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This article is part of our Opinions section, where we invite industry professionals to share their views on the most pressing technology questions of our time.
In 2026, we will see hyper-personalisation continue its transition from a competitive differentiator to something that customers increasingly expect.
We’re already seeing this shift take hold across industries such as insurance, retail and financial services. At an IBM panel session, Natasha Davydova, CIO of AXA UK&I, recently noted how the insurance sector, specifically, is leveraging AI to streamline claims processes and provide proactive support. In one case, she described a future where claims for delayed flights could be processed automatically, without the customer needing to lift a finger.
AI is moving from optimising interactions to dynamically shaping them in real-time, using context, behaviour and intent rather than static segmentation. Beyond streamlining and driving new efficiencies across customer service, this also supports inclusion, bringing customers into systems that previously excluded them due to blunt risk models or limited data.
Driving this will, of course, be data collection and analysis, which requires trust from the consumer. Crucially, UK consumer research shows growing acceptance of AI when it is transparent, trusted and clearly improving outcomes, with 57% of people surveyed in a recent IBM study saying they use it for everyday tasks.
Hyper-personalisation at scale therefore hinges on trust: customers will trade data for value, but only if organisations can demonstrate fairness, explainability and control.
IBM’s recent 2026 trends report found that more than half (56%) are so excited about cutting-edge, AI-enabled services that they’d be willing to accept flaws but – on the other hand – four of five of them said they would trust a brand less if it intentionally concealed use of AI in their experience.
Those that get this right will unlock new revenue models built on continuous engagement with the consumer rather than ad-hoc transactions, while those that don’t risk being outpaced by more adaptive competitors.
Indeed, delivering hyper-personalisation at scale exposes a hard reality: most organisations are not operationally or culturally ready, and skills investment cannot wait.
Enterprises typically run hundreds or even thousands of applications across hybrid environments, with data fragmented across siloes. Scaling AI into this myriad of data without governance risks quickly leading to spiralling costs, duplicated effort and disconnected systems that fail to deliver value.
At the same time, there is a widening gap between the skills organisations have and the skills they need. Our own research shows that while two-thirds of UK firms are already seeing productivity gains from AI, reskilling is the single biggest factor in unlocking further returns.
This is not just about data scientists or engineers; learning agility itself has become a core capability and spans across a whole company. As roles evolve faster than formal job descriptions, organisations must invest in upskilling employees to work alongside AI, redesign processes, and apply judgement where automation stops.
Those that delay will find themselves constrained not by technology, but by people, culture and governance not fully convinced by AI’s capabilities.
The strongest returns will come from organisations that move decisively beyond experimentation and treat AI as a strategic, enterprise-wide capability rather than a series of pilots haphazardly released.
There’s a notable difference between companies dabbling in AI and those embedding it into core functions such as HR, procurement, supply chain and customer operations. The latter are far more likely to quantify impact, reinvest savings and compound value over time.
This shift requires discipline: focusing on high-value domains, integrating AI with trusted enterprise data, and putting governance in place from the outset to manage bias, compliance and model drift. It also requires a pragmatic mindset, using open technologies and hybrid cloud to avoid lock-in and enable scale.
Less than a fraction of enterprise data is currently used in AI models; unlocking this “crown jewel” data responsibly is where the next wave of ROI will come from. By 2026, leaders will not be asking whether AI works, but why their organisation isn’t extracting more value from it.
The answer will increasingly come down to strategy, accountability and trust and not technology. And the real results will come from business’ desire to propel their staff and technology forward and get ahead of the rest of the pack.
