{"resourceId":"ncaa-dii-2025-membership-survey-ai-usage","versions":[{"version":"external-cc7bdb54b7801475fce20d420f506cb76e752a9658183dd1b38a78162b984f4c","resource":{"id":"ncaa-dii-2025-membership-survey-ai-usage","title":"NCAA Division II 2025 membership survey: 19% of athletics directors report AI use, led by game-day stories; 79% cite lack of technical expertise","organization":"NCAA Research / Division II governance","sector":"NCAA Division II athletics administration","geography":"United States (national, Division II)","publishedAt":"May 2025","publicationDate":"2025-05-01","eventDate":"2025-01-22","sourceName":"NCAA Research","sourceLabel":"Membership association survey results","sourceUrl":"https://ncaaorg.s3.amazonaws.com/research/d2/D2Gov_Strategic%20Priorities_Artificial%20Intelligence.pdf","evidenceClass":"public-sector-association","outcomeClass":"mixed","topics":["knowledge-work","accessibility-workforce","governance-procurement","operating-model"],"finding":"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.","sledRelevance":"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":"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.","architectureImplications":"Interpretation: 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.","governanceImplications":"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.","securityPrivacyImplications":"Privacy concern was reported by about a quarter of AI-using ADs; the survey does not describe controls in place.","caveats":"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.","streamIds":["college-athletics"],"roles":{"sales":"Sales framing: the survey frames demand around expertise, not tools. Content drafting leads current use, and the main barriers are skills, resistance to change and budget. A bounded offer is training plus a short use policy for a department starting with communications tools. Do not cite these 2025 Division II percentages as current or as Division I adoption rates.","engineering":"Engineering framing: most reported uses are lightweight content and research tools, so the engineering fit is enablement rather than build. Engineers should set up approved tools with single sign-on, define what athlete data may enter them, and connect drafting tools to the CMS with audit logs. Proof means documented data rules and usage visibility, not model accuracy. Interpretation: these steps also prepare for later compliance or recruiting uses, which ADs flagged as areas of interest.","delivery":"Delivery framing: adoption fails on skills and change management. Delivery should pair any tool rollout with tutorials and workshops, which ADs ranked as the most helpful support, name a departmental owner, and track adoption and issues after a semester. Privacy questions should be answered in writing before expanding into student-athlete support or recruiting uses. Interpretation: these 2025 Division II results are a baseline to check against local conditions."},"roleDetails":{"sales":{"customerProblem":"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"],"discoveryQuestions":["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?"],"valueHypothesis":"Training and a clear use policy may let a department adopt content and research AI with fewer privacy and quality problems.","potentialEngagement":"A short AI enablement package: use policy, approved-tool list and staff training for one department or conference.","unsupportedClaims":["Current 2026 adoption rates","Division I adoption levels","Measured efficiency gains from AI use","Cost savings"]},"engineering":{"fit":"Departments early in AI adoption using commercial content or research tools.","architectureIntegration":"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.","proofOfValue":["Approved-tool list adopted","Staff completion of training","Documented data-handling rules"]},"delivery":{"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.","skillsAdoption":"Online tutorials, webinars and workshops, the support types ADs ranked highest.","governanceCheckpoints":["Use policy approved","Semester review of usage and issues","Privacy review before student-athlete support uses"],"acceptanceCriteria":["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"]}},"retrievedAt":"2026-10-05T22:11:55-05:00","enrichedAt":"2026-10-05T22:20:00-05:00","enrichmentBasis":"retrieved source","accessibilityWorkforceImplications":"Lack of technical expertise is the top barrier; training is the main workforce need.","procurementImplications":"Budget constraints were cited by 44% of AI-using ADs; low-cost tools with clear data terms will dominate early adoption.","operatingModelImplications":"AI use starts in communications and research before moving into compliance, recruiting or athlete support.","sourceVerification":{"responseId":"curl-ncaa-d2-ai-survey-20261005T221155","openedUrl":"https://ncaaorg.s3.amazonaws.com/research/d2/D2Gov_Strategic%20Priorities_Artificial%20Intelligence.pdf","referenceExcerpt":"Game day summaries/stories on your website","promptVersion":"sled-research-v4.0","model":null,"basis":"agent-reported inspection"}}}]}