There’s been a growing discussion about whether we’re entering an AI bubble and it’s prompting business leaders to pause and carefully evaluate their next steps. Analysts point to inflated valuations, speculative investment and widespread hype around generative AI – often detached from actual revenues or proven results. This has led many executives to wonder how to translate AI’s promise into measurable business value and what strategies are needed to unlock positive ROI.
In our AI Readiness Report, only 6% of UK firms reported positive returns across all AI initiatives, while 39% of workers said their company’s AI strategy was mostly hype. These findings align with broader scepticism: when expectations and market sentiment outpace operational reality, even advanced tools struggle to deliver.
These figures point to a fundamental challenge: AI success is not primarily dependent on the technology itself. Crucially, it’s how AI is implemented and how teams are empowered to use it effectively. With the right foundations, clear processes and teams equipped to use AI tools confidently, organisations can move past the hype and begin realising meaningful gains.
Bridging the hype gap
Many organisations are rushing to adopt AI without a clear plan, with 40% of UK respondents experiencing reliability issues with AI projects. There is a tendency to assume that introducing AI into a process will automatically improve outcomes. But without pre-defined use cases, measurable objectives and clear criteria for success, AI initiatives can quickly become an expensive experiment rather than a strategic advantage. Misaligned workflows, poor-quality underlying data, or teams lacking confidence in using the technology can all undermine adoption and prevent AI from delivering meaningful business value.
It’s important to focus on what problem they are trying to solve and how AI will create tangible value to help achieve that goal. Establishing success metrics before deployment ensures that teams have a shared understanding of targets and can assess progress objectively. By focusing on outcomes that accelerate work rather than simply increasing output, leaders can avoid generating unnecessary ‘noise’ or cognitive overhead, ensuring that AI delivers on real value instead of just more content. This focus on outcomes rather than tools also mitigates the risk of hype-driven disappointment, which can erode confidence in AI across the organisation.
Putting people first in AI deployment
Even with the right strategy in place, AI implementation will fall short if employees are not equipped to use it confidently. Our survey found that a third of workers frequently encounter AI tools that come with unclear instructions and inconsistent guidance. The result is frustration, slower productivity, inefficiency and, in some cases, resistance to tool adoption, which can erode trust in the broader AI strategy.
AI is far from a replacement for human insight – it works alongside humans to augment decision-making, speeding up analysis, reducing busywork and revealing blind spots. To get the most value, employees need guidance on when and how to use AI, such as treating it as a sparring partner or co-collaborator rather than a replacement for critical ideas. They also need solid documentation of processes and tacit knowledge to support AI outputs, as well as transparency around the tool’s limitations, so they can make informed decisions.
Providing this guidance builds confidence, which increases psychological safety and autonomy. Employees feel safe experimenting, making mistakes and asking questions, which drives adoption and engagement. By designing AI interventions that support and enhance employees’ work, organisations can build trust and demonstrate real value. Streamlining processes, providing training, and embedding clear guidance into workflows all contribute to that goal. Organisations that take this approach see higher engagement, better adoption and ultimately, stronger business outcomes as a result.
Documentation and workflow readiness
AI effectiveness is also heavily dependent on the readiness of organisational workflows and knowledge. Many teams still face uneven foundations, with almost a third (30%) of UK knowledge workers saying that poor data quality is the biggest barrier holding their organisation back. AI relies on clear, consistent information to deliver value. In organisations where documentation is incomplete or difficult to navigate, teams can end up duplicating work because they cannot find prior insights, wasting time instead of applying AI effectively.
Employees need context on how tasks relate to one another, why certain processes exist, and where AI can add value. Comprehensive, well-organised documentation is therefore a prerequisite for meaningful AI adoption. Clear workflows and accessible information not only improve the efficiency of AI tools, but they empower employees to make informed decisions. Organisations that build this foundation will find their AI initiatives scale more smoothly. They also see tangible gains sooner because teams spend less time troubleshooting or retracing steps and more time using insights to solve real business problems.
Beyond efficiency, documentation also plays a role in accountability and transparency. As AI becomes more embedded in decision-making processes, being able to trace recommendations and actions back to a clear source of truth reduces errors, ensures compliance, and builds confidence in both the technology and the people using it.
Making AI work
Too often, organisations focus on acquiring technology without addressing the human and operational factors that determine success. UK businesses have the tools at their disposal, but positive ROI will remain elusive until employees are equipped, workflows are ready, and AI is integrated effectively. By addressing these challenges, UK organisations can turn AI from a source of hype and frustration into a driver of real productivity and competitive advantage.
