🤔 Dear Lewis, how do I build AI products that work?

A VP of Product struggles with failed AI experiments. Why do some AI products thrive while others flop? Today, we cut through the hype to uncover what actually works.

Here we are again, my friends. Another week, another story from the trenches of leadership and growth. Today, we’re diving into the world of AI, product innovation, and one VP’s struggle to thrive in the era of endless noise, confusion, and hype.

Let’s call him Mark. Mark is a VP of Product at a fast-growing tech company. He’s smart, driven, and deeply frustrated. Over the past six months, he’s been experimenting with AI-infused products, trying to figure out how to make a real impact in the market. But so far, nothing’s landed.

“I feel like I’m just throwing spaghetti at the wall,” he said during our first session. “Everyone’s talking about AI. Everyone’s launching something. But our experiments? They’ve been… underwhelming. Either the AI feels like a gimmick, or it just doesn’t work well enough to justify itself. What am I missing?”

I couldn’t help but smile. Not because his struggle was funny – but because I knew exactly where this conversation was headed.

The AI Gold Rush – And the Fool’s Gold

“Mark,” I said, “have you ever seen one of those old gold rush movies? You know, where everyone’s panning for gold in the same river, and most of them end up with nothing but rocks?”

He nodded. “Yeah, that’s what this feels like. Everyone’s chasing AI, and I’m starting to wonder if we’re just panning in the wrong river.”

“Exactly,” I said. “The thing about gold rushes is that the people who succeed aren’t just the ones who pan faster. They’re the ones who know where to dig – and where not to.”

Mark leaned forward. “So how do I figure out where to dig?”

This is where we started unpacking the truth about AI: where it thrives, where it struggles, and how to cut through the noise.

Where AI Thrives – The Gold Veins

I shared with Mark the patterns we’ve seen in successful AI products. The ones that don’t just look shiny but actually deliver value. Here’s the shortlist:

1. Time-Saving in Complexity:
   AI thrives when it dramatically reduces the time needed to accomplish complex tasks. Think DALL-E for image generation or Synthesia for video creation. They take something that used to require specialized skills and make it accessible to anyone in minutes.

2. Cognitive Assistance:
   AI shines when it makes cognitively demanding tasks easier. Writing assistants like Grammarly or ChatGPT don’t replace writers – they give them a head start, helping them overcome the blank page.

3. Democratizing Expertise:
   The best AI tools put specialized knowledge into the hands of the masses. Zillow’s Zestimate turns everyone into a real estate analyst. Duolingo makes language learning intuitive and fun.

4. Clear Context and Boundaries:
   AI works best when the problem is well-defined, with clear inputs and outputs. Google Translate thrives because language translation is structured. But throw AI into an ambiguous, multi-step process? That’s where things start to fall apart.

Where AI Struggles – The Fool’s Gold

Then we flipped the coin. “Mark,” I said, “here’s where most AI products fail – and why your experiments might be falling flat.”

1. Incomplete Context:
   AI struggles when it doesn’t have enough information to make good decisions. Hiring algorithms, for example, often fail because they’re working with incomplete data about candidates.

2. Error Compounding in Multi-Step Processes:
   Complex workflows with multiple interdependent steps are a minefield for AI. If each step is only 80% accurate, the errors snowball. By the end, you’re left with a mess.

3. Specialized or Rare Knowledge:
   AI falters when it needs to operate in niche domains with limited training data. Diagnosing rare diseases or handling obscure legal cases? That’s still human territory.

4. The Gimmick Trap:
   “AI for the sake of AI” is a common pitfall. If the AI doesn’t solve a real problem or provide clear value, it just feels like a shiny but useless add-on.

The Breakthrough Moment

Mark leaned back in his chair, processing all of this. “Okay,” he said. “So AI thrives when it saves time, makes things easier, or democratizes expertise. And it struggles when the problem is too vague or niche. But… I feel like I’m still missing something. You know, this reminds me of your CIRCLES Method—starting with the customer, right? Should I be thinking about it that way?”

I smiled. “Exactly. You’ve already got it. AI isn’t the product—it’s the tool. The real question is: what’s the problem your customer needs solved? And is AI the best tool for the job?”

We started brainstorming. Not about AI features, but about customer pain points. The noise. The inefficiencies. The moments of frustration. And slowly, a pattern emerged.

The Job to Be Done

Mark’s eyes lit up. “You’re saying I’ve been starting with the technology instead of the problem.”

“Bingo,” I said. “AI isn’t the product. It’s the tool. The real question is: what’s the problem you’re solving? And is AI the best tool for the job?”

We started brainstorming. Not about AI features, but about customer pain points. The noise. The inefficiencies. The moments of frustration. And slowly, a pattern emerged.

The Surprising Twist

Here’s where it gets interesting. As we dug deeper, Mark realized that the biggest pain point for his customers wasn’t something AI could solve directly. It was something more human: decision fatigue.

“They’re overwhelmed,” he said. “Too many tools, too much data, too many decisions. They don’t need another AI feature. They need clarity.”

And that’s when it hit us: the real opportunity wasn’t about building an AI-powered product. It was about building a product that simplifies decision-making – and using AI as a quiet, invisible engine behind the scenes.

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The Takeaway

The next time you’re tempted to jump on the AI bandwagon, remember this: the gold isn’t in the technology. It’s in the problem. The pain point. The job to be done.

AI isn’t the hero of the story. It’s the sidekick. The tool. The enabler.

And if you can focus on solving real problems – not just chasing shiny trends – that’s where you’ll find the gold.

Keep digging where it matters,
Lewis C. Lin

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Simple, right? Well, not always

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