Barriers to AI Adoption in Private Equity

What’s Really Holding Back AI Adoption in PE Workflows?

In last week’s micro-survey, we asked a direct question:
What’s the biggest barrier preventing broader adoption of AI in your workflows?
The responses from our community—spanning PE sponsors, bankers, consultants, and corporate development teams—offer a clear snapshot of what’s stalling momentum across the industry.

ROI Uncertainty and Data Gaps Top the List

Across all respondents, the most common answer was “Unclear ROI or value case” (26%), followed closely by “Data quality or availability” (24%) and “Lack of internal expertise” (20%). These responses paint a picture of an ecosystem where interest is high but execution is shaky—often due to the absence of proof points or operational readiness.

Role-by-Role Breakdown: Friction Takes Different Forms

Private Equity Sponsors are particularly cautious. Nearly one-third (31%) cite unclear ROI as the top hurdle, followed by concerns about data quality (23%) and internal talent gaps (15%). These responses reflect a broader hesitation to divert resources toward AI until its impact on fund performance is more tangible.

Bankers echoed similar sentiments. ROI concerns (33%) were the top barrier here as well, with lack of internal expertise (25%) close behind. Interestingly, bankers were less concerned about resistance to change—suggesting that skepticism is less cultural, and more analytical.

Corporate Development professionals stood out as the group most burdened by data challenges (38%). Many also flagged resistance to change (23%) and regulatory concerns (23%), underscoring how internal systems and org dynamics can stall even the most promising tech deployments.

Consultants, on the other hand, were far more likely to call out resistance to change as the primary hurdle—an eye-popping 50% of respondents chose it. With another 25% citing regulatory friction, it’s clear that advisory teams are up against both internal and client-side adoption headwinds.

The Verdict: The Barriers Are Structural, Not Just Strategic

What this micro-survey reveals is that the biggest obstacles to AI adoption aren’t just philosophical—they’re practical. Poor data infrastructure, unclear use cases, and skill gaps are keeping even the most enthusiastic teams from getting AI off the ground.

But the split across roles also suggests an opportunity: different teams need different enablement. For corporate development, the answer may lie in better data hygiene. For PE sponsors, it's stronger ROI modeling. For consultants, it’s managing change—both internally and externally.