Every Company Wants AI. Few Know What Problem They're Solving.

Scroll through job postings on LinkedIn for ten minutes and you'll notice something odd. "AI-enabled Product Manager." "Manager, Operations, must drive AI adoption." The title leads with the tool. Read further down for the problem this person is supposed to solve, and most postings don't have one. Just a vague line about "leveraging AI to enhance outcomes."

That's a small thing. It says a lot about the state of AI adoption right now. Companies are buying tools faster than they're defining problems, and the gap between the two is bigger than most people realize.

Everyone Is Using AI. Almost Nobody Can Prove It Worked.

Nearly nine out of ten organizations now use AI regularly, according to McKinsey's most recent global survey. Only 39% report any measurable earnings impact, and two thirds haven't moved past small scale pilots (Singla et al., 2025).

RAND Corporation interviewed 65 experienced data scientists and engineers to find out why AI projects fail. More than 80% don't deliver the business value they were built for, roughly double the failure rate of ordinary IT projects (Ryseff et al., 2024).

MIT's Project NANDA reviewed over 300 enterprise AI initiatives. 95% saw no measurable financial return from their generative AI pilots (Challapally et al., 2025).

India isn't exempt. NASSCOM's Digital Enterprise 2025 report found that while 27% of Indian enterprises now have AI agents in production, another 31% are still stuck at the proof of concept stage (Nasscom, 2025). A related Nasscom-KPMG study put it plainly: most Indian organizations aren't behind on adoption at all. The open question is why adoption hasn't translated into results.

Three continents, three research bodies, one conclusion.

The Problem Isn't the Technology. It's the Order of Operations.

RAND's researchers found a recurring root cause: organizations frequently misunderstand, or fail to communicate clearly, what problem they're actually trying to solve before they start building (Ryseff et al., 2024). Gartner found something adjacent. 63% of organizations either lack, or aren't sure they have, the data management practices AI needs to function. Gartner predicts 60% of AI projects without AI-ready data will be abandoned by 2026 (Gartner, 2025).

Notice what's missing from both findings. Nobody's saying the models don't work. They're saying nobody agreed on the target before pulling the trigger.

Any real problem-solving process starts the same way: figure out whether the issue is a skill gap, a motivation problem, or a broken process, before you design a fix. Skip that step and you get a polished solution nobody needed.

That's what those job postings are a symptom of. In India, this gets amplified by a specific kind of pressure. Companies in IT services and GCCs (Global Capability Centres) operate in a culture where being seen as "AI-first" has become a competitive signal to clients and investors, almost independent of whether it solves anything internally. Leadership wants the company associated with AI, so the mandate becomes "use AI" instead of "here's the specific problem AI might help with." The tool becomes the job description before anyone agrees on the job.

What The 5% Do Differently

The organizations actually seeing returns redesign how work happens and agree on what success looks like before they choose a technology, not after (Singla et al., 2025). Diagnosis is step one. The tool is step three. Not the other way around.

The Bottom Line

This isn't an argument against AI. It's an argument for sequence. Name the specific problem in plain language. Figure out who's affected and how it shows up in day to day performance. Only then decide whether AI, or something simpler, is the right fix.

Go back to that job posting. Imagine one that opened with the problem instead of the tool: "Our onboarding takes 40 days longer than industry average. We think a chunk of that is a process, not people." That's a posting that tells you the company knows what's broken. "AI-enabled" can come later, once someone's answered that question. It's a slower line to write. It's a much cheaper one to get wrong.