AI’s Enterprise Problem Isn’t Innovation. It’s Execution

The pilot-to-production gap isn't really a technology problem. It's a design problem. Most enterprises build a pilot to prove a model works, not to prove it can survive contact with a live P&L, and those are two different exercises with two different success criteria. Gartner has said at least 30% of generative AI projects will be abandoned after proof of concept by the end of 2025 because of poor data quality, unclear business value or escalating costs, and MIT's Project NANDA went further, finding that 95% of organizations deploying generative AI saw zero measurable return. That's not a model failing. That's an organization that never defined what "working" meant before it started building.

The fix is simple to describe and hard to execute. Pick a use case with a P&L owner attached to it from day one. Instrument the return before you write a line of code. Treat data readiness as a prerequisite, not a workstream you'll circle back to. At Findability, we don't greenlight a pilot unless there's a named business metric it has to move and someone in the room accountable for moving it. That one discipline eliminates most of the reasons projects quietly die in pilot purgatory.

This comes down to treating AI as an operating system for the business, not a lab experiment that occasionally gets promoted. Our clearest proof point is the AI Factory for Sugar, built with Grupo Pantaleon and our work with the Agricultural Development Trust in Baramati, where Stoma Sense and Stoma Insight take a mill or a farm from reactive decision-making to predictive planning. Yield forecasting, crop stress detection, harvest scheduling, all tied back to a number the CFO already tracks, not a new dashboard nobody opens twice.

The pattern holds outside agriculture. In manufacturing, we've applied the same architecture to predictive maintenance and quality drift detection on production lines, catching a problem before it becomes a stoppage. In retail and QSR, Serva Insight brings that same store-level operational intelligence down to a footprint small enough that a single-location owner can adopt it without a boardroom debate, not just an enterprise chain with a data science team on staff. In financial services, the use case shifts to risk scoring and fraud pattern detection, but the underlying discipline doesn't change: start where the data already lives, attach a hard number to it, and only scale once that number moves.

The thread running through all of this isn't "deploy more AI." It's deploy AI against the handful of decisions in a business that actually move revenue, cost, or risk, and ignore the rest until those are working.

Yes, and I think it's a mistake we'll look back on. GenAI gets the headlines because it's the most demo-able technology our industry has built. You can watch it write something in real time, and that's genuinely compelling in a boardroom. But writing isn't where most enterprises make or lose money. Predictive and prescriptive AI, forecasting demand, optimizing a supply chain, flagging a machine before it fails, are quieter, far less photogenic, and considerably closer to where the balance sheet actually lives.

Anywhere a business makes the same high-stakes decision repeatedly under uncertainty. A sugar mill deciding harvest windows. A manufacturer scheduling maintenance. A bank pricing risk. A retailer managing inventory ahead of a season. These are decisions made thousands of times a year, and a 2 to 3 percent accuracy improvement compounds into real money fast, in a way a chatbot conversation never will. GenAI helps you communicate a decision well. Predictive and prescriptive AI help you make a better one in the first place. Enterprises treating these as competing priorities are choosing style over substance. The ones actually pulling ahead are running both, deliberately, against different problems, not picking a side.

Most organizations aren't stuck because they lack ambition. They're stuck because they've run fifteen disconnected pilots instead of one coherent programme. S&P Global's research shows the share of enterprises abandoning most of their AI initiatives jumped from 17% in 2024 to 42% in 2025, that's not a technology failure, that's a governance failure, plain and simple.

My advice here is unglamorous but it works. First, consolidate. Pick three to five use cases with the clearest line to revenue or cost, and have the discipline to kill the rest; a portfolio of twenty pilots dilutes both budget and executive attention until nothing actually ships. Second, build the data foundation once, centrally, rather than letting every pilot rebuild its own from scratch. This is essentially what our ICUPP model does for clients: Infrastructure, Collect, Unify, Process, Present. It's a sequence, not a menu. Get the infrastructure and data unification right once, and every AI use case after that gets faster and cheaper to ship, instead of every new pilot paying the same integration tax the last one already paid. Third, put a single senior owner on AI outcomes, not a committee, someone who reports a number, not a roadmap. Fourth, hold every AI initiative to the same standard you'd hold any other capital investment: a defined return, a defined timeline, a defined owner. Enterprise-wide adoption isn't a bigger version of a pilot. It's a different discipline entirely, closer to how you'd run a manufacturing line than a research lab.

Don't let AI become another line item you manage. Let it become the way your company sees itself more clearly than it ever has before. Every business already knows, somewhere inside it, which decisions actually shape its future — what to grow, what to fix, what to stop doing. Most of us have just never had the tools to act on that knowledge in time. That's what changes now.

Five years from now, the CEOs who look back and feel genuinely proud of what they built won't be the ones who moved fastest on the newest model. They'll be the ones who used this moment to make their organizations more honest, more decisive, and closer to the people they serve. That's the real promise here. Not efficiency for its own sake, but the chance to run a business the way you always believed you could, with the friction and the guesswork finally out of the way.

So my advice isn't a framework. It's a question worth sitting with, and asking again every year: if we saw our business with total clarity, what would we do differently, and are we brave enough to actually do it once we can? Build your AI strategy around answering that honestly. Everything else, the tools, the vendors, the roadmap, is detail in service of that one thing.