Tabletop and after-action learning assistant

Win statement

Enable operations, security, and mission teams to use Tabletop and after-action learning assistant to reduce friction in the work, with a visible source, an exception path, and a human owner for the decision.

Description

Key benefits

• ·Gives people a safe way to practice a decision or task before it matters in live work.
• ·Connects feedback to a real standard, source, or scenario.
• ·Lets facilitators see where people need better support.
• ·Supports learning without pretending an automated score is the full story.

Potential impact

These are pilot hypotheses, not promised outcomes. Validate them against a real baseline, quality sample, and user feedback.

Success metrics

Services needed

A defined business process needs a conversational front door, connected knowledge, a routed action, an approval, a scheduled or event-triggered flow, or delivery beyond a single user's M365 context. Move to Microsoft 365 Copilot (Premium) when the useful experience is a focused assistant for licensed users in the M365 flow of work. Move to Microsoft Foundry when bespoke code, specialized models, complex orchestration, multimodal processing, or deeper runtime control are central.

Data sources

• ·Approved learning content, policies, scenarios, job aids, and example artifacts
• ·Subject-matter-expert feedback and success criteria
• ·Learning and work-quality feedback

Implementation considerations

• ·Name one accountable business owner, one technical owner, and one content or data owner before the pilot starts.
• ·Define what the agent may advise, what it may do, and what must remain a human decision.
• ·Use representative test cases, including incomplete, conflicting, and out-of-scope inputs.
• ·Design the exception path before measuring straight-through success.
• ·Measure user effort, quality, and rework together. A high interaction count alone does not show value.
• ·Build in a managed Power Platform solution with environment, connection-reference, and data-loss-prevention decisions made up front.
• ·Use deterministic flows and approvals for consequential actions. Do not rely on conversational language to enforce a business rule.
• ·Test the chosen authoring experience and preview status before committing a production design, because current experiences have different feature boundaries.
• ·Category-specific focus: Learning & simulation.

Human review · A named qualified person reviews exceptions, low-confidence output, and any recommendation or action with material consequence.

Executive FAQ

Next actions

• 01Observe 5-10 real examples of tabletop and after-action learning assistant and map the current work, delay, handoff, and exception path.
• 02Name the accountable decision owner, source owner, technical owner, and pilot audience.
• 03Choose the smallest approved content set, data set, and action set that can prove or disprove the value hypothesis.
• 04Create a representative test pack, including success, ambiguity, bad input, and escalation cases.
• 05Run a time-boxed pilot with a measured baseline and a structured user-feedback loop.
• 06Review quality, rework, safety, adoption, and value together. Expand only when the work is demonstrably better.

Estimated timeline

8-14 weeks after discovery

1. Discovery and service design2 weeks

   Map the user journey, existing process, handoffs, exception path, and system of record.

2. Agent and flow build3-4 weeks

   Configure instructions, knowledge, tools, and deterministic flows in a managed solution.

3. Pilot and evaluate2-3 weeks

   Test conversations, actions, permissions, and low-confidence handoffs with a real pilot group.

4. Operationalize1-5 weeks

   Train owners, publish, monitor, and establish an ongoing content and change cadence.

Provenance

Inferred candidate · Copilot Studio

Candidate. Discovery and validation required before any build commitment.