AI-driven SLA manager

Win statement

Enable research security, IT, and project teams to use AI-driven SLA manager to reduce friction in the work, with a visible source, an exception path, and a human owner for the decision.

Description

Key benefits

• ·Gives users a clear next step in a process that previously lived across people, inboxes, and spreadsheets.
• ·Routes work to the right owner with an auditable handoff.
• ·Separates deterministic actions from advisory language.
• ·Makes exceptions visible instead of hiding them.

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

• ·Process maps, SOPs, forms, queues, case records, and approval rules
• ·Approved connectors and system-of-record APIs
• ·Operational logs and exception records

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: Workflow.

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 ai-driven sla manager 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

Workshop-derived · Copilot Studio

Candidate. Discovery and validation required before any build commitment.