Most AI failures do not come from bad technology.
They come from bad questions.
Organizations invest heavily in AI pilots, experimentation platforms, and proof-of-concepts—yet struggle to explain what business problem was actually solved. Budgets get spent. Models get built. Results remain elusive.
Jeff X. Li has spent more than two decades watching this pattern repeat itself across Fortune 500 companies and mid-market enterprises. The common failure mode is not execution capability—it is starting in the wrong place.
AI fails when organizations optimize for adoption instead of outcomes.
Start with Business Objectives, Not AI Capabilities
The first question leaders ask largely determines whether AI initiatives create value or quietly consume budget.
Are we chasing new capabilities because everyone else is talking about them—or are we starting with the business objectives the organization must achieve to survive and grow?
Too many AI strategies begin by declaring AI a priority, allocating funding, and then asking teams to “find use cases.” The result is predictable: technically interesting pilots that demonstrate feasibility but do not move the metrics that matter. AI works, but the business does not change.
Li learned a different approach earlier in his career. After beginning as an SAP consultant, he went on to run Lean Six Sigma programs at Valspar Asia. Rather than positioning himself as a process expert, he told regional leadership he was there to help them achieve their performance objectives—using the best tools available.
That shift in framing changed everything. The program doubled total impact in its first year.
“To me, scaling AI is the same game,” Li explains. “You start with the performance objectives. The tools are different, but the principle is identical.”
When leaders start with objectives, AI initiatives are evaluated on a simple criterion: does this remove a constraint that is preventing the business from hitting its targets?
Match the Right AI to the Right Problem
Even when organizations focus on outcomes, many fail by applying the wrong form of AI to the problem at hand.
Generative AI has dominated executive conversations, leading many teams to apply large language models indiscriminately. But AI is not monolithic. It consists of distinct approaches, each suited to different classes of problems.
At a practical level, executives need to understand three core categories:
- Natural Language Processing, including LLMs, for text and language problems
- Computer Vision for visual inspection and pattern recognition
- Predictive Analytics for forecasting, optimization, and decision support
A manufacturer trying to reduce defects does not need a chatbot—it needs computer vision. A retailer optimizing inventory does not need generative text—it needs accurate demand forecasting.
Leaders do not need to become technologists, but they do need enough fluency to ask the right questions: What type of problem is this? Does the proposed AI approach actually fit?
Build with Capable Teams—and Demand Options
Execution matters as much as intent.
Most internal IT teams are highly effective at keeping critical systems running. That does not automatically translate into AI execution capability. AI requires different skills: selecting appropriate approaches, integrating with existing workflows, and measuring business impact rather than technical performance.
Organizations succeed when they are honest about their capabilities—whether that means assembling focused internal teams or working with partners who have repeatedly delivered AI into production environments.
Equally important is governance. Leaders should demand options, not single answers.
“When vendors present one solution, they are selling,” Li notes. “When they present thoughtful alternatives, they are helping you make a decision.”
Options force trade-offs into the open—cost, risk, timeline, and impact—allowing executives to allocate capital deliberately rather than reactively.
Execute AI—or It Will Execute You
As AI capabilities advance, the distinction between strategy and execution will continue to blur. The organizations that outperform will be those that treat AI not as a collection of tools, but as a managed capability embedded into how the business operates.
AI will not replace leaders. But leaders who fail to integrate AI into core workflows—and align it to real objectives—will be replaced by those who do.
For Jeff X. Li, the lesson is simple:
Start with the objectives the business must achieve. Choose the AI that fits the problem. Build with teams that can execute. Demand options. Measure outcomes.
Scaling AI is not a technology challenge.
It is a leadership one.
