The AI Trough of Disillusionment

A scathing article in the Business Section of The Economist (Welcome to the AI trough of disillusionment) states that, "Tech giants are spending big, but many other companies are growing frustrated."

I can't say I'm surprised. Many of the past articles (browse the full archive at https://aicelabs.com/articles) here discuss the pitfalls of undertaking an AI initiative, whether that's building a bespoke tool or onboarding an off-the-shelf solution.

In a way, this article is vindication for our stance at AICE Labs that AI needs to be done right, from end-to-end, with clear outcomes to evaluate against. It's not enough to download a "10 prompts that will hypercharge your organization" article and call it a day. Neither is it enough to make a top-down decree to do something with this AI stuff, no matter how much money gets thrown at it.

There is no simple solution—and, for that matter, no complex big-consultancy-style 17-step process either—that will guarantee success with any project. Throw the massive product risk of an AI initiative into the mix and stir in overhyped promises from influencers and you have the perfect recipe for disappointment.

We've been here before. Machine Learning went through several cycles of hype followed by disillusionment, and it's no surprise that the cycle repeats anew. What can we do? I hope writing this newsletter is doing a small part shedding light on some of the pitfalls, and I'll expand on a few pieces of them in the next little while.

All this to say: It doesn't have to be like this. It's painful to see so much effort and hard work go to waste and lead to disappointing outcomes for users and businesses, where expensive projects lead to nothing more than a proof-of-concept gathering dust in some forgotten cloud folder. And it's this pain that drives us at AICE Labs to dig deeper into what it takes to deliver outcomes rather than code.