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.
For learning and development leaders, the instinctive response is understandable: identify the new skills employees will need and build the programs to develop them.
But that sequence has a problem. 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 do we train people for the new way of working?”
In an era of AI transformation, that is, increasingly, too late.
The emerging challenge for L&D leaders is not how to become better at reskilling employees. It is moving upstream to help shape how work changes and define the resulting capability requirements.
A useful example comes from Singapore’s health care system.
A different starting point
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 → job redesign
For L&D leaders, the lesson here lies in the sequence of the center’s transformation. Using continuous Plan-Do-Study-Act cycles allows changes to be tested, evaluated and refined.
To start, organizations must first reconsider the work. They must examine processes, challenge inefficiencies and design 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.
In many organizations, learning happens after the final stage. Once the redesigned job role is established, L&D receives a competency list, develops training and supports implementation.
However, involving a learning function earlier is much more strategic.
Skills requirements are consequences of work design
Organizations frequently begin workforce development by asking: “What skills will we need?”
But skills do not exist independently from work. 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. It would be easy to conclude that employees simply need “AI skills,” but that tells L&D very little.
The more useful questions are: What will employees do with the time released? Are they expected to validate AI output? Interpret it? Challenge it? Combine it with contextual information? Advise a client? Make the final decision?
Each answer reveals a different capability requirement. This is why L&D may not design reliable future capabilities without understanding future work. The implication is significant: Capability architecture should follow work architecture. And L&D leaders should have a voice in designing both. Do not automate away the learning system.
Another reason L&D needs to be involved upstream: 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. This can improve productivity. But if organizations remove developmental work without considering how expertise will be built in its absence, they may inadvertently weaken their future capability pipeline.
The question, therefore, cannot be only: Can AI perform this task?
L&D leaders should ask another: What capability has historically been developed through performing this task, and how will we develop it if the task disappears?
That changes the nature of L&D’s contribution. Instead of designing training after automation, the learning function helps leaders understand the capability consequences of automation before implementation.
This is workforce development by design rather than by reaction.
Redesign should create better work, not simply less work
The CHI cases illustrate another principle that L&D leaders should pay attention to: that streamlining work is only half of a successful redesign.
The more strategic question is what replaces it. In one reported transformation, the “Ward of the Future,” physical redesign, workflow changes and technological support were combined with role changes. Nurses subsequently spent 24.8 percent more time providing direct patient care and walked an average of 3.9 kilometers less per shift. Job satisfaction improved, while staff attrition fell from 8 to 6 percent.
The important lesson is not simply that technology saves time. Human capacity was redirected. That distinction is critical as organizations introduce generative and agentic AI.
Suppose automation releases 20 percent of an employee’s capacity. Discussing productivity may focus on the hours saved. A talent discussion would focus on:
- What higher-value contribution will now occupy that capacity?
- Will employees solve more complex problems?
- Spend more time with customers?
- Exercise greater judgement?
- Coach colleagues?
- Interpret information rather than assemble it?
- Develop new services?
- Improve processes?
If no deliberate solutions exist, automation can make space without creating greater capability or greater value. L&D and talent leaders have an opportunity to connect the two.
From course design to work-capability design
This step requires a broader learning model.
Traditional L&D processes commonly start with a training need. Analyze the gap, design the intervention and evaluate the results. That remains useful when training is genuinely the intervention required.
Work redesign demands an earlier starting point. L&D leaders should first understand the business outcome and the work required to achieve it.
A practical sequence might be:
- Define the outcome: What customer, operational or strategic result is the organization trying to improve?
- Redesign the work: Which activities add value? Which create friction? What should be simplified, eliminated or reconfigured?
- Determine the technology contribution: What should AI, automation or other technologies perform, support or improve?
- Define the human contribution: Where will judgement, relationships, creativity, contextual understanding and accountability remain important?
- Redesign roles and capabilities together: How should tasks, decision rights and responsibilities change, and what capabilities will employees consequently need?
- Build capability through the work.
What combination of formal learning, practice, coaching, performance support and workplace experience will enable employees to perform successfully? This turns L&D from the recipient of a redesigned job into a participant in redesigning the system of work.
Learning needs a seat at the pilot
Piloting is another area where L&D leaders can expand their contribution. Organizations typically evaluate transformation pilots using metrics such as processing time, error rates, productivity, adoption and cost.
A redesigned workflow can look efficient but can produce unintended workforce consequences.
Employees may be faster but less capable of handling exceptions. AI may improve output quality while weakening independent judgement. A role may become broader without employees receiving enough practice to perform it confidently.
L&D leaders should help establish capability measures alongside operational ones. Depending on the work, these might include proficiency, quality of judgement, ability to handle exceptions, time to independent performance, use of newly released capacity, transfer of learning, employee confidence and the availability of developmental work experiences.
The CHI approach offers a useful precedent. Its projects assess not only productivity but also workforce and service outcomes. In the reported pharmacy transformation, for example, prescription rework fell from 30 percent to below 5 percent while automation generated workforce savings equivalent to 19 full-time employees. Other projects examined measures including employee satisfaction, attrition and time available for direct patient care.
For L&D leaders, the principle is straightforward: Do not measure only whether the new system works. Measure whether people are becoming capable of working successfully within it.
A new mandate for L&D leaders
AI is likely to blur the boundary between workforce transformation, job redesign and learning even further.
That creates an opportunity for the learning function. For years, L&D leaders have argued that learning should be closer to the business. Work redesign provides a practical way to make that ambition real.
Being closer to the business does not simply mean aligning courses with strategic priorities. It means participating in conversations about:
- How work creates value
- Which activities should be automated or augmented
- Where human judgement should remain
- How responsibilities should move across roles
- What experiences develop expertise
- What work employees should grow into
- How redesigned work will continuously build organizational capability
These are learning questions as much as they are operating model questions.
The L&D leaders of the AI era may need to become, in part, an architect of work. Not because L&D should own job redesign. It should not. Operations, technology, HR, business leaders and employees all have critical roles.
But L&D brings something distinctive to the table: An understanding of how people acquire capability, how expertise develops through experience, how performance can be supported in the workflow and what happens when the experiences through which people once learned are redesigned away.
That perspective is too important to introduce only after the new job description has been written.
Move upstream before the skills gap appears
The conventional sequence is familiar: Introduce technology. Redesign jobs. Identify skills gaps. Train employees. A more sustainable sequence is: Redesign the work. Determine the human and technological contribution. Design roles and capability together. Build learning into the new way of working.
The distinction may seem subtle, but it changes L&D’s strategic position. It moves the function from responding to skills gaps to helping prevent them; from creating learning for work to creating learning through work, and from preparing employees for a future designed by somebody else to helping the organization build that future with human capability deliberately included.
For L&D leaders, that may be one of the most important shifts AI demands. As the nature of work changes, the question is no longer simply whether employees are ready for the redesigned job. The question is whether L&D leaders were present when the work was redesigned in the first place.
