Why AI success starts with teams, not tech

UK organizations are investing heavily in AI tools. According to latest Office for National Statistics (ONS) figures, 29% of UK businesses were using at least one type of AI technology as of June 2026.

Yet despite growing adoption, many organizations are still focused on the wrong question.

Most AI discussions start with the technology. Which models should we use? Which platform should we buy? How quickly can we deploy it?

While these decisions matter, they are not what determines success. The biggest obstacle to AI transformation is not technology but the people.

Organizations have managed to navigate major technology shifts before, from cloud migration and agile development to Zero Trust security. AI, however, represents something different. Rather than simply introducing new tools, it is changing how work gets done.

The businesses that succeed will be those that give AI tools the context they need to be useful and the people who know how to put that context to work.

The first shift: changing the mindset

One of the most striking trends in AI adoption is the gap between individual use and organizational readiness. According to recent research, nearly one in five UK workers now uses generative AI daily, yet only one in ten UK organizations has successfully scaled AI or embedded it into core operations.

Employees are embracing AI, whilst organizations are still figuring out how to turn that enthusiasm into lasting business value.

The people seeing the biggest gains aren’t treating AI like business software. They’re treating it like another worker. They know how to provide context. They know how to set expectations. They know how to review outputs, challenge assumptions and refine results. In short, they’re managing AI rather than simply using it.

That distinction matters. AI isn’t behaving like traditional software. It behaves more like a worker. The people getting the most value from it are those who know how to brief it, direct it and assess its output.

For many employees, that way of working is still emerging. AI is often used as a faster route to information rather than a collaborative tool capable of helping shape and execute work. Increasingly, every employee will need to become an AI manager. The ability to brief, direct and challenge AI will become a core workplace skill, regardless of role or department.

The second shift: treating upskilling as a business priority

Organizations cannot hire their way to AI readiness. There simply aren’t enough people with the necessary expertise, and external hiring alone cannot replace the institutional knowledge already sitting inside the business.

The businesses moving fastest are investing in the people they already have. They’re helping employees develop the confidence, judgement and practical skills needed to work alongside AI every day.

Giving employees access to AI tools is only the starting point. Organizations also need structured training, clear governance and opportunities for practical experimentation. The real opportunity lies not in teaching people to use AI, but in helping them understand how to work alongside it, applying critical thinking and human expertise to deliver stronger outcomes than either could achieve alone.

As AI capabilities continue to evolve, the organizations that treat learning as an ongoing discipline rather than a one-off training program will be best positioned to adapt.

The third shift: redesigning the organization itself

The final shift is organizational.

For decades, businesses have been structured around functions, departments and clearly defined handoffs. These models were designed to create accountability and specialization, but they can also introduce friction.

As AI begins working across teams and systems, some of those traditional boundaries begin to blur. Processes that once relied on handoffs between departments can increasingly be coordinated through connected digital workers. Activities that once required multiple individuals, applications and approval chains can increasingly be coordinated through connected digital workers.

While organizational structures will remain important, leaders will need to reconsider how work is organized and how value is generated.

Businesses will increasingly organize around outcomes rather than functions. As AI takes on more coordination work, we may start to see entirely new operating models emerge, including businesses capable of generating enormous value with far fewer employees than would have previously been possible.

AI runs on context

Many organizations are focused on the latest AI capabilities, but AI can only work with the context it has access to. The more relevant context AI can access, the more valuable its outputs become.

This is why digital transformation remains so important in the age of AI. Not because every organization needs to reinvent how people work overnight, but because information needs to flow more freely across the business.

Too often, transformation is viewed as a large-scale overhaul. Some of the most impactful improvements come from making existing workflows smarter.

For years, organizations have viewed printing as a blocker to digital transformation. Although when connected to modern workflows, cloud platforms and business systems, the print action can become an enabler instead. It can help trigger processes, route information and connect systems in ways that support both employees and AI.

Sometimes transformation starts with a major initiative. Sometimes it starts by making familiar actions more intelligent. The goal is the same: making information easier for both people and AI to access and use.

AI transformation is a people transformation

As organizations race to adopt AI, it’s easy to assume success will be determined by the technology itself.

The real differentiator will be how effectively organizations prepare their people and unlock the knowledge already sitting across the business.

AI can only work with the context it has access to, and much of that context still lives in documents, workflows and everyday processes.

The organizations that see the greatest value from AI won’t necessarily be those with the most advanced models. They’ll be the ones that make information easier to access, enable people to work differently and create an environment where humans and AI can contribute together.

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