Explore new research on how AI is changing jobs, why many leaders are slow to invest in reskilling, and what this means for workforce strategy. Share with your team and contact Lotus Beta Analytics to discuss practical next steps for your organisation.
How is AI changing employees’ expectations about their jobs?
Accenture’s research shows that employees are bracing for significant change from AI:
- 31% of workers expect their job will either be unrecognisable or disappear completely by the end of the decade. That’s roughly double the share who felt this way just 18 months ago.
- 79% of workers expect they will need to reskill.
- 55% say they are likely to change occupations.
At the same time, there is strong willingness to adapt:
- More than half of UK workers (54%) say they have the appetite to reskill in response to AI.
In short, employees largely accept that AI will reshape their roles and are prepared to learn new skills. What they are looking for now is clearer guidance, structured reskilling opportunities, and support from their employers to navigate this transition.
Are employers investing enough in AI reskilling and workforce transition?
The research suggests a clear gap between what workers expect and what employers are currently doing.
On the employer side:
- Only 26% of organisations have conducted a skills audit to assess AI’s impact on roles.
- 27% do not provide AI-related training at scale.
- Just 30% are investing in reskilling and redeployment pathways for at-risk roles.
- Only 7% of executives say their workforce is fully prepared for agentic AI.
At the same time, executive expectations are shifting:
- In 2024, only about one-third of executives believed AI would shrink the national workforce; now 49% expect AI will reduce national employment over the next decade.
- Executives expecting AI to increase demand for entry-level roles dropped from 40% to 15%.
- Those expecting reduced demand for entry-level roles rose from 22% to 37%.
This mindset risks creating a “wait and see” approach, where leaders assume displacement is inevitable and invest less in reskilling. The report argues that turning AI-driven efficiency into inclusive growth will depend on more deliberate investment in skills audits, structured reskilling, and new career pathways.
Why aren’t AI productivity gains showing up at the organisational level?
The data indicates that AI use is growing, but often in a fragmented way that limits impact at scale.
Key patterns from the research:
- 24% of workers are sourcing AI tools themselves, outside formal company systems. This “shadow AI” means usage is ad hoc and not aligned with enterprise processes or governance.
- Employees are mainly adopting AI at the level of individual tasks, not end-to-end workflows.
- Only around a quarter of employees say a major process in their team has been redesigned around AI in the past year.
The result is what the report calls a “two-speed organisation”:
- Some functions operate with AI-enabled productivity.
- Others continue to work as they always have.
As Matt Prebble of Accenture notes, AI has effectively “joined the workforce,” but people are moving faster than their organisations. Personal productivity gains are visible, but they don’t automatically translate into enterprise performance.
To turn AI adoption into economic value, organisations need to:
- Create a current, comprehensive view of workforce capabilities and gaps.
- Redesign workflows and operations around AI, rather than just layering tools on top of existing processes.
- Invest in structured reskilling and upskilling so that AI becomes embedded in how work gets done, not just in isolated tasks.
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