Before rushing into AI, organizations must prioritize decision quality over speed. Learn why focusing on improving decision-making processes is crucial for AI success.
Client/server computing didn’t even work for the first decade it was deployed. Big data focused us on buying massive amounts of storage before we’d figured out how to use it. One of the biggest disasters in that push happened at a massive NSA data center that cost billions and became worthless. Then came “digital transformation”; it sounded great but didn’t focus on very real problems that needed fixing. After their digital transformation, many companies found they’d spent millions only to make operations worse.
With AI, we are focused on productivity, but is productivity our biggest problem — or is it that we continue to make many awful decisions? Maybe we should first focus on making better decisions before we deploy AIs modeled on our process that will make these bad decisions faster — so fast that we may be unable to recover from them.
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How we should rethink AI purchases
When Windows 95 came out, Microsoft did an incredible job creating demand for the product. People were deploying it everywhere, including me, who put it on my CEO’s PC and bricked it. It was not exactly my best career move. Worse was a guy at Intel who put it on a machine that ran an FAB and crashed the entire line. It takes weeks to restart a process like that, or it did back then. To say a mistake like that is career-limiting is an understatement.
Productivity, where AI seems to be focused at the moment, is important, but increasing the speed of anything before you first ensure direction can, and often does, result in the company spending millions of dollars in the wrong direction. Although that was problematic during the client/server, big data, and digital transformation campaigns, it could be deadly with AI.
A case in point was Tesla’s Autopilot effort, which convinced people, many of whom died as a result, that they’d bought a true autopilot when what they had was advanced cruise control. When I was a kid, my father told a story about a guy who flew in from England, rented an RV, and asked what cruise control meant. He was told it was like autopilot, so he got on the freeway, turned on cruise control, returned, and tried making a coffee pot. He survived, but it ended badly.
As people run around like chickens with their heads cut off trying to deploy this largely unreliable tool, they should first ask what needs to be fixed. I’d argue they need to fix the quality of their decisions first, not the speed at which they make them.
They must ensure it is dependable before they deploy it to make decisions. Right now, too many buyers think generative AI is the same as AGI (artificial general intelligence)—not the case, as explained in this Forbes comparison—and that generative AI can be trusted, which is also not true, according to a Wall Street Journal article.
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Final thoughts
Like most tech waves I’ve been involved with, this latest AI wave is way too focused on driving revenue and not enough on ensuring a positive result. To succeed, buyers need to have a clear idea of what they need AI to do and a deep understanding of what it can do. Neither is evident in many of the decisions I’ve seen and made.
We all need AI to be more focused on helping us make better decisions rather than speeding up the impact of our bad ones. We should tune out these industry initiatives, prioritize what we must fix, and choose the best tool. In short, we need to be smarter before we go faster. Otherwise, like that guy in the RV, things are likely to end badly.
