UCX Manchester 2026 made one thing clear for the productivity and automation market: the conversation has moved beyond AI hype. Buyers are still interested in copilots, assistants, and automation layers, but the mood on the ground was more practical than promotional. The real questions now are simpler and tougher. Does this remove work? Does it extend service coverage? Does it connect systems properly? Can it be governed? And will people actually use it?
Across interviews with Intermedia, Algo, Akash Joshi of DevOps Society, and Computer Weekly editor-in-chief Bryan Glick, the same pattern kept surfacing. Productivity and automation no longer look like a race to launch more AI features. They look more like a test of whether organisations can cut friction across workflows, infrastructure, access, and decision-making.
Productivity Is Becoming an Operations Question
That theme came through strongly in Philippe du Fou’s interview. His view was that AI in UC is shifting from novelty to operational relief. In practice, that means tools that support stretched teams, improve responsiveness, and quietly remove low-value work. He pointed to after-hours demand as a particularly important pressure point.
“These days people are more digital. They’ll go online later on, they expect an immediate answer.”
That matters because productivity in 2026 is not just about what happens while staff are online. It is increasingly about whether organisations can use AI to extend service coverage, answer routine questions, and stop simple requests from spilling into the next day’s workload.
Get the full breakdown here.
But Service Coverage Is Only One Layer of the Story
If Intermedia framed productivity as service coverage, Mike Greenwood showed why the infrastructure underneath still matters. His point was that communications infrastructure still decides how much automation is actually possible. The PSTN and ISDN switch-off is therefore more than a telecoms deadline. It is a chance to replace disconnected communications systems with IP-first, API-connected infrastructure that can feed into modern workflows.
“AI is going to help transform our technology and really help API integration into customer portals and customer-centric organisations.”
That is a useful reminder. Automation rarely fails because the AI model is missing. It often fails because the surrounding systems do not connect well enough for automation to act meaningfully. Productivity gains depend on infrastructure that can carry signals, trigger actions, and stay resilient as workflows evolve.
Watch the interview here.
Then the Debate Moves Inside the Organisation
Akash Joshi pushed the argument in a more organisational direction. In his view, many companies still undermine their own AI ambitions by restricting data too tightly and limiting access to a small technical group. His message was blunt: if organisations want stronger AI outcomes, they need to let more non-engineers use the tools and the relevant business data safely.
“The best thing that companies can do right now to remove the hype out of it is to actually hand it to people who are not engineers.”
That shifts the productivity debate away from models and toward enablement. If employees cannot reach the right data, trust other teams, or experiment without organisational friction, AI will stay stuck in pilot mode.
