The future of work: Human-led, AI-enabled

Two things can be true at once.

Artificial Intelligence (AI) is transforming work at remarkable speed. Still, it’s only one of several forces reshaping how organisations operate, compete, and create value.

The future of work is emerging from a collision of geopolitical instability, economic pressure, demographic change, new regulation, environmental disruption and technologies that extend well beyond AI. An organisation may be reducing costs in one part of the business while investing heavily in another, speeding up decisions while navigating more complex regulation, or creating flexibility without sacrificing collaboration.

AI cuts across these pressures and opens new ways to respond to them. But treating every restructuring, skills shortage or change in working practice as an AI story risks misunderstanding both the problem and the opportunity. The question isn’t which single force will define work. It’s whether leaders can make coherent decisions while several forces act on the organisation at once.

The forces changing work

Political and economic factors are changing the practical geography of work. Tariffs, sanctions, industrial policy and conflict affect where organisations invest, source goods and locate people. In March 2026, the World Trade Organisation forecast that merchandise trade growth would slow from 4.6% in 2025 to 1.9% in 2026, with conflict in the Middle East threatening higher energy, transport and insurance costs. Slower growth encourages efficiency, yet tight labour markets leave organisations competing for scarce technical, regulatory and sector expertise. “Most of the time, the need to restructure is not AI-related at all,” says Laurence Renard, Partner and International Head of Employment. “Cost-cutting is almost always due to economic and geopolitical pressures.”

Demography presents a longer-term challenge. The OECD estimates that the working-age population across its member countries will decline by 8% by 2060, and by more than 30% in over a quarter of them. Employers will have to retain skills for longer and manage a wider range of ages and expectations. Nicholas Ngo, Managing Associate in our employment practice group, based in Singapore, puts it plainly: "The future of work is keeping people on terms that are as much theirs as yours." Reward, flexibility, progression and family responsibilities will carry different weight across generations and markets.

The social contract around the workplace is still unsettled. Some employers have brought back office mandates while others retain hybrid models. Lucy Shurwood, Partner and Head of Applied AI, sees renewed recognition that “humans are fundamentally social creatures” who form communities and relationships. Yet the experience of work is also shaped by transport, housing and commuting. The future office will be influenced as much by cities and infrastructure as by corporate policy.

Technology is changing work through more than generative AI alone. Robotics and autonomous systems are altering manufacturing, logistics and transport. Cloud platforms allow work to move across locations, while cybersecurity is becoming part of workforce design. The EU Agency for Cybersecurity analysed 4,875 incidents for its 2025 threat assessment and flagged ransomware as the most consequential threat. Organisations need to decide who can access information, which processes keep running during disruption and whether employees can recognise an attack.

Law is reshaping the employment relationship in parallel. The EU’s pay-transparency regime gives applicants and employees greater access to salary information and requires larger employers to report gender pay gaps. Compliance calls for consistent job architecture, defensible definitions of work of equal value and reliable workforce data across jurisdictions.

Environmental change is more immediate than a distant transition in job titles. The International Labour Organisation estimates that more than 2.4 billion workers, over 70% of the global workforce, are exposed to excessive heat. Extreme weather is affecting working hours, health, travel and business continuity, while the shift towards lower-carbon industry creates demand for new technical and regulatory skills.

These forces reinforce one another. A geopolitical shock can raise energy prices, accelerate infrastructure investment, trigger regulation and shift where skills are needed. An ageing workforce can strengthen the case for automation while making knowledge retention more urgent. Leaders have been training for this environment through the global financial crisis, Brexit, the pandemic and geopolitical turmoil. AI intensifies the challenge, but it didn’t invent it. As Nicholas puts it, AI is “an age-old problem wearing new clothes”: just another chapter in the long back and forth between people and machines.

AI-enabled: from literacy to fluency

The first wave of generative AI adoption was largely about access. Organisations acquired tools, launched pilots and encouraged experimentation. The next phase is about knowing where the technology belongs within a piece of work and redesigning processes around what it can now do.

“It’s not just a degree of literacy, but fluency,” says Ali Chaudhry, Senior Legal Engineer. Fluency means “knowing what technology is suitable for a particular task”.

Fluency develops through use, informed scepticism and an understanding of the bigger goal. A fluent user can frame a problem, pick the right tool, test the output and recognise when another kind of expertise is needed. They’re neither blindly enthusiastic nor reflexively resistant.

This lets organisations go beyond just drafting and summarisation. AI can compare information across documents and jurisdictions, spot gaps in data, pull out institutional knowledge and flag anomalies for a human to look at. It makes huge volumes of information easier to interrogate, freeing specialists to spend more time interpreting what the results actually mean.

Agentic systems push the opportunity even further. Instead of responding to a single prompt, agents can run through defined stages of a workflow, oversee other agents and hand work back to people when judgement is needed. In some organisations, lawyers are already supervising agents, adding tasks and course-correcting outputs on the fly. This creates new roles around configuring systems, evaluating performance and deciding how much autonomy they should have.

Its potential also extends beyond production. Sarah James, Partner and Head of Adaptive, encourages people to think of AI as a “thinking partner”, an educator or a coach. It can stress-test an argument, expose assumptions, rehearse a difficult conversation or accelerate someone’s understanding of an unfamiliar subject. As Sarah notes, "One of the biggest opportunities we may be missing with AI is not simply getting answers faster, but improving the quality of our thinking." This kind of impact is less visible than an automated workflow, but it can be just as consequential. It turns AI from a faster way of producing an answer into a means of improving the thinking that comes before it.

The opportunity is especially big for organisations with knowledge scattered across functions, locations and legacy systems. Many do not lack information. They lack the ability to retrieve it, understand why a decision was made or convert what they know into action. AI can help, but only where data is accessible and reliable, systems connect and permissions reflect the sensitivity of the material.

Just putting AI in employees’ hands doesn’t automatically build organisational capability. Fluency is the difference between isolated use and purposeful application: the confidence to frame better questions, test outputs against evidence, recognise when human context is indispensable and connect the technology to a worthwhile organisational problem. It also means knowing when not to use AI. Governance, in this sense, is part of the enabling architecture. Clear boundaries, accessible expertise and deliberate review points give people room to experiment and learn without obscuring accountability.

The opportunity is to widen an organisation’s field of action: bringing previously inaccessible knowledge into decisions, testing more possibilities, addressing needs that were once impractical to serve and letting people focus on the judgement, creativity and relationships that shape outcomes.

Human-led: deciding where value is created

The forces described above are opening new ways to organise work, access talent and create value. Economic change can redirect investment towards new capabilities; evolving workforce expectations can widen how and where organisations recruit; and regulatory differences can reward businesses that combine global scale with strong local judgement. The opportunity lies in understanding how these developments connect, rather than responding to each in isolation.

Human-led organisations make deliberate choices about where value is created. Their advantage lies not in predicting every development, but in aligning people, investment and organisational design around the capabilities that will differentiate them.

AI has entered this environment as a particularly powerful new force. It feels all-consuming because its potential isn’t confined to one market, function or business problem. It can enhance almost every workflow, make expertise more accessible and expand what individuals and teams are capable of doing. The leadership opportunity is to integrate that capability into the organisation with the same clarity applied to every other source of change.

Doing so requires organisations to decide where AI creates value, where human judgement stays essential and who’s accountable for the outcome. That also requires governance frameworks that allow innovation to scale whilst making responsibility and oversight clear.

There’s a temptation to divide work into routine activity for machines and complex decisions for people. Peter Lee, a Partner in our digital business team and Head of AI Governance, argues that this “doesn’t reflect the direction of travel. Work will not be allocated in a binary fashion. Instead, there will be a fluid interaction between routine and complex tasks, and between human and machine capability.”

Leaders must design work around the outcome they want, rather than inherited structures or the availability of a particular tool.

The tension between pricing and value is forcing organisations to rethink how work gets done and how value is created. When technology can produce a competent first draft, compare thousands of records or answer a standard enquiry, the premium shifts towards interpretation, originality, specialist knowledge and the ability to make a recommendation under uncertain conditions.

A technically sound answer may still fail to account for political sensitivity, cultural differences, an emerging regulatory expectation or the consequences of acting in one market rather than another. AI can bring more information to the table, but human judgement decides what matters, how to weigh competing priorities, and which outcomes the organisation is ready to own.

Judgement is built through experience: seeing what works, what doesn’t, and noticing which details tip the scales. When organisations automate the very activities that built that judgement, they need to swap incidental learning for deliberate development.

“Junior hires get to do more interesting and substantive work sooner because AI tools free them up to think about the problems, rather than the process,” says Andrea Finn, Partner in our employment practice group.

AI supported training could help people advance faster by exposing them to a broader set of situations without waiting for the perfect project. Making that real takes structured feedback, simulations, and supervised exposure to difficult decisions.

Rules still matter, particularly where AI touches confidential data, regulated activities or decisions affecting people. But policies can't predict every use, resolve every difference between markets or replace the conversations through which judgement develops. Leaders have to model good practice, leave room for challenge and make accountability clear.

This is why AI is an adaptive challenge rather than a conventional transformation project. Most change programmes assume movement towards a defined destination. Here, both the technology and the environment around it continue to shift.

Sarah describes it as “an ongoing adaptive challenge. Leaders need to listen, learn and continually recalibrate as the technology and their people evolve.”

The organisations that do this well won't allow the newest technology or the latest external shock dictate their direction. They’ll understand how different forces reshape their choices and stay clear about where they create unique value. They’ll use AI to improve analysis, service and learning while reserving human attention for the points at which context, trust and consequences matter most. They’ll be, in Andrea’s words, “benefiting from the tools, not being driven by them”.

Four questions for leaders

The organisations best placed to succeed aren’t the ones chasing every new technology or reacting fastest to each new pressure. They’re the ones that can answer these four questions:

• Which forces will most reshape where and how the organisation creates value?
• Where can technology expand capability or improve outcomes?
• Where is human judgement essential, and who remains accountable?
• How will people learn and adapt as the model of work changes?

The answers will differ across organisations, markets, functions and even individual tasks. They’ll also shift as political, economic, social, technological, legal and environmental conditions evolve. The goal isn’t to draw a permanent line between people and machines, or a fixed response to uncertainty, but to build an organisation capable of adjusting intelligently.

That means more than just adopting technology. It requires leaders to connect long-term strategy with the daily design of work: deciding which capabilities to build, which knowledge to protect, where collaboration matters and when technology can open a genuinely better route to an outcome. They also need to explain those choices clearly enough for people to act confidently when circumstances change.

The organisations that outperform will be those that understand how external forces, human capability and technology interact. They’ll use AI where it creates real advantage, but they won’t mistake the tool for the strategy. They’ll redesign work, invest in their people and continue adapting as the environment changes.

They will be human-led and AI-enabled.