AI Prompts L&D to Engage Earlier in Work Redesign

Artificial intelligence is forcing organizations to rethink job roles at a speed few learning functions have experienced before. Tasks are being automated. Workflows are being redesigned. Decision-making is shifting between people and technology. Activities once performed by experienced employees are increasingly supported or performed by AI.

Sequence Problem in AI Transformation

The instinctive response from learning and development leaders is to identify new skills employees will need and build programs to develop them. By the time L&D is asked what people need to learn, some of the most consequential decisions may already have been made. The workflow has changed. Technology has been selected. Tasks have been redistributed. Roles have been redefined. Only then does someone ask how to train people for the new way of working. In an era of AI transformation, that sequence is increasingly too late.

CHI Innovation Cycle in Singapore Healthcare

A useful example comes from Singapore’s health care system. The Centre for Healthcare Innovation at Tan Tock Seng Hospital developed an approach known as the CHI Innovation Cycle. The cycle connects three elements: care and process redesign, automation IT and robotics, and job redesign. Using continuous Plan-Do-Study-Act cycles allows changes to be tested, evaluated and refined. Organizations must first reconsider the work by examining processes, challenging inefficiencies and designing for a desired future state. Then teams introduce technology where it can enable future workflow. Only after these steps can job roles be revamped through approaches such as upskilling, shifting appropriate work between occupational groups and expanding employees’ contribution into higher-value activities.

Capability Requirements Follow Work Design

Skills requirements are consequences of work design. Organizations frequently begin workforce development by asking what skills will be needed. What employees need to know and be able to do depends on what work they will perform, which decisions they will own, what technology will support them and what accountability will remain human. Change those variables and the required capabilities change with them. Consider a workflow in which AI takes over the first draft of an analysis that previously required several hours of manual work. The more useful questions are what employees will do with the time released, whether they are expected to validate AI output, interpret it, challenge it, combine it with contextual information, advise a client or make the final decision. Each answer reveals a different capability requirement. Capability architecture should follow work architecture.

Developmental Work and Automation Consequences

Some work does more than produce an output. It develops expertise. Junior employees build judgement by researching, preparing first drafts, handling straightforward cases, observing consequences and receiving feedback. Managers develop decision-making ability partly because they have previously performed the operational work beneath those decisions. AI is capable of taking over many of those activities. If organizations remove developmental work without considering how expertise will be built in its absence, they may inadvertently weaken their future capability pipeline. L&D leaders should ask what capability has historically been developed through performing a task and how it will be developed if the task disappears. This changes L&D’s contribution from designing training after automation to helping leaders understand the capability consequences of automation before implementation.

Redesign should create better work, not simply less work. Streamlining work is only half of a successful redesign. The more strategic question is what replaces it. According to Chief Learning Officer, involving a learning function earlier is much more strategic than reacting after roles are redefined.