InterviewProductivity AI6 May 2026 · 5 min read

Why Digital Twins Could Matter as Much as AI for the Future of Enterprise Communications

Why digital twins and AI could reshape enterprise communications, planning, and business change faster than many leaders expect.

Alex Cole

Digital twins enterprise communications future

AI is dominating enterprise conversations, but Bryan Glick, Editor-in-Chief at Computer Weekly, thinks many businesses still do not know what they actually want it to do.

Speaking to UC Today at UCX Manchester, Glick argued that AI belongs in a longer chain of post-internet technologies such as cloud and big data. Each wave builds on the last. Each one accelerates change a little more. And AI may prove the most transformative of the lot. But his reality check was just as clear: businesses still need outcomes, governance, and a better planning layer if they want AI to deliver anything more than interesting demos.

“AI is just another technology. It has enormous capabilities. Businesses have to understand how to use it, what they want to get from it.”

Also at UCX:

AI ROI Still Depends on Business Change, Not Just Better Chatbots

That is the big takeaway for UC Today readers. Glick drew a distinction between large enterprises that have used machine learning and data science for years, and the wider group of businesses for whom generative AI is the first real exposure to AI at scale. The former already understand the context. They have the skills. They know where the technology can fit. The latter are still working through the basics and chasing the easier use cases first.

The first wins are predictable: chatbots, internal search, summarisation, and similar low-friction deployments. Useful, yes. But incremental, not transformational.

“Where the real ROI will come is when you start thinking, ‘How can we really change our business because of the capabilities of this technology?’”

That is a sharper framing than most vendor messaging. For UC and collaboration buyers, it means the biggest return will not come from sprinkling AI on top of existing workflows. It will come from redesigning how service, support, communication, and decision-making actually operate.

Compliance Leaders Still Have Good Reason to Be Nervous

Glick was equally direct on governance. In highly regulated sectors, compliance teams need to audit decisions step by step. They need to understand why a system produced a result, what data it used, and whether it stayed within policy. That becomes much harder with generative AI.

His point was blunt: for many compliance leaders, today’s models are still a black box. That is why the short-term future will almost certainly include tighter guardrails, slower deployment in regulated workflows, and far more scrutiny around where AI is allowed to act autonomously.

And that lack of explainability is exactly where simulation starts to matter more. If organisations cannot fully inspect how AI will behave in a live environment, they will increasingly want safer ways to test workflow changes, service redesigns, and operational decisions before they reach real customers or regulators.

The Missing Planning Layer: Digital Twins

That is what made Glick’s next point so interesting. Asked which areas of enterprise technology deserve more attention than they get, he pointed to digital twins.

“One area that we’ve written a lot about, which I think is going to have a real impact around this, is the concept of digital twins.”

His explanation was simple and strong: a digital twin creates a digital model of a business, building, city, or operating environment so leaders can simulate change before making it in the real world. Glick compared it to a Formula One simulator for business. Tweak something, test the result, and see what happens before the cost becomes real.

That has obvious relevance to AI, but it also has direct value for UC. In customer service environments, hybrid workplaces, and support operations, digital twins could help leaders model how AI, workflow changes, staffing shifts, or new communication tools affect the business before those changes hit production. That makes them more than an XR curiosity. They could become a planning layer for business change.

In that sense, Glick’s point reaches beyond the current AI cycle. The market may be fixated on assistants and agents today, but one of the more strategic shifts could come from tools that help businesses simulate change before they deploy it. AI may get the headlines. But digital twins may decide whether it actually works.

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FAQs

How does Bryan Glick compare AI with earlier enterprise technology shifts?

He sees AI as part of a chain of post-internet technologies such as cloud and big data, with each wave building on the last and accelerating change further.

Where does Glick think the real ROI from AI will come from?

He argues that the biggest return will come when businesses use AI to reshape how they operate, not just make existing tasks slightly more efficient.

Why are compliance leaders cautious about generative AI?

Because many AI systems still behave like black boxes, making it difficult to audit decisions properly in regulated environments.

What are digital twins in this context?

They are digital models of businesses, buildings, or environments that let organisations simulate changes and test likely outcomes before acting in the real world.

Why do digital twins matter to UC and workplace technology buyers?

Because they could help leaders model the impact of new communication tools, staffing changes, AI deployments, and workflow shifts before rolling them out live.