Harnessing AI for innovative research

Why AI is a defining development for research leadership

In October 2024, the Nobel Committee awarded the Prize in Chemistry to Demis Hassabis, John Jumper and David Bakeropens in new tab/window for using artificial intelligence to solve protein structure prediction — a problem that had defeated generations of researchers for five decades. It was not the beginning of AI in research, but it signaled what becomes possible when AI is applied to scientific challenges with the right data, talent and institutional conditions. 

That moment carries both inspiration and weight for those leading research at their institutions. Academic research leaders want to ensure their universities are prepared to meet intensifying expectations to deliver results faster and with greater impact. At the same time, there are growing questions about what AI offers — and the risks that come with it. Leaders are working in challenging circumstances: the opportunity to accelerate innovation must be carefully balanced against the need for responsible, ethical and safe practice. 

Adding to the pressure are region-specific challenges that look markedly different around the world. In the US, public confidence in higher education ticked up in 2025 for the first time in a decade, but at 42% it remains well below the 57% Gallup recorded in 2015 — and only modestly above the 36% historic low of 2023 and 2024 (Gallup, 2025opens in new tab/window). The research enterprise is not isolated from these broader trends. In a world where AI can produce expert-sounding content on demand, the trustworthiness of research itself is increasingly in question. Research leaders who lead effectively on AI will not only accelerate their institution’s research outputs; they will help restore the credibility of scholarship at a moment when that public trust urgently needs strengthening. 

Elsewhere, those pressures take different shapes: 

• In Japan, which ranks among the world’s fastest-aging societies, with its population projected to fall by nearly 40 million over the next 40 years (Gigante & Gest, 2025opens in new tab/window), AI is framed less as a way to cut costs than as a way to augment a shrinking research workforce. The country’s 2025 AI Promotion Act deliberately favors light-touch, guidance-based regulation to keep innovation moving (World Economic Forum, 2026opens in new tab/window). That policy stance is now backed by substantial investment and international reach: Japan’s draft fiscal 2026 budget allocates ¥502.7 billion (~ US$3.2 billion) to AI, with nearly 90% devoted to strengthening the country’s AI development capabilities (White & Case, 2026). The country's global AI ambitions are also evident in its research partnerships: in June 2026, Japan became the first international partner in the US Genesis Mission — a $1 billion AI-for-science partnership, funded equally by both governments over five years, to accelerate AI-driven scientific discovery (US Department of Energy, 2026). 

• In China, AI is viewed as a strategic technology central to  national importance and competitiveness, prompting institutions to rapidly expand AI education and research. By 2026 more than 600 universities had launched AI  degree programs (Ministry of Education, 2026opens in new tab/window), part of a broader national effort to address a projected shortfall of up to four million AI professionals by 2030. For China, the story is one of rapid capacity building rather than declining public trust (McKinsey, 2023opens in new tab/window). 

• In Singapore, more than S$1 billion (~ US$750 million) has been committed to AI research and talent through 2030, yet research leaders there must navigate intense global competition for the very people they train, as well-funded technology firms aggressively recruit talent from their campuses (MDDI, 2026opens in new tab/window). 

• In Europe, the pressure is largely regulatory: under the EU AI Act, the world’s first comprehensive AI law, university systems used for tasks such as admissions or grading can be classified as “high-risk,” carrying obligations for risk management, documentation and transparency. For research leaders, this is a challenge to navigate rather than a barrier — part of a push toward responsible, trustworthy AI — even as Horizon Europe and the AI Continent initiative channel funding into compliant research (European Commission, 2024opens in new tab/window). 

• In Latin America, ambition is outpacing capacity: Brazil’s 2024–2028 national AI plan commits roughly R$23 billion (~ US$4.3 billion) and a flagship supercomputer, yet the region attracts only about 1% of global AI investment despite accounting for 6.6% of global GDP, leaving leaders to close persistent gaps in computing, funding and advanced talent (ILIA, 2025opens in new tab/window). 

Each country faces its own version of this pressure — demographic, competitive, regulatory, infrastructural or reputational — but all share the same defining moment: a narrow window in which research leaders must define how AI is used and shape the opportunities it opens for their researchers. 

This piece is for those leaders. It is not a primer on AI technology but a framework for thinking through as you weigh the opportunities and challenges that define this moment.

What AI makes possible in academic research

AI breakthroughs accelerating research today

Across disciplines, AI is compressing the time between question and discovery in ways that would have seemed implausible only a decade ago. There are already numerous examples of AI accelerating discovery, such as: 

• Researchers at Stanford used an AI “co-scientist” to identify existing drugs that could be repurposed to treat liver fibrosis — one candidate, Vorinostat, blocked 91% of the chromatin changes that drive scarring (Guan et al., 2025opens in new tab/window).  

• In Japan, Tokyo-based Sakana AI built an autonomous “AI Scientist” that generates hypotheses, runs experiments and drafts full manuscripts end-to-end. One of the system’s AI-generated papers cleared the first round of peer review at a 2025 ICLR workshop, placing it roughly in the top 45% of submissions (Lu et al., 2026opens in new tab/window). 

• A machine-learning model trained on 19,000+ battery cathode experiments across 14 metal-containing species is now helping researchers rapidly identify candidate materials for next-generation batteries (Zhong et al., 2024opens in new tab/window).  

• AI models are helping researchers uncover causes of Alzheimer’s disease that were only visible because AI could predict three-dimensional protein structures with the necessary precision — in one 2025 UC San Diego study, modern AI revealed a previously unknown function of the PHGDH protein that points to a new therapeutic target (Chen et al., 2025opens in new tab/window). 

• Climate scientists are now able to forecast weather with a level of precision that was previously out of reach.  Google DeepMind’s GraphCast produces 10-day global weather forecasts in under a minute and beats the ECMWF’s industry-standard model on roughly 90% of metrics (Lam et al., 2023opens in new tab/window).

How researchers are using AI day-to-day

Examples like these are easy to point to, but they are only the visible tip of a much broader shift. Elsevier’s 2025 Researcher of the Future report, based on responses from 3,200+ researchers across 113 countries, found that AI use among researchers jumped from 37% to 58% in a single year. How researchers are using AI is just as revealing: 

• 61% report using it to find and summarize the latest research. 

• 51% use it for literature reviews. 

• 41% use it to draft grant proposals. 

• 38% use it to analyze data and draft papers.  

AI has moved past the experimental stage — it is now embedded in the workflow of many researchers, often invisibly.

How AI is expanding access to research

AI is also making research more accessible in a deeper sense. Translation capabilities are opening non-English research to researchers globally, addressing the long-standing dominance of English in scholarly publishing. For example, a researcher in São Paulo whose native language is Portuguese can now engage substantively with a Japanese-language methods paper — and, more importantly, identify relevant insights from research published in virtually any language at scale, far faster than traditional translation workflows ever allowed. Researchers at less-resourced institutions are gaining access to analytical capabilities previously available only to well-funded labs. And AI’s ability to surface unexpected connections across fields broadens the scope of what interdisciplinary research can achieve.

What does this mean for research leaders?

For research leaders, the question is no longer whether these capabilities matter for their institution — they already do, in labs and at desks across campus. The question is whether the institutional conditions are in place to turn that a grassroots experimentation into competitive advantage: the data infrastructure researchers can use, the training they need to use it well and the governance that lets people move quickly while managing risks.

The AI adoption-enablement gap in higher education

Despite rapid adoption, researchers reported feeling unsupported. That gap may signal a pressing focus area for research leadership. 

27% of researchers say they have adequate training to use AI effectively in their work. Elsevier, Researcher of the Future, 2025

22% of researchers currently believe AI tools are trustworthy enough to rely on. Elsevier, Researcher of the Future, 2025

Researchers are not waiting for permission — they are already using AI tools. The question is whether they are doing so with adequate guidance, appropriate tools and institutional support. EDUCAUSE’s 2025 AI Landscape Studyopens in new tab/window suggests the majority are not: The survey found that 80% of faculty and staff use AI tools, yet fewer than one in four are aware of a formal institutional policy. More than half of higher education institutions report having no institution-wide AI strategy. When researchers were asked directly what would increase their confidence in AI tools, the answer was striking in its clarity (Elsevier, Researcher of the Future, 2025): 

• Transparency and clear citations (59%) 

• Recency of data and up-to-date literature (55%) 

• Training on high-quality, peer-reviewed content (55%) 

• Regular human validation of AI outputs (49%)

None of these can be achieved by researchers alone. Academic and research leadership can directly influence each of them through procurement decisions, policy frameworks and the culture they cultivate.

Six AI challenges shaping research leadership

Here are six challenges likely to shape your AI leadership agenda over the next three years. 

1. AI hallucinations and research integrity

As is well-known, AI tools can generate plausible-looking but entirely fabricated citations, data and analyses. In late 2025, an analysis by the AI detection company GPTZero of papers accepted at NeurIPS, an AI research conference, identified at least 100 AI-fabricated citations across 53 published papers, roughly 1% of all papers accepted that year (Ansari, 2026opens in new tab/window). A companion analysis found similar contamination in ICLR 2026 submissions. The problem extends well beyond AI conferences: a Lancet audit of 2.5 million biomedical papers found fabricated references rose roughly 12-fold between 2023 and early 2026, with the steepest increase following the wider use of AI writing tools in mid-2024 (Topaz et al., 2026opens in new tab/window).  

As Elsevier has examined in its own work on AI tooling, hallucinations take more than one form — from outputs ungrounded in the underlying sources to outputs that are well-grounded but factually incorrect — and the practical response is to ground AI in trusted, peer-reviewed content and keep human verification in the loop at every stage ( see both Preventing AI hallucinations in our research and health tools and EAI in Higher Education — A Question of Trust ).  

The risk is not theoretical, and it is not confined to fringe cases. Research leadership can work with research integrity offices, journal editors and faculty to establish disclosure standards, verification requirements and meaningful consequences for misuse. Underpinning all of this is a more foundational choice: which AI tools the institution sanctions for research, and whether those tools are built on trusted, peer-reviewed sources in the first place. Policies on disclosure and verification cannot do their work if the underlying content is unreliable. 

2. AI, public trust and the value of expertise

Beyond the internal integrity challenge lies a broader public trust problem. As we saw earlier, public confidence in higher education remains well below where it sat a decade ago — and the trust problem is no longer just about how universities are perceived; it is also about whether the public still believes in the value of human expertise. The 2026 Yale Committee on Trust in Higher Educationopens in new tab/window found that “the rapid technological changes of our moment are contributing to declining public trust in the very idea of human expertise.” When AI can produce expert-sounding outputs on demand, the public increasingly asks: what is the value of a human expert? In academic publishing and research more broadly, trust is the currency of progressopens in new tab/window — what allows ideas to travel, funders to commit and scholarship to accumulate. For research leadership, responsible and transparent AI governance in research is not just an operational question; it is a question of whether the public, funders and the scientific community will continue to trust the research your institution produces. 

3. Research funding pressure and AI infrastructure

The funding context differs sharply by region (see table below), but the direction is strikingly similar: established research economies face acute pressure while China and India are scaling AI investment as a central policy priority. Research leaders are being asked to lead on AI strategy at the same moment they are managing real uncertainty about whether their labs will be funded next year — in nearly every major research economy. Explore examples of how research leaders around the world are navigating AI.

Although institutions are contending with these external pressures, some of the sharpest challenges are internal — starting with making the case for AI investment when core budgets are under pressure. That case rarely rests on the promise of transformation alone. With 58% of researchers already using AI in their work, the practical choice facing most institutions is between supported, governed adoption and ungoverned use. A phased approach, starting with trusted tools and training before larger infrastructure commitments, can help keep the investment conversation grounded in demonstrated demand rather than speculation. Framed well, the case is as much about reallocation as it is about new money: according to BCG in their report, How AI Can Help Higher Education Capture a Once-in-a-Generation Opportunityopens in new tab/window, a well-prioritized AI roadmap can shift millions toward mission-critical priorities, research and innovation among them (BCG, 2026opens in new tab/window).

4. Faculty resistance to AI in research

Not all researchers are enthusiastic AI adopters — and some have principled, well-founded concerns worth taking seriously. Adoption is uneven across disciplines, seniority levels and levels of resource access. Mandates without culture rarely work in research settings. Research leaders can help by building an environment that respects legitimate skepticism while still enabling the institution to move forward purposefully. 

Some of that resistance is better understood as a capability gap: only 27% of researchers say they have adequate AI training (Elsevier, Researcher of the Future, 2025), and skepticism often softens when structured training, protected time to experiment and peer examples are available (Times Higher Education, Boosting AI Literacy Across Your Institution, 2026opens in new tab/window). Research leaders need not build that capability alone — academic libraries are emerging as institutional anchors for AI literacy education, drawing on strategic thinking, technical experimentation and principles of knowledge integrity to build capacity across the institution ( see Elsevier, Rising to the Challenge: Library Leaders on AI Literacy Education).

5. AI governance in higher education

Movement is happening, but slowly. Institutions with AI acceptable use policies rose from 23% in 2024 to 39% in 2025 (EDUCAUSE, 2025 AI Landscape Studyopens in new tab/window). Yet Inside Higher Education reports that only 14% of US provosts say their institution has adopted a comprehensive AI strategy or governance framework; one in five report an intentionally hands-off approach, and the largest share are still developing policy (Inside Higher Ed, 2025 Survey of Chief Academic Officers, n=478opens in new tab/window). The challenge is that governance frameworks risk being obsolete by the time they are ratified. Adaptability and continuous review are requirements, not aspirations.

6. AI and the equity gap in higher education

AI models trained on historical data can perpetuate existing biases — in the research questions that are surfaced, which datasets are privileged and which languages and geographies are represented. In “Can colleges be run using AIopens in new tab/window?,” The Chronicle of Higher Education has called the resulting institutional gap “Digital Divide 2.0:” well-resourced institutions with the infrastructure and talent to leverage AI accelerate further, while those without fall further behind (Chronicle of Higher Education, 2025). The sector recognizes the risk — in EDUCAUSE’s 2025 AI Landscape Study (subtitled Into the Digital AI Divideopens in new tab/window), 83% of respondents said they were concerned about a widening digital equity divide. 

This is not only a US concern. Across the Pacific Rim, the Association of Pacific Rim Universities (APRU)opens in new tab/window has brought member institutions from very different economies together, cautioning that “access remains a critical equity issue — without deliberate intervention, AI risks widening existing digital divides,” and arguing that governance should move beyond restriction to enablement, giving researchers clear guidance while actively encouraging experimentation (APRU, 2025opens in new tab/window).  

Its conclusion is that “individual institutional responses are insufficient for the scale of change required:” APRU points instead to collaborative clusters — universities moving “beyond competition to cooperation in key areas including joint development of generative AI applications and pedagogical approaches, shared frameworks for assessment redesign, coordinated advocacy for equitable access, combined faculty development initiatives and unified governance frameworks that respect local contexts.” 

A question for research leaders to consider is whether their AI strategy narrows or widens the equity gap within and beyond their institution.

Five AI leadership actions you can take now

The good news is that the most important moves are ones research leaders are already positioned to make. These five actions are accessible to every institution, whatever its size, funding picture or technical starting point and the leaders making them now are shaping where their institutions will sit five years from today. For most institutions, the question is no longer whether to adopt AI, or even whether researchers are using it. Enterprise-wide rollouts of tools like ChatGPT and Gemini are already underway, often ahead of any research-specific strategy or governance. The leadership task has shifted accordingly: from granting permission to giving fast-moving, scattered adoption a coherent, research-grade shape. In “How AI can help universities capture opportunityopens in new tab/window,” the Boston Consulting Group (BCG) makes the same point for higher education broadly: value comes not from isolated pilots but from end-to-end transformation of core functions, with research and innovation being one of five domains where AI is already delivering gains (BCG, 2026opens in new tab/window). The five actions below work best when read as one connected strategy, not a menu.  

1. Start by listening. Some of the most useful AI insights at any institution are already in faculty and researcher hands. A quick survey of which tools researchers are using, for what and what they want from you turns that into shared knowledge. Governance built on that baseline tends to land better because it reflects the work people are actually doing. It also tells researchers what most of them want to hear: that policy is being shaped with them. 

Idea: Start with a five-question pulse survey — which tools are you using, for what, what do you want the institution to provide, what holds you back and what would earn your trust — and publish the results back to researchers.

2. Build disclosure and verification standards that fit research. The norms researchers need for AI in their work may be different from those that fit teaching and learning, and they may vary across disciplines — which means the strongest frameworks might come from collaboration, not a single institutional template. Research integrity offices, discipline leaders, chief technology officers, publishers and funders are all useful partners; many are already building pieces of this. The goal is not to restrict AI use but to make it verifiable and properly attributed without limiting legitimate innovation, which is also what most researchers are looking for. 

Idea: Convene a small group; research integrity, a few discipline leads, a librarian and IT. Then agree on a one-page baseline: what to disclose when AI is used, what human verification is expected and where disciplines differ; distribute version one in weeks and revise it openly.

3. Invest in trusted tools and curated content. One of the most consequential AI procurement decisions a research leader can make is which underlying data to trust. Researchers themselves are clear about what they want: AI tools trained on high-quality, peer-reviewed content, with transparent citations and up-to-date literature. Treating data provenance and quality as a primary criterion in procurement is one of the strongest investments in long-term research quality. For why source quality is the decisive factor when evaluating AI for research, see Elsevier's “Why trusted content matters in AI research.” 

Idea: Ask four questions of every AI procurement: Is it built on peer-reviewed, licensed content? Does it return citations and provenance? Is the literature current? Does it meet privacy and IP requirements? Then,  score data provenance rather than treat it as an afterthought.

4. Cultivate community alongside compliance. The strongest AI adoption in research tends to grow from shared norms, peer learning and iterative refinement — the same things that make any research environment work. Champion your early adopters. Create visible, low-stakes forums for researchers to share what is and is not working. Bring in external expertise. Connect with peer institutions. Policy will always matter, but the culture you build around it is what determines whether the policy is used.

Idea: Name three or four early adopters as AI champions, give them a small budget and a monthly forum to show what is and is not working, and make sure skeptics are in the room.  5. Invite ambition, not just efficiency. AI’s most powerful use in research is not just a faster literature review, it is the question that was previously out of reach. The Nobel-winning protein structure work, the AI co-scientist surfacing drug candidates, the climate model that finishes with better-quality data; i.e. these are research questions that AI made tractable, not just tasks AI made faster. Research leaders who explicitly invite their researchers to attempt those questions — and back them with data, compute and protected time — unlock the upside that efficiency alone can’t deliver.

Idea: Put out a small internal call — even a few seed grants or blocks of protected time — for research questions that were previously out of reach, and pair the teams that answer with data, compute and methods support.  Where to start with these actions depends on the institution: if there is no AI policy yet, idea 1 (listening) builds the foundation; if there is a policy but tooling is uneven, idea 3 (trusted tools) tends to deliver the fastest visible improvement; if both are in place, idea 4 (culture) is where the next gains live; and idea 5 (ambition) should run alongside all the others, whatever the starting point.

What a well-supported AI research environment looks like

When these five actions land well, a research environment starts to feel different. A doctoral student running a literature review for a grant deadline finishes a first pass in two days instead of two weeks and uses the time saved to design a stronger study. A faculty member working at the intersection of two fields surfaces a cross-disciplinary connection that would not have been visible at human reading speed. A research office processes proposal due diligence in a fraction of the time, freeing capacity for strategy. And a research team takes on a question that was previously out of reach — a protein structure no one had cracked, a drug candidate hidden in decades of literature, a weather forecast at a resolution no human could compute — because the institution has made room for that ambition. The ideal state is researchers who move faster, with more confidence in their sources and with the institutional support to attempt the work that matters most. That is what harnessing AI for research looks like: not a technology layer, but a research environment that has changed shape.

Why the next move on AI belongs to research leaders

The 2026 Yale Committee on Trust in Higher Education concluded its report with a charge that applies directly to this moment: “trust is built through action, not messaging.” 

That action is now within reach: listen to what researchers are already doing with AI, build standards that protect integrity while making room for innovation, invest in tools grounded in trustworthy content and cultivate a culture where responsible adoption feels natural rather than imposed. The opportunity is bigger than speed: it is a chance to strengthen the credibility of scholarship at the very moment that trust matters most. The institutions investing now in rigorous, transparent, human-overseen AI use will be the ones the public, funders and the scientific community continue to trust — and continue to support and look to for answers. And research leaders are well placed to lead the way. 

How to evaluate an AI tool or data source

Before adopting or scaling an AI tool across the institution, the same qualities researchers say would earn their trust double as a practical checklist. Five questions to ask of any tool  and of the data behind it: 

• Provenance: Where does the underlying content come from: high-quality, peer-reviewed sources, or open web data of unknown quality? This is the most consequential choice, because disclosure and verification policies cannot compensate for unreliable source content. 

• Transparency: Does the tool show its sources, with citations and provenance for every output, so researchers can trace and verify each claim? 

• Recency: Is the literature it draws on current and kept up to date? 

• Human oversight: Does the workflow keep human validation in the loop rather than presenting outputs as final? 

• Fit and security: Does it meet the institution's privacy, IP and compliance obligations, and is access equitable across disciplines rather than widening the gap? 

For why the data behind AI is the decisive factor in research, see Elsevier's “Why trusted content matters in AI research”.

How Elsevier supports the research mission

Elsevier has been a partner to the research community for more than 140 years. The same commitment to peer review, research integrity and trusted scholarly content that has shaped that history is what we are building our AI tools on — and what we bring to the partnership with research leaders to shape what comes next. 

In practice, that means these tools are built on trusted, curated scholarly content, including Scopus, the world’s largest collection of research abstracts. Researchers get AI-assisted insights they can trace back to the source. 

• LeapSpace — research-grade AI workspace enabling your teams to work faster, smarter and with greater confidence in their sources. Built with the global research community, LeapSpace grounds every response in more than 20 million peer-reviewed full-text articles and books from Elsevier and over 1,000 publishing partners, plus over 100 million Scopus records, and every insight carries a Trust Card showing how the claim aligns with its cited source. New agentic tools such as Writing Coach and Claim Radar extend that support from literature review through to writing — all in a secure environment where researcher data is never used to train public AI models. 

• Scopus — AI-assisted literature discovery and citation analysis, grounded in peer-reviewed content. With more than 100 million records curated by an independent review board, Scopus with AI surfaces relevant insights with every response grounded in trusted, clearly cited content, and its metrics and author profiles help leaders identify experts, track emerging trends and evaluate research output at individual or institutional level. 

• SciVal — Research performance analytics to inform strategic decisions about where AI investments will have the most impact. Built on trusted Scopus data, SciVal helps institutions benchmark against peers, map collaboration networks and spot emerging research areas, while Smart Summaries transform complex analyses into plain-language insights for faster, more confident decision-making. 

The global context: examples of how research leaders around the world are navigating AI

Sources for the global context table 

• Australia — Australian Framework for AI in Higher Educationopens in new tab/window (ACSES, 2025) and the Australian Universities Accord (2024); institution-led governance at USYD, UNSW and Melbourne. 

• Brazil — Brazilian AI Plan (PBIA) 2024–2028opens in new tab/window, Ministry of Science, Technology and Innovation (MCTI); Santos Dumont / LNCC supercomputer upgrade (2025). 

• Canada — Pan-Canadian AI Strategy (CIFAR / ISED, 2017; Phase 2, 2022, C$443M); Canadian Sovereign AI Compute Strategy, Budget 2024 (C$2B); Canadian AI Safety Institute (2024); AIDA / Bill C-27 status (2025). 

• China — McKinsey (2023) on the AI talent gap; Ministry of Education / China Daily (2026)opens in new tab/window on the growth of AI degree programs. 

• European Union — European Commission: European Strategy for AI in Scienceopens in new tab/window and RAISE (2025), and the Horizon Europe Work Programme 2026–2027 (€2B+ for AI). 

• Gulf States (UAE & Saudi Arabia) — UAE National AI Strategy 2031opens in new tab/window and MBZUAI; Stargate UAE / G42 (2025); Saudi Arabia SDAIA and PIF’s HUMAIN (2025); US Commerce Department advanced-chip export authorizations (Nov 2025). 

• Hong Kong SAR — Innovation & Technology Development Blueprint (2022)opens in new tab/window; 2025–26 Budget (HK$1B AI R&D Institute); Cyberport AI Supercomputing Centre and HK$3B AI Subsidy Scheme; HKGAI V1, Innovation, Technology and Industry Bureau (2025). 

• India — IndiaAI Missionopens in new tab/window, Ministry of Electronics and IT (MeitY), 2024 (₹10,372 crore, ~US$1.2B); AICTE 2025 initiatives across 14,000+ institutions. 

• Japan — Cabinet Office: 7th STI Basic Planopens in new tab/window and AI Basic Plan; AI Promotion Act (2025); US-Japan Genesis Mission (2026). 

• Kenya — Kenya National AI Strategy 2025–2030opens in new tab/window (Ministry of ICT and Digital Economy); Microsoft-G42 Olkaria data center announcement (2024). 

• Malaysia — National AI Roadmap 2021–2025opens in new tab/window (MOSTI/MDEC); National AI Office and AI Governance & Ethics Guidelines (2024); Higher Education Plan (RPTM) 2026–2035. 

• Singapore — National AI Strategy 2.0 (Smart Nation, 2023); MDDI National AI R&D Planopens in new tab/window, over S$1bn for 2025–2030. 

• South Korea — AI Framework Actopens in new tab/window (2025) and the National Strategy for AI, Ministry of Science and ICT (MSIT); National AI Strategy Committee (2025). 

• Thailand — National AI Strategy & Action Plan 2022–2027opens in new tab/window (NECTEC/NSTDA); AI Ethics Guidelines (2021). 

• United Kingdom — UKRI AI Strategyopens in new tab/window (£1.6B for 2026–2030); DSIT AI for Science Strategy (2025) and AI Opportunities Action Plan. 

• United States — The White House, Winning the AI Race: America's AI Action Planopens in new tab/window (2025); NSF National AI Research Institutes; reported NSF/NIH funding reductions (2025). 

While the policy and funding environment differs significantly by region, the core leadership challenge does not: build an environment where researchers can engage with AI responsibly, rigorously and with national and institutional support.