TeamStation AI / Executive decisions
AI capability planning finds the missing pieces
AI capability planning compares what a workflow requires with what your organization can actually deliver and support. It covers data, integration, evaluation, security, and operating skills, not just model development.
Review the decision guide- Name the task
Describe the work the system and team must perform.
- Inspect the evidence
Check a work sample, test result or operating record.
- Close the real gap
Address missing skills, access or platform support separately.
How it works
Turn a capability claim into observable evidence
Scroll sideways to see the whole diagram. The same steps are written out below.
- Name the task
Describe the work the system and team must perform.
- Inspect the evidence
Check a work sample, test result or operating record.
- Close the real gap
Address missing skills, access or platform support separately.
- Dependency to checkData owner and access policy.
- CapabilityRetrieval
- Useful evidence
A traceable answer linked to permitted source documents.
- Dependency to checkDomain reviewer availability.
- CapabilityEvaluation
- Useful evidence
A versioned test set with expected behavior and failure cases.
- Dependency to checkDeployment access and support coverage.
- CapabilityOperations
- Useful evidence
A monitored service with an exercised recovery path.
A missing artifact means readiness is unverified, not that a person lacks ability. Assess the actual task and avoid treating a capability checklist as a universal score.
The problem
Adding a specialist won't fix an inaccessible dataset or a missing test process. A capability map helps the CTO untangle those messy dependencies before committing the team.
Assess capability through observable work
Current ability
Ask for a recent work sample or operational record, not a skills label alone.
Owner: Engineering lead
Dependency
Identify the upstream permission, dataset, service, or owner that limits progress.
Owner: Architecture and data leads
Gap response
Choose training, reassignment, managed delivery, or a narrower scope.
Owner: Capability owner
Once the missing capability is clear, decide whether to develop it internally or add a delivery role. Compare workforce options.
Illustrative scenario / not a client result
Is a retrieval problem really a hiring problem?
An internal knowledge assistant returns weak answers. The team asks for more AI engineers, but the source documents have conflicting owners and stale permissions. Another specialist might improve retrieval, yet cannot decide who is allowed to see each document or which version is authoritative.
- Fix the data dependency
- Assign source ownership, permission rules and freshness expectations before tuning retrieval.
- Develop the skill
- Use a bounded work sample and supported practice when the team lacks evaluation or retrieval experience.
- Add delivery capacity
- Add a role when the task is understood, access is ready and current capacity cannot cover it.
The decision: Keep unknown readiness separate from demonstrated inability. Ask the team to produce a traceable answer, a denied-access test and an explanation of a failed result. Those artifacts reveal more than a list of tools on a skills matrix.
Put the plan into practice
- Step 1Break the use case into tasks from data intake through support.
- Step 2Record the evidence that a team can perform each task safely.
- Step 3Mark missing evidence separately from a demonstrated skill gap.
- Step 4Sequence the limiting dependencies first and reassess with a practical work sample.
If the missing capability is a shared service, define its interface and owner before staffing around it. Review architecture dependencies.
Capability evidence map
NIST includes competencies and capacity when establishing system context. NIST AI RMF 1.0 (2023), MAP 1.2.
TeamStation planning worksheet informed by the cited reference, not a customer result, certification, or completed assessment.
- CapabilityRetrievalUseful evidenceA traceable answer linked to permitted source documents.Dependency to checkData owner and access policy.
- CapabilityEvaluationUseful evidenceA versioned test set with expected behavior and failure cases.Dependency to checkDomain reviewer availability.
- CapabilityOperationsUseful evidenceA monitored service with an exercised recovery path.Dependency to checkDeployment access and support coverage.
What this does not establish
A missing artifact means readiness is unverified, not that a person lacks ability. Assess the actual task and avoid treating a capability checklist as a universal score.
Questions before you commit
Is an AI capability gap the same as a hiring gap?
No. A gap can be missing data access, unclear ownership, weak evaluation, or an integration constraint. Hiring is only one possible response.
How should capability readiness be verified?
Use task-specific work samples, system records, and practical tests. Record unknowns separately from failed checks and revisit them when evidence becomes available.
From planning to delivery
What to scope with TeamStation
Bring a workflow and the evidence you already have. We can help translate the missing engineering work into role requirements and a bounded delivery scope.
- A task-to-capability map with evidence and missing dependencies.
- Practical role requirements tied to implementation and review work.
- A sequence for training, platform repair and additional capacity.
A capability map isn't a validated score of employee potential. Missing evidence calls for further assessment, not an automatic hiring or replacement decision.
Discuss your delivery scopeYour next decision
Assess AI skills, data access, integration, evaluation and operations. Sequence evidence-backed capability gaps before adding teams.