From the College Athletics edition of October 22, 2026
Teamworks/Front Office Sports State of AI in Sports full findings: about half of college programs use AI, 40% of those run four or more systems, and only programs with an organization-wide AI decision report clear ROI (54%)
Teamworks / Front Office Sports (published via College.town special edition) · Athletic department AI strategy, budgeting and data integration · Global survey with U.S. college athletic department segment
- Publisher
- College.town (sponsored special edition)
- Original publication
- October 20, 2026
- Source retrieved
- 2026-10-23
What happened
The October 20, 2026 College.town special edition, written from Teamworks' perspective, publishes the key findings of the Teamworks and Front Office Sports State of AI in Sports report, a survey of more than 200 sports organizations including college athletic departments, pro league offices, multi-club groups and national governing bodies. Headline results: 90% expect AI use to grow; 15% report clear, measurable ROI; 37% see clear ROI when AI follows an organization-wide strategy versus 2% when deployed silo by silo. College-specific results: about half of college programs report using AI, mostly case by case; 40% of those run four or more systems; 14% plan no meaningful AI investment next year (twice the overall rate, partly due to cost); 54% of college programs that made AI an organization-wide decision report clear ROI, while those that have not report none; 20% of college respondents were unsure whether AI delivered ROI. Field-wide, 24% have a dedicated AI budget line, about half have a named AI owner, 29% do not know how AI is funded, and 9% can see one athlete in a single system.
Why it matters
Why now: published October 20, 2026, ahead of an October 27 Teamworks webinar on the findings. The archive's October 16 record (teamworks-fos-state-of-ai-in-sports-2026-roi-claims-2026-10-16) covered only a short promotional blurb with headline numbers, and the full report was then gated; this is the first openly readable release of the college-segment figures (adoption, tool sprawl, investment hesitancy, ROI by governance). It is a separate URL and publication, so it is recorded as a new source rather than an update. Interpretation: the college figures suggest the main barrier is governance (owner, budget line, connected athlete record) rather than tool availability.
Evidence and measured results
Self-reported survey sponsored and written up by a vendor that sells the integrated athlete record it recommends ('Teamworks is one of the partners this report describes'). No methodology, college subsample size, response rate, ROI definition or confidence intervals are published in the write-up. Correlation between governance and ROI is presented, and the text itself states the survey cannot say which comes first.
Limitations and uncertainty
Vendor-sponsored and vendor-interpreted; methodology and subsample sizes undisclosed; ROI self-reported and undefined; causal direction unknown. The full report beyond the special edition was not inspected. Mixes college and pro respondents, so only explicitly college-labelled figures apply to departments.
Put this evidence to work
Lighthouse Advisory interpretation, grounded in this source. Enriched 2026-10-22; this does not change the original publication date. Each role follows the same checklist so takeaways compare across sources.
Sales
Role takeaway
- Customer problem
- College departments are adopting AI tool by tool without an owner, budget line or ROI measure, and most cannot show returns.
- Stakeholders
- Athletic director
- Athletics CFO or business office
- Deputy AD for sport performance
- Athletics IT
- Compliance and university privacy office
- Discovery questions
- How many AI-enabled tools does the department pay for today, and who approved each?
- Is there a named owner and a separate AI budget line?
- How many systems must staff check to see one athlete?
- How is ROI defined for current AI tools?
- Which data-sharing agreements cover athlete records?
- Value hypothesis
- An AI inventory, owner and ROI definitions could reduce duplicate spending and make returns measurable, consistent with the survey's association between governance and reported ROI.
- Potential engagement
- A four-to-six-week AI inventory, ownership and ROI-definition review for one athletic department.
- Unsupported claims
- That any specific platform causes ROI
- Survey percentages as benchmarks for a given school
- Causal link between governance and returns
- Representativeness of the college subsample
Pre-sales engineering
Role takeaway
- Fit
- Departments running several disconnected athlete-data systems across performance, medical, academic and compliance functions.
- Architecture and integration
- Map systems that hold athlete data, choose a common athlete identifier, and integrate into an existing system of record before adding AI features.
- Prerequisites
- System and data inventory
- Common athlete identifier
- Data-sharing and consent agreements
- Named data owner
- Constraints
- Health and education privacy rules (HIPAA/FERPA where applicable)
- Vendor APIs and export limits
- Small staff capacity at mid-major and smaller programs
- No published survey methodology
- Security
- Apply role-based access and audit logging to any consolidated athlete record; separate medical data access from performance and commercial users.
- Proof of value
- Produce one reconciled athlete view across two or three systems
- Count staff time spent reconciling records before and after
- Test that access controls block non-authorized roles
- Document ROI definitions for each AI use case
Delivery
Role takeaway
- Work
- AI tool inventory, owner designation, budget-line proposal, ROI definitions and an integration roadmap for athlete data.
- Dependencies
- Athletic director sponsorship
- Business office cooperation
- Vendor contract and data-access review
- Ownership
- A named deputy AD or athletics CIO accountable for AI decisions across the department.
- Skills and adoption
- Staff learn to define measurable use cases and baselines; department heads shift from independent tool buying to shared review.
- Governance checkpoints
- Owner named before new AI purchases
- Budget line approved in the next cycle
- Privacy review before records are connected
- Acceptance criteria
- Proposed: complete inventory of AI-enabled tools and their costs
- Proposed: documented ROI definition and baseline for each active use case
- Proposed: named owner and budget line approved
- Risks
- Vendor-sponsored benchmarks drive purchasing
- Consolidated records increase breach impact
- Governance work stalls without executive sponsor
- ROI remains unmeasured
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?
Respondents close to athlete data report 91% checking two or more systems and 37% four or more to see one athlete. Interpretation: integration into existing systems (the top planned investment among owner-and-budget organizations, 75%) is the architectural precondition the survey associates with returns.
Governance
Who approves, reviews and stays accountable for outcomes?
Associates clear ROI with a dedicated AI budget line, a named accountable owner and a connected athlete record; the most common current decision model is each department deciding for itself.
Security and privacy
What data, permissions and controls need testing?
Consolidating athlete records across performance, medical and academic systems concentrates sensitive data; the write-up does not address access controls, consent or health-data rules.
Accessibility and workforce
Who is affected, and what skills or accommodations follow?
Implies new accountable AI owner roles and staff time for integration; 14% of college programs plan no investment partly because of cost, suggesting resource gaps between programs.
Procurement
What should contracts, pricing and exit terms secure?
Most organizations fund AI from existing software budgets (38%) or do not know how it is funded (29%); many buy platforms without a defined use case.
Operating model
Which teams own the service once it runs?
Moves AI decisions from department-by-department purchasing to a single owner, budget line and shared athlete record.
Publication history
- 2026-10-22College Athletics · Issue 433 resources
Stable resource ID: teamworks-fos-state-of-ai-in-sports-full-findings-college-segment-2026-10-20