UCX Manchester 2026 made one factor clear for the productiveness and automation market: the dialog has moved past AI hype. Consumers are nonetheless all in favour of copilots, assistants, and automation layers, however the temper on the bottom was extra sensible than promotional. The actual questions now are easier and more durable. Does this take away work? Does it lengthen service protection? Does it join programs correctly? Can or not it’s ruled? And can individuals really use it?
Throughout interviews with Intermedia, Algo, Akash Joshi of DevOps Society, and Pc Weekly editor-in-chief Bryan Glick, the identical sample stored surfacing. Productiveness and automation now not appear to be a race to launch extra AI options. They appear extra like a check of whether or not organisations can reduce friction throughout workflows, infrastructure, entry, and decision-making.
Productiveness Is Turning into an Operations Query
That theme got here via strongly in Philippe du Fou’s interview. His view was that AI in UC is shifting from novelty to operational reduction. In apply, which means instruments that assist stretched groups, enhance responsiveness, and quietly take away low-value work. He pointed to after-hours demand as a very essential strain level.
“As of late individuals are extra digital. They’ll go browsing afterward, they anticipate a direct reply.”
That issues as a result of productiveness in 2026 is not only about what occurs whereas employees are on-line. It’s more and more about whether or not organisations can use AI to increase service protection, reply routine questions, and cease easy requests from spilling into the subsequent day’s workload.
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However Service Protection Is Solely One Layer of the Story
If Intermedia framed productiveness as service protection, Mike Greenwood confirmed why the infrastructure beneath nonetheless issues. His level was that communications infrastructure nonetheless decides how a lot automation is definitely doable. The PSTN and ISDN switch-off is due to this fact greater than a telecoms deadline. It’s a likelihood to exchange disconnected communications programs with IP-first, API-connected infrastructure that may feed into trendy workflows.
“AI goes to assist remodel our know-how and actually assist API integration into buyer portals and customer-centric organisations.”
That may be a helpful reminder. Automation hardly ever fails as a result of the AI mannequin is lacking. It usually fails as a result of the encompassing programs don’t join nicely sufficient for automation to behave meaningfully. Productiveness positive factors rely on infrastructure that may carry alerts, set off actions, and keep resilient as workflows evolve.
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Then the Debate Strikes Contained in the Organisation
Akash Joshi pushed the argument in a extra organisational path. In his view, many corporations nonetheless undermine their very own AI ambitions by limiting information too tightly and limiting entry to a small technical group. His message was blunt: if organisations need stronger AI outcomes, they should let extra non-engineers use the instruments and the related enterprise information safely.
“One of the best factor that corporations can do proper now to take away the hype out of it’s to truly hand it to people who find themselves not engineers.”
That shifts the productiveness debate away from fashions and towards enablement. If workers can’t attain the correct information, belief different groups, or experiment with out organisational friction, AI will keep caught in pilot mode.
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The Larger Prize Is Enterprise Redesign, Not Workflow Ornament
Bryan Glick’s contribution tied the day collectively. He argued that AI sits in an extended chain of transformative applied sciences, however the true ROI won’t come from making current duties barely quicker. It’s going to come when organisations begin redesigning the enterprise round what the know-how makes doable.
“The place the true ROI will come is once you begin pondering, ‘How can we actually change our enterprise due to the capabilities of this know-how?’”
That’s the place his level on digital twins was particularly fascinating. Whereas AI dominated the occasion, Glick flagged digital twins as an underhyped idea with actual relevance to productiveness. The power to mannequin a enterprise, simulate change, and check workflow shifts earlier than deploying them might change into a serious planning benefit as automation grows extra advanced.
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The broader takeaway from UCX Manchester is obvious. Productiveness and automation have gotten much less about particular person AI options and extra in regards to the working circumstances round them: service protection, infrastructure readiness, information entry, belief, governance, and the power to revamp work itself. The following productiveness winners won’t be the businesses with essentially the most AI options. They would be the ones that take away essentially the most friction from how work really will get accomplished.
FAQs
What had been the principle productiveness and automation themes at UCX Manchester 2026?
The most important themes had been sensible AI adoption, after-hours service protection, infrastructure modernisation, information entry, belief, governance, and the necessity to redesign workflows fairly than merely layering on extra instruments.
Why was AI described as transferring past hype?
As a result of the dialogue centered much less on flashy demos and extra on measurable outcomes akin to workload discount, workflow assist, service responsiveness, and enterprise redesign.
How does infrastructure have an effect on automation outcomes?
Audio system highlighted that disconnected or legacy communications infrastructure nonetheless limits what automation can really do, even when AI instruments can be found.
Why did belief and entry come up so usually?
As a result of organisations that limit information too closely or hold AI instruments confined to technical groups usually battle to scale actual productiveness positive factors throughout the enterprise.
What was the underhyped takeaway past AI itself?
Bryan Glick pointed to digital twins as an essential future planning layer, particularly for modelling enterprise adjustments and testing workflow impacts earlier than deployment.
