AI Talent vs. AI Strategy: Why More Engineers Won’t Save Your AI Project

AI talent is in high demand. Organizations are hiring data scientists, ML engineers, AI engineers, and prompt engineers at record pace.

This is hard for me to say because I’m an engineer at heart…

But here’s the uncomfortable truth: lack of talent isn’t the main reason AI projects fail.

It’s lack of strategy.

The Talent Rush

The AI boom has created a talent rush. In fact, the World Economic Forum estimates that 97 million new AI-related roles could be created by 2025 (WEF Future of Jobs Report).

But many organizations fall into the trap of thinking: “If we just hire enough AI people, we’ll figure it out.”

The result? Lots of activity, but little to no ROI.

(See this article in techradar for recent research results: “AI still isn’t living up to its full potential for many business“.)

The Strategy Gap

Why do projects stall? Because:

• No clear link to business value. Teams optimize for demos, not outcomes.
• No governance or compliance framework. Risks get overlooked until it’s too late.
• No adoption plan. Employees don’t know how or why to use the tools.

This lines up with broader research: IDC found that 9 out of 10 custom AI pilots never make it to production (CIO.com). Not because the talent wasn’t there, but because leadership didn’t provide a strategy.

The Smarter Move

So what should business leaders and decision makers do?

• Define business outcomes first. What problem should AI solve? How will you measure ROI?
• Use frameworks. Proven approaches for compliance, governance, and adoption reduce risk.
• Bring in outside help if needed. It doesn’t have to be a full-time hire. A knowledgeable consultant can help you set the right foundation… and often save money by avoiding costly missteps.

Bottom Line

AI works. Talent matters. But without a strategy, even the most skilled engineers can’t deliver ROI.

Before you grow your AI team, ask: Do we have the strategy to guide them?

Without strategy, your AI project could become another statistic floundering in the demo, proof-of-concept, or pilot stage… never reaching production and never returning on your investment.