Introduction
A few months ago, I started asking junior buyout candidates different questions in the interviews. The classic questions still take place, like simple: “walk me through the transaction process”, “show me where you would flag risk”, etc., but now I remember to put more emphasis on something else:
- What would you double-check before showing this to a partner?
And the reason behind it says something bigger about where the industry is going.
These days a large part of junior PE work is becoming much faster to produce – tasks like market screening, company profiling, comparable analysis, first drafts of investment papers used to take days if not weeks of junior work. It is obvious now that thanks to AI, all of it can now be done with far less manual effort than two years ago.
EY’s November 2025 research found that 84% of PE funds expect AI to have a significant transformative impact on their business. Around two-thirds of EY’s PE clients had already implemented at least one AI initiative in their portfolio by 2024. So the question is no longer whether AI will enter the PE workflow. It already has.
The more interesting question is what AI takes away at the same time.
How the learning ends
For decades, private equity funds trained its junior investors into becoming more mature dealmakers through simple, well known mechanism - learning by doing. Nobody designed this system on purpose, but its logic was clear. Judgement is not taught. It builds through a disciplined repetition. An Investment Analyst builds multiple models from zero and is forced to understand how businesses actually make money. They read the data room line by line and find the inconsistencies that never survive into a clean summary. They rework an IC paper ten times, then observe, listen and slowly learn which questions senior investors ask and what they treat as important.
The value of this system was never in the produced documents but the producing these documents was the training.
This is exactly what AI disrupts. Not the analyst role itself <yet>, but the learning mechanism hidden inside it. When the first draft arrives in two minutes, the firm gains speed and at the same time it loses the friction that was doing the teaching. The repetitive work juniors would happily skip is, inconveniently, the work that builds their instinct.
PwC’s 2026 Global AI Jobs Barometer, based on more than one billion job advertisements across 27 countries, shows the consequences of what I described above - AI-exposed entry-level roles are now seven times more likely to require senior-level skills such as judgement and leadership. The first rung of the ladder is not disappearing but it is being raised. However, nobody has explained how people are supposed to reach it.
Banking, which feeds a large part of the PE junior pipeline, is already living this contradiction. Bloomberg cited Debasish Patnaik from McKinsey, saying that banks are cutting junior analyst classes by as much as two-thirds while sourcing roughly 62% of their AI talent from those same groups. His warning that “senior judgment cannot be manufactured laterally” is worth remembering. JPMorgan has already shortened its analyst-to-associate track from three years to two and a half. Maybe it helps to keep talent or maybe it is an attempt to compress a learning curve that was never built to compress.
To be precise: not every bank is moving the same way. Reuters Breakingviews reported that several large banks and boutiques are not planning to cut analyst hiring. But the tension is visible. The group most exposed to automation is also the group the industry relies on to build future investment judgement. So if the pipeline narrows at the base, the judgement gap does not disappear but it shows up one step later, inside the funds hiring from it.
When the tool becomes the environment
There is a second layer that I think funds have not fully absorbed. The next generation of junior candidates will not come from the same working environment.
Some will come from banks where tools like Rogo AI were part of the workflow from day one. Rogo (and many more tools) is a platform built for finance, used for meeting preparation, company profiling, automated diligence and market maps. From its website we can read that it served more than 35 000 bankers and that it saves analysts more than ten hours per week. A junior trained in that environment learns to ask different questions. Not only “can I build this from scratch,” but “what should I delegate, what should I test, and where is the output too good to be trusted?”.
This creates a gap in the hiring market, and it works in both directions. A junior from an AI-enabled bank will not automatically be better. However, they will operate with a different sense of pace and expectation, and they may read the absence of a modern toolkit not as old-fashioned discipline but as inefficiency. Funds should not assume that brand or deal exposure will cancel out that candidates’ perception. The best juniors will compare not only compensation and deal flow, but the quality of the operating environment itself.
On the flipside - candidates most fluent with these tools are also the ones at highest risk of never learning to challenge them. Did the tool make them sharper, or just faster? Did they learn to question the output, or did they outsource the thinking? Can they still build the logic themselves when the platform is not there? The strongest candidates will not be the ones who had access to better tools. They will be the ones who turned that access into better judgement. Telling those two apart is now, in my view, the central problem of junior assessment.
The shape of the role, not its size
I do not think the junior PE roles will shrink, but I think, it will change shape, and the change will be more demanding than the headlines suggest.
Juniors will spend less time gathering information and more time testing it. Less time producing the first draft and more time asking whether a plausible-looking analysis is incomplete, misleading or wrong. These tasks require earlier exposure to the parts of the process that used to be reserved for seniors: management meetings, lender conversations, portfolio reviews, and the moments where an investment thesis either holds or breaks. Otherwise, firms will produce analysts who process information fast, but struggle to form an investment view.
For students and junior professionals thinking about a career in private equity, the implication is uncomfortable but clear: to use the early years to understand businesses, team management and decisions. If you are a junior considering the buyside career, my advice for you would be to learn to express a view clearly. Stay close to what happens after the presentation is delivered. The work that feels slow may be the work that builds your edge.
Closing thoughts
This is not an argument against AI in the deal process. The efficiency gains are real, and pretending otherwise would be naive. I would like to treat it more as a reminder of the system these tools live inside. Investment judgement does not arrive with a promotion and a “Senior” in your title. It builds through repeated exposure to companies, decisions and mistakes. AI can compress production time but I am not convinced it can compress the learning process. Firms that automate the tasks without redesigning how juniors learn may look efficient this year and find themselves short on judgement in three.
Which brings me back to my interview question. I do not ask it to catch candidates out, but to see: whether someone can step outside the output, whether they see the difference between analysis and understanding, and whether they have started to build the instinct this industry still runs on. Instinct and ability to build relationships, gather insights and see what is not visible in the decks.
AI will make junior work faster. It will not remove the need for judgement.
If anything, it will make your lack of judgement easier to see
