“We’re almost moving to a landscape where we’re working for the AI rather than the AI working for us.” – James Brocklebank – Co-chair of Advent International
The strategic relationship between human investors and algorithmic systems is undergoing a subtle but profound inversion. In private equity, the centre of gravity is shifting from people using tools to augment judgment, toward organisations restructuring their processes, culture, and governance around always-on machine counterparts that scrutinise every decision.1 The emergence of firm-specific AI systems trained on decades of proprietary deal data and investment committee materials changes not only how capital is allocated, but who, or what, effectively sets the agenda.3 That shift lies behind the growing unease that professionals may increasingly find themselves justifying decisions to machines designed to challenge their assumptions rather than merely assist with workflows.1,3
From filing cabinets to institutional memory machines
For much of modern private equity history, the collective memory of a firm lived in archived investment committee papers, informal lore, and the tacit experience of partners. Those assets were powerful but fundamentally inert. They could be revisited, sampled, and informally referenced, yet they did not systematically interrogate new proposals or enforce consistency in how lessons were applied.1 Training an AI system on more than 13 years of investment committee papers converts that hidden archive into a living analytical substrate: a structured dataset of assumptions, decisions, and outcomes capable of pattern recognition at a scale impossible for individual partners.1,3
In this case, the IC Robot developed under James Brocklebank’s leadership ingests the full corpus of deals shown to the committee over that period, including those that were approved and those that were rejected.3,5 The crucial design choice is not simply breadth of data, but the linkage between ex ante assumptions and ex post real-world performance. When a new investment memorandum arrives, the system reads it, maps each assumption back into that historical lattice, and identifies where projected margins, growth, or leverage structures diverge from what has previously been achieved in similar types of companies.3,5 This is not a static database query; it is a dynamic critique baked into the deal workflow.
The practical effect is to convert qualitative experience into quantifiable priors. For example, if the committee sees a proposal projecting EBITDA margin expansion beyond any historical precedent for a given sub-sector and business model, the AI flags that divergence automatically.5 Over time, this creates a de facto standard for plausibility grounded in empirical firm-specific data rather than generic industry benchmarks. That standard is constantly updated as new deals play out, meaning the machine’s view of what is realistic is, in principle, more comprehensive than any single partner’s recollection.3 The underlying issue is whether this institutional memory machine begins to exert its own gravitational pull over human judgment.
Investor, adviser, or procedural gatekeeper?
On paper, systems such as the IC Robot are framed as powerful prompts rather than voting members of the investment committee.5 They surface anomalies, encourage deeper discussion on particular assumptions, and serve as a disciplined reminder of historical outcomes. Yet, within the social dynamics of a committee, even formally non-binding prompts can carry significant weight. When a structured, data-backed system repeatedly questions margin assumptions, leverage profiles, or growth trajectories, human participants may gradually calibrate their proposals in anticipation of those critiques.
That anticipatory behaviour is where the role of AI quietly migrates from adviser to gatekeeper. Associates and principals drafting memos know that every line item will be stress-tested against 13 years of internal performance.1,3,5 They are incentivised to pre-empt objections by aligning projections more tightly with the machine’s inferred norms. Deals whose narratives require a deliberate break from precedent may be framed, justified, and defended in terms that satisfy how the system interprets risk rather than exclusively how human partners do.
The tension is not that the AI is formally empowered to veto deals-it is not-but that the internal definition of a “reasonable” case becomes co-authored by an algorithm. Human participants may, consciously or otherwise, treat the machine’s view as a baseline from which they must deviate only with robust argument and supporting evidence. Over time, this could narrow the space of proposals, favouring those that conform to historically encoded patterns of success, and potentially bias the firm against outlier opportunities that require a more radical leap of faith. That possibility sits at the heart of concerns about working for the AI: the machine criteria start to shape the upstream behaviour of people long before a committee vote is taken.
The Advent context: embracing complexity and codifying edge
James Brocklebank’s public comments on private equity strategy emphasise the idea that complexity can be a source of competitive advantage.5 Advent positions itself as a firm willing to tackle complex markets, intricate capital structures, and sophisticated operational transformations. In that environment, systematising the firm’s accumulated expertise via a bespoke AI is a logical extension of the “specialisation at scale” approach.15 The IC Robot is not an off-the-shelf product but a tailored internal capability aligned with a broader push for AI transformation across portfolio companies.5,11
On the portfolio side, Advent deploys dedicated teams to help businesses undertake real AI transformations, not merely bolt-on side projects.5,11 That stance suggests a belief that competitive edge increasingly depends on deep integration of machine learning into core processes: customer analytics, pricing, operations, and strategic planning. Within the fund itself, applying the same philosophy to the investment process means treating AI as part of the firm’s intellectual infrastructure. Historical committee minutes and memos become training data; decision-making becomes a partially codified discipline that can be interrogated by software.1,3
Against that backdrop, the emotional mix of excitement and terror reported in coverage of the IC Robot is instructive.3,17 Excitement stems from the ability to “see things humans can’t”-hidden correlations, subtle patterns in which types of deals consistently underperform, and the interplay between macro conditions and sector-specific outcomes over long horizons.3 Terror, or at least discomfort, arises from the recognition that once such a system is embedded, it will inevitably start to shape internal norms and expectations. Investors who spent years honing their judgment must now engage with a machine that can challenge their interpretations with empirical counter-evidence drawn from the firm’s own track record.
AI as labour arbitrage and capability amplifier
The Advent experiment sits within a broader trend in private equity: using AI to perform the work that previously required teams of analysts, associates, and research staff. A recent case described by Laura Cooper highlights a private equity firm using AI tools to source investment opportunities at a scale and speed “of several dozen humans”, with higher accuracy and lower cost.2 Similarly, Pilot Growth’s NavPod and other AI-powered deal-sourcing platforms automate market mapping, lead identification, and outreach, displacing much of the manual effort of combing through databases and cold-calling potential targets.2
Advisory firms argue that generative AI allows funds to evaluate far more deals with the same number of people, increasing velocity without formal headcount expansion.4,6,8 Tools filter opportunities, pre-populate diligence workbooks, and simulate scenarios that would previously have required weeks of modelling. From a firm economics perspective, AI delivers both labour arbitrage-doing more with fewer people-and capability amplification, enabling deeper analysis per transaction.6,8 The promise is that professionals are freed from low-value tasks to concentrate on strategy, relationship management, and nuanced judgment.10,16
However, the labour dimension cannot be ignored. When AI performs most of the tasks that constitute a particular role, empirical work suggests the share of people in that role within a firm tends to fall.16 MIT Sloan research tracking AI adoption from 2010 to 2023 finds that occupations whose task content is heavily automatable see employment in those roles decline by about 14%, while roles where AI complements rather than replaces tasks can grow.16 Within private equity, associate and analyst positions are precisely those built on repeatable tasks: data gathering, initial modelling, memo drafting, and market scans. If AI takes over the bulk of that work, the risk is that entry-level pathways constrict, and the human workforce is reshaped around a smaller number of higher-leverage roles.
Autonomy, judgment, and the risk of procedural dependence
One of the most subtle risks in embedding AI deep into the investment process is the gradual erosion of independent human judgment. When every deal memo is read, critiqued, and labelled by a machine trained on historical outcomes, committee members may come to rely on its assessment as a proxy for disciplined thinking. Over-reliance on such systems can lead to procedural dependence: deals that pass the algorithmic checks acquire a presumption of validity, while those that trigger repeated warnings carry a presumption of flaw.
From a behavioural perspective, this raises questions about comparative advantage. Firms are ostensibly paying partners for their ability to synthesise complex information, assess management quality, and navigate ambiguity where quantitative data is incomplete. If the machine’s critique is treated as authoritative on all matters that can be quantified, humans may retreat to a narrower role: relationship management, negotiation, and qualitative pattern recognition. The division of labour becomes one where the AI defines what is “normal” and humans focus on narrative exceptions.
The danger is that the machine’s implicit model of risk becomes conflated with reality. Because it is trained on a firm’s own history, it systematically reflects that organisation’s biases, missed opportunities, and structural preferences. Deals that were rejected but might have been successful elsewhere are labelled failures by omission, while categories of opportunity never seriously considered in the past are under-represented. Over time, the AI can entrench a path-dependent worldview that subtly discourages experimentation. Human judgment, instead of challenging those embedded priors, may become subservient to them.
Debates and objections: will AI really take the investment wheel?
Not all observers accept the narrative that AI will dominate decision-making. Some practitioners argue that AI is nowhere near advanced enough to “steal” private equity jobs and that, properly used, it simply makes professionals better.10,14 From this perspective, AI is a sophisticated calculator and research assistant that can accelerate tasks but cannot replicate the social and psychological complexities of deal-making: persuading founders, structuring bespoke transactions, and guiding companies through difficult transformations.
Others point out that firms adopting AI heavily often see faster growth, which can sustain or expand headcount in high-exposure positions.16 Even in roles highly exposed to AI, overall employment may rise if the firm’s productivity gains outpace automation-driven reductions.16 Under this scenario, AI serves as an engine of growth-enabling the firm to raise larger funds, pursue more transactions, and manage more portfolio companies-generating demand for human leadership, governance, and oversight.
There is also a philosophical objection: capital allocation is fundamentally a human responsibility tied to accountability, trust, and ethics. Investment committees exist not only to maximise risk-adjusted returns but to ensure that capital deployment reflects the firm’s stated values, regulatory obligations, and reputational constraints. Delegating material decision weight to machines, even indirectly, raises questions about how responsibility is allocated when things go wrong. Investors, limited partners, and regulators may be uncomfortable with any suggestion that “the system said yes” substitutes for a human signature.
Why private equity is a test case for AI-human inversion
Private equity is a particularly revealing arena for this tension because its economics, structure, and culture lend themselves to aggressive AI adoption. Funds manage large pools of capital with relatively small teams, making any productivity improvement disproportionately impactful on returns.6,8 The workflow is repeatable: sourcing, screening, diligence, structuring, portfolio value creation, and exit. At each stage, AI can ingest vast data, run simulations, and flag anomalies. Advisory literature already describes AI-driven sourcing tools that consider more targets, better identify prospects, and free people to focus on top candidates.6
Moreover, the industry’s competitors benchmark against each other. If early adopters successfully embed AI into their processes and achieve superior performance, others are compelled to respond. PwC and KPMG both highlight how generative AI can transform deal sourcing, evaluation, portfolio value creation, and fund management, framing adoption as a route to “more informed decision-making and improved fund performance”.6,8 Once that framing takes hold, the question shifts from whether to use AI to how deeply to integrate it-and how much discretion to cede to its outputs.
In that environment, the emergence of internal systems like IC Robot is not an anomaly but a harbinger. When every new deal is scanned against historical assumptions and outcomes before reaching the committee table, the machine effectively becomes the first reader, the preliminary reviewer, perhaps even the unseen co-author of the human memo. If future iterations integrate real-time external data, portfolio analytics, and macro scenarios, the AI’s role may expand further, operating as a continuous risk monitor and opportunity scanner that directs human attention where it deems most warranted.
Design choices that keep humans in charge
The trajectory is not predetermined. Whether investors end up working for AI systems or working with them depends heavily on governance and design choices made now. Several principles are emerging among firms trying to keep humans firmly at the centre of decision-making, even while exploiting AI’s analytical strength.
First, transparency and auditability of AI outputs are critical. Firms experimenting with AI in hiring emphasise that every AI output should sit within a structured workflow where the human owner of the decision is clear and accountable.12 The same logic applies to investment decisions: AI prompts should be logged, critiqued, and, where appropriate, overridden with explicit rationale. That practice builds trust internally and preserves a meaningful record of where human judgment diverged from machine inference.
Second, structured processes act as a defence against over-reliance. In hiring, rigorous scorecards, case studies, and back-channel referencing prevent AI-generated polish from substituting for real experience.12 In investing, disciplined frameworks for underwriting risk, assessing management, and testing scenarios can ensure that AI augments rather than replaces core analytical steps. Committees can require that every AI flag be treated as a question, not a verdict, and that “off-model” opportunities receive deliberate scrutiny rather than quiet exclusion.
Third, firms can deliberately cultivate human capabilities that AI cannot match. Relationship-building skills, empathy, and nuanced negotiation are not optional extras in private equity; they are often decisive in winning deals and supporting portfolio companies through stress.5,12 Leaders who encourage juniors to specialise, ask questions, and build deep sector expertise are effectively investing in comparative advantage relative to machines.7 If the career path is reoriented around those strengths, AI’s rise need not translate into subordination but into a redefinition of what valuable human work looks like.
Why the tension will sharpen, not fade
The coming years are likely to intensify rather than resolve the tension between human autonomy and machine-centric workflows. As more firms adopt AI systems to mine internal archives, challenge assumptions, and drive productivity, the practical question will be how far to let those systems influence decisions and behaviour. In private equity, where marginal improvements in judgment and speed compound into significant changes in fund performance, the temptation to lean heavily on algorithms will be strong.
At the same time, the stakes justify caution. Investment decisions reverberate across companies, employees, and communities; they shape which technologies are funded, which industries are consolidated, and which regions attract capital. The idea that those decisions could be materially steered by systems trained on historical patterns raises difficult questions about innovation, fairness, and resilience. History is not always a reliable guide to future opportunity, particularly in periods of structural change.
Understanding the backstory behind remarks about “working for the AI” requires recognising this broader context: firms like Advent are not simply experimenting with clever tools. They are actively re-architecting how institutional knowledge is stored, accessed, and used to govern billions of capital. The outcome of that experiment will influence not only the internal culture of private equity organisations, but the balance of power between human intuition and machine inference in financial decision-making more broadly.1,3,5,6,8,16
References
1. ‘Excited and terrified’: One of private equity’s top investors built an AI … – 2026-05-26 – https://fortune.com/2026/05/26/james-brocklebank-advent-international-goldman-sachs-ic-robot/
2. Quote: James Brocklebank – Co-chair of Advent International – 2026-06-05 – https://globaladvisors.biz/?p=3164568
3. Private Equity Firm Uses AI Instead of Employees to Source Deals – 2019-06-04 – https://www.workforcebulletin.com/private-equity-firm-uses-ai-instead-of-employees-to-source-deals
4. How the Private Equity industry is using AI with Greg Nieuwenhuys – 2025-07-15 – https://www.youtube.com/watch?v=wOpFle7e110
5. ‘Complexity Is Our Friend’: James Brocklebank on Advent’s Private … – 2026-05-26 – https://www.goldmansachs.com/insights/goldman-sachs-exchanges/complexity-is-our-friend-james-brocklebank-on-advents-private-equity-strategy
6. How private equity survives AI – PwC – 2025-11-03 – https://www.pwc.com/us/en/industries/financial-services/library/private-equity-ai-transformation.html
7. James Brocklebank – Advent International – 2026-04-20 – https://www.adventinternational.com/our-team/james-brocklebank/
8. Generative AI for Private Equity – KPMG International – 2025-02-10 – https://kpmg.com/us/en/capabilities-services/private-equity/generative-ai.html
9. James Brocklebank – Managing Partner at Advent – LinkedIn – https://www.linkedin.com/in/jamesbrocklebank
10. Artificial Intelligence Won’t Steal Your Private Equity Job, But It Will … – 2023-04-25 – https://udu.co/blog/ai-wont-steal-your-pe-job-but-it-will-make-you-better-at-it/
11. ‘Complexity is our friend’: James Brocklebank on private equity, AI … – 2026-06-02 – https://www.adventinternational.com/ideas/complexity-is-our-friend-james-brocklebank-on-private-equity-ai-and-the-advent-edge/
12. AI in hiring: What private equity talent leaders are getting right – 2026-04-01 – https://www.greenhouse.com/blog/ai-in-hiring-private-equity
13. James Brocklebank – Business Roundtable – https://www.businessroundtable.org/about-us/members/james-brocklebank
14. Is it really a matter of when with AI? | Wall Street Oasis – 2025-02-02 – https://www.wallstreetoasis.com/forum/off-topic/is-it-really-a-matter-of-when-with-ai
15. About Us – Advent International – 2026-06-18 – https://www.adventinternational.com/about-us/
16. How artificial intelligence impacts the US labor market | MIT Sloan – 2025-10-09 – https://mitsloan.mit.edu/ideas-made-to-matter/how-artificial-intelligence-impacts-us-labor-market
17. ‘Excited and terrified’: One of private equity’s top investors built an AI … – 2026-05-26 – https://finance.yahoo.com/sectors/technology/articles/excited-terrified-one-private-equity-120000499.html
18. AI upside in PE still not there? : r/private_equity – Reddit – 2026-03-16 – https://www.reddit.com/r/private_equity/comments/1rus1cu/ai_upside_in_pe_still_not_there/
19. James Brocklebank – Alternative Investments Conference – https://www.lseaic.com/speakers-2024/james-brocklebank
20. Will AI wipe out the private equity associates? | Dan Herr – LinkedIn – 2025-06-05 – https://www.linkedin.com/posts/danielherr_will-ai-wipe-out-the-private-equity-associates-activity-7336349832111902721-Xzjo
