Higher Education's next AI challenge: moving from experimentation to operationalisation

AI has quickly become one of the defining topics in Higher Education!

Over the past two years, Universities have explored hundreds of AI use cases across teaching, research, administration and student support. But the challenge facing institutional leaders in 2026, and beyond, is no longer whether to adopt AI, but how to scale it safely, sustainably and effectively. As such, one sector imperative becomes increasingly clear: Universities must move beyond collections of pilots and experiments towards institution-wide AI operating models to begin delivering consistent experiences and benefits for all.

Across Higher Education, AI activity is becoming concentrated in four big key areas: learning and assessment, research and partnerships, operational efficiency, and governance and security. Universities have been exploring some assessment redesign, personalised student support, research productivity tools, service automation, knowledge assistants and workflow orchestration. However, many institutions are discovering that successful AI adoption requires considerably more than simply providing access to new tools. Governance, policy, upgraded security and sensible guardrails all underpin successful adoption.

Long term success with AI

The biggest barrier to scaling AI is often not the technology itself but the underlying foundations that support it. Many Universities continue to operate around fragmented data sets, legacy infrastructure, complex application portfolios, vast distributed infrastructure and inconsistent governance arrangements. Without trusted data, effective identity management, clear ownership and consistent policies, AI initiatives can struggle to move beyond limited departmental deployments. Trusted data, composable platforms, automation and decision intelligence are becoming essential foundations for long-term AI success.

Governance is emerging as another critical challenge. Universities must balance innovation with academic integrity, transparency, accountability and security. Questions surrounding intellectual property, research data protection, AI literacy, assessment integrity and acceptable use require institutional answers rather than faculty-level or personal responses. Increasingly, University leaders are recognising that AI governance must become part of mainstream corporate governance, with clear accountability, policies and risk management frameworks.

Security considerations are also rising rapidly up the agenda. Security must remain Job Zero, not an after-thought or bolt-on consideration later. As AI becomes integrated into core business processes, institutions must ensure sensitive student, staff and research data remains protected too. Concerns around AI security, data leakage, access controls, model governance and supplier assurance are all becoming as important as traditional Cyber Security controls. The convergence of AI governance and cyber resilience will likely become a defining characteristic of digital strategy over the coming years.

This is where Softcat can help

Softcat has specialist teams that work with Universities to move beyond experimentation and towards sustainable AI adoption at scale. We do this through a combination of Higher Education advisory services (built on expertise from lived experience), Microsoft AI technologies, cyber security expertise and data transformation capabilities.

Our approach focuses on these practical areas:

• Establishing AI governance and operating models that align innovation with institutional risk appetite.
• Modernising data platforms to create trusted, secure and AI-ready information assets.
• Transforming legacy infrastructure and associated operating models
• Implementing identity, security and compliance controls that enable safe adoption.
• Identifying high-value operational use cases that deliver measurable productivity improvements.
• Supporting cultural change, skills development and organisational readiness.

Most importantly, Softcat helps Universities to focus on outcomes rather than technology. Whether the objective is improving student experience, increasing staff productivity, accelerating research, reducing operational costs or improving decision-making, successful AI adoption starts with institutional priorities and measurable benefits, not individual tools.

The Universities that will realise the greatest value from AI over the next few years are unlikely to be those running the largest number of pilots. Instead, they will be those that build robust foundations, establish clear governance and operationalise AI as a strategic institutional capability. The opportunity is significant, but realising it requires a move from experimentation to execution. Softcat can help Universities make that journey with confidence.

Please connect with your dedicated Softcat Account Manager to find out more, or visit our Education Hub.