The Talent Divide Arrives: What the 2026 PwC AI Jobs Barometer Means for Healthcare and Life Sciences Hiring

Key takeaways

• PwC’s 2026 Global AI Jobs Barometer, based on more than a billion job adverts across 27 countries, puts the average wage premium for AI-skilled workers at 62%, up from 57% a year earlier.
• PwC separates ‘professionalised’ roles, where AI extends expert judgement, from roles AI simply makes easier. The first group is growing twice as fast and its salaries are rising 42% faster.
• In the UK, specialist AI postings rose 61% in a year (from 112,000 to 180,000) while vacancies across the economy fell 6.6%, and the UK AI wage premium rose from 11% to 34.2%.
• Health accounts for less than 1% of AI job growth, the lowest share in PwC’s sector table, which gives healthcare organisations that hire now less competition than they will face later.
• AI-exposed entry-level roles are seven times more likely to require senior-level capabilities, so organisations can rely less on growing AI capability from junior hires and more on experienced leaders.

Why this report matters for hiring plans

PwC publishes its Global AI Jobs Barometer each summer. The 2026 edition, released in July, analyses more than a billion job advertisements across 27 countries and six continents, which makes it one of the broadest datasets on AI hiring available, and its figures will be quoted in board papers for the rest of the year.

The headline figures are straightforward. AI-skilled workers now earn an average premium of 62%, up from 57% last year. AI-specific roles grew 69% against 9% for the job market as a whole. In the UK, specialist AI postings rose 61% in a year in which overall vacancies fell 6.6%. Claire Reid, PwC’s UK Chief Technology and Innovation Officer, described the change this way: “The experimentation phase is over and businesses want to scale and embed the technology properly.”

The figure that matters most for the sectors we work in appears further down PwC’s sector table and has had less attention: health accounts for less than 1% of AI job growth.

Two kinds of AI-affected work

The most useful part of this year’s Barometer is its distinction between two kinds of work affected by AI.

In professionalised roles, AI extends expert judgement: the clinician who reads model output against twenty years of experience, or the data science leader who can tell which validation failure matters. In democratised roles, AI makes tasks easier for people without that expertise.

The two groups are moving apart. Professionalised roles are growing at twice the rate of democratised ones, and their salaries are rising 42% faster. Companies most exposed to AI grew headcount by 52% since 2018, against 36% for other companies, and the top fifth of those AI-exposed companies recorded labour productivity gains of 163%, nearly five times the wider group.

Joe Atkinson, PwC’s Global Chief AI Officer, put it this way: “Across the global economy, we’re beginning to see a new divide emerge between different models for talent and value creation.” In our reading, the divide PwC describes runs between organisations that hired people able to combine AI with deep expertise and organisations that bought the tools without those people.

Where the premiums are highest

The premium data does not show the highest pay in technology companies. The largest premiums appear where AI skills are scarcest relative to demand: consumer markets pay a 118% premium globally, reflecting how few people with AI skills work in that sector.

The UK figures point the same way. A 34.2% national premium, three times last year’s 11%, emerged while overall vacancies fell. Employers kept paying more for AI skills in a year when they cut hiring elsewhere, which suggests these are roles they are not prepared to leave unfilled.

For healthcare and life sciences organisations, the premium they pay today reflects the scarcity they face. It is also a guide to what competitors will pay once they reach the same conclusion. We covered how that escalation works in The AI Compensation Arms Race, and the 2026 Barometer supports that argument with a much larger dataset.

Health’s share of AI job growth

Technology, media and telecommunications account for 11% of AI job growth and professional services for 6%. Health accounts for less than 1%.

On one reading this is a serious gap: the sector with some of the highest-stakes uses of AI, including diagnostics, clinical language processing, drug discovery and patient-risk prediction, is hiring very few people to build and run it. The obligations are arriving regardless. Regulatory requirements such as the EU AI Act’s high-risk provisions (which we set out in March) apply whatever an organisation’s headcount plan says, and organisations deploying clinical AI without senior AI leadership are taking on risks they may not be able to see.

On the other reading it is an opportunity. Healthcare and life sciences organisations are, for now, competing with fewer peers for AI leaders than technology or professional-services firms are. Those building their healthcare AI leadership team now face less competition than they are likely to later. If health’s share of AI job growth moves towards that of other regulated sectors, which is our expectation rather than PwC’s forecast, the premium for these hires will rise with it.

Fewer routes in at junior level

One further finding matters for hiring leaders. Analysing 2.4 million US entry-level roles, PwC found that AI-exposed junior positions are now seven times more likely to require senior-level capabilities such as judgement, leadership and persuasion. These ‘seniorised’ entry-level roles have grown 35% since 2019, while other entry-level roles have shrunk by 10%. Pete Brown, PwC’s Global Workforce Leader, said: “AI is removing some of the routine work that once acted as an apprenticeship, while increasing demand for judgement, leadership and adaptability much earlier in careers.”

That weakens a common plan: hire capable juniors, grow AI capability internally and avoid paying the senior premium. The routine work juniors used to learn on is increasingly the work AI does. More of the capability now has to come from experienced leaders who have taken models from pilot to production and can develop judgement in the people below them.

That changes which hire comes first. The first AI appointment is less about who builds the first model and more about who can advise the board in a market paying a 62% premium, and make each later hire quicker and less likely to fail.

What hiring leaders should do now

1. Re-price open roles against the new data. Compensation bands set before this report do not reflect a 62% global premium or a UK premium that tripled in a year, and benchmarks from January are already out of date.

2. Classify roles honestly. Decide which open positions are professionalised, relying on judgement your organisation needs, and which are democratised work that AI will absorb. Fund the first group properly and avoid over-specifying the second.

3. Hire the experienced leader first. One AI leader who has run systems in production changes the cost and success rate of every hire beneath them. Our executive search work in healthcare and life sciences AI is built around this appointment.

4. Replace the training junior roles used to provide. If entry-level roles no longer teach, senior people have to. Favour leadership candidates with a record of developing teams as well as shipping models, then use structured talent acquisition for the roles beneath them.

5. Hire before competition in your sector increases. Health’s low share of AI job growth is unlikely to last, and a premium deferred this year is likely to be higher next year, with more competing offers.

The 2026 Barometer confirms a divide many organisations already suspected. For healthcare and life sciences, the sector table shows that the competition for AI leaders is still at an early stage, which is the best time to hire.

Fergal Nolan is the founder of Banba, a specialist executive search firm for Healthcare AI and Life Sciences AI, with offices in New York, London and Berlin. He has spent more than 25 years in talent acquisition, including as a founding team member at SThree / Real Staffing and as founder of Upstream, where he has built Data Science & AI teams and AI leaders for start-ups, scale-ups and global enterprises.