AI Strategy Quickstart: Objectives, Use‑Case Map, and a Pragmatic 90‑Day Roadmap

Welcome

This quickstart helps teams translate business goals into a tight, prioritized set of AI initiatives that produce measurable learning and value within 90 days. It focuses on solving meaningful problems, protecting resources from unfocused pilots, and building a foundation you can scale.

Use this playbook as a facilitator’s guide during a half‑day to two‑day planning session. You’ll leave with:

• A clear outcome statement for AI investments
• A mapped set of candidate use cases (value vs feasibility)
• Capacity & dependencies identified
• A 90‑day sprint plan with owners and success criteria
• Risks, mitigations, and go/no‑go acceptance rules

1. Frame Strategic Outcomes (30–60 minutes)

Begin with the outcomes you care about, not the technology. A concise outcome frames the hypothesis you’ll test with each pilot.

1. State the business outcome in one sentence (who, result, timeframe). Example: "Reduce average customer support response time from 8 hours to <2 hours for Tier 1 issues within 90 days."
2. Define 2–3 supporting metrics (primary success metric + leading indicators). Example: Primary = average response time; Leading = % of messages triaged automatically, % of escalations avoided.
3. Set a minimal acceptable improvement (pilot acceptance criteria). Example: 30% reduction in response time and >80% triage accuracy in production‑like testing.

2. Stakeholder & Capability Map (30 minutes)

Quickly list stakeholders, decision owners, and the internal capabilities required.

• Stakeholders: Sponsor, Product Owner, Data Owner, IT/Platform, Legal/Privacy, Operations lead, End user representatives.
• Capabilities: Data access & quality, Model development, MLOps/deployment, UX/integration, Monitoring & analytics, Change management.

Exercise: For each candidate use case note the primary sponsor and one person accountable for delivery.

3. Use‑Case Mapping: Value vs Feasibility (45–90 minutes)

Collect candidate use cases from the team. For each candidate, estimate expected business value and feasibility (data, engineering complexity, regulatory constraints) using three levels: High / Medium / Low.

Use a simple 2×2 map: Value (High/Low) on the vertical axis; Feasibility (High/Low) on the horizontal axis. Prioritize High Value / High Feasibility for early pilots. Reserve High Value / Low Feasibility for later investment or research tracks.

Suggested quick scoring template (per use case):

• Problem statement (1 sentence)
• Expected impact (qualitative + rough $ or time estimate)
• Primary metric(s)
• Data availability (Good/Partial/None)
• Technical complexity (Low/Medium/High)
• Compliance or regulatory constraints (Yes/No; brief note)

Example shortlist after mapping:

1. Auto‑triage of Tier 1 support (High value, High feasibility)
2. Sales lead prioritization (High value, Medium feasibility)
3. Automated document classification for contracts (Medium value, High feasibility)

4. Capacity & Dependencies Checklist

Before committing, confirm you can run a meaningful experiment. Use this checklist to decide whether to greenlight a pilot.

• Data: Is sample data accessible, labeled, and representative? (Yes/No — note gaps)
• Compute & Platform: Is there an environment for prototyping and secure testing? (Yes/No)
• Integration points: Which systems must integrate for a pilot to run? (List)
• People: Assigned engineer, data scientist, product owner, and business SME (Named people)
• Governance: Privacy, security, and legal reviews scheduled? (Yes/No)
• Budget & Time: Rough budget estimate and availability of 2–3 sprints in next 90 days

If critical items are missing, convert the task into a readiness activity (data prep, sandbox provisioning) rather than an immediate pilot.

5. 90‑Day Sprint Plan (Template)

Structure the 90‑day plan as three 4‑week cycles focused on learning and delivering an MVP that demonstrates measurable impact. For each pilot, assign an owner and one clear acceptance rule.

Weeks 1–4: Discovery & MVP Design

• Confirm outcome & KPIs, finalize data access, build initial proof of concept (POC) approach, and define acceptance criteria.
• Deliverable: POC that demonstrates feasibility on a test dataset, and a runbook listing data, tech, and integration needs.

Weeks 5–8: Pilot Build & Controlled Test

• Develop MVP, integrate with a limited production‑like environment, execute controlled A/B or canary tests, capture metrics.
• Deliverable: MVP running in a pilot environment with dashboards for KPI measurement and an initial cost/time estimate for scale.

Weeks 9–12: Validation & Decision

• Run validation, measure against acceptance criteria, document lessons, and recommend go/no‑go for scaling or further iteration.
• Deliverable: Pilot report (metrics, risks, roadmap to scale, estimated budget & org changes required).

Example acceptance criteria (support triage pilot): "Achieve ≥30% reduction in average response time and ≥80% triage accuracy during a 2‑week controlled test with no major privacy incidents."

6. Risks & Mitigations

Common risks and practical mitigations:

• Data quality gaps — Mitigate: run a 1‑week data discovery, sample labeling, or synthetic data augmentation.
• Integration overhead delays — Mitigate: start with offline/batch evaluation before real‑time integration.
• Over‑ambitious scope — Mitigate: split into MVP features and a deferred roadmap for secondary features.
• Unclear ownership after pilot — Mitigate: require a sponsor who agrees to resource scaling if acceptance criteria met.
• Regulatory/privacy issues — Mitigate: early legal review and a privacy impact assessment before testing with live data.

7. Worksheets (copyable templates)

Use these short templates during the session. They are intentionally minimal so teams can complete them live.

Business Value Hypothesis

Problem statement: ________________________

Outcome (who, what, timeframe): ________________________

Primary metric: ________________________

Target improvement (minimum acceptable): ________________________

Data Needs Snapshot

Data sources: ________________________

Exists / Quality / Notes: ________________________

Privacy or compliance constraints: ________________________

Pilot Budget & Team

Estimated budget (people, infra, tools): ________________________

Core team (roles + names): ________________________

Pilot Acceptance Criteria

Success metric & threshold: ________________________

Minimum non‑negotiables (e.g., accuracy, latency, safety): ________________________

8. Governance, Ethics & Scaling Considerations

Plan governance in parallel with pilots. Define where decisions live (who approves production rollout), what monitoring is required, and what ethical guardrails must be enforced.

Key checks before scaling:

• Reproducible performance on production data and over time
• Operational support (on‑call, monitoring)
• Cost model and ROI projection
• Change management plan for affected teams

9. How to Use This Playbook

Facilitator tips:

• Run the playbook with cross‑functional representation: business, data, engineering, legal, and end users.
• Timebox exercises to keep decisions forward‑leaning; prefer a small validated pilot to a perfect plan.
• Capture decisions, assumptions, and open risks in a single shared document so the team can iterate.

After the session, produce a one‑page investment memo summarizing outcome, pilot plan, acceptance criteria, budget, and go/no‑go decision points.

10. Example One‑Page Pilot Summary (deliver with pilot)

Title: Auto‑triage for Tier 1 Support

Outcome: Reduce avg response time for Tier 1 from 8h to <2h in 90 days

Primary metric: Avg response time. Target: 30% improvement in pilot, 50% on scale.

Team & Budget: Product Owner (Name), Data Engineer (Name), Data Scientist (Name). Budget: $XXk

Acceptance: 30% response reduction AND ≥80% triage accuracy in a 2‑week controlled test.

Next steps: Week 1 data discovery → Week 3 POC → Week 6 pilot → Week 12 decision.

Closing

This playbook is intentionally pragmatic: pick a small set of pilots that demonstrably connect to business outcomes, learn fast, and build the scaffolding for scale. If you want, this structure can be turned into interactive worksheets (to collect and store team responses), a repeatable kit for other sites or business units, and a standardized pilot report template.