A VP of Operations recently described an AI initiative that sounded impressive on paper.
The company had spent nearly $400,000. They hired consultants. Subscribed to an enterprise AI platform. Presented “AI transformation” updates at every board meeting.
Nine months later, no one was using it.
When asked a simple question—“What problem were you trying to solve?”—the answer came after a long pause:
“We needed an AI strategy to stay competitive.”
That response is more common than most leaders realize. And it explains why so many AI initiatives quietly stall before delivering any value.
Wanting to stay competitive sounds reasonable. But it’s not a business problem—it’s a talking point.
Real problems are specific, measurable, and painful enough that people are already working around them. “We need AI” doesn’t meet that standard.
This gap between ambition and clarity shows up consistently in the data.
A 2025 report from McKinsey found that only 33% of senior leaders can clearly explain how AI creates value for their business. Two-thirds cannot articulate why they are investing at all.
Even more telling:
The issue isn’t adoption. It’s focus.
Most AI initiatives fail before they start because they begin with the wrong question.
Organizations ask:
Winning organizations ask something very different:
“What could we accomplish if AI wasn’t a constraint?”
That shift reframes AI from a goal into a tool—and exposes where value actually lives. Sometimes, businesses often fall into the cost reduction trap, perceiving it as a tool to cut costs on labor instead of an innovative solution to streamline processes at scale, along with the added cost of integration and training.
In the VP’s organization, “AI transformation” was the headline initiative. Meanwhile:
None of these issues required cutting-edge AI. They required clarity about where time, money, and energy were being wasted.
The transformation project was killed. Instead, the team started with accounts receivable automation.
Two months later, DSO dropped from 52 days to 34 days.
Suddenly, no one needed convincing about AI’s value. Momentum took over.
Many organizations frame AI as a strategic initiative rather than an operational enabler. That’s backwards.
AI doesn’t create value on its own. It amplifies existing processes—good or bad. When those processes aren’t understood, AI simply accelerates confusion.
This is why large, abstract transformation efforts struggle. They aim high but land nowhere.
Smaller, well-defined problems create something far more important early on: proof.
Another common failure point is measurement.
Teams launch AI initiatives without defining what success looks like. When results are unclear, adoption stalls, budgets get questioned, and projects quietly fade away.
Companies that succeed do three things early:
This discipline turns AI from an experiment into an asset.
AI initiatives don’t fail because they’re too small. They fail because they’re too abstract.
Early wins do more than deliver ROI—they change behavior. Teams begin to look for problems worth solving. Leaders gain confidence. Resistance drops.
This is the same principle behind the AI Trifecta: impact, effort, and adoption must move together. When any one is missing, progress slows or stops entirely.
The companies seeing real returns aren’t the ones with the most sophisticated AI strategies.
They’re the ones that can clearly answer:
At OntracAI, this problem-first approach is how we help organizations move from stalled initiatives to measurable outcomes, by anchoring AI to real operational pain, not abstract transformation goals.
Explore practical AI solutions built around real business problems here.
There’s another pattern that quietly derails AI initiatives: leading with cost reduction alone.
While reducing expenses matters, starting there can damage trust, suppress adoption, and limit long-term value—especially early in an AI journey.
That dynamic is explored further in our other piece: The Cost Reduction Trap
Most AI initiatives don’t fail because the technology isn’t ready.
They fail because leadership can’t clearly explain what problem they’re solving—or how success will be measured.
AI delivers ROI when it’s used deliberately, narrowly, and in service of real operational needs. Not when it’s treated as a signal to investors or a checkbox on a roadmap.
Before investing another dollar, every leadership team should be able to answer one question clearly:
What problem are we solving—and how will we know if AI actually helped?
If that answer isn’t clear, the initiative has already failed—before it even started.