The AI valuation haircut: what PE buyers are now scoring in franchise due diligence

Franchise operators heading toward a capital event in the next two to five years need to answer one question: what does a sophisticated PE buyer see when they open the hood on your technology stack? Not what you see - what they see.

Due diligence has changed in ways that aren't fully visible from the operating side of the table. AI readiness has become a standard evaluation criterion alongside financial and operational diligence. Bain & Company's Asia-Pacific Private Equity Report 2026 identifies assessing AI impact on acquisition targets as the number one generative AI priority for PE general partners. That shift reflects a broader repricing of what "digital maturity" is worth and what its absence costs.

What changed in how buyers assess value

Industry research tracking PE valuation criteria over the past several years shows that the share of investors weighting digital maturity among their top three factors has roughly tripled since 2020. That's a structural reset in what buyers consider when setting entry multiples.

The mechanism behind that repricing is concrete. Digital maturity improves EBITDA through three channels: revenue growth from better demand capture, cost reduction from operational automation, and improved asset utilization from tighter system integration. Companies with mature digital pipelines aren't just capable of deploying AI; they're already generating the financial outcomes AI is supposed to create. That distinction matters at the deal table.

On the inverse side, the logic is direct. Outdated legacy systems without a migration strategy create identifiable deductions in valuation models. IT technical debt is no longer treated as a post-close problem to be managed. It's priced in at entry.

The gap between aspiration and execution

The FTI Consulting 2026 Private Equity AI Radar offers a precise view of where most portfolio companies actually sit. 95% of PE funds report that their AI initiatives are meeting or exceeding their original business case criteria. That sounds like success. But only 7% of portfolio companies have achieved enterprise-scale AI deployment.

Those two numbers coexist because the success cases concentrate in firms that moved early, built the right foundations, and had the operational discipline to execute. The remaining gap isn't a capital problem or an intent problem. FTI identifies talent as the primary constraint: 35% of PE respondents cite human capability as the primary barrier to scaling AI adoption. You can fund an AI roadmap. You can't compress the time it takes to build the team and infrastructure to execute it.

For a franchise system entering a deal process, this gap matters in a specific way. If a buyer's operating partners have spent the past two years building playbooks for AI deployment across portfolio companies, their expectations for what a target should have already done are calibrated to the 7% that got there, not the 93% still in early or mid-stage experimentation. The deal gets priced against what the buyer would have to do to close that gap, not what the seller thinks they're worth.

What an AI readiness discount actually looks like

The "AI readiness discount" has a specific trigger: targets that show no visible movement on AI adoption. Targets with no experimentation, no pilots, and no evidence of a structured approach get priced differently than targets that have begun the work, even if that work is early-stage.

Research on PE digital transformation premiums finds that buyers are willing to pay 20%+ more for digitally transformed portfolio companies compared to financially identical non-digitalized companies. Companies with mature digital pipelines command measurably higher valuation multiples than less digitalized competitors.

For franchise systems, the practical implication is that the window for building digital maturity that reads well in due diligence is not the six months before a deal process opens. It is the two to three years before that. Infrastructure decisions made now - ERP modernization, CRM integration, cloud migration, data architecture - determine what the diligence team finds when they run their assessment.

BCG's work on AI deployment velocity adds a specific dimension to that timing pressure. Systems and portfolio companies that modernize core systems first report faster AI deployment timelines once the implementation begins. The foundation determines more than whether diligence goes smoothly; it compresses the value creation timeline the buyer is paying for at close.

What buyers are looking for and what most systems haven't built

The modern due diligence framework for technology and AI readiness includes several evaluation streams: AI readiness and data maturity, system architecture and cloud readiness, IT technical debt and migration strategy, evidence of AI experimentation or pilot outcomes, and the operational capability to execute a digital roadmap post-acquisition.

EY reports that 53% of PE firms are actively hiring specialists in digital transformation, and 51% are seeking data scientists and AI experts. Building that internal capability is not overhead. PE firms are investing in it to assess and execute digital and AI due diligence with more precision than they could three years ago. The sophistication of the buyer's team is increasing. Standards for what reads well in diligence are rising with it.

McKinsey's research on AI in private markets frames AI as a distinct value lever for PE portfolio companies: one that expands EBITDA, improves capital efficiency, and produces structurally stronger exit narratives grounded in demonstrable productivity gains. That tells you what buyers are underwriting when they set multiples: a transformation roadmap. A franchise system that walks into a deal process with infrastructure that can support that roadmap commands a different price than one that requires the buyer to rebuild the foundation before the roadmap can begin.

The timing problem most operators underestimate

The shift in how PE buyers evaluate digital maturity is not a future trend. Research on PE valuation criteria already documents a roughly threefold increase in digital maturity weighting since 2020. Bain already treats AI due diligence as its top generative AI priority for 2026. The advisory infrastructure for assessing digital maturity is already formalized.

What lags is operator awareness on the sell side. Franchise systems that haven't started building toward digital maturity in earnest are already operating under a compressed timeline if a capital event is in view within three to five years. Infrastructure changes take time. Data architecture decisions take time. Building the operational discipline to demonstrate AI readiness to a sophisticated buyer takes time that can't be bought back in a deal process.

The gap between aspiration and execution that FTI documents at the portfolio company level exists at the individual system level too. 95% of PE funds are getting AI returns where they've actually deployed. 7% of portfolio companies have reached enterprise scale. The systems that will look most valuable at the next deal cycle are the ones that close that gap now, not at close.