03/11/2025
AI personalisation is usually discussed in the context of customers — but the next competitive frontier is inside the workplace. Employees increasingly expect tools and support that adapt to their role, context, and urgency, rather than forcing everyone into one-size-fits-all workflows.
McKinsey’s research on AI in the workplace suggests that while adoption is widespread, true maturity remains rare — and the biggest barrier to scaling is often organisational, not technical. That makes personalisation a practical leadership question: how can AI help people find the right information faster, reduce friction in daily work, and make better decisions without adding complexity or risk?
In this article, we explore what “people-first personalisation” looks like across industries — and how organisations can build the foundations that make it real.
Why personalisation is still the future
Across industries, expectations are growing faster than most organisations can keep up. Customers want bespoke services that feel perfectly curated and tailored to their interests and needs. So, why shouldn’t employees deserve the same? Unfortunately, most employees feel stuck working with legacy systems, data and processes that are built around uniformity.
AI can help close that gap — but only when it’s grounded in real context. If data is siloed, content is outdated, or workflows can’t adapt, personalisation becomes superficial and quickly erodes trust.
The organisations that scale personalisation well focus less on “more AI” and more on the conditions that make AI relevant: high-quality knowledge, integrated data, clear governance, and teams that understand how recommendations are generated. The use cases below show what that looks like in practice across industries.
Banking: Making knowledge personal
Banks know the importance of trust and personal connection, yet many still deliver standardised digital experiences to their employees. AI can change that, but only if it helps people do their jobs better and faster.
Imagine a relationship manager who can instantly see which clients need advice because their spending habits or life events have changed. Or a compliance analyst who gets tailored insights on risk exposure without having to sit and read hundreds of pages of reports. That’s what AI can enable when it’s trained on quality data and paired with human judgment.
In many banks, investment is rising — especially in risk and compliance — but perceived value often lags behind expectations. That gap is rarely about “choosing the wrong tool” alone. More commonly, employees don’t see clear relevance in their day-to-day work, recommendations aren’t explainable enough to act on, or the change effort is underestimated.
Making workplace personalisation worthwhile starts with selecting use cases that remove real friction (search, onboarding, case preparation, compliance interpretation) and backing them with change management: clear communication, training, and feedback loops that improve relevance over time.
Insurance: Personalisation through better understanding
Insurers have been working to personalise customer journeys for years, but not always successfully. Many policies still look more-less the same, and customer contact tends to follow fixed scripts. AI could finally change that by giving employees a 360-degree view of each customer’s situation.
Large language models can learn from real customer behaviour and claim patterns. This delivers insights that can help insurers tailor cover or advice to a customer’s actual needs. The same applies internally. A claims handler might get context that fits how they approach assessments, or an underwriter might see patterns linked to their portfolio instead of a standard dashboard.
But personalisation only works when everyone involved understands how those suggestions come about. A “personalised” recommendation that feels generic erodes trust. It all comes down to transparency. If your people can see how an AI tool works, they’re far more likely to use and trust it.
Manufacturing: from production lines to personalised learning
Manufacturers understand the value of personalised products. But personalised employee experiences have so far been under-prioritised in the industry. Engineers and line workers still train through static modules or manuals that don’t reflect their actual equipment or experience levels.
AI personalisation can change that. Digital twins and predictive systems are already reshaping maintenance, but the same technology can also tailor learning and shift planning. If a technician tends to make certain adjustments faster or spots faults more accurately, the system can adapt training accordingly. That builds confidence and speeds up progress without adding more pressure.
Companies like Siemens and Beko are already using AI in production to save energy and reduce downtime. The next step is to bring that intelligence to people’s daily routines. A good place to start would be using AI insights to personalise learning and development. It keeps skills fresh and creates positive, meaningful user journeys.
Retail: Personalisation behind the scenes
Retailers have led the way in coming up with innovative AI use cases, but those have largely focused on customers, not employees. It’s become standard practice to automatically retarget customers with personalised offers. But retail employees are often stuck working with rigid, standard procedures and inflexible tools.
Everyone benefits when the tools are transparent and well-understood. Yet even in retail, the signal is clear: Gartner reported that 69% of organisations suspect or have evidence that employees are using prohibited public GenAI tools (“shadow AI”). That’s a strong indicator that people want smarter support — but will route around controls when approved tools don’t meet real needs.
What real personalisation looks like
Truly personalised user experiences all have one thing in common: relevance. AI should give people the context they need, when they need it, and then get out of the way. In practice, that can mean fewer repetitive tasks and more space for human judgment. It also means smarter onboarding that adapts to each person’s background. Or genAI assistants and support tools that are always there when employees need them.
Personalisation is moving beyond customer experience and becoming a workplace expectation. The differentiator won’t be who deploys the most AI features — it will be who delivers the most relevant, explainable support at the moment people actually need it.
If you’re exploring how to implement people-first AI personalisation responsibly, start with three questions: What work friction are we removing? What data and knowledge make recommendations credible? And what governance ensures transparency, privacy, and accountability as personalisation scales?
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