From the Campus Operations edition of September 15, 2026
IHE/Hanover CTO survey: only 29% say AI investments met or exceeded ROI expectations
Inside Higher Ed / Hanover Research · Higher education technology leadership · United States
- Publisher
- Inside Higher Ed
- Original publication
- May 12, 2026
- Source retrieved
- 2026-09-15
What happened
Among 130 campus CTOs (about 8 percentage-point margin of error), half say AI ROI is unclear or below expectations; only 29% say investments met or exceeded expectations. Most cited tangible value is employee productivity (55%), then IT operations/service management (30%) and administrative efficiency (29%). Skills/capacity (55%), cost (48%), and governance uncertainty (38%) limit impact; 49% call the pace of tech change unsustainable without new resources.
Why it matters
Cross-campus scrutiny for campus-operations AI spend: productivity and ITSM value claims are common, while institutional ROI and capacity remain contested.
Evidence and measured results
Independent trade reporting on Hanover survey of 130 CTOs; methodology summarized. Companion survey PDF key findings align on 29% met/exceeded. Not a causal evaluation of any single deployment.
Limitations and uncertainty
Self-selected CTO survey; MoE about 8 points; ROI is perception-based; article is May 2026 but newly relevant against continuing enterprise AI deals; sponsored project disclosure present.
Put this evidence to work
Lighthouse Advisory interpretation, grounded in this source. Enriched 2026-09-15; this does not change the original publication date. Each role follows the same checklist so takeaways compare across sources.
Sales
Role takeaway
- Customer problem
- Institutions are spending on AI while CTOs doubt clear institutional return and face talent and cost constraints.
- Stakeholders
- CIO/CTO
- CFO
- Provost/cabinet
- Cybersecurity leadership
- Discovery questions
- How is AI ROI defined and by whom?
- Which ITSM or admin metrics will move in 90 days?
- What staff capacity exists to absorb change?
- Where does governance uncertainty block adoption?
- Value hypothesis
- Bounded AI investments tied to employee productivity and IT service metrics are more credible than transformation claims while only 29% of CTOs report met or exceeded ROI.
- Potential engagement
- ROI diagnostic workshop plus one ITSM/admin pilot with pre-registered metrics.
- Unsupported claims
- Typical campuses already achieving strong AI ROI
- Teaching and learning gains as primary CTO-reported value
- Peer adoption as proof of local value
Pre-sales engineering
Role takeaway
- Fit
- Advisory and solution design under conditions of mixed AI ROI perception and capacity limits.
- Architecture and integration
- Prefer integrations that reduce ITSM toil or admin cycle time with observable telemetry; defer broad agent platforms without governance.
- Prerequisites
- Agreed ROI definition
- Baseline metrics
- Named capacity owners
- Cyber risk review
- Constraints
- Survey MoE and perception bias
- Unsustainable change pace reported by about half of CTOs
- Governance lag
- Security
- Plan for AI-amplified cyber demand and third-party exposure; staffing is a limiting factor per CTO worries.
- Proof of value
- Pre-registered KPI movement
- Capacity hours tracked
- Post-pilot ROI judgment by CTO/CFO
Delivery
Role takeaway
- Work
- Instrument baselines, deliver one narrow pilot, report realized vs expected value, and document capacity burn.
- Dependencies
- CTO sponsorship
- Finance ROI definition
- Security review
- Unit operations owners
- Ownership
- CIO/CTO portfolio with unit business owners for each use case.
- Skills and adoption
- Upskill existing staff; survey says capacity, not only tools, limits impact.
- Governance checkpoints
- Problem definition gate
- ROI mid-pilot review
- Scale/no-scale decision
- Acceptance criteria
- Documented baseline and post metrics
- Explicit CTO ROI assessment
- Capacity impact statement
- Risks
- Imitation-driven scope creep
- Fatigue from undefined problems
- Cyber incidents during rollout
Implementation considerations
Lighthouse Advisory interpretation across the operating dimensions a public-sector buyer must settle before this evidence becomes a design. Each note answers the question under its heading for this specific source.
Architecture and integration
What must connect, and where does the AI sit in the workflow?
Prioritize use cases with measurable ITSM/admin baselines before agentic or institution-wide platforms; avoid peer-imitation architectures without problem definition.
Governance
Who approves, reviews and stays accountable for outcomes?
Governance/policy uncertainty is a top limiter; ROI discipline and shared definitions of digital transformation are prerequisites called out by commentators.
Security and privacy
What data, permissions and controls need testing?
CTOs rank cybersecurity breach among top 2030 worries (59%); AI raises both attack and defense demand amid staffing shortages.
Accessibility and workforce
Who is affected, and what skills or accommodations follow?
Skills and staff capacity are the top reported limiter of AI impact (55%); training and hiring strategy are part of any campus-ops AI program.
Procurement
What should contracts, pricing and exit terms secure?
Evaluate purchases with cost, ROI, and return-on-value assessments before large investments; resist peer-pressure licensing.
Operating model
Which teams own the service once it runs?
Without shared digital-transformation meaning and cabinet-level CTO leverage, AI programs risk fragmented pilots and unsustainable change pace.
Publication history
- 2026-09-15Campus Operations · Issue 107 resources
Stable resource ID: ihe-cto-ai-roi-expectations-20260512