The first hurdle is the hardest in generative AI adoption – and businesses keep falling

AI adoption is ramping up at a rapid pace globally, of that there’s no doubt. But there’s more to successful adoption than just buying shiny new tools and finding yourself in a productivity paradise.

While that might seem like stating the obvious, it’s still a problem a significant portion of enterprises find themselves facing – and there’s little sign that it will improve any time soon.

Research from Amazon Web Services (AWS), released to coincide with its annual AWS Summit London this week, showed progress on the adoption front compared to last year.

The study, which focuses primarily on UK-based firms, has some positive takeaways, with British enterprises outpacing adoption compared to their European counterparts.

Clearcut gains with the technology are also being made, according to AWS, with workers now basking in the productivity and efficiency gains promised by it and other big tech providers.

Speaking during the keynote address at the summit this week, Alison Kay, AWS’ VP and managing director for UK&I, waxed lyrical about the benefits of AI for customers and the company itself.

Developers are working at lightning speed delivering projects previously deemed insurmountable while customers such as Motorway are recording the same gains.

“We’re living through just an incredible moment in history, a breakthrough moment in technology advancement,” Kay told attendees.

“And what makes this moment so exciting is that the things that are being built, they almost defy belief. They almost feel like magic.”

AI might look and feel like magic, but IT leaders aren't living in a fantasy land – they're facing the harsh reality of how difficult AI implementation can be. They've got the trendy new tools and staff are loving them, yet they’re not seeing any significant progress on a business-wide level.

AI pilot purgatory persists

AWS’ study found the actual use of AI in the enterprise is still very basic, with employees using the technology to summarize documents or answer basic questions using chatbots.

That’s so 2023, but at least they’re enjoying it. More than two-thirds (68%) of employees are reporting marked productivity and efficiency improvements.

AWS’ findings align closely with similar research from Accenture earlier this month, which pointed toward “localized” gains whereby individual workers are seeing gains, but the companies themselves are still struggling.

So what’s stopping enterprises from making the leap toward more “advanced” AI uses? The same old problems many businesses have with digital transformation projects.

Skills and expertise, legacy systems, and outdated infrastructure are stifling progress.

It’s been a common recurring theme with AI adoption over the last three and a half years and one that must have IT leaders bashing their heads off the wall over.

Research from Google Cloud in August 2024 warned British firms in particular were stuck in a “pilot purgatory”.

At that point, only two-in-five firms had pushed generative AI projects to production and were facing a “substantial hurdle” in scaling the technology internally - an issue the aforementioned Accenture study found is still a key problem.

The situation is even worse with agentic AI, with a January study from Dynatrace showing that half of agent adoption schemes are wallowing in the proof of concept (PoC) stage.

Phil Le-Brun, executive in residence at AWS, told ITPro that a “lack of imagination” is often a key factor in this inability to scale AI adoption and reach more advanced uses.

An increasing volume of research also points toward the frantic nature of the generative AI boom as a key factor behind failed adoption efforts.

Research from Forrester in early April suggested that a “lack of understanding” when it comes to AI, combined with the rabid appetite to invest in the technology, has resulted in a slap-dash approach to AI innovation.

Executives have been caught up in the hype, and all they can see are dollar signs and the marketing materials promising huge productivity improvements.

But Forrester noted those who take a more conservative, “deliberate” approach to AI adoption have recorded far better results – and a key factor here is focusing on creating value for customers rather than productivity and cost reductions.

Speaking to ITPro, Le-Brun touched on this customer-centric approach with AI, noting that some of the more agile companies it engages with, such as startups, are making great strides in this regard.

Simply put, keep an eye on the productivity and cost benefits, but sharpen your focus on actually delivering for customers.

“The application of AI to fundamentally rethink how you’re delivering a product or service to the customer would be an example of advanced AI,” he said. “That’s where we see startups doing some amazing things.”

“They’re willing to really dive into what it is the customer wants, and how can we apply this new technology to change, rather than for large enterprises - it’s much harder because they have a lot of established processes.”

A long road ahead

The current pace of progress in pushing from pilots and basic use-cases toward more advanced AI uses highlights the sorry state of affairs for many businesses - and there’s still a long road ahead. A very long one, if AWS findings are to be taken at face value.

Just 24% of firms have reached an advanced AI phase in 2026, according to AWS, marking just a single percentage increase compared to last year. If things continue at this pace of progress, it’ll be 2102 before all AI adopters in the UK reach an advanced stage.

As Le-Brun told ITPro, businesses will be in a good position “by the end of the century”.