The AI Rat Race: Fix Your Product Before Chasing the Hype

Foreword

This article stems from several candid conversations I’ve had with engineering executives from leading companies, all eager to integrate AI into their products or processes. Their question was often the same: “Do you have a quick AI solution I can put in place for my use case? I need to show incredible results to my board within one or, at most, two quarters.”

While I admire their drive to innovate and stay ahead in today’s hyper-competitive AI-driven landscape, I found myself offering a much-needed reality check. These conversations were a delicate balancing act—exploring the "art of the possible" while highlighting the risks, costs, and trade-offs involved in hastily adopting AI.

The challenge? Helping them understand that AI is not a quick-fix solution but a powerful tool that, when wielded wisely, can unlock transformative value. Yet, I felt these discussions often lacked a foundational understanding of what AI can—and cannot—achieve.

This article is my humble attempt to address that gap. It’s not intended to be a professorial guide but rather a personal reflection based on those discussions. My hope is to bring clarity and offer practical insights to those who are striving to stay relevant in today’s AI-driven world.

Let’s delve into it—no buzzwords, no lofty promises, just an honest conversation about AI, its potential, and its pitfalls.

The AI Hype of 2024: A Cautionary Note for Today’s Executives

AI is everywhere. In 2024, it feels like every software company is rushing to add “AI” to their product portfolio. The promise is alluring: increased value, enhanced user experience, and elevated status as a forward-thinking innovator. But let’s face it: in too many cases, these promises fall flat. What often happens is that a chatbot gets hastily bolted onto a broken product, resulting in a complicated, unnecessary addition that frustrates users.

This AI bandwagon is fueled by a mix of ambition and pressure:

• Perceived Innovation: Companies want to look cutting-edge.
• Marketing Leverage: “AI-powered” grabs attention and raises perceived value.
• Competitive FOMO: No one wants to be the company that missed the AI train.
• Financial Opportunity: AI allows for higher price tags, regardless of its actual utility.

Yet, this rat race often overlooks a crucial truth: adding AI won’t fix underlying product issues. Instead, it risks amplifying existing flaws.

Reality Check: AI Won’t Fix a Broken Product

The idea of quickly slapping AI onto a legacy system to deliver “amazing value” sounds tempting. But it’s a misconception that AI can act as a magic wand. Here’s why:

1. User Experience (UX) Still Reigns Supreme

Bad UX doesn’t become good with AI. If your interface is confusing or your workflows are clunky, users won’t care about the AI features—they’ll leave. AI should complement an already intuitive product, not compensate for its shortcomings.

2. Legacy Systems Are a Barrier

Outdated architectures often struggle with the computational demands of AI. Worse, they create integration headaches, making your “quick AI” anything but quick. Modernizing these systems isn’t glamorous, but it’s critical.

3. Broken Features Erode Trust

Imagine adding predictive analytics to a product whose core functionality is buggy. Users won’t stick around long enough to appreciate your AI—they’ll abandon it due to frustration with the basics.

4. AI Complexity Can Overwhelm

Overloading a product with AI features risks alienating your audience. Instead of delighting users, you may confuse or even alienate them. Remember, simplicity is often more valuable than over-engineered complexity.

A Better Approach: Ask “Should We?” Before “How Can We?”

The best companies aren’t asking, “How can we add AI to our product?” They’re asking, “Should we?” This mindset ensures AI serves a purpose rather than becoming a superficial feature. Here’s how to think about it:

1. Start with the Problem

Focus on real pain points. What frustrates your users the most? What bottlenecks slow down operations? AI should solve these issues, not add to them.

2. Build a Solid Foundation

AI thrives on clean, accessible data and scalable infrastructure. Without these, your efforts will result in subpar outcomes. Modernize your systems first—AI can wait.

3. Think Long-Term

A flashy AI feature might win you short-term applause, but lasting success comes from aligning technology with your strategic vision. Be patient. Invest wisely.

4. Simplify, Don’t Overcomplicate

AI should streamline workflows, not burden them. If your product is a simple bar of soap, don’t try to turn it into a soap dispenser. Instead, focus on perfecting what your customers already love about it.

Lessons from the Field: A Tale of Two Approaches

Let me share two stories, both from companies eager to adopt AI.

Case 1: The Chatbot Misstep A SaaS company rushed to launch an AI-powered chatbot for customer support. While the chatbot could answer basic queries, the product’s interface was clunky, and its search function was unreliable. Users found the chatbot more frustrating than helpful and abandoned the platform. The problem wasn’t the AI—it was the lack of attention to the product’s fundamentals.

Case 2: The Analytics Win Another company took a more measured approach. They modernized their data architecture and improved the performance of their analytics platform. Only then did they introduce AI-driven insights, which helped customers make smarter decisions. This thoughtful integration enhanced user satisfaction and boosted renewals, proving that a strong foundation is the key to success.

Closing Thoughts: The Soap and the Soap Dispenser

As executives, it’s natural to feel the pressure to innovate quickly. But remember: AI isn’t a silver bullet. It’s a tool—an incredibly powerful one—that can amplify your product’s value when used correctly. However, the foundation of your product must already be solid.

Sometimes, a bar of soap is all your users need. Perfect it first. Then, if it makes sense, build the soap dispenser. AI should simplify, not complicate. It should enhance, not obscure. Most importantly, it should serve your customers, not your quarterly targets.

In this AI-driven world, relevance comes from thoughtful innovation, not rushing to check a buzzword off your list. Let’s stay grounded, stay strategic, and remember that technology is here to serve people—not the other way around.