Confidence in AI is Growing Faster than the Proof it Works
By Sai Mali A., Ph.D., Chief AI Officer, Teragonia
I keep having a version of the same conversation with investment and operating leaders: they’re often proud of the margin work AI has enabled: things like tighter cost structures, automated workflows, and maybe a few real points of EBITDA. That work is real and certainly important. However, true enterprise value (unlike margin) scales with growth, predictability, and differentiation, so something I’ve been thinking a lot about lately isn’t whether a model is accurate, but if that accuracy ever reaches the multiple.
Investment keeps climbing, but the proof hasn’t caught up.
According to KPMG‘s Venture Pulse, global venture investment hit $330.9 billion in Q1 of 2026 alone, more than doubling from the prior quarter, with AI capturing +80% of that total.
What’s lagging is proof. In a survey of business leaders earlier this year, Grant Thornton (US)found private equity firms growing more confident about AI faster than they’re producing evidence it’s working. I see the mechanism behind that finding constantly.
For example, AI gets deployed inside a portfolio company before the team has agreed on data definitions, reporting ownership, or what “this works” really means.
You can’t measure your way out of a problem you haven’t defined, and we can only fix what we can define and see.
And even where ROI does show up, it’s concentrated in the wrong place to move a multiple. For example, EY‘s recent private equity research found 68% of firms reporting significant ROI from AI on operational efficiency, and 66% on competitive advantage. Those are solid numbers, but neither is the same claim as enterprise value creation.
Efficiency gains get noticed in diligence but they don’t, on their own, get paid for at exit.
Another way to think about it is a version of Goodhart’s Law playing out at portfolio scale: measure use-case volume, and firms optimize for use-case volume. Measure enterprise value, and the roadmap starts to look very different.
From forecasting to orchestration
Three patterns separate the firms turning AI into multiple expansion from the firms using it to build better dashboards.
1. AI as a business model input
- The dashboard era is giving way to AI embedded directly into revenue mechanics. Things like dynamic pricing, segmentation that shapes go-to-market, churn models that trigger real interventions vs just flagging risk.
- We’ve seen this directly with our clients. For example, a PE-backed subscription education platform was losing customers at a churn rate near 75%. However, once their churn signals were connected to automated, targeted retention plays, the result was $90M enterprise value uplift delivered within a six-week execution cycle.
2. Cross-functional signals that are truly actionable
- Predictions sitting in a spreadsheet don’t move anything to action. The firms seeing real returns connect AI outputs to actions that fire automatically across teams: a procurement renegotiation, a working capital unlock, a targeted campaign that distributes directly to the person who needs to own it and drive that change.
- One of our healthcare portfolio companies had the opposite problem: leadership was spending more time reconciling numbers than acting on them. Once the reporting layer was replaced with a system that routed staff directly to next actions, the unbilled claims backlog dropped -58%, claim submissions increased +33%, and the team eliminated +300 hours per month of manual reporting.
3. Full lifecycle deployment
- Advanced firms aren’t just ‘running AI’, but are using it for sourcing and target scoring all the way through to exit planning. The key understanding is that AI is compressing the time diligence teams spend building conviction on a deal.
Most Firms Can’t Trace their AI Spend to a Multiple
Buyers pay for operations that are predictable, intelligent, and scalable. This further highlights the importance of proving cross-functional adoption, operational discipline, and decision loops that hold up under a new owner’s scrutiny.
A pile of disconnected AI use cases won’t produce that proof, no matter how accurate or technically sound they are.
Firms that pull ahead over the next 12 months will be those who name what decision AI changed, and which number(s) it actually moved. A dozen AI initiatives with no traceable result is a busy quarter, while one initiative with a number attached to it is a diligence answer. Buyers don’t pay for effort; they pay for that answer, and most firms don’t have it yet.
Dr. Sai Mali has a PhD in Operations Research, M.Math in Optimization, Electrical Engineering from Columbia University Data Science Institute, University of Waterloo, IIT Madras.
He is responsible for building AI systems that PE-backed operators use to underwrite capital decisions. Previously, he led data science and AI teams at Houlihan Lokey and has solved large scale data science problems for in production for Barclays, GE Research, major US airlines, and NYC government.
Sources:
[1] KPMG Private Enterprise, “Global VC investment surges to record $330.9 billion in Q1’26 on back of AI megadeals,” Venture Pulse Q1 2026, April 15, 2026. https://kpmg.com/xx/en/media/press-releases/2026/04/global-vc-investment-surges-to-record-330-9-billion-dollar-in-q1-26.html
[2] Grant Thornton, Private Equity Insights: 2026 AI Impact Survey Report (survey of 950 business leaders, including a 100-respondent private equity subgroup, fielded Feb. 23–Mar. 18, 2026). https://www.grantthornton.com/insights/survey-reports/private-equity/2026/private-equity-insights-2026-ai-impact-survey-report
[3] EY, “Beyond implementation: PE’s AI evolution into differentiated growth,” US
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