Stop piloting, start platforming - the AI-ready campus stack for 2026

Everyone’s running GenAI pilots in universities. But far fewer are building the durable capabilities that make AI safe, useful, and scalable. Two new heavyweight reads point higher education leaders towards their next step: the 2025 EDUCAUSE report on teaching and learning, and World Bank’s report on AI in higher education. 

Together, they outline what to build into your stack so AI moves from experiments to outcomes.

What these two reports agree on 

EDUCAUSE surfaces six technologies and practices that will shape teaching and learning: 

1. AI tools for teaching and learning
2. Faculty development for generative AI
3. AI governance
4. Shoring up cybersecurity
5. Evolving teaching practices
6. Critical digital literacy

It also stress-tests plans with four potential futures (growth; constraint; collapse; transformation) because regulation, budgets, and culture will affect each institution’s path. 

The World Bank report complements this with a practitioner’s map of 20 AI tool types across five categories (students, faculty, and staff use cases), distilled from an initial scan of 438 tools – and it adds concrete evidence and case examples for what already works. 

Six non-negotiable capabilities for an AI-ready campus stack

1. AI tools with a learning-impact pathway 

It’s time to move beyond novelty. Prioritise use cases where you can measure learning or operational impact (metrics like feedback speed, time-on-task, progression, attrition). EDUCAUSE places ‘AI tools for teaching and learning’ at the centre, while the World Bank lays out concrete categories (from AI tutoring and adaptive learning to assessment, research support, and admin automation) – so you can choose by outcome, not hype. 

2) Faculty development for GenAI (but not just tool demos)

Workshops that stop at ‘prompting tips’ won’t change assessment, feedback, or integrity. EDUCAUSE explicitly calls out faculty development for generative AI and ‘evolving teaching practices’. Design micro-credentials for people working across your institution that cover assignment redesign, disclosure norms, accessibility, and academic integrity. 

3) Governance that fits higher ed (not borrowed from generic IT)

Write down your stance on model choice, data flows, IP, disclosure, and acceptable use. Then align it to risk and values. EDUCAUSE highlights AI governance and flags regulatory uncertainty in its political-trend scan; use that to justify a living governance note you update quarterly. 

4) Security and privacy by design

GenAI introduces new connectors and plugins, along with shadow AI. EDUCAUSE singles out cybersecurity measures for AI tools, and its trends section warns that regulations for AI are lacking or ineffective – so you’ll need to raise your own bar. Embed data-classification, logging, and red-team prompts into pilots. Run a privacy/security threat-model on your top two AI use cases before you scale them. 

5) Critical digital literacy for students and staff

If AI can fabricate fluently, your community must verify fluently. EDUCAUSE elevates ‘critical digital literacy’, and its collapse scenario shows what happens when unchecked AI erodes trust in knowledge. Build short, required modules that teach verification, citation with AI, and bias detection. Pair a literacy micro-course with in-tool ‘reflection prompts’ that nudge good practice. 

6) Replace anecdotes with effective pipelines

Treat AI like product ops, leveraging baselines, instrumentation, A/Bs. The World Bank’s cases show why: AI-powered assignment platforms (for admissions/placement) increased student placement efficiency by 20% and improved options for under-assigned students by 38%, and an RCT of an AI chatbot (‘Pounce’) at Georgia State reduced ‘summer melt’ (that’s the number of students who don’t actually enrol by the start of term) by 3.3%. Impact follows when tools are embedded, measured, and iterated.

Stop pilot fatigue – build for scale in 90 days 

Based on the studies by EDUCAUSE and the World Bank, here are our quick tips to move beyond the pilot to genuinely useful AI:

• Pick two outcome-aligned use cases (maybe AI feedback for large first-year modules, or applicant guidance in admissions). Map success metrics before kickoff.)
• Publish a living governance note (model selection, data residency, disclosure norms). Include a one-page plain-language explainer for students and staff.
• Ship faculty micro-credentials tied to assessment redesign and accessibility, not just tool clicks.
• Instrument security/privacy (data-flow diagrams, audit logs, red-teaming prompts) and run a risk review prior to scale.

And then stress-test your plans using EDUCAUSE’s four possible futures: 

• Growth: can you scale quickly without widening inequity?
• Constraint: if compliance tightens, do your disclosures, logs, and human-in-the-loop controls hold up?
• Collapse: if misinformation spikes, do your literacy and assessment designs protect credibility?
• Transformation: if workforce alignment dominates, can you defend critical thinking and breadth?

OK but…why should universities do this now? 

The World Bank’s 2025 brief shows that AI in higher ed is already delivering value when implemented transparently and measured rigorously, and it catalogues practical tool classes and cases to copy. 

EDUCAUSE, meanwhile, tells you what to build around the tools – faculty capacity, governance, cybersecurity, and literacies – and reminds you to plan for multiple futures. 

Take them together, and the strategy emerges: platform AI for long-term usefulness now.