“How do we actually do this?”
For me, that sums up, as well as anything, the moment that enterprises are in when it comes to AI adoption. As part of a great article surveying the enterprise-level challenges facing AI, Esther Shittu at AI Business interviewed analyst David Nicholson of Futurum Group, who put it succinctly: “Once you get into that realm of, ‘How do we actually do this,’ it gets very complicated very quickly.’”
In every transformation, the emerging technology requires enterprise IT pros to learn whole new skill sets and terminology – and the tech always winds up being the (relatively) easy part. The real challenges for IT tend to come when hype and pressure to adopt a new technology collide with fundamental enterprise requirements that simply can’t be compromised or ignored, such as security, governance, installed base (which implies interoperability), and business processes.
In a recent post on No Jitter, enterprise communications expert Rob Hilsen sums up these factors, and how they impact enterprises’ efforts to take AI from shiny object to transformative reality. He writes:
“Increasingly, deployments inside large organizations slow down when companies realize autonomous [AI] systems must operate inside governance structures involving permissions, auditability, escalation paths, and regulatory accountability. In practice, operational trust means confidence that the workflow around the AI system can be governed reliably at scale.”
Of course AI is as much a cultural phenomenon as a technology transformation, and that’s only making things worse. The Internet was similarly a cultural phenomenon, but communications technology pros were largely insulated from the worst of the resulting pressures. Companies needed to have websites, intranets, e-commerce strategies and all the rest of the building blocks of the Web-driven enterprise; IT teams that specialized in communications did need a roadmap for adopting voice over IP; but recognizing that the technology wasn’t ready to deploy at scale was a viable position to take with the business during the early days of VOIP.
Corporate leadership tends not to want to hear that answer when it comes to AI. So end users are scrambling to apply AI to scenarios where it may not even be the optimal technology, or even provide value equal to or greater than its cost (there’s a couple of good articles here and here providing details). IT is left to manage these users’ struggles and try to help them use the technology safely and cost-effectively, while also driving the AI strategy for the technology estate overall.
We used to talk about wanting to plan for a “graceful” migration from one technology to its successor. I always found that an amusing term, not least because it’s like talking about a “graceful” sausage-making process. But what we really meant was that everything needed to be in order in the planning and execution of a major technology rollout. It was true then and still is today. AI is no exception to that rule.
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