Executive summary. Two long-running waves — the relational turn in engaged, responsible scholarship and the technological turn in generative and agentic AI — have converged in ways business schools did not anticipate. The relational turn was argued for societal relevance; in a research landscape where the empirical substrate is proprietary and trust-mediated, it turns out to be the essential access layer for the current technological turn, too. The strategic answer is a Signature Faculty Model: composing faculty around combinational careers, a portfolio of distinctive scholarly identities, and humancratic recognition — producing schools whose authenticity is structural, deepening and durable rather than a marketing claim.
Key takeaways
- Business research depends on proprietary, trust-based data. Schools that invested in engaged, collaborative scholarship now have the access needed for AI-driven research. What began as a push for relevance has become a strategic advantage, but the window to capitalise on it is narrowing.
- Few scholars can excel equally in both relationship-based and technology-intensive work. Yet many schools still expect them to. The Signature Faculty Model solves this by building complementary faculty strengths, rather than asking every scholar to do everything.
- The Signature Faculty Model reinforces a school’s distinctive mission and character. Over time, this becomes visible in the questions faculty pursue, the partners they attract, and the problems they solve, creating an identity that is more durable and valuable than rankings alone.
Why the relational turn has quietly prepared business schools for the AI era — and how a signature faculty model lets them lead it.
When One Turn Quietly Prepares You for Another
A new question is emerging in dean’s offices across the sector with growing regularity. A school’s best AI-centric researcher — a scholar whose work on generative-AI adoption in pedagogical practice and research is already in use in policy and practice — declines promotion. The reason is simple. The tenure criteria still reward solo-authored A-journal articles and treat the industry partnerships, the open-source AI governance toolkit and co-authored work with doctoral students as ‘service’. The faculty member does not want a promotion built on evaluating what they are no longer doing.
The dean’s honest strategic question: “Does our school actually want the work we are asking him to do?” The pattern recurs often enough across schools to deserve a structural reading.
Two long-running waves — once comfortably separate — have begun to reinforce each other in ways the sector did not anticipate. The first, the relational turn, has been argued over decades by accreditation leaders, including Eric Cornuel from the EFMD community, and by senior business and management scholars, including Anne Tsui, Andrew Hoffman and — most recently — SMU President Lily Kong: a gradual shift from Publish or Perish through post-2008 Publish and Perish toward a Scholar-to-Society paradigm of engaged, responsible, co-created research.
The relational turn has long been structural rather than decorative: EFMD has treated Connections with Practice as a transversal element of EQUIS for years, and its Business School Impact System (BSIS) has given schools a dedicated instrument for evidencing local and regional impact. The 2026 accreditation landscape now makes the commitment sector-wide — EQUIS Chapter 10 retains Connections with Practice as a thread through every other chapter, and AACSB’s renamed Standard 9 (Societal Impact and Engagement) anchors a new “Pathways to Impact” block — while UN PRME, national evaluation initiatives such as the Dutch Recognition & Rewards reform feeding into the reshaped national Strategic Evaluation Report SEP 2027–2033, and coalitions such as RRBM, DORA and CoARA complete the scaffold. The relational layer has become the operating logic for research governance and evaluation.
The second wave is the technological turn: human-AI collaboration – generative AI as default working partner by 2026 and agentic AI through the late 2020s. Core faculty themselves acknowledge the direction. In the Digital Education Council’s 2025 global faculty survey (1,681 respondents across 28 countries), two-thirds saw AI as an opportunity, with 86% expecting to use it in teaching within the coming five years. AACSB’s Lily Bi reports that 97% of accredited schools now address AI explicitly, and frames the pedagogical shift as moving the faculty contribution “from knowledge to judgment.” Thomas Hartung’s “co-pilot to lab-pilot transition” — AI no longer merely interpreting knowledge but acting on it — is the most visible edge of that shift.
What the latest Stanford AI Index 2026 makes unmistakable, however, is that the technological turn is not only about how scholars work, it is also about what they study. Organisational AI adoption has reached 88%; within three years of the creation of ChatGPT, generative AI has reached 53% population-level adoption; and over 90% of notable frontier models now come from private industry. The research object itself — firms, markets, organisations, workers, consumers — is being fundamentally transformed by AI systems whose development sits outside the research community. The subjects of our research are no longer simply using AI; they are being remade by it.
The asymmetry across disciplines is instructive. Within the STEM fields, biomedicine, chemistry and the physical sciences domain LLMs have been systematically purpose-built by the hundred. Within business and management, finance has developed some 15–25 purpose-built FinLLMs. Management, strategy, organisational behaviour, marketing, accounting and operations are still in a different place, lagging behind with purpose-built domain models. The reason is structural: the empirical substrate of business and management research — internal firm data, consulting and audit records, administrative HR data, transaction flows — is largely proprietary. It cannot be harvested for open pretraining at scale (including via the large financial-data platforms on which the FinLLMs stand), and it cannot be reached without trust relationships with the organisations that hold it. As firms start integrating proprietary data into commercial AI model weights, that substrate becomes simultaneously more valuable and less accessible through arms-length means.
This is where the two turns quietly converge. The relational turn was argued for the sake of societal relevance. In a research landscape where the empirical substrate is proprietary and accelerating into AI-transformed organisations, the same relational competencies — trust, co-design, longitudinal field partnerships, ecosystem convening — have become the essential access layer for the technological turn in business and management research itself. Without them, the technological turn in our field has no material substrate to operate on. The technological turn in business and management is not a substitute for the relational turn; it is its unexpected and valuable inheritor. That coincidence is a genuine strategic advantage — and the natural entry point to thinking about the faculty composition that can carry both forces together.
The Tightrope Most Deans Are Now Walking
The result in many schools is a now-familiar pattern — quiet attrition, mid-career stall, the best people steering around a rubric that no longer fits their intrinsic motivation or the work they are doing. The workload pressure is sharpening with it. Most scholars cannot carry both turns at full intensity. Yet most schools still expect everyone to perform like the proverbial five-legged sheep — a single creature asked to be everything at once. That is the tightrope.
The workload side of the tightrope is now being addressed at the sector level. Eric Cornuel and colleagues have placed mental health, wellbeing and collegiality on the EFMD agenda through the 2026 Global Focus Annual Research special issue. Elsewhere in the same volume, SMU President Lily Kong observes that an unreflective “Publish or Perish” culture has produced patterns of mistreatment, misconduct, and manipulation — over 10,000 papers retracted globally in 2023, coercive citation reported by one in five authors. Her direction of travel from “Publish or Perish” to “Purpose and People” is one articulation of the relational turn. Once faculty work is expected to contribute visibly to society and to relate to a research object that is itself AI-transforming, asking every scholar to embody every dimension at every stage is no longer a high aspiration. It is a design that quietly strains the people it depends on.
A Signature Faculty Model: Three Moves That Make it Work
The strategic answer to the tightrope is not to work every scholar harder. It is to compose the faculty differently. The Signature Faculty Model is based on three design principles: combinational careers at the individual level, a portfolio of signatures at the school level, and humancratic recognition at the evaluation level. Each move is within any dean’s agency; together they resolve the tightrope that no individual scholar can walk alone.
The first move is combinational careers. Within a single scholar’s working life, AI carries more of the codifiable layer, and the recovered time is reinvested in trust-based, co-designed, societally-engaged work. Frontier research on academic work puts the contrast plainly: administrative and literature-synthesis tasks carry high automation exposure; mentorship, ethical reasoning and contextual judgment retain strong human centrality — the three control points AACSB’s Lily Bi describes as framing the right questions, assuring the honesty of AI outputs, and governing which tasks stay human. The engaged AI-centric career is this reinvestment, consciously made.
The second move is a portfolio of signatures — signature career pathway thinking as a design language for difference. Rather than asking every scholar to fit one archetype, the question becomes: which combination of relational and AI-centric elements, competencies and expertise comes together in this scholar to form a distinctive, productive, genuinely held scholarly identity?
Consider three colleagues in the same faculty. Maria’s signature is ecosystem convening — a sustained partnership with a consortium of healthcare providers — amplified by AI-enabled stakeholder analytics; her output is policy-grade evidence that peer review alone could not generate. James’s signature is deep disciplinary authority in strategy, augmented by AI orchestration across large firm datasets he reaches through long-standing corporate relationships; his A-journal publications are still there, now enriched by fieldwork no competitor can replicate. Priya’s signature is the design of engaged doctoral formation and AI governance for the school’s research infrastructure — less visible in citation counts, indispensable to everything the other two produce. None of them is the legacy all-rounder; none of them needs to be. Together, they are a faculty that the school’s mission can actually deploy.
Each school, reading its own mission, people and unique regional/societal context, will discover its own portfolio. The OECD’s 2024 evidence review on academic careers points the same way, urging systems to “take into account the variety of activities of academics such as teaching, research, entrepreneurship, management or leadership.” The 2026 accreditation frameworks already provide an operational grammar: EQUIS Chapter 4 asks schools to justify composition “in accordance with the strategic objectives of the school” and anticipates “new forms of specialisation within the faculty;” AACSB Standard 3 recognises four faculty classifications — Scholarly Academic, Practice Academic, Scholarly Practitioner and Instructional Practitioner — each with its own qualification currency, and asks for “intentional alignment between faculty deployment and strategic priorities.” The legacy scholar who genuinely fits the all-rounder disposition keeps a valued place within the portfolio — as one signature among several. And because no signature career is self-sufficient, a strategically cultivated constellation of signature faculty becomes more than the sum of its parts: problems are framed more richly, team science triangulates across methods, the knowledge base becomes more robust, and the school absorbs shocks better than a soloist faculty ever could.
The third move is humancratic recognition — a gradual shift from counting soloist academic output to evaluating the distinctively human contribution. Essentially, this means that evaluation focuses on five key questions.
- Did this scholar choose research questions that mattered?
- Was the co-design with practice partners authentic and productive?
- How many doctoral and junior scholars were formed, and how well?
- Did their AI governance work actually change institutional practice?
- What ecosystem contributions did they make visible?
These are harder to count than citations and more truthful. The business case is on the same side: Deloitte’s 2026 Global Human Capital Trends reports that tech-focused approaches to AI are 1.6? more likely than human-centric ones to fall short on AI investment returns. Humancratic recognition is both responsible and quietly pragmatic.
There is a deeper strategic reason still. A school whose faculty composition is the result of deliberate, signature-based cultivation becomes, over time, a more authentic institution — not as a marketing claim (like triple-crown exclusivity) but in the structural sense that its scholarly identity is visible in the work its faculty produce, the partners they attract and the problems they choose. Authenticity of this kind is the emerging currency of strategic engagement. Corporate, policy, peer-institutional and societal partners increasingly choose their engagement and technology counterparts by the credibility of the faculty behind them, not by ranking position. A strong signature faculty base, therefore, makes a school more attractive to exactly the partners the combined model depends on — and makes its institutional strategic narrative more impactful in the long run.
Most scholars cannot do both turns at full intensity. Yet most schools still expect everyone to try. The Signature Faculty Model resolves the tightrope by composing the faculty differently — not by stretching each scholar harder.
Where to Start on Monday Morning
Adapting a faculty composition takes time. Recruitment, promotion, doctoral formation and recognition operate on multi-year cycles; the signature careers cultivated in 2026 will shape a school’s capacity in 2035. The first move, therefore, is not a policy document. It is simpler and harder: start mapping the faculty you have against the mission you claim. Ask a faculty council or a small working group to describe, person by person, the signature each colleague is already building — relational, technological, combinational, legacy. Compare that map with the school’s stated mission and its articulated ambitions for 2030 and 2035. Most schools will find a gap. That gap is where the strategy begins — not in a new framework, but in an honest conversation about what the school wants to become.
Five further questions can then guide the design.
- Composition: where are the signatures our mission most needs, and where are we thin?
- Signature careers: do we have legible promotion pathways for combinational identities, alongside a valued pathway for the legacy scholar?
- Recognition: Does our evaluation actually reward the five humancratic criteria, or does it still count with the old metrics?
- Infrastructure: Are AI literacy and engagement capacity budgeted, staffed and governed as institutional assets, or left to individual goodwill? (DEC 2025: 40% of faculty still self-identify as beginner in AI literacy; only 6% agree their institution provides sufficient resources.)
- Shared governance: Are we designing this with faculty — through elected councils and shared evaluation — or to them from the top?
Begin by mapping the faculty you have against the mission you claim. Most schools will find a gap. That gap is where the strategy begins.
An invitation — But Not a Soft One
None of this is a finished blueprint. The conversation has a natural home in the EFMD community, where the relational side of this argument has long been openly expressed — through EQUIS peer review, BSIS impact assessment and the sustained doctoral-programme dialogue — and where adding the technological thread to those same conversations is in many ways the most natural next step.
A companion instrument to this article, the Engaged AI-Centric Career Navigator — an interactive board built in Claude — supports that dialogue: it maps the two forces onto the four career stages (doctoral, tenure, professor, eminency) and lets dean’s teams, faculty career managers and individual scholars walk through how combinations of relational depth and AI-centric fluency assemble into distinctive scholarly identities. It is designed to help a school test which signatures already exist in its faculty, identify any gaps, and what humancratic recognition criteria would actually look like in its own mission voice.
The invitation is genuine, but it is not soft. The relational turn and the technological turn have not given business schools a choice between them; they have given the sector an integrative moment and a narrowing window of institutional runway. The faculty logic you start building in 2026 will define your school’s capacity in 2035 — the partners you can attract, the questions you can credibly answer, the research objects you can still reach when access runs through trust. It is my belief that the single most strategic asset a business school possesses in the AI era is the signature composition of its faculty. Everything else follows from that, or fails because of it.
The dean’s question at the start of this article deserves a better answer than a tenure rubric written for a previous era. The window is wide enough to lead with intention — and narrow enough to reward those who start now.
References
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Wilfred Mijnhardt is Policy Director General at Rotterdam School of Management (RSM), Erasmus University, and Honorary Professor at Edinburgh Business School (EBS), Heriot-Watt University. His work focuses on institutional development — supporting deans and faculty leadership through the strategic transitions that make business schools better, with particular attention to responsible research assessment, engaged AI-centric scholarship and faculty composition. He is the designer of the pilot Engaged AI-centric Career Navigator referenced in this article.
