I expect AI governance to move from an afterthought to a priced line item in private equity deals. The regulatory picture is still unsettled: Colorado delayed and substantially narrowed its own AI law twice, and the EU has now also pushed back its AI Act's high-risk deadlines, from August 2026 to December 2027 and August 2028. What has not moved is the SEC, which named AI directly in its FY2026 examination priorities. Combined with Grant Thornton's 2026 finding that only 9 percent of PE leaders are very confident they could pass an AI governance audit within 90 days, the conditions for this to eventually price into deals are forming, even though the specific laws firms might point to keep getting delayed.
I expect AI governance to move from an afterthought to a priced line item in private equity deals. It is not one yet. The regulatory ground underneath it is moving fast, and not always in the direction of more requirements sooner.
This applies most directly to a portfolio built on AI: companies whose product or value is the AI itself, not a company that merely uses AI tools the way most businesses now do.
What the regulatory picture actually shows
Colorado is a useful lesson in how fast this ground is moving. Its original AI Act would have required documented impact assessments for high-risk systems. The legislature delayed it twice and then substantially narrowed it, replacing the risk-assessment framework with a disclosure and transparency law that does not take effect until January 1, 2027.
The EU has moved the same direction. Its Digital Omnibus on AI, now binding law, pushed the AI Act's high-risk compliance deadlines from August 2, 2026 to December 2, 2027 for standalone high-risk systems, and to August 2, 2028 for AI embedded in already-regulated products. Two of the most-cited AI laws in the world were both delayed in the same year.
What has not been delayed is the SEC. Its FY2026 examination priorities, released in November 2025, name AI directly, testing whether firms meet their existing fiduciary and compliance obligations in how they use and oversee it. That exam tests a firm's own compliance program, not a portfolio company's product directly. But a firm that carries a fiduciary duty of care to its LPs is not well positioned to say it never looked at material AI risk sitting inside its own portfolio, regardless of whether a specific state or EU deadline has arrived yet.
Why I still expect this to move toward pricing
Cybersecurity diligence followed a specific path a decade ago: low internal confidence inside firms, then a regulatory and institutional push, and only once both were in place did it become a line item that moved price. ESG diligence followed a similar arc.
Grant Thornton's 2026 Private Equity AI Impact Survey found that only 9 percent of PE leaders are very confident they could pass an AI governance audit within 90 days, compared to 22 percent of leaders across industries broadly. Confidence is low. The SEC has already signaled where its own scrutiny is going. The specific state and EU deadlines keep moving, but the underlying direction, examiners and regulators paying closer attention to how AI is overseen, has not reversed.
What changes for a fund when this catches up to price
None of this requires a new diligence workstream. It is the layer sitting underneath the technical and legal diligence a firm already runs, the question of who is accountable underneath the question of whether the model works and the contract is sound.
I have not found evidence that this is yet standard or widespread practice, but a buyer's diligence team pricing an AI-native company could reasonably start asking how the AI functions, what it produces, who is responsible for the outcome, and whether the company can document its own accountability. An unresolved answer becomes a discount. The same logic runs in reverse at acquisition, with an ungoverned target priced down before the fund owns the exposure.
Fund personnel are not outside this exposure either. A board seat or an observer role puts them inside the investigation if an AI-driven decision causes harm and no one can show who had the authority to prevent it. And the safety net a firm might assume is standing behind all of this is thinner than it looks: WR Berkley has already filed an absolute AI exclusion across its D&O, E&O, and fiduciary liability forms, and ISO's matching general liability exclusions took effect in January 2026. A liability that used to be insurable is not automatically insurable anymore. See the full picture on AI insurance exclusions
The exposure is also correlated rather than isolated across a portfolio of AI companies specifically. Several companies leaning on the same vendor or the same model would turn one ungoverned gap into a fund-wide event, not five separate problems on five different timelines. And the upside runs the other way too: a company that can show evidence of how its AI functions, what it produces, and how it is overseen tends to travel better at the next raise or exit, the same way clean compliance documentation already reduces friction elsewhere in diligence.
Reading a pattern like this, across many companies rather than one file at a time, is forensic work. It is a different discipline than reviewing a single contract or a single model in isolation.
How to know where a portfolio stands today
The standard is the same across every company in the fund. How it applies flexes by risk, but every company answers the same five questions, drawn from the G.U.A.R.D. Framework™:
1. Governance. Is there a named person responsible for how each AI system functions and what it produces? Neither ISO/IEC 42001 nor the NIST AI Risk Management Framework mandates that exactly one person hold this authority, but a named owner is the clearest way to demonstrate what both are actually asking for. 2. Understanding. Can the firm produce a current AI and vendor inventory across the whole portfolio, including where multiple portfolio companies share the same model or vendor dependency, and is it tracking which state and EU requirements apply to each one, given how often those deadlines are moving? 3. Authority. Has the Human Authority Line™ been drawn for every high-risk workflow across every portfolio company? The EU AI Act's human oversight standard, that a person must be able to effectively oversee and override a high-risk system, is a useful benchmark even where its deadline has been pushed back. 4. Reputation. If an AI-driven decision at a portfolio company went wrong publicly, could the firm show who was authorized to respond and what they did? 5. Design. Does each portfolio company own and run its own AI governance architecture? The firm's role is oversight, not operation, the same relationship it already has with a portfolio company's financial reporting: standardized, reviewed on a set cadence, without the firm doing the bookkeeping itself.
Falkovia's point of view
The AI Governance Maturity Scorecard™ identifies exactly where a portfolio company's AI human architecture has gaps, the same gaps that determine whether adoption actually holds and whether the ROI a firm underwrote shows up, regardless of which specific law has or has not taken effect yet.
The industry's current AI work already goes deeper than a policy check in two ways. Legal teams review vendor terms and generative AI representations and warranties as a matter of course, and operating partners track adoption, since usage is what drives the ROI a firm underwrote. Neither measures the layer underneath: how the AI functions, what it produces, and who is responsible for that. AI adoption is ten percent technology and ninety percent human architecture, and a portfolio company can show strong adoption numbers while carrying exactly the exposure this piece describes.
Building that architecture ahead of where the direction is heading, rather than waiting for a specific deadline that keeps moving, is the same human governance work this practice builds across every sector it serves, private equity included.