The Next Move: What Learning Live Revealed About AI, Human Behaviour and Organisational Readiness

The most revealing moment at Learning Live came at the close of a formal debate on whether traditional learning and development is still fit for purpose. One side argued that AI had broken the sequence of learn first, practise later. The other defended the enduring value of human judgement, practice and behavioural change. An observation from the audience brought the two positions together: L&D has always evolved as work changes, adopting new tools and approaches while working towards the same fundamental purpose of enabling people to learn, adapt and perform. In other words, AI may change how that happens, but not why it matters.

Charlie Swan, Alchemist Group, sharpened the debate further, arguing that AI can solve the knowing problem while leaving the doing problem untouched. The same distinction surfaced across the conference. Elena Ivanushkina, HARTMANN added a behavioural perspective: learning builds capability, but workplace conditions such as time, tools, incentives and social norms determine whether that capability translates into action. ServiceNow asked leaders to define where people create value before deciding how AI should support them.

Different speakers approached the subject from different angles, yet a common challenge emerged. Access to knowledge is expanding faster than many organisations can convert it into reliable performance. And at the same time, learning leaders are being asked to support AI adoption, adaptability and new ways of working, but many are still measured through attendance, completion and satisfaction.

The underlying business issue is organisational readiness, which reaches far beyond the learning function alone. That made The Learning and Performance Institute (LPI)’s invitation to consider “the next move” especially appropriate. Like strong chess players, learning leaders need to read several moves ahead, anticipating how work, behaviour and organisational conditions must change for new capabilities to translate into business value.

Drawing on the sessions we attended at Learning Live, we explore three connected challenges for learning leaders:

Jayney Howson and Martin Hill from ServiceNow drew a useful distinction between teaching people to use AI and preparing the organisation to integrate it safely and effectively into everyday work. Its sequence was deliberate: understand the work, then workforce capability and finally select the tools. In the readiness discussion, Saood Kidwai from KNOLSKAPE made a related point: instant access to information raises the value of application, judgement under pressure and consistent execution.

AI is changing more than the speed of learning, while also shifting authority inside the organisation. When an employee consults an AI tool before a colleague, manager or policy library, leaders need clarity about the sources the tool can use, the decisions it can influence and the moments that require human review.

The executive trade-off is speed against assurance. Excessive control can suffocate useful experimentation. Loose controls can create inconsistent decisions, regulatory exposure and misplaced confidence in plausible answers. Resolving that tension requires governance and work design alongside training.

Leaders should start with the business outcome, trace the decisions and tasks that produce it, then determine where AI can automate work, support human judgement or should remain outside the process. Only then can they define the capabilities people need.

Reversing that sequence creates a familiar problem: employees are trained to use a tool before its role in the workflow, decision rights and measures of value are clear. Adoption then depends on individuals finding useful applications for themselves, which creates inconsistency and makes governance harder.

An effective AI operating model brings these decisions together across people, processes, technology, data and governance. It should clarify where experimentation is encouraged, which decisions require human accountability, who owns data quality and how roles will change as AI becomes embedded. The learning agenda can then focus on the capabilities that matter most, including judgement, escalation, critical challenge and responsible use.

See how we applied this approach to an AI-enabled content generation capability: AI in Life Sciences: A Disruptive Approach to Content Generation

Ivanushkina’s session on behavioural science moved the discussion from content to context. Capability, opportunity and motivation combine to shape behaviour, while social norms, habits, priorities and time pressure can overwhelm good intentions. Their effects on the workplace still depend on opportunities within the environment, including whether people have the space, tools and permission to apply what they have learned.

Organisations frequently diagnose a performance gap as a knowledge gap because training is visible, fundable and easy to measure. The underlying cause may sit elsewhere. A manager can complete inclusive leadership training and still reward the most available voice when meetings are rushed. A project team can learn a new governance model while senior sponsors continue to bypass it.

In both cases, more content adds cost without removing the friction. The real levers may be process design, access, incentives, decision rights or the signals sent by leaders.

Capability is just one condition of readiness. Whether people use what they have learned also depends on the environment around them: the tools available, the time they have, the behaviour their manager reinforces and the consequences attached to the new way of working.

Before commissioning learning, programmes should define the required behaviour in terms that can be observed in the workplace. They can then examine whether workloads, systems, permissions, performance measures and management routines support that behaviour. Our 6Lever™ Change Readiness Assessment helps teams assess these wider conditions before they become barriers to adoption.

Resistance and workarounds provide useful evidence. Employees who return to a spreadsheet after learning a new system may be responding to poor usability, missing functionality or unrealistic time pressures. Treating that response as a lack of commitment misdiagnoses an operational issue.

Each barrier therefore needs a named owner with the authority to act. L&D can identify capability gaps and surface patterns, while operational leaders, technology teams, sponsors and managers must address the conditions that reinforce existing behaviour. This is where understanding resistance becomes a source of programme intelligence rather than a label applied to employees.

See how we combined capability building with practical support for adoption during a major integration: Driving People-Centred Post-Merger Integration

While AI shaped many of the conversations at Learning Live, some sessions deliberately stepped away from the technology to focus instead on the human experience of learning and change. Gavin Oattes from Tree of Knowledge session on leading with joy explored what he called “glimmers of joy”, those small moments of connection, curiosity and learning that stick with people long after the moment itself. Elspeth Briscoe from Learning with Experts session added that storytelling, community and immersive practice can strengthen capability and motivation. Together, these sessions challenged a purely technical view of readiness. People experience change through their interactions with the organisation, including whether they feel heard, whether managers make space to learn and whether their efforts lead to visible progress.

Employee experience shapes how people respond to change. Frustration can expose broken processes, while silence may signal that people do not feel able to contribute. More positive experiences, including moments of curiosity, creativity, connection and visible progress, can help people engage with change and embrace their role within it.

This is why change communications and learning interventions need to do more than transfer information. Storytelling, immersive experiences and opportunities to contribute can make change feel relevant to people’s work, while creating spaces where they can explore, practise and learn with others.

Change is more likely to stick when people are active participants rather than passive recipients. Creative and immersive interventions can help employees understand what is changing, connect it to their own experience and practise new behaviours in a supportive environment.

That might mean using storytelling to bring change to life, creating simulations that reflect real workplace decisions or involving employees in shaping solutions. The aim is not creativity for its own sake, but to build understanding, strengthen connection and help people translate change into action. Storytelling, role modelling and psychological safety can all help to keep people engaged during AI-driven change as they experiment with new ways of working.

See how we used storytelling and creative communications to bring a complex strategy to life: Communicating Strategic Value Through an Engaging Narrative – Afiniti

Learning Live exposed a widening gap between the capabilities organisations say they need and the systems through which people are expected to demonstrate them. AI raises the stakes: as access to knowledge accelerates, organisations need to be more deliberate about how people apply it, exercise judgement and translate it into action.

Three practical moves to follow:

The LPI and ServiceNow framed the challenge as deciding the next move. Strong learning and transformation leaders are able to read several moves ahead, considering what people, processes, managers and governance must absorb before that first move is made. This broader change approach connects readiness, adoption and capability with the outcomes the organisation needs to achieve.