How to Spot Fake AI Innovation: A Guide for Leaders

CES, the Consumer Electronics Show, is the mecca for tech enthusiasts, where global startups and Fortune 500 giants gather to unveil the "next big thing." I went in ready to be dazzled by cutting-edge AI advancements. Instead, what I found was this: “AI” slapped on products like a cheap sticker, masking a disturbing lack of true innovation.

I spoke with founders, marketers, and engineers who proudly paraded their "AI-powered" solutions. But after digging into their pitches, I realized something unsettling: most of it wasn’t AI at all. It was classic innovation theater—fluff designed to grab attention, dazzle investors, and inflate perceived value without delivering any real intelligence.

After a day of wading through the hype, I saw through the illusion. And I know I’m not the only one. By next year, I’m already bracing for the buzzword “Agenting AI.” Because when AI becomes too mainstream to impress, the tech world finds a new way to dress it up.

Here’s the truth: Not all AI is created equal, and much of what’s being sold as groundbreaking is little more than automation or simple algorithms. So how do you cut through the noise? After dozens of conversations, here are three lessons I learned on how to spot fake AI innovation.

1. Real AI Learns—Fake AI Just Automates

One of the easiest ways to spot fake AI is to ask a simple question: Does this technology actually learn?

Too many products at CES relied on predefined logic rather than real intelligence. For instance, I saw a “smart” device that adjusted temperature settings based on user input. When I asked how it worked, the rep enthusiastically said, “It’s AI-powered!” But when pressed, they admitted it was just basic automation with fixed rules.

True AI goes beyond static programming. It learns from data, adapts to new inputs, and makes decisions that evolve over time. If the product doesn’t improve with use, it’s likely automation dressed up as AI.

2. Buzzwords Are a Warning Sign

If a pitch is drowning in buzzwords—“AI-driven,” “machine learning-enhanced,” “deep neural networks”—it’s often a red flag. At CES, I heard a lot of these phrases without any concrete explanation of what the technology actually did.

I started asking: What’s the model training on? How does it improve over time? What’s the measurable impact? In most cases, the answers were vague, or worse, non-existent.

Real AI companies don’t need to hide behind jargon. They can clearly explain the technology and its value without relying on trendy terms. So when you hear a wall of buzzwords, ask yourself: Is this innovation or just clever marketing?

3. Follow the Data Trail

True AI innovation is data-driven. Without quality data, there’s no intelligence—just guesses. But here’s the kicker: Many of the “AI” products I saw at CES weren’t using data at all.

I spoke to a founder pitching an “AI-powered shopping assistant” that claimed to personalize recommendations for users. When I asked how it gathered data, they confessed it wasn’t actually integrated with any meaningful datasets yet. Translation: There’s no AI here—just marketing.

If a product claims to be AI-powered, ask: What data is it using? How is that data being analyzed? If they can’t answer, you’re looking at a hollow promise, not real innovation.

A Call for Accountability in AI

As I left CES, I couldn’t shake the feeling that the industry is doing itself a disservice by diluting the meaning of AI. Real innovation gets lost in the sea of smoke and mirrors.

I’m not saying automation and basic algorithms aren’t valuable. They are. But let’s call them what they are. Slapping an “AI” label on everything erodes trust and makes it harder for genuinely transformative technologies to stand out.

Next year, when I return to CES, I’ll be ready for the buzzword du jour—maybe “Agenting AI” or “Quantum-Ready Intelligence.” But I’ll also be armed with these three takeaways, ready to separate the true pioneers from the pretenders.

Who else has seen through the hype? Let’s keep the conversation going and hold the industry accountable for what AI truly means.

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