Case Patterns

What an AI workflow case can look like

These are anonymized example patterns, not public client claims. They show how a workflow can be framed, evaluated, designed, and measured before AI implementation.

Support policy replies

Situation: A service team answers repeated questions about delivery, refunds, and policy exceptions.
Workflow: Incoming email or chat is classified, matched to approved policy sources, drafted, and reviewed before sensitive replies are sent.
Where AI helps: Classify request type, retrieve policy references, draft consistent replies, and flag unusual cases.
Risk control: Refunds, complaints, contract issues, and unclear policy cases stay under human review.
Measure: First-response time, manual triage time, escalation accuracy, answer consistency.
Likely next step: Pilot Design Sprint

Internal SOP lookup

Situation: Employees repeatedly ask where to find procedures, templates, and operational rules.
Workflow: AI searches approved sources, answers with references, and flags missing or outdated documents.
Where AI helps: Retrieve source-grounded answers and reveal which knowledge areas need cleanup.
Risk control: Answers cite sources. Missing or conflicting sources are escalated rather than guessed.
Measure: Search time, repeated questions, onboarding speed, user confidence.
Likely next step: AI Workflow Diagnostic

Document-heavy operations intake

Situation: Operations teams receive emails and documents that must be summarized, checked, and routed.
Workflow: AI extracts key fields, summarizes exceptions, prepares a structured task, and routes it to the right owner.
Where AI helps: Extract data, summarize context, identify missing information, and prepare handoff notes.
Risk control: Incomplete or high-value cases require confirmation before downstream action.
Measure: Processing cycle time, rework rate, missing information rate, handoff quality.
Likely next step: AI Workflow Implementation

Inbound sales qualification

Situation: Inbound requests arrive with uneven detail, making priority and routing inconsistent.
Workflow: AI extracts intent, asks for missing details, scores readiness, and routes leads to the right follow-up path.
Where AI helps: Structure intake, identify buyer intent, draft follow-up questions, and suggest routing.
Risk control: AI recommends priority but does not reject opportunities without review.
Measure: Qualification speed, response delay, handoff quality, qualified lead conversion.
Likely next step: Pilot Design Sprint

Have a workflow that looks similar?

Start by testing whether it has the repetition, source material, review structure, and ownership needed for a realistic AI pilot.

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