How AI Is Changing the VC and PE Investment Process — and What Won’t Change
Over the past 20 years, I’ve seen the venture capital and private equity market evolve through multiple cycles. Processes have become more structured. Data has become more abundant. Competition has intensified. Each time, there has been talk of structural change.
AI is different, not because it will replace investing, but because it will automate large parts of how investment processes are currently run.
The direction of travel is clear. But so is something else: preparation, process, and execution will continue to determine outcomes.
The mechanics of investing are being automated
The last six months have been an eye-opener for me personally. Moving from a VC role into deep AI capability building has made one thing obvious: much of the mechanics of investing are automatable.
Inside VC and PE teams today, people are already using AI to:
Summarise pitch decks and data rooms
Accelerate market research and comparable analysis
Draft first-cut investment memos
Structure due diligence questions
The next step is predictable. Repeated prompts become templates. Templates become internal tools. Tools evolve into specialised agents. Over time, those agents are stitched together into structured workflows.
This is not speculative. Much of it is already possible.
McKinsey estimates that 60–70% of knowledge-worker tasks are technically automatable with generative AI . A large proportion of what junior and mid-level investment professionals do today sits squarely in that category.
The result is faster diligence, more consistent analysis, and shorter time-to-decision.
The mechanics of investing become commoditised.
Judgement does not.
What changes first inside VC and PE firms
Based on both experience and current AI capability, the sequence of change is relatively predictable.
First, traditional analyst-heavy work compresses. Data gathering, competitive mapping, and memo drafting become faster and more structured. BCG has already estimated that AI can reduce diligence timelines by 30–50%.
Second, investment committee materials standardise. The structure becomes more consistent. Style matters less. Signal matters more.
Third, headcount compresses at the junior level. AI produces structured first drafts; humans refine and apply judgement.
Fourth, roles shift. Analysts and investment managers spend more time on business development, relationship management, and increasingly, managing AI workflows rather than building spreadsheets from scratch.
Fifth, senior time moves up the stack. Less debate about formatting and data collation. More focus on management quality, strategic positioning, and risk under pressure.
What feels fast today becomes normal.
Second-order effects most people miss
Where this becomes more interesting is outside the investment firm itself.
As VC and PE processes standardise:
Corporate finance advisers and consultants will need comparable AI capability to prepare companies properly.
Founders will increasingly use AI tools to pre-empt diligence questions, stress-test narratives, and identify weaknesses before investors do.
The VC/PE process itself becomes more transparent and more repeatable.
In effect, the “black box” narrows.
There is also a quieter dynamic around LPs. As structured “VC/PE-in-a-box” workflows become more viable, some institutional investors will inevitably ask harder questions about the full GP stack. Governance and reputation will remain critical, but automation lowers certain barriers to entry.
Even small shifts in behaviour at the margin can alter long-term dynamics between LPs and GPs.
What does not change: preparation, process, execution
Despite all of this, the core determinants of a successful capital raise or transaction remain the same.
AI can accelerate analysis. It can structure thinking. It can surface blind spots. But it does not replace:
Running a disciplined, institutional-grade process
Creating competitive tension
Understanding investor psychology
Negotiating terms under pressure
Maintaining execution discipline over months
Over the last two decades, I’ve seen businesses with strong fundamentals underperform in a raise because the process was weak. I’ve also seen average businesses outperform expectations because the preparation and execution were exceptional.
AI will not remove that reality. If anything, it will amplify it.
Once process mechanics are automated, what remains visible is judgement and execution quality.
Implications for founders and investment teams
For founders, this likely means faster decisions and more structured diligence. There will be fewer arbitrary hurdles, but also fewer places to hide weak preparation.
For GPs, process will stop being a differentiator. Value-add, judgement, and access will matter more. Once the mechanics are standardised, fees must be justified by outcomes, not by process opacity.
For LPs, transparency increases. Not because AI simplifies investing, but because it removes excuses.
A final perspective
Every structural shift in private markets over the last 20 years has ultimately rewarded those who understand fundamentals.
AI is a powerful accelerant. It will change workflows inside VC and PE firms. It will compress timelines. It will reshape roles.
But it will not change the need for disciplined preparation, well-run processes, strong execution, and the right mindset.
Those fundamentals have outlasted every cycle I’ve worked through so far. I expect them to outlast this one too.
This belief underpins both my advisory work and the tools and platforms I build: technology evolves, but optimal outcomes still depend on how well you prepare and execute.
Sources:
McKinsey (Generative AI economic potential)- “The Economic Potential of Generative AI”
BCG has published multiple pieces on AI reducing diligence timelines and increasing productivity in PE workflows.
