AI Use Cases

Use Case Strategy, Intake, Alignment

AI Use Case Intake Model: Set up a standardized intake form, submission channels, and triage workflow to avoid “random ideas in slide decks.”
Strategic Alignment Filters: Define decision principles and alignment criteria (strategic priorities, KPIs, customer promises, cost takeout goals).
Value Thesis Library: Build reusable value hypotheses by function (sales, service, finance, ops, HR) to accelerate idea generation.
Use Case Definition Standards: Create a consistent “use case one-pager” format (problem, user, workflow, inputs/outputs, success metrics).

Executive Use Case Workshops: Facilitate structured workshops to surface high-value opportunities across functions.
Frontline Workflow Mining: Identify pain points and repetitive work through observations, ride-alongs, and process walk-throughs.
Data-Driven Opportunity Scans: Use operational data (tickets, cycle times, defects, spend, churn signals) to pinpoint value pools.
External Benchmark Inspiration: Translate competitor moves, vendor patterns, and cross-industry analogs into actionable candidates.

Use Case Taxonomy Design: Classify use cases (automation, augmentation, prediction, personalization, genAI knowledge, agents).
Portfolio Segmentation: Separate “quick wins,” “platform bets,” “frontier experiments,” and “compliance-required” use cases.
Duplicate/Overlap Resolution: Consolidate similar ideas and standardize naming to prevent fragmented efforts.
Ownership and Sponsorship Mapping: Assign business owners, product owners, and accountable executives per use case.

Benefits Estimation Models: Build repeatable sizing templates (labor time, error reduction, revenue lift, risk reduction, cash impact).
Baseline Definition and Measurement: Establish pre-AI baselines and measurement methods so “value” is provable later.
Cost and Effort Estimation: Estimate build/run cost, data work, integration effort, and change/adoption lift.
Value Realization Plan: Define benefit owners, timing, dependencies, and tracking cadence to prevent “ROI drift.”

Data Feasibility Review: Assess availability, quality, access, labeling needs, and unstructured data readiness.
Process Feasibility Review: Evaluate workflow stability, exception rates, handoffs, and whether process redesign is required first.
Technology Feasibility Review: Assess integration points, latency needs, model hosting constraints, and tooling fit.
Organizational Feasibility Review: Confirm decision rights, operator readiness, training needs, and whether incentives will support adoption.

Risk Tiering Framework: Categorize use cases by risk (customer-facing, regulated, safety-critical, material financial impact).
Privacy and Security Pre-Screen: Identify sensitive data exposure, access controls, retention needs, and threat model implications.
Model Risk Considerations: Assess explainability needs, bias risk, hallucination tolerance, and error consequences.
Controls and Human Oversight Design: Define required review steps, approvals, audit trail, and human-in-the-loop guardrails.

Prioritization Scoring Model: Score by value, feasibility, risk, strategic fit, time-to-impact, and dependency complexity.
Dependency Mapping: Identify prerequisite data products, process changes, integrations, vendor decisions, and policy blockers.
Roadmap Sequencing: Build a 3/6/12-month delivery roadmap balancing quick wins and foundational work.
Capacity and Funding Fit: Match use cases to available teams/budget; shape a portfolio that can actually be delivered.

PoV Design (Hypothesis-Based): Define testable hypotheses, success criteria, evaluation plan, and timeline.
Prototype and Pilot Planning: Stand up rapid pilots with realistic users, representative data, and constrained scope.
Evaluation Metrics and Test Sets: Define accuracy/quality measures, error types, acceptance thresholds, and benchmark baselines.
Pilot-to-Scale Decision Gates: Establish objective go/no-go criteria and escalation for ambiguous results.

MVP Definition: Translate the use case into an MVP scope with clear user journeys and “done means done” criteria.
Requirements for Engineering: Produce PRDs, workflow specs, data requirements, integration needs, and non-functional requirements.
Operating Model Requirements: Define support needs, monitoring, retraining triggers, and escalation paths.
Change and Adoption Requirements: Specify training needs, comms, role impacts, and adoption metrics required to scale.

Use Case Governance Forums: Set up practical routines (intake council, prioritization board, risk review) with clear decision rights.
Portfolio KPI Dashboard: Track pipeline stages, time-to-decision, time-to-pilot, value delivered, and adoption outcomes.
Backlog and Lifecycle Management: Manage use case lifecycle (ideation → pilot → scale → retire) and prevent zombie pilots.
Stakeholder Communication Cadence: Create crisp status reporting and executive narratives that keep momentum and funding.

Reusable Use Case Playbooks: Package repeatable patterns (ticket triage, call summarization, pricing guidance, AP automation).
Reusable Components Library: Promote reuse of prompts, evaluation harnesses, UI patterns, data connectors, and guardrails.
Replication Strategy: Define how to replicate a use case across business units/geographies while preserving local constraints.
Benefits Replication Tracking: Track value realization by site/unit and manage variance drivers to prevent dilution at scale.