Yesterday, 7 June 2026, the deadline to transpose the EU Pay Transparency Directive (Directive (EU) 2023/970) into national law passed. Most of the coverage this week treats it as a compliance event: new reporting obligations, salary ranges in job adverts, a ban on asking candidates what they currently earn. All true. But for organisations competing for the scarcest leaders in healthcare and life sciences — the people who can stand up a regulated clinical-AI function or lead an AI-driven drug-discovery programme — the directive is something more interesting than a compliance headache. It quietly changes the economics of how those people are hired, and who wins them.

The conventional wisdom is that transparency helps the underpaid and constrains employers. The evidence is more subtle, and for senior technical hiring it points somewhere counter-intuitive: pay transparency tends to compress exactly the kind of large, individually negotiated packages that scarce AI leaders have learned to command. The advantage is shifting — away from whoever can quietly outbid the rest, and toward whoever can make a credible, defensible, mission-driven offer in the open.

Key takeaways

• The directive’s transposition deadline passed on 7 June 2026 with only Slovakia and Italy having fully transposed. The rest of the EU is a patchwork, but the direction — mandatory pay ranges, a salary-history ban, uncapped remedies — is now settled and applies to employers of every size.
• There is no executive carve-out. Recital 18 explicitly covers management, so the senior AI and data-science roles you most need to fill are squarely in scope — equity-heavy packages included.
• The law exists because Europe’s gender pay gap has barely moved — 11.1% overall, but 27.1% among managers — and the evidence shows it is now driven by who holds the senior roles, not by paying men and women different rates for the same job.
• Peer-reviewed economics suggests transparency reduces average wages by around 2% by weakening individual bargaining power — an effect concentrated precisely among high-leverage hires like senior AI leaders — even as salary-history bans raise pay for previously underpaid groups.
• The firms that win scarce AI leadership in healthcare and life sciences will be those that build defensible, gender-neutral pay structures now and treat a published range as an employer-brand asset, not a disclosure they resent.

Pay transparency tends to compress exactly the kind of large, individually negotiated packages that scarce AI leaders have learned to command.

Why this law exists

It is worth being clear about what the directive is for, because it explains why it bites the way it does. At heart this is an equal-pay instrument. The principle of equal pay for equal work has been written into European law since the Treaty of Rome and now sits in Article 157 of the Treaty on the Functioning of the EU. The problem is that the principle has never fully translated into practice: women in the EU still earned, on average, 11.1% less per hour than men in 2024 (Eurostat), a figure that has narrowed only marginally in a decade. The European Commission’s own assessment is blunt — the largest part of that gap cannot be explained by measurable differences between workers, and it is not expected to close on its own. Transparency is the chosen intervention.

What makes this directly relevant to senior hiring is where the gap concentrates. Among managers, the EU pay gap was 27.1% in 2022 — more than double the economy-wide figure. The higher up an organisation you look, the wider it gets. And the most useful recent evidence on European technology pay shows why: the compensation-analytics platform Ravio found that the raw, unadjusted gender pay gap across European tech is around 25%, but once you compare like with like: the same role, the same level, the same country, it collapses to roughly 2.5%. In other words, the problem is overwhelmingly about who holds the senior, higher-paid roles, and what they were anchored to when they were hired, rather than about paying men and women different rates for identical work. That distinction runs through everything that follows.

It also collides with a representation problem that is sharpest in exactly the roles this article is about. Women are still only around a fifth of the EU’s ICT specialists (19.5%, Eurostat). Narrow the lens to AI specifically and the most current data; LinkedIn’s Economic Graph, reported through the World Economic Forum, puts women at 29.4% of those listing AI-engineering skills in 2025, up from 23.5% in 2018, but still under a third. The scarcity compounds with seniority: an analysis of some 1.6 million AI professionals (the think tank interface, drawing on Revelio Labs data) found women hold around 22% of AI roles globally and under 14% of senior AI executive positions. The talent pool the directive’s senior-hiring rules govern is both small and heavily male before pay is even discussed.

What actually changed

At the centre of the directive are three obligations that touch hiring directly. First, applicants are now entitled to know the initial pay or pay range for a role before they ever negotiate, typically in the job advert or before the first interview. Second, employers may no longer ask candidates about their current or historical pay. Third, and this is the part senior hiring teams routinely underestimate, “pay” is defined broadly enough to capture bonuses, equity-like instruments, allowances and occupational pensions, not just base salary. For AI leadership offers, where the headline base is often the smallest part of the package, that breadth matters enormously.

Two features make this bite at the top of the org chart. The directive contains no seniority exemption: Recital 18 expressly includes workers in management positions, and the recruitment rules apply to employers of any size, public or private. And where an employer has failed to meet its transparency obligations, the burden of proof shifts — it falls to the employer to show that any pay difference was not discriminatory. The combination means improvised, undocumented executive pay is no longer just a fairness question; it is a litigation exposure, backed by remedies the directive deliberately leaves uncapped.

The legal map is, for now, fragmented. As the deadline passed, only Slovakia and Italy had fully transposed; major economies including Germany, France, Spain and Ireland were late, and the European Commission has confirmed it will not “stop the clock” and is prepared to open infringement proceedings against the states, reportedly at least ten, that miss it. For a multinational hiring across several EU countries, that patchwork is itself the problem: the obligations are arriving on different dates and, in several places, in stricter-than-minimum forms.

And the practical distance to travel is large. Across Europe, salary information is still the exception rather than the rule in hiring: only about 12% of German job postings disclose pay today, against 56% in the UK and 48% in the Netherlands (Indeed Hiring Lab). For most employers, in other words, this is a genuine change of practice, not a ratification of what they already do.

The counter-intuitive part: transparency can shrink the premium

Here is where senior AI hiring diverges from the general narrative. The most rigorous study of pay transparency’s market effects; Zoë Cullen of Harvard and Bobak Pakzad-Hurson, published in Econometrica in 2023  , found that transparency laws lead average wages to fall by roughly 2%. The mechanism is the important bit: once pay is visible, employers credibly refuse to pay any single individual far above their peers, because doing so triggers costly renegotiation with everyone else. Crucially, that wage-suppressing effect is smallest where workers have little individual bargaining power — which means it is largest exactly where bargaining power is greatest. Senior AI leaders, with multiple competing offers and genuine scarcity value, sit at the high-leverage end of that spectrum. The bespoke, opaque mega-offer is precisely the thing equilibrium transparency erodes.

The picture isn’t one-directional. Salary-history bans, a core plank of the directive, work the other way for those who have historically been underpaid. Research by James Bessen and colleagues at Boston University, published in the Journal of Economic Inequality, found that after such bans employers advertised pay more often and raised offers for job-changers, with the largest gains going to women and to non-white candidates. Both findings can be true at once, and together they describe the new reality: transparency narrows gaps and lifts the floor, while compressing the negotiated premiums at the ceiling. For the kind of leaders we are discussing, it is the ceiling that moves.

This evidence comes from the United States rather than the EU, which is part of why it is useful. American states have lived with pay-transparency and salary-history rules for several years, so they are the closest thing we have to a preview of where Europe is now heading; the mechanisms are general, even where the jurisdictions differ. Read it as a strong directional signal rather than a precise forecast. But the direction is consistent and well replicated, and it is the opposite of what most boards assume transparency will do to their senior compensation.

It matters, too, that the European starting point is far lower than the US figures that dominate the headlines. The median machine-learning engineer in Germany earns around €83,000 in total compensation, against roughly $270,000 in the United States (Levels.fyi), before the frontier-lab packages that sit far above even that. Transparency operates on top of an already-compressed European base, which is part of why its effect on the bespoke premium is felt so directly here.

Why this lands hardest in healthcare and life sciences

Two things make these dynamics acute in the sectors we work in. The first is simple scarcity, and in Europe it is documented in the official statistics, not borrowed from the US. The EU had an estimated 10.3 million ICT specialists in 2024, against a Digital Decade target of 20 million by 2030: on the current trajectory, a shortfall approaching ten million (Eurostat). Of the EU enterprises that tried to recruit ICT specialists, 57.5% could not fill the roles. Cedefop, the EU’s skills agency, ranks ICT professionals as the single most shortage-prone occupation in Europe, followed, tellingly, by medical doctors and nurses. Healthcare and life sciences sit at the intersection of two of the continent’s deepest talent shortages at once. The World Economic Forum’s Future of Jobs Report 2025 tells the same story from the demand side: AI and machine-learning specialists are among the fastest-growing roles globally, and skills gaps are the single biggest barrier employers cite to transformation.

The second is that healthcare and life sciences each add their own pressure, in different ways.

In healthcare: health systems, payers, digital health, clinical-decision support, medical imaging, the binding constraint is regulation. Under the EU AI Act, AI that functions as, or within, a medical device is automatically classified as “high-risk,” bringing obligations around risk management, data governance and human oversight. That regulatory weight has created demand for a specific and rare kind of leader: one who can deploy clinical AI and govern it to a standard that regulators and clinicians will accept. The scale of the gap is visible in the numbers: a 2026 WHO/Europe survey found that 74% of EU member states already use AI-assisted diagnostics, yet only 8% have a national health-AI strategy. The technology is already in the building; the leadership to run it safely is what is missing. US health-tech firms expanding into Europe and EU health systems appointing their first Chief AI Officers are now competing for the same small pool of people who can do both.

In life sciences: pharma, biotech, drug discovery, computational biology, the constraint is competition. The organisations that need AI/ML drug-discovery leaders and principal computational biologists are bidding for them against Google DeepMind, the large technology platforms and quantitative finance. The signals are everywhere: Alphabet’s Isomorphic Labs has signed major discovery alliances with Eli Lilly and Novartis, and Lilly appointed its first-ever Chief AI Officer, the computational-pathology specialist Thomas Fuchs, in late 2025. This contest is intensifying across Europe’s life-sciences hubs, the UK’s “golden triangle” of London, Oxford and Cambridge, the biotech clusters of the Netherlands, Switzerland and Germany, and a fast-growing AI-bio scene in Paris, and one UK industry projection puts the sector’s skilled-talent need at around 145,000 people over the coming decade once new and replacement roles are counted. These employers cannot always win on cash, the packages at frontier AI labs are in a different universe, with Meta reported to have assembled AI compensation worth hundreds of millions over several years and OpenAI’s stock-based pay averaging in the seven figures per employee in 2025. They have to win on mission, science and the credibility of the offer. Transparency makes that even more true, because it removes the option of quietly closing the gap with an oversized, invisible number.

There is a gender dimension running underneath all of this that is easy to miss, and worth stating honestly, including where the data runs out. One might expect to be able to put a precise number on the gender gap within this specific talent pool: AI roles, in healthcare and life sciences, in Europe. One can’t. No credible source measures it cleanly, because official pay data is cut either by sector or by occupation, rarely both, and “AI” is not yet a recognised statistical occupation. That absence is itself revealing, the measurement gap mirrors the talent gap. What the evidence does show is directional and consistent. Women are markedly under-represented in computational biology and bioinformatics; typically 20–30% of researchers in the published literature. And there is a particular irony in healthcare: women make up close to 70% of the health and care workforce worldwide but only around a quarter of its senior leadership (WHO), so the very sector where women are most numerous is one where they are scarcest at the technical and executive level that AI leadership occupies. We are hiring into a pool we cannot yet fully measure, which is all the more reason to set pay openly and defensibly.

I have watched this dynamic up close. Across more than two decades placing data and AI leaders, I have seen how unforgiving these searches can be: handled without specialist support, they routinely run six months or longer, and prising the right person out of a major technology platform or a quantitative fund has typically meant a premium of forty to sixty per cent on their existing package. The directive does not change that scarcity. What it changes is that the premium, once paid, is now visible to the rest of the organisation, which is precisely why it has to be defensible before it is offered.

These two arenas are distinct, a point worth holding onto. The AI Act’s research exemptions mean most internal drug-discovery AI is not high-risk, so the regulatory burden that shapes clinical-AI hiring does not fall the same way on life-sciences R&D. The talent can look similar from a distance; the hiring context is not.

The advantage flips: from secret offers to credible ones

If you cannot win by outbidding in secret, how do you win? The answer the evidence points to is uncomfortable for organisations used to competing on money alone, and encouraging for those with a genuine story to tell.

Start with the fact that transparency, done well, is an attraction tool rather than a cost. A 2025 survey across six European countries found that a majority of jobseekers everywhere, from 61% in Germany to 82% in Ireland, are more likely to apply for a role when the advert shows a salary range, and that women agreed more strongly than men, which speaks directly to the directive’s purpose (Indeed Hiring Lab). The pattern holds outside Europe too, with LinkedIn’s research putting the equivalent US figure at 91%. A published range, set with conviction, expands and pre-qualifies the field of senior candidates rather than shrinking it. The organisations that treat the range as something to be dragged out of them will read, to a discerning candidate, as exactly that.

Then there is the part of the offer that transparency cannot commoditise. When the number is visible to everyone, the differentiators become the things that were always the real reasons senior people move: the significance of the problem, the quality of the team, the scientific or clinical autonomy, the conviction behind the mission. For a Chief AI Officer choosing between a health system, a biotech and a technology platform that can simply pay more, those are the levers that decide it. Transparency does not weaken that case, it removes the distraction of the bidding war and forces the case to the front.

What hiring leaders should do now

Three moves are worth making regardless of where your specific EU jurisdictions stand on transposition.

First, stop asking candidates about pay history across all EU hiring today, and remove pay-secrecy clauses from contracts. This is a core obligation, it is good practice ahead of national law, and in Germany the courts are already moving in this direction: in October 2025 the Federal Labour Court ruled that a single higher-paid opposite-sex comparator can be enough to presume pay discrimination and shift the burden onto the employer. Median-only benchmarking is no longer a defence there.

Second, build a defensible, gender-neutral pay range for every senior AI and data-science role  expressed in total-compensation terms, because that is how the directive defines pay and document the reasoning while you set it, not afterwards. Germany is the cautionary case worth watching most closely. It will miss the deadline, but the law it is preparing is strict: the reporting threshold is expected to drop from organisations of 500 employees to 100, and the right to pay information is set to become near-universal. Late does not mean lenient.

Third, treat the published range as part of your employer brand, not a concession. The organisations that will struggle are those bolting transparency onto an unexamined pay structure under deadline pressure. The ones that will pull ahead are those that have already done the work to know what each role is worth, why, and how they can defend it and can therefore compete in the open with confidence.

The pay transparency era rewards a particular kind of employer: one that knows what its work is worth, can justify it, and has a reason for the right person to choose it that does not depend on a number nobody else can see. For healthcare and life-sciences organisations trying to hire the rare leaders who will decide whether their AI ambitions succeed, that is not a constraint. It is, if you choose to see it that way, a problem worth solving well.

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.