCG and CPG companies to maximize revenue and profit margins without relying solely on higher sales volume. It optimizes the entire commercial mix to ensure the right product reaches the right consumer on the right occasion." - Revenue Growth Management (RGM) - FMCG / CPG

Margin pressure in fast-moving consumer goods rarely comes from a single source. Retailers push for lower net prices, shoppers become more price sensitive during inflationary spikes, private labels advance, and input costs fluctuate unpredictably. Under these conditions, simply selling more units often destroys value rather than creating it, as volume is bought with discounting, deep promotions, or costly innovation that does not pay back. The central challenge is therefore not how to grow sales at any cost, but how to configure prices, packs, promotions, channels, and trade terms so that every additional unit sold contributes positively to profit.

The commercial problem RGM is trying to solve

Traditional top-line management in FMCG has often relied on blunt tools: across-the-board price increases, promotional intensity ramp-ups, or broad portfolio extensions. These approaches can yield short-term gains but frequently erode long-term brand equity, confuse shoppers, and complicate retailer relationships. When price architecture is inconsistent, promotions undercut base prices, and assortment is bloated, manufacturers end up transferring value to retailers and consumers rather than capturing it themselves.

The underlying mechanism is misalignment between three perspectives that must be reconciled. First, shoppers have discrete willingness-to-pay segments and occasion-based needs that are not fully captured by average price elasticities. Second, retailers focus on category profit, traffic, and basket size, not on any single brand, and they use their shelf space as a scarce asset to be allocated to the most productive SKUs. Third, manufacturers need sustainable gross margin and contribution to cover marketing, overheads, and innovation. Without a structured approach, these perspectives collide in annual negotiations, ad hoc promotions, and reactive pricing decisions, generating volatility instead of disciplined value creation.

Revenue Growth Management (RGM) arises as a response to this multi-sided optimisation problem: it builds a system to align shopper value, retailer economics, and manufacturer profit by orchestrating the full commercial mix with data and analytics.

Substantive meaning beyond the label

In practical terms, RGM is a cross-functional discipline that brings together trade marketing, category management, sales, finance, and revenue management to shape how the portfolio makes money in each market. Rather than treating price, promotion, pack, and trade terms as isolated levers owned by different teams, RGM defines a coherent strategy for how the business should earn its net revenue and margin across channels, customers, and shopper segments.

The aim is to grow both revenue and margin simultaneously by identifying and monetising hidden pockets of value. This may involve monetising convenience or premium attributes at the top of the portfolio, rationalising tail SKUs that dilute margin, redesigning pack sizes to better match occasions and price thresholds, or reallocating trade investment to the promotions that truly change shopper behaviour.

For FMCG and CPG manufacturers, RGM is not only about internal profitability. It also shapes how they collaborate with retailers: setting category growth agendas, defining the role of each brand and pack in the shelf, and agreeing on promotion mechanics that build the category instead of triggering price wars.

The core RGM levers in FMCG

Most frameworks converge on a small set of commercial levers that RGM systematically optimises. Commonly cited levers include pricing, promotion, assortment, pack architecture, trade terms, and sometimes channel or mix.

1. Pricing

Price is the most visible signal of value to shoppers and the main driver of net revenue per unit. In RGM, pricing decisions move from generic increases to carefully crafted price ladders across SKUs, brands, and channels. The goal is to define a price architecture that reflects perceived value tiers, minimises intra-portfolio cannibalisation, and respects retailer value equations.

Data-driven pricing under RGM involves analysing price elasticity by segment and channel, identifying optimal price points for different pack sizes, and simulating the margin impact of alternative list and net price scenarios. Instead of uniform changes, teams vary price moves by brand strength, role (traffic builder versus premium margin driver), and competitive intensity.

2. Promotions

Promotional investment is often one of the largest P&L lines in FMCG but historically has been poorly measured. RGM introduces rigorous promotional effectiveness analysis, seeking to understand which promotions generate true incremental volume versus subsidising base sales. The focus shifts from frequency and depth to efficiency, payback period, and long-term equity impact.

Practices include defining promotion floors and ceilings, limiting unprofitable mechanics, and calibrating event timing to category seasons and competitive activity. Leading companies link promotion plans to precise objectives such as switching, stock-building, or trial, and adjust mechanics accordingly.

3. Assortment

Assortment decisions determine which products appear in which stores and formats. Overexpansion of SKUs increases supply chain complexity and ties up working capital, while underrepresentation reduces availability on key occasions. RGM uses store-level and shopper-level data to identify the contribution of each SKU to category growth and profit, then rationalises or tailors assortments by channel and customer.

The objective is to focus shelf space on productive items that add incremental value rather than duplicating existing offers. This can imply eliminating low-rotation variants, elevating high-margin premium lines, or developing channel-exclusive SKUs that align with retailer strategies.

4. Pack architecture

Pack architecture links physical format, size, and configuration to price points and consumption occasions. By designing a logical ladder of packs that address different affordability thresholds and usage needs, manufacturers can tap into both premiumisation and downtrading trends without eroding margin. RGM analyses demand patterns to define optimal pack sizes for single-serve, family, and bulk formats across channels.

In inflationary contexts, pack resizing and format innovation become particularly powerful levers to manage perceived price increases while maintaining unit margins. Value packs, multipacks, and channel-specific formats (for discounters, e-commerce, convenience) are tuned to local shopper behaviour.

5. Trade and channel terms

Trade investment, discounts, and rebates determine the net price manufacturers realise after the complexities of retailer negotiations. RGM frameworks increasingly treat trade terms as a strategic lever: harmonising conditions across comparable customers, rewarding growth behaviours, and linking investment to joint business plans.

Channel strategy is often considered alongside trade terms, as different routes to market (modern trade, traditional trade, e-commerce, on-premise) require distinct price and pack architectures, as well as differentiated promotional mechanics. Advanced RGM decomposes performance by channel to decide where to allocate scarce commercial resources.

Data, analytics, and mathematical specification

Although RGM is fundamentally commercial, modern practice relies heavily on quantitative modelling. At a basic level, pricing and promotion decisions draw on demand models where sales volume depends on own price , competitor prices , promotion flags , and seasonality : . Elasticities derived from these models help simulate the impact of different actions on volume and revenue.

For example, a simple log-linear model might specify the relationship as , where is own-price elasticity and measures the proportional uplift from promotion. RGM teams use such estimates to project how a price increase or promotion depth change will affect both revenue and margin.

Margin optimisation frequently involves expressing profit as , where is unit cost and is the demand function. The task is to identify price levels that maximise , subject to competitive and retailer constraints. Portfolio-level models extend this to multiple SKUs , considering cannibalisation: .

On promotions, incremental volume is estimated by comparing promoted weeks to a counterfactual baseline , with ROI calculated as . Events falling below threshold ROI are candidates for redesign or elimination. In assortment work, decision rules may be grounded in metrics such as incremental profit contribution or transferability of demand to alternative SKUs, derived from choice models.

While not all organisations deploy complex econometrics, even simpler elasticity tables, price ladders, and promo scorecards embed the same logic: using quantitative relationships between price, volume, and cost to systematically steer the commercial mix rather than relying on intuition alone.

Key parameters and capabilities

For RGM systems to function, a set of parameters and organisational capabilities must be defined and maintained. At a technical level, core inputs include baseline volume, price elasticities by segment, incremental lifts by promotion mechanic, gross-to-net waterfalls by customer, cost-to-serve by channel, and SKU profitability contribution. These parameters underpin scenario simulations and decision guidelines.

On the organisational side, leading FMCG companies build dedicated RGM teams with clear mandates, governance, and links to the annual planning and budgeting cycles. Typical responsibilities include designing the price and pack strategy for a planning period, setting promo guardrails, providing analytical support for customer negotiations, and monitoring post-event performance. RGM often sits at the interface of marketing and sales, with strong involvement from finance to ensure that top-line decisions align with profit and cash generation objectives.

Technology enablers range from data lakes and pricing tools to promotion optimisation platforms and dashboards that track net revenue performance. However, advisory firms consistently highlight that tools alone are insufficient: capability building, incentives, and decision rights are responsible for the majority of impact.

Major schools of thought and frameworks

Although terminology varies, most consultancies and practitioners converge on similar RGM architectures for FMCG and CPG. One school emphasises the five commercial levers: pricing, pack architecture, promotions, trade terms, and channel strategy, and demonstrates how orchestrating these levers together reveals hidden value in portfolios. Another approach, often labelled net revenue management, frames RGM within a broader strategy-levers-enablers model, where a clear revenue strategy and organisational capabilities are seen as prerequisites for effective lever execution.

Some organisations lean towards shopper-centric RGM, starting from occasion-based segmentation and working backwards to define the optimal price-pack architecture and promotion role for each segment. Others adopt a more finance-driven lens, focusing first on gross-to-net leakage, mix effects, and structural margin improvement, then translating insights into commercial tactics. Both perspectives remain compatible and are increasingly integrated into end-to-end frameworks.

Differences also exist in how centralised RGM should be. One school advocates strong global guardrails and tools with local adaptation for market specifics, while another argues for heavily localised teams given the heterogeneity of retailer landscapes and shopper behaviour. Hybrid models, in which central teams define methodologies and platforms and local teams own decisions within those frameworks, have become common.

Tensions, trade-offs, and debates

RGM operates in a landscape of inherent tensions. A recurring debate concerns short-term promotion-driven revenue versus long-term brand equity and pricing power. Aggressive discounting may deliver quarterly targets but teach shoppers to wait for deals, undermining base price and future margin. Conversely, overly rigid adherence to premium positioning can lead to share loss in highly price-sensitive segments or in economic downturns. RGM provides the analytical clarity to quantify these trade-offs but cannot fully resolve the underlying strategic choices.

Another tension lies in retailer relationships. Optimising price and trade terms purely from the manufacturer perspective can harm collaboration if it ignores category profitability or the retailer's competitive context. Many successful RGM programmes actually deepen joint planning, using shared data and models to identify win-win interventions that grow category value rather than simply shifting margin. However, this demands transparency, trust, and advanced data-sharing arrangements, which are not always present.

There is also an internal cultural debate around who owns RGM decisions. Sales teams may view centrally imposed price or promo guidelines as constraints that make customer negotiations harder, while central revenue managers may see local deal-making as a source of margin leakage and complexity. Governance models, incentive schemes, and communication become critical in ensuring that RGM is perceived as an enabler of better deals rather than an administrative constraint.

Finally, the growing use of advanced analytics and AI in RGM raises questions about explainability and human judgment. Algorithmically optimised price and promo recommendations can be difficult to communicate to retailers or internal stakeholders. Many organisations therefore adopt a human-in-the-loop approach, where algorithms generate scenarios and humans adjudicate based on market knowledge and strategic intent.

Why RGM remains strategically important

Several structural trends in FMCG and CPG ensure that RGM will remain central to commercial strategy. Inflationary episodes and cost volatility heighten the need for disciplined price and pack management; manufacturers must pass through cost increases without triggering disproportionate volume losses or retailer conflict. RGM allows them to sequence price moves, adjust formats, and calibrate promotions to protect both share and profitability.

The rise of discounters, e-commerce, and direct-to-consumer channels multiplies price points and promotional environments. Without a coherent RGM system, pricing architecture becomes fragmented, leading to cross-channel conflicts, grey markets, and shopper confusion. Structured management of channel price corridors, promo intensity, and assortment is essential to maintain a stable brand value proposition across touchpoints.

Data availability is another driver. Loyalty data, ePOS feeds, and panel data provide granular visibility into shopper behaviour at the level of store, basket, and occasion. RGM frameworks are the mechanism by which this data is translated into actionable commercial decisions: which SKUs to list, where to place them, how to price and promote them, and where to invest trade budgets.

At the same time, investors increasingly scrutinise organic growth quality rather than just headline revenue expansion. The ability to demonstrate disciplined net revenue management, healthy price-mix contributions, and resilient margins has become a key component of equity narratives for major consumer goods companies.

In a world where unit volume growth is often constrained by demographics, saturation, or sustainability considerations, the ability to extract more value from each unit sold without alienating shoppers or partners becomes a competitive differentiator. Revenue Growth Management, as practised today in FMCG and CPG, provides the structured, data-driven means to do exactly that: orchestrating prices, promotions, packs, assortment, and trade terms so that profitable growth is designed rather than hoped for.