Most organisations aren’t struggling to access new technology anymore. They’re struggling to make it behave inside real work. That means people with different incentives, half-documented processes, edge cases everywhere, and systems that were never meant to talk to each other. The trends that matter in 2026 aren’t the flashiest ones – they’re the patterns that decide whether change becomes normal, or quietly fades into another unused tab.
AI confidence is high,but clarity is missing
0%
strongly agree leadership has clearly communicated how change will be navigated in 2026
0%
understand how AI agents will affect their roles and skills
0%
feel like active co-creators in how AI is shaping their jobs
0%
say they have a clear understanding of their organisation’s AI strategy
What this tells us
2026 begins with a paradox. Leadership confidence is strong, yet clarity has not reached the workforce. Employees sense that decisions are being made with good intentions, but they are not being meaningfully involved in shaping outcomes.
This gap between empathy and agency matters. When people do not understand how technology will affect them personally, adoption slows, trust erodes, and transformation stalls. The defining challenge here is not communication volume, but shared ownership of change.
Investment is easy. Value is structural
0%
of executives plan to increase AI investment in 2026
0%
now view AI as more beneficial to revenue growth than cost reduction
0%
say stronger data strategy and core digital capabilities would most accelerate AI scale
0%
report low-quality or misleading AI outputs that waste time and reduce productivity
What this tells us
Money is flowing into AI, but value remains fragile. The data shows a clear relationship between quality and confidence. Where foundations are weak, outputs are unreliable.
Where outputs are unreliable, trust collapses. 2026 exposes a hard truth: AI value does not scale on investment alone. It scales on clean data, robust systems, and disciplined integration.
Without these, enthusiasm turns into frustration at the point of work.
Deployment is outpacing redesign
0%
of organisations are redesigning processes to accommodate AI
0%
are redesigning roles
0%
of employees say training has prepared them for role changes
0%
drop in regular AI agent usage since summer
0%
try AI tools before asking a colleague — down 15 points
What this tells us
Technology is moving faster than organisations themselves. AI is being layered onto workflows that were never designed for it, while roles and incentives remain largely unchanged.
The decline in usage is a warning signal. When people are unsure how AI fits into their responsibilities, they stop using it — not because it lacks capability, but because it lacks context.
In 2026, redesign must precede automation, not follow it.
Executives expect disruption, employees feel exposed
0%
of leaders expect higher levels of change in 2026 than last year
0%
feel prepared for technological disruption
0%
rank digital tools as their primary strategy for managing change
0%
of employees believe their organisation can respond effectively to disruption
0%
feel confident about how talent disruption will be handled
0%
feel job security has been maintained, down from 59%
What this tells us
Change is accelerating, but confidence is uneven. Leadership sees opportunity; employees see uncertainty. This imbalance creates drag.
When workers feel exposed rather than supported, resistance grows quietly — not through protest, but through disengagement.
The organisations that succeed in 2026 will be those that match their investment ambition with visible commitment to workforce stability and adaptation.
AI conviction is strong, but impact is shallow
0%
of leaders report sustained, enterprise-wide AI impact
0%
of employees say they regularly work with AI agents
0%
feel comfortable delegating tasks to AI
0%
frequently encounter misleading or low-quality outputs
0%
say enjoyment of using AI has dropped, down from 21%
What this tells us
Belief in AI’s potential remains high, but lived experience tells a more cautious story. Where AI outputs are inconsistent or poorly integrated, confidence declines.
Comfort with delegation remains low because trust has not been earned. In 2026, AI maturity is no longer about access or experimentation — it is about reliability.
Systems that cannot consistently deliver value lose momentum, regardless of how advanced they appear.
Skills alone are not enough
0%
of employees say clearer training would improve confidence using AI
0%
report improved learning capability
0%
say AI helps them innovate
0%
feel training has prepared them for role changes
What this tells us
People want to engage with AI. They see its benefits and are willing to learn. But training without vision does not build trust.
Skills without context do not create confidence. 2026 exposes the limits of treating enablement as a learning problem rather than an organisational one.
Employees need to understand not just how to use AI, but why it is being introduced and how success will be measured.
What defines success in 2026
Success in 2026 won’t come from chasing the newest capability. It will come from making change feel coherent.
The organisations that pull ahead will be the ones that remove friction everywhere it hides: between what leaders say and what teams actually hear, between launching new tools and reshaping the way work is done, and between ambitious goals and the quality of the data those goals rely on.
They’ll treat clarity as an operating requirement, not a comms exercise. They’ll design workflows people can trust, with outputs that hold up under pressure. And they’ll involve the workforce early enough that change is something people help build, not something that happens to them.
In 2026, the winners won’t be defined by speed.
They’ll be defined by alignment that’s visible in day-to-day work.