Stage 5: What must pass technical, licensing and AI review?

Run three separate checks: can it work, may the club use it that way, and can its outputs be trusted? Begin permission checks before the trial; finish the full review before production use or signature.

Can the player data join reliably?

Use a Player Data Joinability Check rather than accepting “API available” as proof of integration readiness. SkillCorner’s July 2026 integration material distinguishes aggregated-data workflows from event-level and video-synchronised implementations, illustrating why the required implementation scope should be specified. (skillcorner.com)

Recommended acceptance tests:

• Preserve supplier IDs and maintain an auditable mapping to internal IDs.
• Challenge duplicate names, name variants, transfers and changed team affiliations.
• Manually validate a sample of automatic matches, including apparently easy matches.
• Report wrong-person merges separately from unmatched records.
• Confirm units, coordinate conventions, time definitions and missing-value meanings.
• Test pagination, rate-limit handling, corrections and repeat imports without duplication.
• Record schema and metric versions and assign ownership of maintenance.
• Quarantine unresolved identities rather than silently forcing a match.

For the proposed pilot, treat an unresolved wrong-person merge affecting a priority candidate as a blocker. Zero errors observed in a small sample is not proof of an error-free feed.

Does the licence permit the intended workflow?

Hudl’s public User Terms restrict automated extraction and certain AI-model uses unless an applicable exception or express written permission applies. They also state that a customer’s separate Hudl agreement applies to organisational use. This is a concrete reason to inspect the full agreement, not a conclusion about any individual club’s negotiated rights. (hudl.com)

Use this Recruitment Data Licence Stress Test with counsel:

Record the exact clause, applicable schedule, ambiguity and responsible reviewer. Seek contractual clarification where necessary; do not treat silence or a generic sales assurance as approval.

For confidential uploads, require approved access controls, a stated retention period, deletion arrangements and explicit processing terms. The FTC has highlighted the risks of confidential business information entering AI services and the importance of providers honouring privacy and confidentiality commitments. (ftc.gov)

Can AI scouting claims be checked?

Hudl announced AI Player Summaries on 3 August 2026, describing summaries based on existing platform data and traceable to metrics and video. Before buying another summarisation layer, test the functionality already available under the club’s agreement. Treat the announced traceability as a claim to verify. (hudl.com)

NIST’s Generative AI Profile recommends checking sources and citations in generated outputs and cautions against generalising from narrow, anecdotal assessments. (nvlpubs.nist.gov)

For the club’s AI Evidence Check, annotate each material statement as supported fact, analytical inference, unsupported or contradicted. Test thin-data cases, mistaken premises and conflicting evidence. Require the system to acknowledge unavailable information, and include human checking time in the workflow result.

Exit condition: technical ownership, permitted use and evidence quality are signed off independently. Escalate sensitive personal information or unresolved legal questions to qualified counsel; this guide is procurement diligence, not legal advice.