Private equity has moved past asking whether AI matters. The more useful question is where it actually creates value.

That distinction is becoming important. Firms are deploying AI across sourcing, diligence, portfolio operations, and investment research. But adoption is moving faster than measurable returns, and some of the most common assumptions about AI are beginning to look increasingly shaky.

Here are six myths the evidence is starting to kill.

Myth 1: AI is primarily a cost cutting tool

The first wave of AI investment focused heavily on productivity. Automate repetitive work, reduce headcount, expand margins.

The bigger opportunity may be elsewhere.

McKinsey studied 471 PE backed companies across 31 industries and found that more advanced AI adoption was associated with materially stronger revenue economics. Companies using AI to build products, services, and new businesses reached a median revenue multiple of 31x, compared with 13x for companies at the earliest level of adoption.

The distinction matters. Cutting costs can improve EBITDA. Using AI to improve pricing, personalization, conversion, and product development can change the growth algorithm itself.

Myth 2: The best model wins

Access to sophisticated AI models is quickly becoming abundant. Competitive advantage is not.

PwC argues that defensibility is increasingly moving toward proprietary context, domain expertise, and integration into critical workflows. In PE, that could mean years of investment committee materials, operating data, expert calls, portfolio benchmarks, and sector knowledge.

Two funds can use the same model. The fund with better information and better processes should still get the better answer.

Myth 3: AI adoption equals AI value creation

This may be the biggest misconception.

KPMG found that 68% of asset management and PE leaders are piloting AI agents, with another 24% already deploying them. Yet only 40% expect measurable ROI within the next 12 months.

In other words, AI activity is already widespread. Proof of economic value is not.

For sponsors, counting AI pilots is therefore a weak measure of progress. The better question is whether those initiatives have moved revenue, margins, working capital, retention, or another KPI that ultimately reaches the investment case.

Myth 4: Productivity automatically becomes EBITDA

Saving an employee ten hours a week does not mean ten hours of cost disappear from the P and L.

BCG finds that only 6% of companies report meaningful financial value from AI despite widespread experimentation. One reason is that many organizations add AI to an existing process without redesigning the process itself.

BCG research on AI enabled operations found cost reductions approaching 60% when companies redesigned workflows around the technology, versus less than 20% when AI was simply layered onto existing workflows.

The technology matters. What management does with the capacity it creates matters more.

Myth 5: More AI means more value

McKinsey's PE research suggests the opposite. The biggest valuation step up does not appear at the beginning of the AI maturity curve.

Median revenue multiples rise only modestly from 13x for opportunistic adopters to 14x for companies using AI to enhance the operating model. They then increase to 20x when AI becomes embedded in products and services, and to 31x when companies use AI for new business building.

The market appears to place much more value on AI that changes what a business sells than on AI that merely helps employees do the same work faster.

Myth 6: AI will replace investment judgment

AI can dramatically expand coverage. It can screen more targets, process more diligence materials, and synthesize information faster than an investment team ever could manually.

That does not eliminate judgment.

Deloitte found that 90% of organizations studied already use generative AI somewhere in M and A, while human review remains the leading requirement for consequential applications.

That may be the central lesson for PE. AI is making analysis cheaper and faster. It is not making conviction automatic.

The emerging AI advantage is therefore less about access to technology and more about execution. Models will become easier to obtain. Proprietary data, redesigned workflows, management adoption, and disciplined measurement will remain scarce.

For private equity, that is where the real edge is likely to sit.

McKinsey, Beyond Productivity: How AI Creates Value in Private Equity.https://www.mckinsey.com/capabilities/business-building/our-insights/beyond-productivity-how-ai-creates-value-in-private-equity#/

KPMG, Quarterly AI Pulse Survey, Asset Management and Private Equity.  https://kpmg.com/us/en/articles/2026/quarterly-ai-pulse-survey-asset-management-private-equity.html

BCG, Why AI Pilots Rarely Deliver Value and Reinventing the Operating System of Work with AI. https://web-assets.bcg.com/pdf-src/prod-live/why-ai-pilots-rarely-deliver-value.pdf