All academic institutions with research ambitions need an AI strategy.
The most successful institutions focus on three pillars: infrastructure, interdisciplinary culture, and strategic industry partnerships.
1. Build Shared, High-Performance Infrastructure
The biggest bottleneck for academic AI research is compute power. Universities cannot compete with the massive budgets of private tech companies, so they must optimize resource allocation.
- Centralized Compute Clusters: Instead of allowing individual departments to buy fragmented hardware, establish a centralized high-performance computing (HPC) cluster managed by the university. This pools resources and provides equitable access to GPU clusters for researchers across campus.
- Data Architecture Support: Academic research lives and dies by data. Universities should hire dedicated research data engineers to help faculty build, clean, and host proprietary datasets safely and legally.
2. Institutionalize Interdisciplinary AI
The most breakthrough AI innovations in the coming years will not be in AI theory but in applied AI—using machine learning to solve complex problems across domains such as biology, materials science, the humanities, and law.
- Cross-Disciplinary "Joint Appointments": Create faculty positions that sit between departments (e.g., a joint professor in AI and Molecular Biology). This prevents AI researchers from being siloed in engineering departments.
- Seed Grants for Novel Fusion: Offer internal funding explicitly earmarked for projects where an AI expert pairs with a non-technical domain expert.
- Universal AI Literacy: Implement baseline AI literacy and ethics courses for all graduate students, regardless of their major, so every discipline learns how to responsibly leverage these tools.
3. Reimagine Industry Partnerships and Tech Transfer
Moving an AI innovation from a university lab to a real-world application often takes too long, causing institutions to lose top talent and intellectual property (IP).
- Frictionless Tech Transfer: Simplify the IP licensing process for AI startups spun out of university research. Traditional university bureaucratic timelines (which can take months or years) don't align with the breakneck speed of the AI industry.
- "Embedded" Industry Labs: Invite private AI labs and tech companies to establish research outposts on or near campus. This creates co-funded research projects, gives students direct pathways to internships, and exposes faculty to industry-scale problems.
Lionel C. Briand is professor of software engineering and has shared appointments between (1) The University of Ottawa, Canada, where he holds a Canada Research Chair (Tier 1), and (2) Ireland's Lero Centre for Software Research, where he holds the position of Director. In collaboration with colleagues, over 30 years, he has run many collaborative research projects with companies in the automotive, satellite, aerospace, energy, financial, and legal domains. Lionel has held various engineering, academic, and leading positions in seven countries. He was one of the founders of the ICST conference (IEEE Int. Conf. on Software Testing, Verification, and Validation, a CORE A event) and its first general chair. He was also EiC of Empirical Software Engineering (Springer) for 13 years and led, in collaboration with first Victor Basili and then Tom Zimmermann, the journal to the top tier of the very best publication venues in software engineering.
Lionel was elevated to the grades of IEEE Fellow and ACM Fellow for his work on software testing and verification. He was granted the IEEE Computer Society Harlan Mills award, the ACM SIGSOFT outstanding research award, and the IEEE Reliability Society engineer-of-the-year award, respectively in 2012, 2022, and 2013. He received an ERC Advanced grant in 2016 — on the topic of modelling and testing cyber-physical systems — which is the most prestigious individual research award in the European Union. He was elevated to the rank of fellow of the Academy of Science, Royal Society of Canada, and Academia Europaea, the European Academy of Science. He currently holds a Canada Research Chair (Tier 1) on "Intelligent Software Dependability and Compliance". His research interests include: Trustworthy AI, software testing and verification, applications of AI in software engineering, model-driven software development, requirements engineering, and empirical software engineering.