From the College Athletics edition of October 5, 2026
NCAA Division II 2025 membership survey: 19% of athletics directors report AI use, led by game-day stories; 79% cite lack of technical expertise
NCAA Research / Division II governance · NCAA Division II athletics administration · United States (national, Division II)
- Publisher
- NCAA Research
- Original publication
- May 2025
- Source retrieved
- 2026-10-06
- Event date
- 2025-01-22
What happened
Of 216 Division II athletics directors, 19% said their department used an AI application for operations. Among the 40 users, 63% used it for game-day summaries or website stories, 30% for research, 28% for game-day operations and 15% for recruiting. Among 39 users, 79% cited lack of technical expertise, 51% resistance to change, 44% budget constraints and 26% data privacy concerns. Of non-users, 48% were considering AI.
Why it matters
Why now: Washington State announced FanWord AI storytelling on Oct 5, 2026. This survey is the only national NCAA baseline found tonight showing that content drafting is the leading athletics AI use and that staff expertise, not tools, is the main barrier.
Evidence and measured results
Survey of all 304 active and transitioning-in Division II institutions, Jan 22 to Feb 21, 2025 (presidents reopened to Mar 7); 2,234 respondents from 302 institutions and 23 conferences. AI questions answered by athletics directors with Ns of 216, 40, 39, 175 and about 200 depending on item. Self-reported; Division II only; results dated May 2025.
Limitations and uncertainty
Published May 2025, so adoption has likely changed; Division II only; small N (40) for AI users; self-reported. Included as a baseline because of the current WSU deployment, not as a measure of current adoption.
Put this evidence to work
Lighthouse Advisory interpretation, grounded in this source. Enriched 2026-10-05; this does not change the original publication date. Each role follows the same checklist so takeaways compare across sources.
Sales
Role takeaway
- Customer problem
- Athletics departments want AI efficiency but lack staff expertise and budget to adopt it well.
- Stakeholders
- Director of athletics
- Communications staff
- Compliance
- Campus IT
- Conference office
- Discovery questions
- Which AI tools are staff already using informally?
- What training would staff actually use?
- What athlete data may enter AI tools?
- Who approves new AI tools?
- Value hypothesis
- Training and a clear use policy may let a department adopt content and research AI with fewer privacy and quality problems.
- Potential engagement
- A short AI enablement package: use policy, approved-tool list and staff training for one department or conference.
- Unsupported claims
- Current 2026 adoption rates
- Division I adoption levels
- Measured efficiency gains from AI use
- Cost savings
Pre-sales engineering
Role takeaway
- Fit
- Departments early in AI adoption using commercial content or research tools.
- Architecture and integration
- Approved SaaS tools behind campus SSO, with data-handling rules and optional CMS integration.
- Prerequisites
- Campus IT review process
- Data classification for athlete information
- Usage visibility
- Constraints
- Limited staff expertise
- Budget constraints
- Survey reflects 2025 Division II only
- Security
- Classify athlete data, block sensitive data from unapproved tools, and log usage.
- Proof of value
- Approved-tool list adopted
- Staff completion of training
- Documented data-handling rules
Delivery
Role takeaway
- Work
- Pair AI tool adoption with training, policy and a named owner.
- Dependencies
- Campus IT and privacy office
- Training resources
- Leadership sponsorship
- Ownership
- Director of athletics or a designated senior administrator.
- Skills and adoption
- Online tutorials, webinars and workshops, the support types ADs ranked highest.
- Governance checkpoints
- Use policy approved
- Semester review of usage and issues
- Privacy review before student-athlete support uses
- Acceptance criteria
- Proposed: written AI use policy published to staff
- Proposed: training completed by all staff using AI tools within one semester
- Risks
- Unmanaged informal tool use
- Privacy exposure of athlete data
- Stalled adoption from resistance to change
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?
Reported uses are mostly off-the-shelf content and research tools rather than integrated systems, so near-term architecture work is identity, data-sharing rules and CMS integration rather than custom models.
Governance
Who approves, reviews and stays accountable for outcomes?
With 79% of AI-using ADs citing lack of expertise and 26% citing privacy concerns, departments need simple use policies and approved-tool lists before adoption widens.
Security and privacy
What data, permissions and controls need testing?
Privacy concern was reported by about a quarter of AI-using ADs; the survey does not describe controls in place.
Accessibility and workforce
Who is affected, and what skills or accommodations follow?
Lack of technical expertise is the top barrier; training is the main workforce need.
Procurement
What should contracts, pricing and exit terms secure?
Budget constraints were cited by 44% of AI-using ADs; low-cost tools with clear data terms will dominate early adoption.
Operating model
Which teams own the service once it runs?
AI use starts in communications and research before moving into compliance, recruiting or athlete support.
Publication history
- 2026-10-05College Athletics · Issue 294 resources
Stable resource ID: ncaa-dii-2025-membership-survey-ai-usage