Transformation Is the Priority. But Are We Close Enough to the Work to Get It Right?

Transformation, learning and the real work of change

My recent conversations with colleagues, partners and clients keep coming back to two themes. Everything about work is faster, yet momentum feels slower. And while investment in transformation and AI is at an all-time high, the returns are not meeting expectations. Underneath both is a common experience: committed people are trying to do good work inside organisations that keep making it harder. Are we close enough to that reality when we decide what needs to change?

That question matters because transformation is no longer a programme at the edge of the business. It is becoming the business. AI is accelerating it, economic pressure is sharpening it, and leaders are making bigger decisions faster, with less certainty.

PwC's 2026 Global CEO Survey captured that pressure. Only 30% of 4,454 CEOs were confident about revenue growth, while 42% said their biggest concern was whether their organisation was transforming fast enough to keep pace with technology and AI. Yet 56% reported no significant financial benefit from AI. That is quite a gap. The urgency is real. The investment is real. The return, for many, is not.

I do not think the answer is to transform faster for the sake of it. I think it is to get closer to the work before we decide what transformation needs to be. The work usually knows first.

Transformation is often designed from a distance. A strategy is agreed, a platform selected and a process redrawn. Each decision can make sense on its own. Then it lands in the working day, where one person has to stitch it together across competing priorities, old systems and customer needs.

That person sees what the plan cannot. They know which approval adds no value, which system creates a second version of the truth and which workaround is quietly holding everything together. Managers see it too. They hear the frustration before it reaches a dashboard, but are often too stretched to do more than keep things moving.

Getting closer to the work is not a soft engagement exercise. It is commercial intelligence. It shows us what to remove, what to redesign, where learning is needed, where AI could help and where human judgement must stay firmly in control.

Deloitte's 2026 Global Human Capital Trends research makes the same gap visible. Seven in ten business leaders said their competitive strategy is to be fast and nimble, yet only 6% were making progress in deliberately designing human-AI interactions. Technology is moving into the work faster than many organisations are redesigning the work around it. That is where value leaks. It is also where trust does. Start with the problem people are living.

KBank offers a practical 2026 example. Faced with uneven AI readiness and anxiety about jobs, the Thai bank began with AI literacy, responsible-use boundaries and a simple question: what would make your work easier? Employees brought forward real operational problems within the strict rules of financial services. The bank started at individual level, then looked for patterns that could move into wider workflows. Microsoft reports productivity improvements of up to 50% in some areas, with 90% of staff wanting to continue.

KBank's investor material shows the foundations underneath: business and technology teams working together, data governance, responsible AI controls, skills development and AI embedded into daily work.

That sequence matters. People were not handed a finished answer and told to adopt it. Their experience of the work helped shape where the technology went. Learning, governance and workflow design moved together.

STADLER, a 230-year-old manufacturing business in the recycling industry, tells a similar story. Its rollout combined leadership support and clear guardrails with bottom-up experimentation. Teams used AI where time was already being lost: turning technical knowledge into usable drafts, translating, summarising and preparing decisions. In a March 2026 case study, the company reported 30% to 40% time savings on common knowledge tasks and daily active use above 85%.

The interesting measure is not the number of tools created. It is that people came back and used them without being chased. Adoption became a signal that friction had genuinely been removed.

Neither story is a case for dropping AI on every workflow. It is a case for beginning with the work, involving the people who understand it, setting the boundaries properly and measuring whether the day got better.

Human-first is not human-comfortable

Human-first transformation does not mean avoiding hard decisions or preserving every role, task and process exactly as it is. That would not be transformation. It means making those decisions with people, evidence and consequences in view.

A 2026 Harvard Business Review article reported on video diaries from 85 frontline workers across six industries. More than three-quarters were dissatisfied with the training they had received for new technologies. It is a small study, but it names a bigger risk: when transformation is done to people, uncertainty fills the space that involvement should have occupied.

We shouldn't mislabel that as resistance. People are asking reasonable questions, that we should be listening to - and using as drivers for transformation. What will change in my day? What am I still accountable for? When should I trust the output? Will the time saved become space for better work, or simply more work? If we cannot answer those questions, we are not ready to talk about adoption.

This is where learning belongs in transformation. Not arriving at the end to explain a decision already made, but alongside the business while the work is understood and redesigned. Learning helps people make sense, practise judgement, build confidence and adapt as change happens. It also helps the organisation listen, and that part is too often missed.

The measures need to move with us.

If transformation is meant to improve the work, its measures should be visible there too. Did approval time fall? Did rework reduce? Can a new starter contribute sooner? Are managers spending less time translating confusion and more time coaching? Has risk reduced? Did the customer notice? Can someone finish the day without carrying an avoidable process home in their head?

Time, cost, quality, risk and effort are not cold business measures when you get close enough to see them. They describe the human experience of work as much as the operating result. Wasted effort is a cost to the business and a cost to the person absorbing it.

The new world of work will not be won by the organisation with the most AI, the biggest transformation portfolio or the fullest learning catalogue. Most technology will be available to everyone. Advantage will come from seeing the work clearly enough to make better choices, then helping people learn and adapt as part of the work itself.

Transformation is a global priority.

But priority alone does not create value. Proximity does.

Get close to the Tuesday that is not working. Listen to the manager quietly holding it together. Ask where time, judgement and energy are being lost. Put governance around the decisions that matter, keep human accountability clear and measure whether the work improves.

Because when we start there, transformation stops being something the organisation announces and becomes something people can actually feel.

And it's my view, that is when the business case becomes real - and transformation happens.