Bridging the Gap: Why Your GenAI Project Might Be Starting on the Wrong Foot

Let me share something that really stuck with me: A seasoned venture capitalist once said he stops listening to startup pitches the moment founders begin with "We're building a..." Instead of "We're solving..."

Think about that for a moment. It's a mic-drop insight that perfectly captures what's wrong with many technology projects today, especially in the GenAI space.

The "Solution Looking for a Problem" Epidemic

I've seen this before, and I bet you have too. A new technology hits the scene (hello, GenAI!), and suddenly everyone's scrambling to wedge it into their projects. It’s a trap of "solution-first thinking" – We've all been guilty of falling in love with solutions before understanding the problems. It's like buying a Swiss Army knife and then walking around your house looking for things to cut, file, or screw. Sound familiar?

Here's what typically happens:

• Tech teams get excited about the new shiny tool
• Leadership feels pressure to "innovate"
• Someone inevitably says, "We need an AI strategy!"

Projects get launched before anyone asks, "Wait, what problem are we solving?"

The Real Cost of Misalignment

When technology projects prioritize technical sophistication over business value and user needs, the consequences can be severe:

• Expensive implementations that users resist adopting
• Solutions that solve theoretical rather than actual problems
• Wasted resources on features that don't address core business challenges
• Decreased trust between technology teams and business stakeholders

Redefining Project Success Metrics

I cringe a little when I see teams obsessing over sprint velocities, burndown charts, code coverage and deployment cycle. Don’t get me wrong but It's like measuring how fast you're driving without checking if you're going in the right direction.

What if instead we tracked:

• User adoption rates
• Business process improvement metrics
• Customer satisfaction scores
• Return on investment (ROI)
• Actual problem resolution rates

A Better Approach to GenAI Projects

Here's what I believe works better:

1. Start with the Problem Like that wise investor, don't let anyone pitch you a solution until they've clearly articulated the problem. And no, "We need to implement AI" is not a problem statement.
2. Embrace Design Thinking (But Make It Snappy) Understand your users (yes, actual conversations, not just surveys) Define the real problem (the one keeping people up at night) Consider if GenAI is genuinely the right tool (shocking, I know) Prototype fast, take feedback, fail faster, learn fastest Iterate or Pivot
3. Keep It Real Ask yourself: Will this make someone's job easier? Is GenAI really better than a simple automation script here? Are we overcomplicating things because "AI" sounds cooler?

Key is Finding the Sweet Spot

The key is balancing analysis with action. You don't want analysis paralysis, but you also don't want to be that team that built a sophisticated AI solution to automate a process that nobody wanted to use in the first place (we've all seen those projects – they're now case studies in what not to do).

Conclusion

The next time someone on your team suggests a GenAI solution, channel that wise investor and ask, "What problem are we solving?" If the answer starts with "We're building..." rather than "We're solving..." maybe it's time to take a step back.

The goal is also to maintain a bias for action while ensuring that action is directed toward solving real problems. By combining design thinking principles with rapid experimentation and a focus on business metrics, organizations can build technology solutions that deliver genuine value.

And hey, if you've read this far and are thinking, "But we've already started building our GenAI solution..." – it's never too late to pause and ask the right questions. Your users will thank you!!