I think AI transformation begins with a hypothesis about how work and value might change. We can set an intended outcome but a fixed target culture or operating model may be premature when we are still learning what AI makes possible. How do your approaches use evidence from the work to revise the target and move from adoption to absorption?

AI transformation is different from most change programmes because the destination is still emerging as organisations learn what AI can do. The most successful organisations are clear about the outcomes they want to achieve, but flexible about how they get there. Rather than treating AI as a one-off implementation, they use evidence from employees, customers and operational performance to continuously refine their approach.

This is where culture becomes critical. Instead of defining a fixed target culture, organisations can focus on creating the conditions for ongoing adaptation: experimentation, empowerment, learning and collaboration. These make it easier to adjust ways of working as new opportunities emerge. The shift from adoption to absorption happens when AI stops being a separate initiative and becomes embedded in how work is designed, decisions are made and value is created. At that point, people are no longer asking whether they should use AI. They’re simply using it as part of how they work every day.

How are you testing which differences in employee experience actually affect AI absorption, particularly to redesign work, shift decisions and turn released capacity in value?

We can look beyond adoption metrics, eg. access, training completion or usage rates, to understand which aspects of the employee experience genuinely influence AI absorption. For example, are people empowered to experiment? Do they feel confident using AI? Are decisions being made faster and are teams working differently? We can combine employee feedback with operational outcomes to identify the factors that lead to meaningful change. The next question is what the organisation does with the capacity AI creates. The strongest examples we’ve seen, including IKEA and Morgan Stanley, have deliberately redirected that time into higher-value activities that improve customer experience, drive growth and increase innovation.

Ultimately, AI absorption happens when organisations redesign work around the strengths of both people and technology, rather than simply adding AI to existing processes.

Several times in the webinar we’ve talked about helping people adapt to AI. Is there a risk that organisations focus so much on helping people adapt to existing work that they miss the opportunity to fundamentally redesign work itself? What are the signs that an organisation is optimising the old model rather than creating a new one?

Absolutely and one of the biggest risks is focusing on how AI can make today’s work more efficient, rather than rethinking how work should be done altogether. (We’ll shortly share the recording from Perry Timm’s recent keynote at the Business Culture Connected conference on exactly this topic). A warning sign is when success is measured only by time saved, cost reduction or productivity gains. Those outcomes matter, but they don’t necessarily represent transformation.