The jobs AI creates may not be AI jobs

The jobs AI creates may not be AI jobs

Five careers emerging from the redesign of work

When we think about the jobs created by AI, we tend to think about the people building it: AI engineers, data scientists, developers, and machine-learning specialists.

Those jobs are growing - LinkedIn’s 2026 global Labor-Market Report finds that 1.3 million new AI-enabled jobs have emerged over the past two years - but another category of work is shaping up alongside them.

As AI becomes part of how organizations operate, new responsibilities are emerging between people and intelligent systems – and someone (not some technology) will have to take ownership for those relationships.

Hiring patterns are already reflecting this need in the market: Microsoft’s global Work Trend Index found that 78% of leaders are considering hiring for AI-specific roles. Significantly, 28% of managers are considering AI workforce managers to lead hybrid teams of people and agents, while 32% plan to hire AI agent specialists in the next 12 – 18 months.

Our recent whitepaper, On the threshold: AI, the future of work and the rise of hybrid labor markets, points in the same direction. AI is changing tasks, workflows and skills faster than it’s eliminating entire occupations. It introduces the idea of hybrid workforce orchestration: redesigning work so people, AI agents, automation and physical AI each contribute where they create most value.

That prompted the Financial Times to ask whether the workforce orchestrator could become “the next hot job”.

It won’t be the only one. Not all the jobs AI creates will belong to the people who build the technology - some will belong to the people who work out how the rest of us work with it.

For example:

1. Hybrid workforce orchestrator

Traditional workforce planning starts with people: how many are needed, with which skills, where and at what cost. But the workforce itself is becoming more complex, bringing together employees, contractors, specialist partners, copilots, AI agents and automation.

The hybrid workforce orchestrator starts with the work itself and would design that workforce as a system, working out how human and technological capability should combine to deliver the best outcome.

Where is human judgement essential? Where can AI extend someone’s capability? What could an agent execute independently?

Part workforce strategy, part organizational design and part AI strategy, it shifts workforce planning from asking "how many people do we need?” to “what combination of human and technological capability does this work require”.

2. Human-AI work designer

If the orchestrator designs the workforce, the human-AI work design redesigns the work itself.

Putting AI into an existing process does not necessarily transform it. You can automate a task, save a few minutes and leave everything around it untouched.

Human-AI Work Designers would go deeper, breaking work into tasks, decisions and outcomes and rebuilding workflows around what humans and AI each do best.

In a recent World Economic Forum article, Ariki Ono, founder and CEO of Nexgen Japan, described an emerging “AI work architect” – someone who determines what should be delegated to AI, augmented by it or remain human-led.

The title may vary, but the need won’t: organizations looking for more than incremental productivity gains will need people who can redesign work, not simply automate parts of it.

3. AI workforce coach

Giving people access to AI doesn’t mean they know how to use it well, or that that feel psychologically safe navigating new technologies.

Employees need to understand where AI genuinely helps, where it falls short, how to challenge and when its output needs to be challenged.

An AI workforce coach would work alongside teams, helping them identify useful applications, redesign habits and workflows, experiment safely and recognize when not to rely on AI.

Their expertise would sit somewhere between learning, change management, operations and technology. As intelligent systems keep changing, the durable capability will be keeping pace and knowing how to work effectively with them.

4. Agent governance Lead

There’s a fundamental difference between AI that advises and AI that acts.

Scheduling a meeting is one thing. Issuing a customer refund, selecting a supplier, rejecting an application or making a financial commitment is another.

Some of the roles needed to make AI work in practice are already emerging. Akkodis already deploys forward-deployed engineers, who work closely with clients to connect technology with the realities of their business – understanding workflows, processes and problems and translating them into solutions that work in practice. Demand for these hybrid roles is growing as companies move from AI experimentation to implementation.

Agent governance leads could build on the same combination of technical understanding, business knowledge and human judgement. They would turn responsible AI from policy into everyday practice, defining what agents can decide, when a human must intervene, how activity is monitored and who remains accountable when something goes wrong.

That need is becoming more immediate. Microsoft found that 81% of leaders expect agents to be moderately or extensively integrated into their AI strategy within 12–18 months.

As agents take on more consequential work, the people around them will matter just as much as the technology – particularly those able to understand context, question decisions and know when human judgement needs to take over.

5. Human capability strategist

Machines are becoming ever more capable and proficient in certain tasks, but people will always excel over technology in others. Understanding the areas where human skills continue to create disproportionate value therefore becomes an investment strategy.

The World Economic Forum estimates that 39% of workers’ existing skills will be transformed or become outdated by 2030. Technological skills are rising quickly, but so are creative thinking, resilience, leadership and collaboration.

A human capability strategist would translate that shift into choices about recruitment, learning, leadership development, job mobility and job design.

The work comes first. The title comes later.

These won’t necessarily be brand new roles either. In many organizations, the work and responsibilities will emerge inside existing roles first; for example, workforce planners may begin orchestrating people and digital labor. Organizational designers may restructure workflows around agents. Learning teams may take responsibility for AI adoption. Governance specialists may increasingly define agent decision rights.

Some of those responsibilities may eventually become careers in their own right - others will permanently reshape jobs that already exist.

AI is creating a new organizational layer of work around its design, governance and integration. What matters more than the titles is the organizational capability emerging underneath them: deciding how people and intelligent systems work together, where authority sits and where human judgement still matters most.

The next generation of AI jobs may be less about building AI, and more about redesigning work around it.