hile it may seem like AI is being adopted by every company, most aren’t using it to its full potential.
That was the core message IBM CEO Arvind Krishna expressed in the opening keynote for the company's annual Think conference: "Most enterprises run AI at the margin." Rather than reimagining their core processes, he argued, businesses are settling for incremental improvements at the edges of their workflows.
“But the core, end-to-end processes – and a process is, in the end, how an enterprise makes money, makes revenue, … those are largely untouched in the vast majority,” added Krishna.
That’s because we are currently in “day zero” of the AI revolution, Krishna said, which means that the revolution is here and companies have to take advantage of it now.
Yet, in a separate Q&A with analysts and select members of pressed, he attributed people’s hesitation of adopting it now to a fear of the unknown, such as risk, employee’s reactions, and ROI. But Krishna contests this fear with a simple question: If so many people are sharing success stories, why is your company so special that those use cases wouldn’t work for you?
Rather, he advises companies to start slow.
“The world has shown that there is a better way to do something, and if you don't embrace it, it will be a slow oblivion,” said Krishna. “So my advice to them is always pick two or three – I'm not saying pick 10 – areas where it could scale massively, and then, as opposed to doing 100 experiments, pour your energy into making sure that those work.”
He added that those two projects should be chosen only if the leadership of those operations are interested, not just because of what AI can do, as they will be motivated to work around the roadblocks. Once those projects are deployed, Krishna advised to use the confidence as a jumping off point to take it a step further and accelerate other AI projects.
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In my years covering the AI beat, I've spoken with leaders across industries and roles, and nearly every one of them has echoed the same advice as Krishna, to start small with a few purposeful projects, then scale accordingly. There's a reason this guidance keeps surfacing: it holds up. Among the biggest challenges plaguing the industry today are rising compute costs, elusive ROI on AI initiatives, and the all-too-common trap of getting stuck in the pilot phase, unable to bring projects fully to production. Taking a bite-sized approach is one of the most effective ways to minimize those risks.
Disclosure: Sabrina Ortiz's travel to IBM Think was paid by IBM. The Deep View's coverage is editorially independent from the companies we cover.