AI is changing work faster than people can adapt. Is your workforce ready to execute your strategy?
As organisations adopt artificial intelligence (AI), workforce planning faces a challenge beyond automation: whether employees can build the capabilities needed to keep pace with changes to their work. Roles may retain their titles while the expectations attached to them shift substantially.
Sarah Stone, co-founder and director of linkABLE and ThriveABLE AI, said employers needed to look beyond technology investment and headcount. “What if the greatest workforce risk isn't simply AI replacing people, but the capability required to perform our jobs changing faster than our people can adapt?” she said.
Stone said the implications extended to strategy execution, employee development and future leadership pipelines. “This is why workforce planning can no longer focus solely on how many people an organisation will need,” she said. “We also need to ask: What will we need those people to be capable of?”
The World Economic Forum's Future of Jobs Report 2025 identifies skills gaps as the biggest barrier to business transformation among surveyed employers. LinkedIn's Work Change Report estimates that 70% of the skills used in most jobs will change by 2030, with AI accelerating that shift.
PwC's 2025 Global AI Jobs Barometer, based on analysis of close to a billion job advertisements, found that skills sought by employers were changing 66% faster in occupations most exposed to AI than in those least exposed. The findings suggest that a role can change substantially without disappearing.
An employee previously valued for process knowledge, technical expertise and quality output may now also need to interrogate AI outputs, interpret data, redesign workflows and solve ambiguous problems. “Same job title. Completely different capability requirement,” Stone said.
AI access and workforce capability
Stone cautioned against equating technology deployment with workforce readiness. “Giving 5,000 employees access to an AI tool does not suddenly create 5,000 AI-capable employees,” she said. Access, literacy, fluency and capability represent different stages of readiness.
Real capability involves understanding where AI creates value, knowing when to use it, critically evaluating its output and applying human judgement. “Capability determines whether someone knows what question to ask, whether the answer is good and what to do with it,” Stone said.
As AI takes on routine tasks, capabilities such as critical thinking, judgement, curiosity, leadership, collaboration, influence and problem solving remain central. Employees may need AI fluency alongside critical thinking, data literacy alongside commercial judgement, and technical expertise alongside communication. Employers need to understand where technology can augment people and where human capability creates value.
The transition also raises questions about how graduates and junior employees develop expertise. Researching, drafting, analysing, preparing first versions and receiving feedback have traditionally provided practical learning experiences. “It might have looked like junior work, but it was also capability-building work,” Stone said. “Remove the task and you may inadvertently remove the learning experience with it.”
Stone said organisations should assess the learning value of tasks before automating them. “What capability was being developed through that task—and how will we develop it differently?” she asked. That assessment can help employers identify experiences that need deliberate replacement rather than assuming development will happen through everyday work.
Development pathways may need to start with the capabilities an exceptional senior employee will require in three to five years, then work backwards. Coaching, simulations, case work, shadowing, rotations, stretch assignments and structured decision-making may replace experiences that diminish. “It's to ensure we don't accidentally automate the pathway through which high-value capability was created,” Stone said.
From workforce planning to capability planning
Stone described the widening gap between future requirements and current development as capability debt. Technology, processes and customer expectations change; experienced employees leave; and knowledge may remain concentrated in individuals. Development pathways may not evolve at the same speed.
“Eventually, strategy demands something the workforce has not been prepared to deliver,” Stone said. “That is when capability debt becomes a business issue rather than simply a learning issue.” The gap can remain difficult to see until the organisation needs capabilities it has not deliberately built.
Executive teams can begin by looking 24 to 36 months ahead. What will strategy require people to do differently? Which capabilities will become more important? Which skills will AI augment, and which tasks will disappear or change? What will employees need to learn?
Leaders should also examine where critical knowledge is concentrated, how graduates will develop expertise if traditional learning tasks disappear, and where capability debt is already accumulating. Mapping current capability against future requirements provides a basis for building development pathways.
“Because ultimately, an organisation's strategy is only as executable as the capability behind it,” Stone said. She argued that organisations positioned to succeed would understand how work was changing, identify the capabilities needed next and begin developing them before the gap became a crisis.
For Stone, that changes the executive conversation. “The question for executive teams is no longer simply, ‘What is our AI strategy?’” she said. “It is: ‘Do our people have the capability to execute it?’”
This article was produced in partnership with linkABLE
