Data is the fuel and AI is our engine, but where are we going?

P: I've been thinking about our AI strategy meeting yesterday. Everyone kept talking about which models to use, how much compute we need, what data lakes to build...

K: Let me guess - three hours of technical architecture and zero minutes on what we're actually trying to accomplish?

P: Exactly! It's like we're obsessing over building the perfect race car engine without knowing if we're heading to the grocery store or the Indianapolis 500.

K: That's a good analogy. AI is the engine, data is the fuel, but we're just revving in the driveway. Where's our destination?

P: Right? Mike from engineering spent twenty minutes explaining why we need the latest GPU cluster. Sarah from data kept talking about cleaning our customer databases. But when I asked what business problem we're solving, everyone went quiet.

K: I see this everywhere. Companies think that having powerful AI and clean data automatically equals success. It's like assuming that premium gas and a Ferrari engine will somehow magically get you where you need to go.

P: The thing is, without knowing our destination, we can't even judge if our engine is appropriate. Maybe we need a reliable sedan for daily operations, not a Formula 1 car.

K: Exactly. Are we trying to reduce customer service costs by 30%? Increase sales conversion rates? Detect fraud faster? Each destination requires different routes, different vehicles, different fuel efficiency considerations.

P: And different success metrics! If we're trying to improve customer satisfaction, we shouldn't be measuring model accuracy in isolation. We should be tracking whether customers are actually happier.

K: That's what bothers me most. Teams spend months perfecting their AI models, achieving 95% accuracy on test datasets, then deploy something that doesn't move the business needle at all.

P: Because they optimized for the wrong destination. They were trying to win a technical race when the business needed them to deliver groceries efficiently.

K: I remember that startup I consulted for last year. They built this incredible recommendation engine - beautiful data pipelines, state-of-the-art algorithms. But they never defined what "good recommendations" meant for their users.

P: What happened?

K: The recommendations were technically sophisticated but felt random to users. Engagement actually dropped. They had built a rocket ship when they needed a bicycle.

P: So how do we fix this? How do we get teams to start with the destination?

K: I think we need to flip the conversation. Instead of "What AI can we build?" we should ask "What outcome do we need?" Then work backwards to figure out if AI is even the right solution.

P: And if it is, then we can design the engine and choose the fuel based on our specific route requirements.

K: Exactly. Maybe our destination only requires simple automation, not complex machine learning. Maybe we need real-time predictions, which changes our data architecture completely. Maybe we need explainable results, which rules out certain models entirely.

P: It's like trip planning. If I'm driving cross-country, I need different preparation than if I'm going to the corner store. The destination determines everything else.

K: Right. And just like you wouldn't plan a road trip by first buying the most expensive car and the most premium gas, you shouldn't plan AI initiatives by first acquiring the most powerful models and the biggest datasets.

P: We should start our next meeting differently. Not "What AI capabilities do we want to build?" but "What problems do our customers have that we could solve better?"

K: And then ask: "How will we know if we've solved them?" Because if you can't measure whether you've arrived, you don't really have a destination - you just have wishful thinking.

P: That's the missing piece. Clear success metrics that tie back to business value, not just technical performance.

K: Think about it - Google didn't set out to build the best search algorithm. They set out to help people find information faster. The algorithm was just the engine they built to reach that destination.

P: And they measured success by user behavior, not just algorithmic elegance. Did people find what they were looking for? Did they come back?

K: Exactly. The engine and fuel were means to an end, not the end itself.

P: I'm going to propose we restart our AI initiative. Same team, same budget, but we begin with a completely different question: "Where do we want to go?"

K: Now you're talking. Once you know your destination, choosing the right engine and fuel becomes much clearer. And more importantly, you'll actually end up somewhere meaningful.