Part 4/5: AI Just Made the Product Gap Expensive
For a long time, the missing product seat on value creation teams was overlooked. Roadmaps meandered along as nice slides containing lists of features vs. a set of measurable outcomes. The company’s differentiation wore down slowly — even if the baseline bookings maintained pace with the overall plan. Now it’s started to show up at exit as a lower multiple, but rarely as one visible failure anyone could point at. It’s also showing up as lower GRR (in case you hadn’t noticed). On this last point, dig a bit deeper into the numbers and trends and you will likely find some interesting gymnastics being done by the CFO, CCO and CRO to hold on to the GRR trend in particular. How many at risk accounts were offered “free extensions” or other incentives (free additional module or discount to rescue the account) so as not to screw up the 2 last quarters’ trend? No one wants to show a declining GRR trend during a transaction process. Portcos have become expert in massaging these numbers. None of it is false per se, but it’s hiding something important.
AI has changed the dynamic pretty radically. The questions buyers now ask about AI are mostly product questions, and most value creation teams aren't set up to answer them. Buyers are also reexamining their software spend in light of the enormous incremental AI spend they’ve taken on in their IT budgets. Think about it for a moment, the money has to come from somewhere and the CFOs & CIOs I’ve spoken to in the last 24 months have sharpened their pencils. The easiest move to make is to threaten to cancel a SaaS subscription that has been around for a long time, but not shown any significant new outcomes recently. On the new logo sale front, ask any CRO what’s happening and to a person, they will tell you that late stage deals are pushing or going to “no-decision” because the buyer is being forced to justify the expense vs. the company’s new internal AI push. The remainder of “jump balls” are even more hotly contested between the usual set of competitors and the new “AI first” competitors. How many value creation teams or operating partners are zeroed in on this dynamic vs. the GTM playbook that doubles down on more top of the funnel pipe? The latter is an easier playbook, the former requires significantly different skillsets, experience and lead time. I’ve worked with REALLY good CRO + CMO combinations who have gone all out on the GTM playbook successfully, the problem is that it’s an unsustainable firefight without the product strategy-execution process moving into a different gear.
Funds know they need the new product talent. BDO's 2026 predictions describe operating partners with AI, data, and digital experience as "essential, not optional enhancements," and expect competition for those people to intensify. That's right, but look at what most AI value creation plans actually contain. There's an internal productivity program: coding assistants, support automation, back-office efficiency. There's a long list of possible AI features, usually from a workshop and there's a vendor or model decision. In other words, a set of well intentioned, but stale blue prints.
That ain’t gonna cut it…and btw, the CPO and CTO (if they’re any good) know it. There’s a lot of smiling and waving at the unchanged value creation playbook or operating partner who hasn’t been in the line of fire recently. I was talking to two separate PE managing partners recently who shared with me their frustration with their respective value creation teams. I’ll paraphrase the quote: “our portcos ignore our product value creation team… all we’re doing is providing coaching vs. transformational value creation!”
The productivity work is valuable, and it belongs in every plan, but it doesn't answer what a buyer will ask about a software asset in 2026: will this product still matter when customers' own DIY AI efforts are or could be doing the work? Who gives a f*** what the volume of pull requests (PRs) is or what the manufactured “10x engineering productivity” stats say if we’re not focused on the right problems and responding to the market in a coherent and compelling fashion. Hey everyone, remember the story point and burn down velocity charts in the appendix of the board deck? WHO CARES and btw, the CPO & CTO know that it’s bullshit?
That's a question about the product's position, not the company's efficiency. Is AI in the core workflow or sitting next to it? Does the product have proprietary data and context that an agent can reason over? Have we reduced or increased the cognitive load for the user? Does the pricing model survive if customers need half as many seats? Can a customer's agent call the product, or does it have to work around it?
Those are strategy, pricing, and architecture calls and they belong to whoever owns product.
I've shipped AI products at companies since 2020. The model was never the hard part. The hard parts were deciding where AI belonged in the customer's day, pricing it so it grew revenue instead of cannibalizing seats, earning enough trust that customers would let it act, and getting the product organization to build differently. None of that shows up in a use case or workflow library.
The gap is also getting wider over a typical hold, for a few reasons. AI-native competitors can go from idea to a credible product in months, and a portfolio company with a slow, sales-driven roadmap loses ground every quarter. Productivity wins are easy to measure and fit the existing playbook, while repositioning the product is harder to measure and has no natural owner, so it keeps getting pushed and diligence has caught up. Software valuations corrected HARD in early 2026 on AI fears, and buyers now test AI claims directly. A company that spent two years on internal productivity and has an AI slide for its product is going to be priced accordingly.
If I were building the capability, I'd look for people who can do the following things: judge where AI creates a real advantage in a specific product and where it's just table stakes, rethink pricing without breaking the revenue base, understand architecture well enough to know what agentic workflows will require, change how the product and engineering team builds, and point to AI products they've actually shipped to paying B2B customers.
That last one is the scarce part, there's no shortage of AI strategy advice right now. There's a real shortage of people who have put AI in front of enterprise customers and lived with the results.
