AI Needs Clear Outcomes and Better Planning, Not Just Demos
Businesses still do not know what they want AI to do, according to Bryan Glick, Editor-in-Chief at Computer Weekly. Speaking at UCX Manchester, he 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 accelerates change a little more. But his reality check was direct: businesses need outcomes, governance, and a better planning layer if they want AI to deliver anything beyond 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."
Large Enterprises vs. Everyone Else
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?'"
For PR and communications professionals, this means the biggest return will not come from adding AI features to existing workflows. It will come from redesigning how service, support, communication, and decision-making actually operate. That requires rethinking processes, not just automating them.
Compliance Teams Have Legitimate Concerns
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.
For many compliance leaders, today's models are still a black box. That lack of explainability is a real problem. 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.
Communications leaders managing corporate reputation and regulatory messaging need to understand this limitation. If your compliance team cannot audit an AI system's decisions, your organization cannot confidently deploy it in customer-facing or public-statement roles.
Digital Twins: The Planning Layer Missing From Most AI Strategies
When asked which areas of enterprise technology deserve more attention than they get, Glick pointed to digital twins. A digital twin creates a digital model of a business, building, or operating environment so leaders can simulate change before making it in the real world. He 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 direct value for communications and customer service operations. In hybrid workplaces and support environments, digital twins could help leaders model how AI, workflow changes, staffing shifts, or new communication tools affect the business before those changes hit production.
They become more than a technical curiosity. They become a planning layer for business change. Leaders can test how a new communication platform affects team productivity, how AI-driven customer service affects satisfaction scores, or how staffing changes affect response times - all before deployment.
The Real Strategic Shift
The market may be fixated on AI 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. Digital twins may decide whether it actually works.
For communications professionals, that means understanding both the capabilities and the limits of AI. It means knowing when simulation and testing matter more than speed to market. And it means recognizing that the organizations that win will be those that plan carefully before they deploy, not those that move fastest.
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