Open Roles
EN
- English
- DEUTSCH
- SPANISH
- FRENCH
The h-index became academia’s shorthand for research quality. In our work at Perrett Laver, having carried out thousands of research-led appointments for universities and research institutions across the globe, we’ve watched the increase of metrics, with citation counts, journal impact factors, and bibliometric indicators becoming the default language for assessing research leaders.
But this simplicity comes at a cost. As AI tools now make metric-based assessment effortless, the human elements of research leadership assessment risk being automated away. Drawing on over a decade of experience of responsible metric use, we argue that expert judgment has never been more critical to identifying diverse, excellent research leadership.
The problems with metric-based assessment are well documented. A decade ago, the independent review titled “The Metric Tide” examined the role of metrics in research assessment and management in the UK, proposing a framework for responsible metrics. Around the same time, the San Francisco Declaration on Research Assessment (DORA) was published, challenging the use of journal-based metrics in evaluating individual researchers. Now signed by over 2,200 organisations globally, these initiatives have made the case clearly: metrics ignore disciplinary differences in citation cultures, penalise career breaks, overlook contributions beyond publications, and favour established research ecosystems over emerging ones.
Yet here we are, a decade later, still making the same arguments. Despite widespread acknowledgement of these problems, metric-based shortcuts remain commonplace in research assessment. And now, with AI tools making these shortcuts easier than ever, we risk not just continuing these practices but turbocharging them.
We see these blind spots daily: researchers doing exceptional work in fields with modest citation rates, academics who took career breaks, those whose greatest contribution was building research groups rather than their own publications, or scientists working in emerging fields. Perhaps most troublingly, over-reliance on metrics drives perverse behaviours, from publishing for numbers rather than impact to neglecting teaching, mentoring, and wider institutional service.
We were early adopters of citation databases. Over a decade ago, we partnered with a global data analytics firm to use them as research tools long before “find a researcher” functions existed, pioneering their use for discovering academics rather than simply assessing research outputs. But we’ve always used metrics to discover talent, never to exclude it.
This distinction matters. We use citation analysis to scan the globe for excellence in unexpected places and geographies, helping us identify mid-career researchers at teaching-focused universities doing exceptional work, or scientists in smaller research ecosystems punching above their weight. Responsible research assessment emphasises the importance of recognising research quality across diverse contexts, not just at elite institutions. Metrics help us shine a light on overperformance, not just established prestige.
But they’re starting points, not endpoints. We always dive deeper.
Our role includes education, helping clients understand what metrics can and cannot tell them, and why threshold-based exclusions risk missing exactly the kind of innovative, diverse leadership they want.
Yet we’re acutely aware of the contradiction inherent in our approach. We use the very tools whose limitations we acknowledge. This is why we’ve built rigorous processes to actively combat the biases these metrics can introduce. We work deliberately to ensure that our searches surface candidates from diverse backgrounds, career paths, and institutional contexts. We scrutinise our own shortlists for demographic and geographic diversity. We challenge ourselves when patterns emerge that might reflect systemic bias rather than genuine quality differences. Using metrics responsibly means constantly questioning whether they’re expanding or narrowing our view of excellence.
Now we’re entering a new phase. Citation databases have caught up, offering sophisticated researcher discovery tools. AI can scan global research landscapes at unprecedented scale. If metric-based shortcuts were tempting a decade ago, AI makes them effortless.
This is precisely when the human element becomes most critical. AI and metrics still cannot capture cultural fit, leadership potential, communication skills, ethical judgment, or capacity for strategic thinking beyond a research domain. They cannot assess the researcher who contributes through exceptional mentoring, or the one whose interdisciplinary vision doesn’t fit neatly into citation patterns. They cannot weigh the context of someone’s career trajectory or recognise forms of excellence that don’t generate easily quantifiable outputs.
The case for dedicated expert assessment has never been stronger. It requires time to understand institutional needs deeply, expertise to interpret research quality within disciplinary context, and judgment to weigh diverse forms of excellence. It means resisting the temptation to reduce complex academic careers to numbers, especially when the technology makes this trivially easy.
The goal isn’t to reject metrics or AI. These tools have genuine value in expanding our reach and identifying talent we might otherwise miss. But they must serve human judgment, not replace it.
As research assessment evolves, those of us identifying research leaders have a responsibility to lead by example. That means using available tools whilst maintaining rigorous human oversight. It means investing the time for deep assessment rather than taking algorithmic shortcuts. And it means championing the principle that diverse, excellent research leadership cannot be reduced to a number.
Jack is Partner & Sector Lead, AI, Data & Technology.
His experience of senior level executive search includes multiple Chair and Professorial appointments globally across all academic disciplines, as well as leadership appointments in higher education and industry research and development.
Jack is expert in the use of metrics and analytics for the identification and assessment of world leading academics.
Jack holds a BSc in Physiology from the University of Liverpool, an MSc from the University of Leeds in Ion Channels in Disease and a PhD from Kings College London in Cell Signalling. His research focused on the role of ion channels in hormone release.
