Many L&D programmes teach people to use AI for tasks they already do. That helps, but it misses most of the value.
McKinsey's State of Organizations 2026 found 88% of organisations experimenting with AI. Yet 81% report no meaningful bottom-line gains.
Shift 1: From tools to work design
Tool training ties skills to features that change every month. The bigger skill is deciding how work should be split between people and AI.
Deloitte's 2026 Global Human Capital Trends found organisations that prioritise deliberate work design are twice as likely to exceed their AI return expectations. Only 14% of leaders say they are good at it.
Shift 2: From using AI to checking it
More roles are moving from doing the work to overseeing it. That means setting direction, reviewing output and handling exceptions.
People need practice in checking AI well. Deloitte found 42% of executives worry about employees relying on AI for essential thinking.
Shift 3: From events to enablement
The Josh Bersin Company's 2026 research describes a move "from static training to dynamic enablement". Learning happens inside the work, often supported by AI.
The University of Phoenix's 2026 Career Optimism Index found half of US workers are already learning AI skills on their own. L&D can channel that energy.
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Growth Performance designs bespoke programmes that turn insight into measurable behaviour change.
Shift 4: From efficiency to judgement
AI handles routine efficiency well. The skills that grow in value are judgement, relationships and adaptability.
The World Economic Forum's 2026 report The Human Advantage urges pairing AI rollouts with deliberate investment in these brain skills.
Shift 5: From routine tasks to hard problems
Training AI on email drafting signals that AI is a minor productivity tool. Hard, real problems teach more.
Give teams a challenge that would once have been out of reach. Let them use AI to tackle it, then review what they learned.
The implication for L&D
McKinsey quotes one executive who suggests spending $5 on people for every $1 on technology. L&D is where much of that people investment lands.
These shifts map onto three conditions. Adoption is whether people take on the change and make AI part of their work. Adaptation is whether people can direct, combine and check AI agents in real work. Acceleration is whether the organisation is ready as change keeps speeding up.
Try this
Review your current AI learning offer. How much teaches tool features, and how much teaches work design and checking?
Then design one challenge-based pilot with the Experiment Planner. Use ROI Squared to agree what success looks like.