Operations | Monitoring | ITSM | DevOps | Cloud

Today, I’m very happy to share a new report, Bridging the AI Acceleration Gap, from Harvard Business Review Analytic Services and sponsored by Nexthink. It examines how employee-led AI adoption is reshaping the role of IT—and what technology leaders need to do next.

For the last two years, enterprise AI strategy has largely focused on one thing: AI adoption.

At Nexthink’s DEX Day in Raleigh, NC, IT leaders shared how they’re tackling vulnerability response, compliance, and the growing expectations on end-user computing (EUC) teams.

IT teams don’t need another place to look for problems. They need a faster way to understand what is happening, decide what to do next, and act before disruption spreads. That has always been the promise of Workspace. It gives IT teams a conversational way to investigate issues, surface insights from Nexthink data, and understand what needs attention across the digital workplace. Now, Workspace is entering its next phase.

Manufacturing organizations have spent years strengthening the systems, processes, and supply chains that keep production moving. Yet some of the disruption affecting operations begins in places that are much harder to see. A slow engineering workstation, inconsistent access to a production application, a login delay at shift change, or a device that needs repeated intervention may not look like a plant-wide outage, but each one can add friction to work that is already tightly sequenced.

Business services firms are built on people’s time, judgment, and credibility. When a consultant loses half an hour before a client workshop, a legal team is stuck waiting for a document system, or a service delivery group has to move conversations elsewhere because collaboration tools are unreliable, it may not register as a major IT event. It still changes the economics of the work, because skilled time is being spent compensating for the environment instead of serving the client.

For many organizations, digital employee experience (DEX) is still viewed as an IT project. It measures endpoint health, identifies performance issues, and helps service desks resolve incidents faster. While that’s useful, it’s also far too small a vision. In order to get the greatest return from DEX, organizations have to stop treating it as exclusively an engineering capability and started treating it as an equally powerful intelligence capability.

For the last two years, enterprise AI strategy has largely revolved around one message: use AI as much as possible. CIOs encouraged experimentation, CFOs approved budgets, and organizations pushed employees to adopt tools like ChatGPT, Claude, Copilot, and Gemini in the hope that productivity gains would naturally follow. The prevailing assumption was that simply increasing usage would accelerate innovation and unlock efficiency across the organization. But the enterprise conversation is changing quickly.