ARTIFICIAL INTELLIGENCE
An AI-native strategy firmGlobal Advisors: a consulting leader in defining quantified strategy, decreasing uncertainty, improving decisions, achieving measureable results.
A Different Kind of Partner in an AI World
AI-native strategy
consulting
Experienced hires
We are hiring experienced top-tier strategy consultants
Quantified Strategy
Decreased uncertainty, improved decisions
Global Advisors is a leader in defining quantified strategies, decreasing uncertainty, improving decisions and achieving measureable results.
We specialise in providing highly-analytical data-driven recommendations in the face of significant uncertainty.
We utilise advanced predictive analytics to build robust strategies and enable our clients to make calculated decisions.
We support implementation of adaptive capability and capacity.
Our latest
Thoughts
Global Advisors’ Thoughts: How Daniel Rowland is relevant to your business success
Recently Global Advisors hosted multi-stage ultra-marathon runner Daniel Rowland as he gave a talk about his training and racing approach. The talk happened prior to Daniel racing in the Kalahari Augrabies Extreme Marathon 2013 – a 250km multi-stage race that takes place over 7 days through the extreme heat and difficult terrain of the Kalahari Desert. Competitors carry their own food, bedding, etc – water and sleeping tents are provided.
Daniel Rowland winning the Kalahari Augrabies Extreme Marathon 2013 (picture Hermien Webb Photography, Facebook)
Daniel won in a course record time. Earlier this year Daniel won the Atacama Crossing – another 250km multi-stage self-supported desert race across the Atacama Desert (the driest place on earth) and part of the prestigious Four Desert Series. The Four Desert Series attracts some of the finest Ultra-Marathon athletes in the world who compete only for the prestige – there is no prize money. These two races are Daniel’s first two multi-stage races in his first year as a professional runner.
Daniel Rowland leads the Atacama Crossing 2013 field en-route to victory (Picture Shaun Boyte in Trail Magazine Issue 7 – dwrowland.com)
There is no doubt that Daniel is a talented sportsman – he represented Zimbabwe as a triathlete and trained as a potential Olympian. But there are many talented athletes that fail to achieve sporting success. What makes Daniel as successful as he is?
Daniel abandoned his Olympic ambitions to study a Business Science degree at UCT. He was selected as a McKinsey intern. Following this he went on to work for Anglo American in South Africa, Alaska and Chile. Two-and-a-half years ago, Daniel entered his first ultra-marathon beyond 50km – the 100 mile Sustina race through the depths of the Alaskan winter, battling snow and night. Daniel finished fourth. Following Daniel’s blog (www.dwrowland.com) as an interested spectator and recreational runner with no real ambitions of running an ultra myself, I was struck by the regimen that Daniel adopts to life in general and running in particular. Even in his early ultra exploits, Daniel exemplified a simple approach that underlies most key management theory – Plan, Do, Review (PDR).
Plan appropriately for the execution against the goals that you aim to achieve, Do what you planned to and Review your execution against the plan.
This is not a once-off process – it can be repeated many times within a broader cycle and even within execution itself. Organisations might set five year strategies and budgets and then repeat the cycle on a yearly, quarterly or even short interval basis (for example, agreeing and reviewing plans at the beginning and end of shifts – a process typically referred to as short-interval controls). A rugby team might agree a game plan and evaluate its success during stoppages and breaks.
The PDR cycle is followed either consciously or subconsciously by outstanding performers in every field from arts to sport to business. It is a discipline. And like all disciplines it takes practice and fine-tuning to meet the needs of individuals and companies.
Daniel exemplifies the approach. He chooses a goal that is aligned to his interests and who he inherently is. He chooses a race goal and works backwards to fit in all the aspects of training, testing and recovery. He prepares a tailored program with his coach based on his knowledge, Daniel’s input and past performance. Daniel describes execution as doing what he knows he needs to do to achieve his goals.
Daniel is fastidious about all these aspects. He trains in blocks that ramp up to race distances and conditions. He tests all aspects of race conditions, including the diet he will live on in the desert, with the exact pack and equipment he will run with and in conditions on the race course or as close to these as he can find. His approach to optimizing his back pack illustrates this.
Daniel’s Augrabies pack weighed 6kgs and included 3,6kgs of food. Most of his competitors’ packs were 10kg or more. To accomplish the optimum pack weight required much more than selecting equipment against a recipe. He chose a pack that was lightweight and that he found comfortable. He trimmed excess strap lengths. He took the required equipment list (things like eating and cooking utensils, emergency equipment, etc) in their most minimally adequate form. He created a race diet that had the highest calorie to weight ratio possible. And he trained with the pack and on the diet, gradually tuning his choices and becoming utterly familiar with the diet and running with the pack for the periods and conditions matching those of the race. He blogs about all of his choices and tracks progress with data from his heart rate monitor supplemented with his logs of how he felt and his thoughts on what worked and what could be improved.
Every two weeks, Daniel runs a test on the same 12km route, in the same heart rate zone with the same pack weight and as-close-to-optimal body weight. He tracks his time on the test for improvement over the 30 week program leading up to a race.
While Daniel is clinical about the technical aspects of a race, he recognizes the critical importance of emotions – confidence and enjoyment are key to his success. He underpins what he does with a healthy and sustainable lifestyle. This includes enough sleep and recovery time and the community he surrounds himself with. Besides the general community of friends, Daniel’s core team is made up of people he trusts and who create confidence for him because they are present and contributing with a collective goal in mind. This team is comprised of his partner, coach and sponsor – a small and completely trusted core team.
It is an impressive routine and discipline for someone who not long ago, started out training in the very early hours of the morning prior to a demanding corporate job. All the discipline would mean little without stressing himself to the optimum level, and showing incredible willpower and drive. Daniel has willpower and drive in bucket-loads. What stood out to me is that while a level of willpower and drive is a product of who we are, Daniel manages this aspect carefully too. He takes care to push himself enough to develop greater levels of performance while managing the risk of illness and injury – an optimal stress level. Technical training, emotion and health all contribute to setting this optimum. Daniel recognises willpower is a limited resource and ensures he makes focused use of his reserves through routines and removing obstacles. He creates drive through seeing the excellence in others, momentary inspiration, his enduring motivation and the performance of competitors.
Daniel with some of the Global Advisors team after his presentation
When Daniel left Global Advisors after his presentation, we were in little doubt that Daniel had done everything he could to prepare for the Augrabies race. We were confident he stood every chance of winning the race. But more importantly, so did Daniel – in his quiet, unassuming way.
Pick up most management books or business textbooks and you will find the PDR elements described above. We see them in place in the best businesses and clients. They are expressed in tools such as well articulated strategies, balanced scorecards, project management approaches, management and financial reporting. What is far more difficult than the adoption of a set of tools is the institution of the accompanying processes and culture. Discipline is hard enough for an athlete – successfully inculcating the PDR disciplines in a corporate setting requires strong leadership with a soft touch. It relies as much on the belief and cooperation of the team as a well-thought out approach. Just as hard as it must be for Daniel to ensure he preserves the space around him for a community that reinforces his process, beliefs and spirit, it is unbelievably hard to do the same in a business setting. My personal experience is that successful leadership requires walking a fine line between creating and implementing an optimal PDR approach / culture and creating some space to allow those who fit within that culture to find a place within it. That won’t always work out. You will lose some good people along the way as well as those who don’t belong. It is critical to ensure your team sees the benefits of your chosen PDR approach to ease their journey. It takes time – years – for the PDR approach and culture to develop a rhythm, The role of a core supportive team at a management level or on a project is critical to reinforce the PDR disciplines and build confidence. Daniel believes in “controlling the controllables” – the role of his core team illustrates this.
Daniel Rowland meeting Bruce Fordyce after his Kalahari Augrabies Extreme Marathon win (picture from @brucefordycerun on Twitter)
Daniel’s approach is not unique in its elements. Bruce Fordyce famously kept detailed notebooks of his training and races and was meticulous en-route to nine Comrades victories, the London to Brighton Marathon three years in a row and the 50 mile and 100km world records. He too focused on a holistic approach and kept mood records along with the details of his technical performance.
What I have become convinced about is that just as a management / PDR approach is required to prepare and practice for execution, so the approach must be applied and finely tuned over time. As I watched another amazing Kenyan marathon performance, I tweeted how ridiculously easy the lead runners ran at below 3 minutes to the km. Elana Meyer responded, “Practice makes excellence in action look easy.”
Daniel Rowland, Bruce Fordyce and Elana Meyer are inspirational examples of the power of a well-executed PDR process in sport. The same process exists in a well-executed dance routine, well-written academic career and of course in winning businesses.
What is your approach to running your business? How does it incorporate Planning, Doing and Reviewing? Would your approach support the creation and maintenance of a world-class athlete? Is your PDR approach communicated and understood? Is your culture supportive of the approach? Are you practicing the approach and adapting it for your company? What is the PDR mood?
Daniel is racing the Sahara Race (part of the Four Deserts series) in February 2014. You can follow his progress on the Four Deserts website or on Daniel’s blog.
This photo essay of the Atacama Crossing 2013 by Richard Bray will give you some idea of the challenge posed by multi-stage desert running and Daniel’s accomplishments.
Strategy Tools
Strategy Tools: Opportunity/vulnerability matrix – “The Bananagram”
Logic suggests that high relative market share (RMS) should translate into higher profitability (unless the firm was not using its potential advantages or pricing to penetrate the market further). This suggests that a “normative curve / band” exists to describe this phenomenon i.e. the expected profitability of the average business segment in a particular industry according to normal expectations conditional on the segment’s relative market share. This normative band is shown in the figure below as the area between the two curves.
The Opportunity / Vulnerability Matrix
The curve is best explained using data / businesses that have been correctly segmented. In practice such data can only be obtained after analysing the organisation and having a good understanding of any relationships. The band used to be shown coloured yellow, hence the chart became known as a “bananagram”.
The implication of the curve is that high relative market share positions, correctly segmented, are valuable segments / businesses. Managers should therefore strive to achieve / participate in these segments / businesses.
Another implication, in some ways obscured focusing primarily on the growth share matrix (especially where “dogs” are concerned), is that it is useful to improve relative market share in a business segment whatever the starting position. The bananagram enables one to calculate a rough estimate of the equilibrium profitability to be expected from any particular position (relative market share). Therefore it is possible to estimate the potential benefit of moving any particular segment position against the cost of doing so – extra marketing spend, product development or lower prices. This allows one to quantitatively assess whether it is worth trying to raise RMS and which segment / business investments give the best return to shareholders.
Empirical evidence suggests that the majority of observations would fall between the two curved lines and it would be unusual for businesses to fall outside this band. There are two possible positions where a business segment can find itself outside of the two curved lines – this is depicted in the figure below.
The Opportunity / Vulnerability Matrix – Example
Business A is earning (for example) 45 per cent return on net capital employed, a good return, but is in a weak relative market share position (say 0,5x, or only half the size of the segment leader). The theory and empirical data from the matrix suggests that the combination of these two positions is at best anomalous, and probably unsustainable. Business A is therefore in the “vulnerability” part of the matrix. The expectation must be that in the medium term, either the business must improve its relative market share position to sustain its profitability (the dotted arrow moving left), or that it will decline in profitability (to about break-even). Why should this happen? Well, the banana indicates that the market leader in this business may well be earning 40 percent or even more ROCE in the segment. What may be happening is that the leader is holding a price umbrella over the market, that is, is pricing unsustainably high, so that even the competitors with weak market share are protected from normal competition (especially where pricing is concerned). What happens if the market leader suddenly cuts prices by 20 percent? They will still earn a good return, but the weaker competitors will not. The leader may opt to provide extra product benefits or services, instead of lowering prices, but the effect would still be a margin cut. It is as well to know that business A is vulnerable. If relative market share cannot be improved, it is sensible to sell it before the profitability declines.
Now let’s look at business B. This is a business in a strong relative market share position – the leader in its segment, five times larger than its nearest rival. It is earning 2 percent ROCE. This is a wonderful business to find. The theory and practical data suggest that such a business should be making 40 percent ROCE, not 2 percent. Nine times out of ten when such businesses are found, it is possible to make them very much more profitable, usually by radical cost reduction (often involving restructure), but sometimes through radical improvement of service and product offering to the customer at a low extra cost to the supplier, but enabling a large price hike to be made. Managements of particular businesses very often become complacent with historical returns and think it is impossible to raise profits in a step function to three, four or five times their current level. The bananagram challenges that thinking for leadership segment positions, and usually the bananagram is proved right. After all, high relative market share implies huge potential advantages; but these must be earned and exploited, as they do not automatically disgorge huge profits.
Source: Koch, R – “Financial Times Guides Strategy” – Fourth edition – Prentice Hall – page 313-316
Fast Facts
Trends show a decline in traditional pay TV offerings as consumers seek more convenient options
Cable TV has been declining steadily since 2002. Digital Satellite Services increased since 2002 but has seen no growth since 2012. Pay TV subscribers are switching to streaming TV services as broadband adoption grows. Advances in compression technology and 4G have...
Selected News
Quote: John Furner – President, CEO Walmart US
“The transition from traditional web or app search to agent-led commerce represents the next great evolution in retail. We aren’t just watching the shift, we are driving it.” – John Furner – President, CEO Walmart US
When John Furner speaks about the shift from traditional web or app search to agent-led commerce, he is putting words to a structural change that has been building at the intersection of artificial intelligence, retail strategy and consumer behaviour for more than two decades. His quote does not describe a marginal optimisation of online shopping; it points to a reconfiguration of how demand is discovered, shaped and fulfilled in the digital economy.
John Furner: An operator at the centre of AI-led retail
John Furner built his leadership reputation inside one of the most operationally demanding businesses in the world. Before being named President and CEO of Walmart U.S., and then incoming President and CEO of Walmart Inc., he held a series of roles that grounded him in the realities of store operations, merchandising and labour-intensive retail at scale.1,4 That background matters to the way he talks about AI.
Unlike many technology narratives that begin in the lab, Walmart’s AI story has been forged in distribution centres, supercentres and neighbourhood markets. Under Doug McMillon, and increasingly under Furner, Walmart framed AI not as a side project but as a new backbone for the business.1 Analysts note that as Furner steps into the global CEO role, the board describes the next chapter as one “fueled by innovation and AI”.1 His quote about agent-led commerce sits squarely in that strategic context.
Furner has consistently emphasised pragmatic, measurable outcomes from technology adoption: better inventory accuracy, improved shelf availability, faster fulfilment and fewer customer headaches.1,4 He has also been explicit that every job in the company will change in some way under AI – from collecting trolleys in car parks to technology development and leadership roles.4 In other words, for Furner, agent-led commerce is not simply a new consumer interface; it is a catalyst for rethinking work, operations and value creation across the retail stack.
The specific context of the quote: Walmart, Google and Gemini
The quote originates in the announcement of a partnership between Walmart and Google to bring Walmart and Sam’s Club product discovery directly into Google’s Gemini AI environment.2,3,5 Rather than treating AI search as an external channel to be optimised, the collaboration embeds Walmart’s assortment, pricing and fulfilment options into an intelligent agent that can converse with customers inside Gemini.
In this setting, Furner’s words perform several functions:
- They frame the shift from keyword-driven search (type an item, browse lists) to goal- or task-based interaction (“help me plan a camping trip”), where an agent orchestrates the entire shopping journey.2,3
- They signal that Walmart is not content to be a passive catalogue inside someone else’s interface, but intends to shape the emerging standards for “agentic commerce” – an approach where software agents work on behalf of customers to plan, select and purchase.2,3,4
- They reassure investors and partners that the company sees AI as a core strategic layer, not as an optional experiment or promotional gimmick.1,4,6
The Walmart – Google experience is designed to allow a shopper to ask broad, life-context questions – for example, how to prepare for spring camping – and receive curated product bundles drawn from Walmart and Sam’s Club inventory, updated dynamically as the conversation unfolds.2,3 The system does not simply return search results; it proposes solutions and refines them interactively. The agent becomes a kind of digital retail concierge.
Technically, this is underpinned by the pairing of Gemini’s foundation models with Walmart’s internal data on assortment, pricing, local availability and fulfilment options.3 Strategically, it positions Walmart to participate in – and influence – the universal protocols that might govern how agents transact across merchants, platforms and services in the coming decade.
From web search to agent-led commerce: why this is a step-change
To understand why Furner describes this as “the next great evolution in retail”, it is useful to place agent-led commerce in a longer history of digital retail evolution.
1. Catalogue search and the era of the query box
The first wave of e-commerce was built around catalogue search: customers navigated static product hierarchies or typed keywords into a search box. Relevance was determined by text matching and basic filters. Power resided in whoever controlled the dominant search interface or marketplace.
This model mapped well onto traditional retail metaphors – aisles, departments, categories – and it assumed that the customer knew roughly what they were looking for. Retailers competed on breadth of assortment, price transparency, delivery speed and user interface design.
2. Personalisation and recommendation
The second wave saw retailers deploy recommendation engines, collaborative filtering and behavioural targeting to personalise product suggestions. Here, algorithmic theories drawn from machine learning and statistics began to shape retail experiences, but the core unit remained the search query or product page.
Recommendations were adaptively presented around known products and purchase history, nudging customers to complementary or higher-margin items. Many of the leading ideas came from research in recommender systems, one of the most commercially influential branches of applied machine learning.
3. Conversational interfaces and agentic commerce
Agent-led commerce represents a third wave. Instead of asking customers to break down their needs into discrete product searches, it allows them to:
- Express goals (“host a birthday party for ten-year-olds”), constraints (“under £100, dietary restrictions, limited time”) and context (“small flat, no oven”).
- Delegate the planning and selection process to an AI agent that operates across categories, channels and services.
- Iterate interactively, with the agent updating recommendations and baskets as the conversation evolves.
In this model, the agent becomes a co-pilot for both discovery and decision-making. It can optimise not only for price and relevance, but also for timing, delivery logistics, dietary requirements, compatibility across items and even sustainability preferences, depending on the data and constraints it is given. The underlying technologies draw on advances in large language models, planning algorithms and multi-agent coordination.
For retailers, the shift is profound:
- It moves the locus of competition from web page design and keyword bidding to who supplies the most capable and trustworthy agents.
- It elevates operational capabilities – inventory accuracy, fulfilment reliability, returns processing – because an agent that cannot deliver on its promises will quickly lose trust.
- It opens the door to autonomous or semi-autonomous shopping flows, such as automatic replenishment, anticipatory shipping or continuous cart management, where the agent monitors needs and executes under defined guardrails.
Furner’s assertion that Walmart is “driving” the shift needs to be understood against this backdrop. Internally, Walmart has already invested in a family of “super agents” for shoppers, associates, partners and developers, including Sparky (customer assistant), My Assistant (associate productivity), Marty (partner and advertising support) and WIBEY (developer tooling).1,4 Externally, initiatives like integrating with ChatGPT for “instant checkout” and partnering with Google on Gemini experiences demonstrate a strategy of meeting customers inside the agents they already use.1,3,4
Agent-led commerce inside Walmart: from vision to practice
Agent-led commerce is not just a phrase in a press release for Walmart. The company has been progressively building the capabilities required to make it a practical reality.
AI-native shopping journeys
Walmart has rolled out AI-powered search experiences that allow customers to describe occasions or problems rather than individual items – for example, planning a party or organising a kitchen.1 The system then infers needs across multiple categories and pre-populates baskets or recommendations accordingly.
At the same time, the company has been piloting “replenishment” features that create suggested baskets based on past purchases, letting customers approve, modify or decline the auto-generated order.1 This is an early expression of agentic behaviour: the system anticipates needs and does the heavy lifting of basket formation.
Super agents as an organisational pattern
Internally, Walmart has articulated a vision of multiple domain-specific “super agents” that share core capabilities but specialise in particular user groups.1,4
- Sparky supports customers, operating as a front-end conversational assistant for shopping journeys.
- My Assistant helps associates draft documents, summarise information and interact with data, freeing them from repetitive tasks.1,4
- Marty works with partners and increasingly underpins the advertising business, helping brands navigate Walmart’s ecosystem.4
- WIBEY accelerates developer productivity, contributing to the internal fabric of AI tooling.4
Additionally, Walmart has built a generative AI assistant called Wally for merchandising tasks, using AI to support complex assortment, pricing and space decisions.4
Operational AI as the foundation
Critically, Walmart has recognised that agent-led commerce cannot function if the operational substrate is weak. AI agents that promise two-hour delivery on items that are out of stock will immediately erode trust. As a result, the company has deployed AI and automation deep into its supply chain and fulfilment network.1,4
This includes large-scale investment in warehouse automation (for example, through partnerships with Symbotic), sensor-based tracking to improve inventory accuracy, and forecasting models that help move products closer to expected demand.1 The philosophy is that data quality is strategy: without reliable, granular data about where products are and how they move, agentic experiences will fail at the last mile.
The intellectual backstory: the theorists behind agents, recommendations and AI commerce
While Walmart and Google are prominent practitioners, the transition Furner describes rests on decades of work by researchers and theorists in several overlapping fields: information retrieval, recommender systems, artificial intelligence agents, behavioural economics and commerce design. A brief backstory of these fields helps illuminate what is now converging under the label “agent-led commerce”.
Information retrieval and the search paradigm
The idea of representing information needs through queries and ranking results based on relevance traces back to mid-20th century information retrieval research. Early work by scholars such as Gerard Salton introduced the vector space model of documents and queries, which underpinned term-weighting schemes like tf-idf (term frequency – inverse document frequency). These ideas influenced both academic search engines and, eventually, commercial web search.
As web content exploded, researchers in IR refined ranking algorithms, indexing structures and relevance feedback mechanisms. The prevailing paradigm assumed that users could express needs in terms of keywords or structured queries, and that the system’s job was to approximate relevance as accurately as possible given those inputs.
Agent-led commerce departs from this model by treating language not as a set of keywords but as an interface for describing goals, constraints and preferences in natural form. Instead of mapping queries to documents, agents must map intentions to actions and sequences of actions – choose, bundle, schedule, pay, deliver.
Recommender systems and personalisation pioneers
The science of recommending products, films or content to users based on their behaviour has roots in the 1990s and early 2000s. Key theorists and practitioners include:
- John Riedl and colleagues, whose work on collaborative filtering and the GroupLens project showed how crowd data could be used to predict individual preferences.
- Yehuda Koren, whose contributions to matrix factorisation methods during the Netflix Prize competition demonstrated the power of latent factor models in recommendation.
- Joseph Konstan and others who explored user experience and trust in recommender systems, highlighting that perceived transparency and control can be as important as accuracy.
These researchers established that it is possible – and commercially powerful – to infer what customers might want, even before they search. Their theories informed the design of recommendation engines across retail, streaming and social platforms.
Agent-led commerce builds on this tradition but extends it. Instead of recommending within a narrow context (“people who bought this also bought”), agents must manage multi-step goals, cross-category constraints and time-sensitive logistics. This requires integrating recommender logic with planning algorithms and conversational interfaces.
Software agents and multi-agent systems
The concept of a software agent – an autonomous entity that perceives its environment, makes decisions and acts on a user’s behalf – has deep roots in AI research. Theorists in this area include:
- Michael Wooldridge, whose work on multi-agent systems formalised how agents can reason, cooperate and compete in complex environments.
- Nick Jennings, who explored practical applications of autonomous agents in business, including negotiation, resource allocation and supply chain management.
- Stuart Russell and Peter Norvig, whose widely adopted AI textbook set out the rational agent framework, defining intelligent behaviour as actions that maximise expected utility given beliefs about the world.
In this tradition, agents are not simply chat interfaces; they are decision-making entities with objectives, models of the environment and policies for action. Many of the recent ideas around “agentic” systems – where software components can autonomously plan, call tools, execute workflows and coordinate with other agents – derive conceptually from this line of research.
In retail, agentic commerce can be seen as a large-scale deployment of these ideas: shopper-facing agents negotiate between customer preferences, product availability, pricing, promotions and logistics, while back-end agents manage inventory, routing and labour scheduling.
Conversational AI and natural language understanding
The move from query-driven search to conversational agents has been enabled by advances in natural language processing (NLP), particularly large language models (LLMs). Theorists and practitioners in this domain include researchers who developed transformer architectures, attention mechanisms and large-scale pre-training techniques.
These models provide the linguistic and semantic fluency required for agents to engage in open-ended dialogue. However, in commerce they must be grounded in reliable data and constrained by business rules. Walmart’s AI strategy, for example, combines general-purpose language models with retail-specific systems like Wallaby, which is tuned to Walmart’s own data on catalogues, substitutions and seasonality.1
Behavioural economics and choice architecture
The design of agent-led experiences also draws on insights from behavioural economics and psychology. Researchers such as Daniel Kahneman, Amos Tversky, Richard Thaler and Cass Sunstein have shown how framing, defaults and choice architecture influence decisions.
In an agentic commerce environment, the agent effectively becomes the architect of the customer’s choice set. It decides which alternatives to present, how to explain trade-offs and what defaults to propose. The ethical and strategic implications are significant: the same technologies that can reduce friction and cognitive load can also be used to steer behaviour in subtle ways.
Leading thinkers in digital ethics and AI governance have therefore argued for transparency, contestability and human oversight in agentic systems. For retailers, this becomes a trust question: customers need to believe that the agent is working in their interests, not solely maximising short-term conversion or margin.
Google, Gemini and open standards for agentic commerce
On the technology platform side, Google has been a central theorist and practitioner in both search and AI. With Gemini, its family of multimodal models, Google is positioning AI not just as a backend enhancement to search results but as a front-end conversational partner.
In the joint Walmart – Google initiative, the companies highlight a “Universal Commerce Protocol” designed to let agents interact with merchants in a standardised way.3 While technical details continue to evolve, the ambition reflects a broader movement towards open or semi-open standards for how agents discover, price, bundle and purchase across multiple commerce ecosystems.
Sundar Pichai, Google’s CEO, has spoken of AI improving every step of the consumer journey, from discovery to delivery, and has explicitly framed the Walmart partnership as a step toward making “agentic commerce” a reality.3 This aligns with the longer arc of Google’s evolution from ten blue links to rich results, shopping tabs and now conversational, transaction-capable agents.
Strategic implications: trust, control and the future of retail interfaces
Furner’s quote hints at the strategic contest that agent-led commerce will intensify. Key questions include:
- Who owns the interface? If customers increasingly begin journeys inside a small number of dominant agents (Gemini, ChatGPT, other assistants), traditional notions of direct traffic, branded apps and search engine optimisation will be reconfigured.
- Who sets the rules? Universal protocols for agentic commerce could distribute power more widely, but the entities that define and maintain those protocols will have disproportionate influence.
- How is trust earned and maintained? Mistakes in retail – wrong products, failed deliveries, billing errors – have tangible consequences. Agent-led systems must combine probabilistic AI outputs with robust guardrails, validation checks and escalation paths to humans.
- How does work change? As McMillon has noted, and Furner will now operationalise, AI will touch every job in the organisation.4 Theorists of work and automation have long debated the balance between augmentation and substitution; agentic commerce will be one of the most visible test cases of those theories in practice.
Walmart’s own AI roadmap suggests a disciplined approach: build AI into the fabric of operations, prioritise store-first use cases, move carefully from assistants to agents with strict guardrails and develop platforms that can be standardised and scaled globally.1 Furner’s quote can thus be read as both a declaration of intent and a statement of competitive philosophy: in a world where AI agents mediate more and more of daily life, retailers must choose whether to be controlled by those agents or to help design them.
For customers, the promise is compelling: less time on search and comparison, more time on what the purchases enable in their lives. For retailers and technologists, the challenge is to build agents that are not only powerful and convenient but also aligned, transparent and worthy of long-term trust. That is the deeper context behind Furner’s assertion that the move from web and app search to agent-led commerce is not just another technology upgrade, but the “next great evolution in retail”.
References
2. https://pulse2.com/walmart-and-google-turn-ai-discovery-into-effortless-shopping-experiences/
4. https://www.digitalcommerce360.com/2026/01/08/how-walmart-is-using-ai/
6. https://www.emarketer.com/content/walmart-tech-first-strategy-shapes-growth
7. https://www.futurecommerce.com/podcasts/predictions-2026-prepare-for-the-age-of-autonomy
Polls
No Results Found
The page you requested could not be found. Try refining your search, or use the navigation above to locate the post.
Services
Global Advisors is different
We help clients to measurably improve strategic decision-making and the results they achieve through defining clearly prioritised choices, reducing uncertainty, winning hearts and minds and partnering to deliver.
Our difference is embodied in our team. Our values define us.
Corporate portfolio strategy
Define optimal business portfolios aligned with investor expectations
BUSINESS UNIT STRATEGY
Define how to win against competitors
Reach full potential
Understand your business’ core, reach full potential and grow into optimal adjacencies
Deal advisory
M&A, due diligence, deal structuring, balance sheet optimisation
Global Advisors Digital Data Analytics
14 years of quantitative and data science experience
An enabler to delivering quantified strategy and accelerated implementation
Digital enablement, acceleration and data science
Leading-edge data science and digital skills
Experts in large data processing, analytics and data visualisation
Developers of digital proof-of-concepts
An accelerator for Global Advisors and our clients
Join Global Advisors
We hire and grow amazing people
Consultants join our firm based on a fit with our values, culture and vision. They believe in and are excited by our differentiated approach. They realise that working on our clients’ most important projects is a privilege. While the problems we solve are strategic to clients, consultants recognise that solutions primarily require hard work – rigorous and thorough analysis, partnering with client team members to overcome political and emotional obstacles, and a large investment in knowledge development and self-growth.
Get In Touch
16th Floor, The Forum, 2 Maude Street, Sandton, Johannesburg, South Africa
+27114616371