Everyone’s talking about AI. But when the buzz fades, only a few strategies will prove they were more than press releases in disguise.
, tech companies were adding “.com” to their names just to inflate valuations. Now, it’s “AI.” In almost every pitch deck and press release these days, AI shows up somewhere, somehow. But the reality of many AI products today is much closer to hype than to real value. Oftentimes, many CEOs promise disruption and transformation but end up outsourcing the hard work to third-party APIs or, worse, deliver dashboards that do nothing.
Yerushalmi points to a systemic mislabeling problem: “Companies and consumers alike confuse AI with language models. But LLMs are just one aspect of the broader AI landscape.” Indeed, the 2023 Garter Hype Cycle for Generative AI predicted that “80% of enterprises would use GenAI APIs by 2026,” but using an API is not the same as building an intelligent system.When companies stretch their AI claims beyond reality, the fallout goes beyond embarrassment.
At Fetcherr, that innovation takes the form of a real-world system that merges the traditionally siloed pricing and revenue management functions within the airline sector. It’s one good example of how AI can truly re-architect workflows, beyond just automating tasks. For investors, identifying that sort of distinction is essential.
These stats show that there’s a growing credibility crisis for AI and organizations, more than ever before, must build and deploy AI tools in ways that preserve trust and bolster their credibility, not erode it. “The main damage lies in the loss of trust,” Yerushalmi reiterated. And when AI is deployed at scale, across customer interactions and regulated workflows, that loss of trust can be catastrophic.So how can enterprises cut through the noise? For Yerushalmi, the answer lies in A/B testing.
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