Most organizations approaching AI transformation ask the wrong first question. They want to know which tools to buy, which models to deploy, and how quickly they can get something into production. Jeff X. Li, AI strategy and solutions leader at Datrix and a global IT executive, has watched that instinct derail more AI initiatives than any technical failure ever could. The problem, in his experience, is almost never the technology. It is everything that happens or fails to happen before the technology is touched. “Most AI programs don’t fail because of bad technology,” Li says. “They fail because nobody owns the result.” That single observation, he argues, explains the gap between AI ambition and execution that continues to widen across industries.
Ownership Determines Whether It Is a Strategy or a Project
The first thing Li looks for when assessing an AI initiative is not the roadmap or the tech stack. It is accountability. Specifically, he looks for a business sponsor, not a technology lead, whose professional outcomes are tied directly to the success of the initiative. The distinction matters more than most leadership teams recognize. Technology teams can build, integrate, and deploy. What they cannot do is generate revenue. “If no one in a business is accountable for the outcome, it is not a priority,” Li says. “It is a project.”
The difference between those two words lies in whether an initiative commands resources, decisions, and executive attention, or quietly stalls when competing priorities emerge. Turning AI initiatives into measurable business outcomes requires someone in the business, not in IT, to stand behind the result.
Answer One Question Before Writing a Single Line of Code
The second failure point Li identifies is one of strategic clarity, and it surfaces before any technical work begins. Leadership teams – eager to demonstrate AI progress – frequently move into execution before they have resolved a more fundamental question: what, precisely, is this initiative designed to move? “Before a single line of code is written, leadership needs to answer one question,” Li says. “Which number moves? Revenue? Margin? Customer retention? Working capital?”
The question sounds straightforward. In practice, many organizations cannot answer it with the specificity required to build an AI roadmap aligned to actual business outcomes. “If you cannot answer that clearly, do not start,” he says. “You are not ready.” For B2B SaaS companies and PE-backed organizations, where the pressure to demonstrate AI adoption is high, this is precisely where the gap between real AI transformation and marketing talk begins to open.
The Execution Model Outweighs the AI Model
The third principle Li advances is the one most at odds with how AI initiatives are typically structured. Organizations invest heavily in selecting the right model, the right platform, and the right architecture. What they underinvest in is the alignment required to execute across functions, and that misalignment is what kills programs that were technically sound from the start. “The execution model matters more than the AI model,” Li says. “The programs that succeed are the ones where product, data, business, and IT are all aligned around the same outcome, not their own metrics. The same outcome.”
Functional alignment around individual metrics, where each team optimizes for its own KPIs, produces fragmented execution and diluted results. Scalable AI architecture means nothing if the humans deploying it are pulling in different directions. The organizations that close the gap between AI ambition and execution, Li argues, are the ones that treat alignment as a precondition rather than an afterthought. “AI becomes real the moment someone’s bonus depends on it,” he says. “That is not cynicism. That is how change actually happens.”
Follow Jeff X. Li on LinkedIn or visit his website and company website for more insights on AI strategy, enterprise modernization, and building scalable AI solutions.
