Most organisations are already well underway with AI. Your business probably has several pilots running, while teams across the organisation are experimenting with different applications. But if you’re honest: is this approach actually delivering results?
Serious use cases are rarely pointless. But are they connected to what your organisation genuinely wants to achieve? The difference between experimenting with AI and actually creating value with it usually isn’t the technology. Real value comes from connecting AI to what your organisation is trying to achieve.
Three areas where AI can create value
To make that connection, it helps to look at AI through three areas of value: expertise, experience and execution. Understanding these three areas can help you identify where AI could have the greatest impact within your organisation.
Expertise: how do you make knowledge available in a way people can trust?
Take an insurer that can use data to accurately predict which customers are likely to be ready for a new car. These are valuable insights that the insurer wants to share with its network of affiliated dealerships. A win-win, you might think.
In reality, hardly anyone acted on them. Not because the data was wrong, but because the dealerships didn't trust it. An insurer suddenly offering sales advice didn't feel quite right, however good the insights might have been. How could the salespeople be sure the insights were reliable? And even if they were, what should they do next?
Expertise alone isn't enough to convince people. They also need to trust the source.
That's exactly where AI can help. If you conduct thousands of sales conversations every year, AI can be used to distil shared insights from all those interactions. One important condition is that all relevant context is accessible and usable by AI. Think insights from conversations, customer data, benchmarks and other signals that are currently scattered across the organisation.
With that knowledge and context at their fingertips, advisers can go into every conversation with:
targeted opening questions based on current signals from the field
specific points of attention that might otherwise only emerge during the conversation
shared intelligence that enables advisers to advise proactively rather than reactively
insights that help advisers stand out as trusted partners
With AI-powered insights, advisers can get to the heart of the matter faster, provide more value and feel better prepared. The expertise is already there. AI helps make it accessible and useful at the right moment. This allows advisers to provide better advice while building trust and authority, strengthening their position as experts.
Experience: how do you create the right experience at the right moment?
The same principle applies when AI becomes part of the customer experience. A home appliance manufacturer, for example, can turn a transaction into an opportunity to provide personalised support. Using context, AI can identify where someone is in their customer journey and tailor the experience accordingly. For example:
providing instructions on how to activate the warranty to someone who has just bought their first washing machine
offering tips on how to extend the life of a washing machine to someone who bought theirs several years ago
providing a discount voucher to someone whose washing machine is more than ten years old
This creates an experience that reflects both the individual and where they are in their customer journey. The value doesn't come from simply being present all the time. It comes from understanding what someone needs at a particular moment.
Take a look at your own customer journey. At which moments do people need explanation, reassurance or guidance? AI can help you recognise those moments more effectively and stay relevant throughout the entire journey. The result is an experience that not only feels more personal, but also strengthens the emotional connection with your brand.
Execution: how do you safeguard quality at scale?
As teams produce more content or run more processes, maintaining quality and consistency becomes increasingly difficult. Sooner or later, the same question arises: how do you stay in control of quality as volume increases? Manual reviews work up to a point. Beyond that, keeping an overview becomes increasingly difficult. This is where AI can create significant value.
For one of the organisations we work with, we used AI to translate content guidelines into concrete evaluation criteria. Editors received support in structuring, writing and reviewing content, making quality control both more effective and more scalable. This resulted in:
50% less time spent gathering and structuring information
75% faster production from start to finish
consistently high quality at scale
That's the value of AI in execution: managing repeatable work while safeguarding consistency and quality at scale. This gives people more room to focus their attention on the work where their expertise creates real value.
From isolated use cases to an operating model
The three examples above have one thing in common: they weren't built around an isolated AI solution or tool. They started with a different question: how can AI help us do what we already do, only better?
That distinction matters more than it might seem. Use cases are isolated. They can create value locally, but often do little to change how an organisation works as a whole. An operating model connects AI to everyday ways of working, teams, customers and, ultimately, the organisation's mission. In practice, that means AI is no longer a standalone project, but a structural part of how your organisation operates, makes decisions and creates value.
What does an operating model look like in practice?
Between FY21 and FY23, IKEA used AI in customer service to automatically handle around 47% of all enquiries. That amounted to approximately 3.2 million interactions and €13 million in operational savings. The remaining 53% of enquiries were largely related to interior design advice.
AI freed up a significant amount of capacity. Rather than making those employees redundant, IKEA retrained them as interior design consultants. This new service generated €1.3 billion in revenue, built around IKEA's mission to create a better everyday life for people.
What's particularly interesting is how IKEA arrived at that decision. Instead of looking only at what AI could take over, the company also examined the questions AI couldn't answer. That's where it found an opportunity for human expertise and an entirely new service. Crucially, IKEA also safeguarded the jobs of the employees who were retrained.
The example highlights something else too: moving towards an operating model requires change on an entirely different scale from rolling out a few smart tools. The whole business needs to be part of that change, not just the teams that happen to be experimenting with AI already.
Where can you start today?
The first step is not to immediately choose an AI tool or launch another pilot. Start by mapping out where your organisation creates value. Where could AI genuinely contribute to your expertise, experience and/or execution?
A few questions can help you get started:
Identify knowledge or recurring tasks within your organisation that currently take up significant time or are fragmented across teams and systems
Determine which of these areas contribute directly to an important organisational objective
Only then explore where AI could help
The answer is probably already somewhere on your website, in your mission, vision or brand promise. The challenge is to connect that promise to what AI can make possible, then build an approach that brings the whole organisation along with it.
