AI’s Fool’s Gold Promise to Fix Everything

Hot AI companies are raising ungodly amounts of funding. Record-breaking rounds flowing to the latest infrastructure darlings. Billions allocated to platforms promising revolutionary data processing and transformative machine learning operations.

The funding announcements are breathless. Revolutionary AI infrastructure. Transformative data platforms. The future of business automation. Reading the coverage, you’d think these companies had solved every strategic challenge facing product leaders today.

But here’s what the headlines miss: the most expensive technology in the world can’t solve a strategic thinking problem.

Over the past 30 years, you and I have witness businesses implement solutions without understanding the challenge. NFTs. Blockchain. VR. CRM systems. Analytics platforms. Dashboards. Even SEO optimization, responsive design, and choosing to build native apps over web-based solutions. Every few years, a new technology wave promises to transform business outcomes, and leaders rush to adopt it whether it makes sense for their specific situation or not.

The pattern is always the same. Technology gets hyped. Early adopters share impressive case studies. Fear of missing out spreads through executive teams. Budgets get allocated. Solutions get implemented. And then, quietly, it gathers dust and burns dollars while we try to figure out a problem it can solve.

Sometimes the solution is right but the implementation is wrong. Sometimes the timing is off. The only real issue is that a real challenge was never clearly defined.

This post isn’t about whether AI is valuable. It absolutely is. These well-funded AI companies are building sophisticated technology and showing impressive growth. AI will continue transforming how work gets done across industries. However, the question we should be asking isn’t whether to invest in AI capabilities. The question is: what specific problems are we trying to solve, and is AI the right solution for our context?

Sadly, that’s not the first question most folks ask.

Instead, we hear: “What AI tools should we buy?” “How do we get our teams using ChatGPT?” “Should we build or buy our AI capabilities?” “What’s our AI strategy?”

These are solution-first questions. They assume the decision to invest in AI has already been made and now it’s just a matter of execution. This approach skips the most important step: understanding what outcomes we’re trying to achieve and whether AI is the best path to those outcomes.

The organizations that succeed with new technology don’t start with the technology solution. They start with crisp, clear problems, specific outcomes, and an honest assessment of what is and isn’t working in their current approach.

It starts with simple, but challenging questions. “What’s preventing us from achieving our strategic goals?” “Where are our current processes creating bottlenecks or waste?” “What problems do our customers have that we’re not solving well?” “What would success look like, and how would we measure it?”

Only after those conversations can you reasonably evaluate whether any technology, AI included, makes strategic sense for your business.

This approach takes more time upfront. It requires putting our bias and institutional knowledge to the side. It means having difficult conversations about current performance and organizational capability. It’s not as exciting as announcing your new AI initiative. But it prevents expensive mistakes and positions you to actually benefit from technological investments instead of simply implementing it.

The leaders who figured this out early in previous technology waves separated themselves from the competition. While others chased trends, they solved real problems. While others optimized for technology adoption, they optimized for business outcomes. While others measured success by implementation speed, they measured success by impact.

The same pattern will play out with AI. The winners won’t be the organizations with the biggest AI budgets or the fastest adoption rates. They’ll be the organizations with the clearest strategic thinking about what problems AI should solve and how success should be measured.

If you’re feeling pressure to invest in AI capabilities without a clear strategic foundation, let’s talk.

Because the right solution at the wrong time, or for the wrong problem, is still the wrong answer.