AI, capability and pace

Artificial intelligence is changing how work gets done faster than most organisations are changing the capabilities that support it. While attention often focuses on tools and technology, the real constraint emerging for many leaders is organisational pace. KPMG’s Tania Kuklina explores how quickly people, roles, skills and structures can adapt as AI becomes embedded in everyday work.

Article highlights:

Pace of change

AI is changing roles faster than organisations are changing workforce capability
Pace constrained by the organisation

Pace is an organisational constraint, not a technology problem
Supporting roles

AI’s impact will likely support work, not eliminate roles – but only if capability keeps up

The capability challenge leaders are facing right now

Over the past year, AI has moved from pilots and proofs of concept into core processes from recruitment and learning, to decision support and automation. In many organisations, adoption is happening unevenly and informally, driven by teams experimenting at their own speed. The result is a growing gap between what AI can enable and what the organisation is actually capable of absorbing.

That gap matters. Because AI is not just a technology shift. It is a workforce and operating model shift. And for leaders, the question becomes: how confident are you that your organisation can absorb change at the pace AI is setting?

As that gap widens, many organisations are also rethinking strategy, oversight and adoption choices through engaging in change management processes.

How will AI change teams?

AI is already reshaping roles, tasks and decision‑making across organisations. Some work is being automated. Much more is being augmented. But in many cases, organisations lack a clear view of how this plays out across their workforce.

Leaders may ask where AI will “replace” roles. The better question is how roles will change - what tasks are automated, what judgement remains with people, and what new capabilities are required as a result. Without that clarity, how can you make confident decisions about where to invest, where to slow down, and where to move beyond ad‑hoc experimentation?

When AI outpaces workforce readiness

Many organisations want to move faster, but their foundations are simply not yet ready.

Tania Kuklina

Director

KPMG in Ireland

This is particularly visible in people‑centred functions. AI is already supporting recruitment, performance reviews, and learning and development. Yet the underlying data, workforce metrics and role definitions needed to scale these changes are often missing. Many organisations want to move faster, but their foundations are simply not yet ready.

Crucially, this is not just a large‑enterprise issue. Public sector bodies and mid‑sized organisations face the same pressures, often with less flexibility to absorb missteps. Moving too slowly risks falling behind. Moving too fast, without capability in place, risks hollowing out critical skills.

Why pace matters – and is often misunderstood

AI creates a tension between speed and sustainability. Leaders feel pressure to act quickly, driven by competitive, cost and productivity concerns. But pace is not simply about how fast technology can be deployed. It is about how well the organisation can adapt alongside it.

Consider the points below:

Automation or augmentation

Research and client experience consistently point to two distinct impacts of AI on work: automation and augmentation. Automation removes tasks. Augmentation changes how tasks are performed. Most roles will experience far more of the latter than the former.
Recognise experience

If you focus narrowly on cost optimisation, you risk stripping out experience and institutional knowledge that remains critical.
Emphasise productivity

In contrast, framing AI around productivity and redeployment is more likely to preserve your capability while increasing your teams’ capacity.
Future focus

In Europe and Ireland particularly, this distinction matters. Many organisations are less focused on immediate cost reduction and more concerned with competitiveness, resilience and future skills mapping. The challenge is turning that intent into practical decisions.

How can leaders build and sustain organisational resilience?

Leaders need to get comfortable with three realities.

Pace is an organisational capability, not a project timeline

AI adoption is constrained less by technology than by data quality, role clarity, and workforce readiness. You cannot skip these fundamentals without creating friction later.
Most AI impact is evolutionary, not revolutionary

Roles rarely disappear overnight. They change incrementally. Leaders need visibility at task and capability level to manage that change deliberately.
People and technology decisions are converging

As organisations consider whether work should be done by a person, an AI agent or a combination of both, traditional boundaries between HR and IT begin to blur. Decisions about roles, skills and systems increasingly sit together.

At its core, this is about organisational design and change, not just innovation.

How to build change capacity with AI

The actions you need to take are practical, not theoretical. They are about sequencing, clarity and realism.

Build a fact‑base before setting the pace

Organisations need a clear view of roles, tasks and skills today before they can credibly plan for tomorrow. Workforce decomposition - breaking work into its component parts - allows you to see where AI can augment, automate or create new demands.
Anchor AI decisions in productivity, not cost

Focusing solely on cost efficiency can undermine long‑term capability. Ask yourself how AI will enable your team to do higher‑value work, and how expertise can be retained and redeployed as roles evolve. Ask yourself: what are we freeing people up to do, and does it matter?
Match ambition to organisational maturity

Many businesses want advanced AI solutions before foundational data and metrics are in place. Leaders need to be honest about readiness and sequence change accordingly. In some cases, the right decision is to slow down now - in order to speed up later.

These actions do not require perfect foresight. They require disciplined choices.

What are the best leadership practices for AI change management?

Organisations getting this right tend to approach AI through the lens of workforce planning and change.

They use data and industry insight to understand how roles are shifting.
They invest in capability where it matters most.
They communicate clearly with people about how work will evolve.

Crucially, they also resist the temptation to over‑hype AI. Practical examples, credible timelines and realistic expectations build trust and momentum. Pace becomes something that is actively managed, not reactively absorbed.

AI challenges to watch in the coming months

Pressure on organisational pace will intensify. Tools will improve. Expectations will rise. Leaders will be asked not just what AI they are using, but how their workforce is changing as a result.

In practice, that means being able to explain how roles, skills and decision‑making are evolving - and whether the organisation is genuinely building the capacity to absorb change at the speed AI is setting.

Building capacity though clear leadership

Those who invest now in understanding roles, skills and capability will have far more freedom to move quickly later. Those who do not may find that speed becomes their biggest constraint.

For organisations formalising that response, KPMG’s People & Change team can help you create workforces that power strategy and deliver lasting results.