Editor’s note: This article draws on insights from a May CIO Dive and CX Dive virtual event. You can watch the sessions on-demand.
The ambitious promises of AI often fall short when applied to real-world scenarios. Data woes, talent shortages and risk exposure can prevent tools from reaching beyond the initial project stage. Poor customer and employee experiences can also derail enterprise plans.
“Launching AI tools is a very human exercise,” said J. P. Gownder, VP and principal analyst at Forrester, speaking earlier this month during a CIO Dive and CX Dive virtual event. “This is the part that a lot of people are leaving out of their equation.”
For CIOs looking to deploy AI and reap its purported productivity gains, the focus should shift away from vendor hype and more toward the intended users of the technology, underscoring the importance of a solid people strategy to steer AI adoption.
“Don’t hitch your wagon to one technology and assume that that’s going to be what’s most important tomorrow,” Gownder said. “Often, we’re wrong in our prognostication.”
Customers, too, should be a critical focus for executives looking to make the most of their AI efforts — especially as agentic AI opens up new enterprise use cases for the technology. Danny Fisher, CTO at home remodeling company West Shore Home, described using an AI tool to give customers support off-hours, when the alternative would be leaving a voicemail.
“We look for opportunities where it’s upside-only to figure out how we can get real-life testing examples without sacrificing performance,” Fisher said during the panel.
Transparency helps build trust
As mass layoffs attributed to AI adoption emerge across industries — especially in tech — employee concerns are growing about how the technology’s deployment will affect them.
One in three companies expect AI will lead to workforce reductions in the next year, according to an April survey from the Stanford Institute for Human-Centered AI. And in the process, employee confidence is fraying.
“The base case for most employees right now is fear,” Gownder said. “That’s not a good place to be.”
Transparency and communication can help leaders increase employee trust in how tools will be deployed and the effect they will have on operations.
In addition to transparency, leaders must put in place intentional learning strategies to help convey the value of the tools in a given process, according to Gownder. The strategy could include weekly events, video testimonials with success stories and mentorship programs to help spread the message across an organization.
“You need to invest in social learning,” Gownder said. “Formal learning has its place and it is important, but it is not nearly as important as social learning.”
At West Shore Home, Fisher said employee productivity can serve as an indicator of enterprise AI success. Token usage is one metric the company is tracking in order to help spur more adoption across its employee base, Fisher said.
“It is such an interesting insight mechanism for senior leadership to be able to see who’s poking around,” Fisher said. “It leads to very educated conversations with your people, and you’ll understand the different skill sets within pockets of your organization. Then you can work on building teams together with those skill sets.”
In addition, the company looks for an open disposition toward AI in its evaluation of new hires.
As AI efforts unfold, CIOs are grappling with concerns over tool-driven risks and “workslop” — a novel term for low-quality AI generated outputs. It’s up to leaders to ensure their workers have the necessary training to use AI responsibly, Gownder said.
“We want people to actually have that moment where they pause and they say: ‘Should I use AI right now for this particular scenario?’” Gownder said. “Workslop is a big issue right now. It’s a productivity killer if you’re sending out AI-generated documents that no one wants to read.”
