Generative AI is no longer a geeky tech term only technology enthusiasts or industry trendsetters are interested in. It has entered the c-suite boardroom and strategic roadmaps of enterprises, promising transformative possibilities across every sector. Yet, as Oakland’s recent report, Plugged In or Left Out, reveals, the UK’s journey towards meaningful adoption of generative AI is not straightforward.
Oakland is a data consultancy with a 40-year legacy rooted in rigorous research and operational excellence. We wanted to investigate the UK’s readiness for AI, so we embarked on this study with YouGov’s help to help us cut through the hype.
Research has always been a cornerstone of keeping up with modern business. We centre everything we do around our customers rather than relying on the perspectives of technology vendors and analysts. The report focuses squarely on businesses navigating the realities of integrating generative AI into their operations. Here’s what we discovered – and why it matters.
Generative AI The Hype vs. Reality Gap
A disconnect between potential and practical application marks the generative AI landscape. Businesses are awash with promises of revolutionary capabilities, yet many lack the foundational skills, processes, and AI governance frameworks necessary to make those promises a reality. Our report supported what we’ve found on the ground from speaking to many organisations: most companies aren’t yet in a position to really take advantage of the technology.
This isn’t something we are surprised by, and it is, in fact, something we’ve seen before; buzz around new technologies often masks the complexity of real-world implementation. Businesses must navigate the technical challenges and the organisational readiness to adapt.
This mismatch is particularly evident in the UK, where many still grapple with fundamental data challenges in their everyday jobs. From data quality issues to incomplete data pipelines to insufficient expertise, the prerequisites for successful generative AI adoption remain unmet for many.
Generative AI’s Two Waves of Innovation
Oakland’s findings align with a broader observation about technological revolutions: they unfold in two distinct waves. The first wave sees the emergence of the technology itself – in this case, large language models (LLMs) and generative AI tools like ChatGPT and Microsoft Copilot. The second wave, however, is where real transformation occurs. This is when we work out what to actually do with this technology and develop meaningful applications that integrate the technology into processes and products. It’s a phase that demands more than excitement; it requires thoughtful engineering, governance, cultural adaptation, and realistic expectations.
The impressive capabilities of LLMs are currently best demonstrated in scenarios like chatbots or simple content generation. However, translating those capabilities into reliable, high-value enterprise solutions remains a significant challenge. Showcasing that we have a really powerful intelligence is a very different challenge to embedding it into business processes especially ones that are of sufficient importance to the business to drive a return on investment that everyone is happy with.
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Hard Lessons from Early Generative AI Adoption
The report highlights a cautious approach among practitioners, many of whom have scars from previous waves of technological over-promise. Data science and machine learning – two fields that experienced similar hype cycles – serve as cautionary tales. Data leaders have had their fingers burnt before, so they understand the pitfalls of inflated expectations and the high costs of overinvestment in unproven solutions.
Oakland’s findings show that early adopters in the UK are deliberately narrowing their focus to manageable, well-defined use cases. For example, generative AI excels in tasks like reading product descriptions to categorise items or identifying abbreviations in catalogues. These tasks are repetitive and internally focused and benefit from generative AI’s ability to handle high volumes of structured input. However, these projects require significant expertise and investment to deliver tangible results.
The Cost of Complexity
Despite AI’s potential, generative AI solutions are not plug-and-play. Custom applications require weeks of engineering effort, rigorous testing, and extensive oversight.
“Even a seemingly straightforward proof of concept can quickly escalate into a six-figure project, which, in today’s climate, where budgets for R&D and new technology are often the first activities put on hold, can cripple activity. For small and medium enterprises, this level of investment can be prohibitive, underscoring the divide between early adopters and those waiting on the sidelines to see what happens.”
Joe Horgan – Oakland Principal Consultant.
Learning from the Plateau of Productivity
For businesses taking a “wait and see” approach, there is wisdom in watching early adopters navigate these challenges. This mirrors other technological journeys, such as electric vehicles, where initial teething problems gave way to broader adoption as solutions matured and costs decreased. Similarly, the report predicts that generative AI’s most exciting phase will emerge post-hype – once expectations are tempered and businesses focus on realistic, high-value use cases.
So, what steps should a business take to become ready for generative AI implementation?
Building a solid business case for Generative AI is fundamental:
- Be Strategic with Use Cases: Focus on simple, repetitive tasks where generative AI can deliver immediate value. Avoid overly ambitious projects that hinge on unproven capabilities. For example, Generative AI is brilliant at performing simple tasks that would take humans far too long to do.
- Invest in Expertise: Whether through hiring or developing partnerships with people like Oakland, having skilled AI engineers is critical to navigating this complex landscape. The pace of change with this technology is mind-blowing, and you need to be able to keep up.
- Set Realistic Expectations: Understand what generative AI can and can’t do today. The technology’s future potential is vast, but meaningful application requires a grounded approach. You need to know what the LLM is capable of before you can assess if it can solve your problem.
- Learn from Others: Monitor the successes and failures of early adopters. Use their insights (mistakes) to inform your strategy and reduce the risks of premature investment.
But what exactly are the benefits of using Generative AI and Intelligent Agents in your business? Read our blog to find out!
Is Your Data Ready for Generative AI?
Generative AI represents a powerful new tool in the digital toolbox, but it is just another tool. The massive hype is quite unhelpful because it creates ridiculous expectations. People throw money at it, and it creates bad vibes by diverting resources into older, more proven technologies. It’s when the technology appears on Gartner’s trough of disillusionment that you can start to think clearly and seriously about your Generative AI initiatives and how they can help drive your operational efficiency and competitive advantage. Its transformative potential will only be realised through careful, deliberate application. As the UK moves beyond the initial frenzy of excitement, organisations have an opportunity to define the second wave of innovation – where the focus shifts from possibility to productivity.
Oakland’s Plugged In or Left Out report underscores the importance of patience, planning, and pragmatism in navigating this transformative era. The report highlights that while generative AI’s capabilities are impressive, they are often limited in scope today. For example, simple, repetitive tasks like labelling products or categorising data represent the “low-hanging fruit” where AI can currently excel. However, even these cases require a disciplined approach to implementation.
The road ahead may be longer than the hype would indicate, but it also offers greater opportunities for sustainable growth. By focusing on realistic use cases, investing in expertise, and learning from early adopters, businesses can position themselves in a prime position to get the sort of meaningful value that the businesses’ key stakeholders will be satisfied with. For those willing to embrace these principles, the future of generative AI is bright.
