Strategy and Value-Case Readiness

AI transformation succeeds when strategy and economics travel together. Ambition without economics becomes theater; economics without ambition degenerates into local optimizations. This chapter helps you test whether your organization has a clear, value‑anchored AI strategy and a portfolio of use cases that can be funded, governed, and scaled with confidence. The focus is practical: a small set of decisions and artifacts that prove you know where value will come from, how you will measure it, what you will not do, and how you will move quickly without taking on unacceptable risk.

You will evaluate both the “north star” and the “work on Monday.” At the top, we look for a concise, 12–24‑month AI thesis tied to enterprise strategy and stakeholder promises. In the middle, we assess whether your portfolio intake, triage, and stage‑gates channel resources to the highest‑return opportunities. At the ground level, we examine whether each priority use case has an owner, a causal measurement plan, a clear risk classification, and economics that hold up under scrutiny—including unit costs for generative workloads. If these elements are in place, downstream work on data, platforms, risk, and change moves faster and with fewer surprises.

5.1. Diagnostic Criteria

The diagnostic evaluates Strategy and Value‑Case Readiness across six sub‑dimensions. Each criterion is written so you can confirm it with evidence and score it consistently.

Strategic intent and ambition.

There is a brief, signed AI strategy that explains why AI matters for your organization over the next 12–24 months, what value themes you will pursue (growth, productivity, risk reduction, resilience), and what you will not do. It names the domains where you intend to lead versus follow, the customer and colleague experiences AI will reshape, and the principles that will guide hard choices (for example, human‑in‑the‑loop for external content, data residency boundaries, model risk tolerance by decision type). The document is current (reviewed within the last year), referenced in planning materials, and visible to product, technology, and risk teams. Evidence includes the strategy memo, leadership readouts, and the planning rhythm that cascades it into roadmaps.

Value‑case economics and measurement.

Every priority use case has a quantified value hypothesis tied to operating KPIs, a baseline, and a signed measurement plan that distinguishes AI impact from non‑AI drivers. Economics cover both benefits and full costs to achieve, including model training and inference, retrieval, storage, platform overhead, enablement, and change. Acceptance tests convert the hypothesis into pass/fail conditions at each gate. For generative use cases, task‑quality metrics (e.g., grounded answer rate, retrieval precision/recall, safety thresholds) and cost‑per‑task targets are defined before build. Causality is addressed explicitly: where A/B or quasi‑experimental methods are feasible, they are planned; where they are not, you specify credible leading indicators and lagging confirmation. Evidence includes business cases, instrumentation plans, and dashboards prepared to receive the signals.

Portfolio and prioritization.

There is a visible funnel from idea to discovery, proof, pilot, production, and scale. Intake is open but triage is disciplined: prioritization balances value, feasibility, time‑to‑impact, and risk class using a standard rubric. WIP limits prevent thrash, and stage‑gates release funding based on validated learning and control adherence—not activity. The pipeline is right‑sized to capacity; kill/pivot decisions occur at predictable intervals and are recorded with rationale. Time‑to‑decision is measured, and cross‑functional forums resolve trade‑offs on a weekly cadence. Evidence includes a portfolio register (owner, stage, economics, risk class), gating criteria, decision logs, and throughput metrics (lead time from idea to first value).

Enterprise alignment and guardrails.

Simple, actionable guardrails exist to reduce rework and escalation: build‑buy‑partner boundaries; content risk tolerance by channel; minimum human oversight by use‑case class; standard patterns for data access, retrieval‑augmented generation, and model deployment; naming and documentation conventions; and portability expectations for vendor integrations. Teams can cite these guardrails and show how they apply to their current work. Evidence includes a short “principles and patterns” pack, reference integrations, and examples where guardrails accelerated a decision.

Risk appetite and boundaries.

Risk is expressed in plain language by use‑case class—assistive internal tools, internal decision support, external content, and automated decisions. The appetite statement sets the conditions under which you will and will not deploy, the additional controls required as autonomy and exposure increase, and the exceptions process with expiry and mitigation. For regulated contexts, explainability, fairness, and auditability requirements are embedded at the strategy level, not discovered late in delivery. Evidence includes a risk appetite memo endorsed by business and risk leadership, classification rubrics, and an exception register with ages and owners.

Board and stakeholder oversight.

There is a predictable rhythm for explaining AI strategy and outcomes to the board and key stakeholders in business terms. Materials cover value realized vs. plan, risk posture and incidents, major decisions pending, and emerging obligations. The pack fits into existing governance (no bespoke theater), and it is supported by a single source of truth for metrics and decisions. Evidence includes the latest board/executive readout, agenda cadence, and a list of decisions escalated and closed.

What “ready” looks like in measurable terms.

Use these indicative thresholds to calibrate. Adjust for your context, but make the bar explicit before scoring.

Top three value cases account for a majority share of the year‑one value (often ≥60%) and each has an owner, baseline, and acceptance tests.
Every in‑scope use case has a risk class, a safe‑to‑operate minimum, and a measurement plan that a delivery team can implement without reinterpretation.
Portfolio governance shows WIP limits, time‑to‑decision ≤ 10 business days for priority items, and a visible kill/pivot rate at discovery (healthy ranges often 20–40%).
Generative use cases define and meet pre‑launch task‑quality gates (for example, retrieval precision/recall thresholds for RAG and content safety pass rates) alongside cost‑per‑task targets.
Stage‑gates tie funding to validated learning, benefits, evidence, and control adherence; at least one gate must be passed before committing multi‑quarter spend.

Domain‑specific gating rules (apply caps if any fail).

No named business owner or KPI baseline for a top use case → do not advance beyond discovery.
No signed measurement plan or acceptance tests for the next stage → hold funding at current gate.
No risk classification or safe‑to‑operate minimums for the use‑case class → block external pilots and cap the Strategy & Value domain until addressed.
No portfolio view with decision logs and WIP limits → cap the domain; prioritization is not credible.

Minimum evidence required to score confidently.

One current AI strategy memo or equivalent planning artifact with explicit non‑goals.
Portfolio register covering all in‑scope use cases with owner, stage, economics, and risk class.
Business case and measurement plan for each priority use case, including cost‑per‑task for GenAI where relevant.
Gating criteria and recent decision logs that show how funding moved (or did not).
Risk appetite statement and exception register with expiry dates.

Anti‑patterns to watch for.

“Strategy by shopping list”: an unprioritized catalogue of use cases without value concentration or sequencing.
“Platform first, value later”: large platform spends without a line of sight to funded, measured use cases.
“Metric myopia”: model metrics substituting for business outcomes or task‑quality thresholds.
“Eternal pilot”: prolonged proof with no stage‑gates, no kill decisions, and no adoption plans.
“Compliance theater”: risk language without a risk appetite, exceptions policy, or evidence of applied boundaries.

If the criteria above are met with traceable evidence, you have a strategy that is investable and a portfolio that can move at speed without compromising safety. In the next section, we will translate these criteria into a step‑by‑step assessment you can run in days to establish a credible baseline and a sequenced plan of action.

5.2. Strategy Assessment – Step-by-Step Guide

This guide translates the diagnostic criteria into a fast, disciplined sequence you can run in days. The goal is to produce an investable AI strategy and a portfolio you can govern: clear value, explicit non‑goals, risk boundaries, stage‑gates, and a 90‑day plan. Assume you have the raw inputs from Chapter 4 (surveys, interviews, inventory, and baseline analytics). Work through the steps in order. Keep the bar high on evidence and low on ceremony.

Step 1 — Fix scope, decisions, and the clock

Start by writing down the decisions this assessment must enable in the next 30–90 days: which value cases to fund, what guardrails to adopt, which investments to pause, and what to measure. Time‑box the run (e.g., 10 business days for a BU baseline; 30 for enterprise). Name the in‑scope use cases and explicitly list what is out of scope.
Acceptance tests: decisions are written; end date is on calendars; scope and non‑goals are captured in a one‑page charter.

Step 2 — Assemble the minimum evidence pack

Pull the freshest artifacts, not the prettiest. You need the current AI strategy memo (or equivalent), portfolio register, gating criteria, last two decision logs, risk appetite note, top‑use‑case business cases, and measurement plans. For GenAI, add corpus registry, retrieval evaluation results, and content safety policy.
Acceptance tests: every artifact has an owner and last‑updated date; gaps are logged with owners and dates.

Step 3 — Establish the value thesis

Summarize where value will come from over the next 12–24 months: growth, productivity, risk reduction, or resilience. Link to three to five priority value cases and quantify plausible ranges. Use baseline analytics (4.4) to ground cycle times, adoption, reliability, and unit costs.
Acceptance tests: a one‑page value thesis exists with ranges, not point guesses, and cites the metric sources.

Step 4 — Clarify risk appetite and boundaries

Classify in‑scope use cases by risk class (assistive internal, internal decision support, external content, automated decisions). For each class, specify “safe‑to‑operate” minimums and where you will not deploy. Record exceptions and expiries.
Acceptance tests: a signed risk appetite page exists; each use case has a risk class and named minimum controls.

Step 5 — Pressure‑test value‑case economics

For each priority use case, confirm the benefit logic (causal path to KPI), baseline, ramp profile, and the full cost to achieve. For GenAI, calculate cost per task (inference, retrieval, platform overhead, enablement) and stress test with token volume and caching scenarios. If causality tests (A/B or quasi‑experimental) are feasible, plan them; if not, define leading indicators with lagging confirmation.
Acceptance tests: each use case has a quantified hypothesis, baseline, and a measurement plan a delivery team could execute tomorrow.

Step 6 — Define acceptance tests at each gate

Convert value hypotheses into crisp gates. Discovery exit requires an owner, baseline, risk class, and instrumentation plan. Pilot exit requires pre‑launch reviews completed, task‑quality targets met, and early value signal. Production exit requires adoption plan, rollback drills, and signed metrics contracts. Scale exit requires demonstrated value and stable unit economics.
Acceptance tests: written gate criteria exist with pass/fail tests and evidence sources.

Step 7 — Run portfolio triage and sequencing

Apply a simple scoring rubric: value, feasibility, time‑to‑impact, and risk. Enforce WIP limits that match capacity. Sequence the top five items to create a rolling, three‑wave roadmap: quick wins (≤90 days), foundation fixes that unlock multiple use cases, and strategic bets. Record kill and pivot decisions.
Acceptance tests: a visible funnel exists with owners, stages, decision dates, and WIP limits; time‑to‑decision ≤ 10 business days for priority items.

Step 8 — Codify enterprise guardrails

Write the short list of design guardrails that speed decisions: build‑buy‑partner boundaries, content safety posture, human‑in‑the‑loop requirements, data residency rules, retrieval patterns for RAG, and portability expectations for vendors. Keep it to one page; include examples.
Acceptance tests: teams can point to the guardrails and show how they apply to a live decision.

Step 9 — Align funding and stage‑gates

Time spent at gates. Release just enough funding to clear the next acceptance tests; hold multi‑quarter money until value proof and control adherence are demonstrated. For platform spend, require line‑of‑sight to the top value cases and measurable enablement benefits (e.g., reduced lead time, lower unit cost).
Acceptance tests: a funding memo exists; every use case shows the next gate, evidence required, and the amount at risk.

Step 10 — Lock measurement and instrumentation

Translate the measurement plan into events, fields, and dashboards. Confirm owners for data capture, KPI calculation, and value attribution. For GenAI, lock retrieval quality metrics, grounded answer rate, safety filters, and human‑in‑the‑loop thresholds.
Acceptance tests: an instrumentation checklist exists per use case; dashboards or placeholders are created before build.

Step 11 — Write the two‑page AI strategy

Capture the essentials: ambition and non‑goals; value thesis; top five value cases with owners; risk appetite and safe‑to‑operate minimums; guardrails; funding and gates; and the operating rhythm for decisions and measurement. Avoid jargon.
Acceptance tests: the sponsor signs; the document is used in the next planning or investment forum.

Step 12 — Synthesize constraints and actions

From surveys, interviews, inventory, and analytics, name the five constraints that most block value (e.g., access lead times, missing rollback, unclear decision rights, RAG retrieval quality). Quantify value at risk and time‑to‑relieve. Assign one remediation per constraint with an acceptance test.
Acceptance tests: a constraints list exists; each item has an owner, date, and measurable pass condition.

Step 13 — Run the executive readout and capture decisions

Tell the story in this sequence: value, risk, time. Start with the value thesis and top use cases; explain the risk posture; show the portfolio and gates; present constraints and the 90‑day plan. Ask for three approvals: funding by gate, guardrails, and operating rhythm. Log decisions and dissent.
Acceptance tests: decisions are recorded in the decision log; funding and gates are locked; communications go out within 48 hours.

Step 14 — Mobilize the 90‑day plan

Translate decisions into calendarized actions. Book the first gate reviews, instrument dashboards, and schedule a portfolio forum with WIP enforcement. Publish the owner list and acceptance tests.
Acceptance tests: actions are on calendars, not slides; metric owners are named; the next reassessment date is set.

Step 15 — Red‑team the strategy before you launch

Spend an hour trying to disprove your plan. Attack the biggest assumptions: adoption, data readiness, content safety, and unit cost. Adjust gates or investments where the evidence is weak.
Acceptance tests: a short red‑team note exists; at least one assumption is tightened or one risk is explicitly accepted with mitigation.

Fast‑track sequence (5 business days)
Day 1: scope, decisions, evidence pack.
Day 2: value thesis and risk appetite; confirm top five use cases.
Day 3: economics check for each use case; define gates and guardrails.
Day 4: triage and sequencing; constraints and 90‑day plan.
Day 5: executive readout; decisions and mobilization.

Deep‑dive sequence (15 business days)
Add cross‑BU alignment, fuller economics and instrumentation design, portfolio simulations under different funding levels, and a board‑ready risk posture statement.

Artifacts you should produce

Two‑page AI strategy with non‑goals.
Portfolio register with owners, stages, economics, risk class, and decision dates.
Gate criteria and funding memo.
Measurement and instrumentation plans per top use case.
Guardrails one‑pager.
Constraints list with acceptance tests.
90‑day action plan and communications note.

Quantitative bars to keep you honest

≥60% of near‑term value concentrated in the top three use cases with owners and baselines.
Time‑to‑decision for priority items ≤ 10 business days.
Kill/pivot rate at discovery 20–40% (too low means you’re not testing; too high means intake is noisy).
For GenAI, pre‑launch retrieval precision/recall thresholds met on representative tasks and grounded answer rate defined for external content.
Funding released by stage‑gate evidence, not activity volume.

Common failure modes and how to avoid them

Strategy by shopping list: enforce value concentration and sequencing; cut the tail.
Platform first, value later: require line‑of‑sight from platform features to use‑case lead time or unit‑cost improvements.
Metric myopia: insist on operating KPIs and task‑quality metrics, not just model AUC or BLEU.
Eternal pilots: put a date on every stage; kill or scale.
Compliance theater: publish a risk appetite and minimum controls; embed them into gates and pipelines.

Readiness scoring hook

When you finish, score the Strategy & Value domain using Chapter 3’s rubric. Cite the two‑page strategy, portfolio register, gate criteria, and measurement plans as evidence. Apply caps if any gating rules fail (no owner, no measurement plan, no risk classification, no portfolio view). Feed constraints into the 90‑day plan and the executive heat map.

Run this play with discipline and you’ll replace AI aspiration with investable intent, a credible portfolio, and a funded plan that moves value in the next quarter—safely and at speed.

5.3. Strategy Alignment Checklist

Use this checklist to compress months of debate into a single aligned plan you can fund and run. The aim is simple: one page of ambition and non‑goals, a short list of value cases with owners and economics, explicit risk boundaries, stage‑gates tied to evidence, and a 90‑day action plan. Work through the items in order. If an item is missing, document the minimum viable version and move on—momentum matters.

Ambition and non‑goals

A two‑page AI strategy is signed by the sponsor and refreshed within 12 months.
Value themes (growth, productivity, risk reduction, resilience) are prioritized for the next 12–24 months.
Non‑goals are explicit (what you will not pursue this year).

Top value cases (focus and ownership)

The top three value cases account for most near‑term value and each has a named business owner.
Each value case has a quantified hypothesis (benefits, timing) and a baseline tied to operating KPIs.
Each value case has acceptance tests for the next stage (what evidence must be true to pass).

Measurement and instrumentation

Measurement plans exist for every priority use case (events, fields, dashboards, attribution logic).
Leading indicators (pre‑value) and lagging KPIs (realized value) are both defined.
For GenAI, task‑quality metrics and cost‑per‑task targets are specified (e.g., grounded answer rate, retrieval precision/recall, safety thresholds).

Risk appetite and boundaries

Use cases are classified by risk class (assistive internal, internal decision support, external content, automated decisions).
“Safe‑to‑operate” minimum controls are defined and right‑sized per class; exceptions have owners and expiry dates.
Conditions under which you will not deploy are written in plain language.

Guardrails that speed decisions

One page of design guardrails exists (build‑buy‑partner boundaries, data residency, human‑in‑the‑loop rules, content safety posture, portability expectations).
Teams can show where guardrails were applied to a live decision.

Portfolio and prioritization

Intake → discovery → pilot → production → scale is visible, with WIP limits that match capacity.
Prioritization uses a simple, consistent rubric (value, feasibility, time‑to‑impact, risk).
Kill/pivot decisions at discovery are recorded with rationale and dates.

Funding and stage‑gates

Funding is released by gate, based on validated learning and control adherence—not activity volume.
Platform spend is tied to measurable enablement benefits (lead time, reliability, unit cost).
A funding memo lists the next gate and the evidence required for each priority use case.

Sequencing and dependencies

A three‑wave plan exists: quick wins (≤90 days), foundation fixes that unlock multiple use cases, and strategic bets.
Cross‑use‑case dependencies are called out (data products, platform capabilities, risk reviews) with owners and dates.

Operating model linkages

Decision rights for data access, model approval, deployment, and funding are unambiguous and documented.
A single portfolio forum with a weekly cadence resolves trade‑offs; time‑to‑decision ≤ 10 business days for priority items.
A change‑management lead is named for each value case; adoption is part of “done.”

Board and stakeholder oversight

A standing executive/board pack template covers value realized vs. plan, risk posture/incidents, major decisions, and emerging obligations.
Cadence is on the calendar; the source of truth for metrics and decisions is referenced.

GenAI‑specific alignment (use if in scope)

Approved corpora are enumerated, with inclusion/exclusion rules and lifecycle governance for documents and embeddings.
Retrieval‑augmented generation patterns are standard (chunking, embedding, evaluation harness).
Safety filters and human‑in‑the‑loop thresholds are defined for sensitive content; prompts/outputs are logged and protected; third‑party training opt‑out is enforced.

Financial discipline

Unit economics are visible at the use‑case level (LLM tokens, retrieval, infrastructure, platform overhead).
Targets exist for cost per task and are used in design choices (model selection, caching, routing).

Ecosystem and exit

Critical vendors are mapped to value cases; SLAs are outcome‑oriented (latency, safety, reliability).
Exit paths are designed (data/model artifact portability, API abstraction); a portability drill is scheduled.

Minimal evidence you should have in hand

Two‑page AI strategy and non‑goals.
Portfolio register with owners, stage, economics, and risk class.
Gate criteria and the funding memo.
Measurement and instrumentation plans for priority use cases.
Guardrails one‑pager and recent decision logs.
Risk appetite statement with exceptions register.

Ten yes/no questions that expose misalignment

Do the top three use cases each have a named business owner and a KPI baseline?
Can you state the acceptance test that must be passed to release the next tranche of funding?
Is there a written rule for when you will not deploy a use case (by class)?
Can a product owner get governed access to required data within the target SLO?
Is rollback a tested, default path for AI releases?
Do GenAI use cases have defined retrieval quality and content safety thresholds pre‑launch?
Are WIP limits enforced in your portfolio forum?
Can you trace use‑case spend to unit economics (including LLM tokens and retrieval)?
Is there one forum where cross‑domain trade‑offs are decided weekly?
Could you switch a critical vendor or model class in a bounded drill without rewriting your stack?

Acceptance tests for “aligned enough to proceed”

A signed two‑page strategy exists and is used in the next planning forum.
Each priority use case has owner, baseline, risk class, measurement plan, and gate acceptance tests.
A funding and gating memo is approved; quick wins and foundation fixes are scheduled.
Guardrails and decision rights are documented and referenced in live work.
Metrics and dashboards (or placeholders) exist before build begins.

Common misalignments and quick fixes

Strategy by shopping list → Concentrate ≥60% of near‑term value in three use cases; cut the tail.
Platform first, value later → Tie platform tasks to measurable enablement benefits for top use cases.
Eternal pilots → Put dates on every stage; require evidence to advance; celebrate kills.
Compliance theater → Publish a risk appetite and minimum controls; embed them in gates and pipelines.
Metric myopia → Replace model metrics as “success” with operating KPIs and task‑quality thresholds.
Shadow decision rights → Publish a one‑page RASCI for approvals and funding; enforce it in the portfolio forum.

60‑minute alignment huddle (run this when momentum stalls)

Ambition and non‑goals (10 minutes): sponsor states outcomes and boundaries.
Top value cases (15 minutes): owners, baselines, acceptance tests.
Risk appetite and guardrails (10 minutes): confirm “safe‑to‑operate” minimums.
Funding and gates (10 minutes): lock the next tranche by evidence.
Constraints and quick wins (10 minutes): name the five blockers; assign owners and dates.
Close (5 minutes): confirm the readout date and publish artifacts.

If you can answer “yes” to the ten questions and pass the acceptance tests, you have the alignment needed to move from ambition to execution. If not, use this checklist as your working agenda until each gap is closed, then proceed to translate alignment into a sequenced, funded roadmap.

5.4. Value-Case Template

This template turns an idea into an investable, governable value case. It is designed to be filled out by the business owner with input from product, data, platform, and risk partners. Keep it concise but complete. Aim for 6–10 pages plus appendices. Every field below ties to a readiness dimension and a stage‑gate decision. If a field does not apply, state “Not applicable” and explain why; do not leave blanks or “TBD.”

How to use this template

Complete Sections 1–8 before discovery starts; Sections 9–15 must be completed before pilot.
Quantify wherever possible; tie all metrics to owners and systems of record.
Link to artifacts in your governed repository (Chapter 4.3).
Use the acceptance tests in Section 14 to set your next gate. Funding is released only when those tests pass.

1) Header and Ownership

Value case name and short description.
Business owner (name, role), accountable executive sponsor, product lead.
Delivery triad: product, tech/platform, data; risk partners: privacy, security, model risk.
Current stage (idea, discovery, pilot, production, scale) and next gate date.
Risk class (assistive internal, internal decision support, external content, automated decisions).
Decision sought now (fund, pause, kill, scale) and amount at risk to next gate.

2) Business Objective and KPI

Objective stated in business terms tied to one or two operating KPIs.
Baseline values for those KPIs (measurement period, data source, owner).
Target range and time to impact (near‑term leading indicators and lagging benefits).
Problem/opportunity statement and why now (mandates, customer promise, cost pressure).

3) Scope and Boundaries

In scope: processes, channels, geographies, segments, user roles.
Out of scope and explicit non‑goals for this phase.
Assumptions and constraints (e.g., data residency, change freeze, vendor commitments).
Dependencies (data products, platforms, approvals) with owners and dates.

4) Users and Workflow

Target user personas and volume (who, how many, where).
As‑is workflow summary and pain points; to‑be workflow with AI in the loop.
Definition of “adopted” behavior change; usage metrics you will track.
Human‑in‑the‑loop roles and decision boundaries (what remains a human decision, when, and how it is evidenced).

5) Measurement and Attribution Plan

Causal path to value: how the solution moves the KPI (diagram or narrative).
Experiment design (A/B, staggered rollout, quasi‑experimental) or, if not feasible, leading indicators and lagging confirmation.
Event and field list for instrumentation; dashboards to be updated (links/placeholders).
Acceptance tests for this gate with pass/fail thresholds and data sources.
Attribution logic (distinguish AI impact from seasonality, mix, or non‑AI levers).

6) Economics and Funding

Benefits model: expected range by month/quarter for 12 months; key drivers and sensitivities.
Cost to achieve by stage: build, data work, platform consumption, LLM tokens, retrieval/vector queries, storage, enablement, change, licenses, vendors.
Unit economics: cost per task (inference + retrieval + platform overhead ÷ tasks) and target; assumptions for volume, caching, model choice.
Financial risks and mitigations (price caps, rate‑limit strategies, routing, model selection rules).
Funding request for the next stage and what evidence will release further funds.

7) Data and Knowledge Requirements

Critical datasets/data products with owners, SLAs, lineage links; known quality issues.
Access plan and SLO (request to usable credentials), masking/minimization choices, DPIA need (yes/no; if yes, status).
For GenAI: approved corpora, inclusion/exclusion rules, chunking/embedding standards, retrieval evaluation plan and target thresholds (precision/recall at k).
Data retention and redaction rules for prompts/outputs; training opt‑out status for any third‑party model.

8) Technical Approach

Solution pattern (traditional ML vs. GenAI; RAG vs. fine‑tuning; hybrid).
High‑level architecture: data sources, feature store, training/eval, serving, retrieval layer, guardrails, observability.
SLOs (latency p95, availability), rollback strategy (canary/blue‑green, automatic rollback triggers).
Evaluation harness: model metrics and task‑quality metrics (for GenAI: grounded answer rate, hallucination detection, content safety tests).
Portability plan (API abstractions, model/provider fallback, portability drill schedule).

9) Responsible AI and Risk Controls

Risk class rationale and applicable obligations (privacy, security, model risk, explainability, fairness, content safety).
Safe‑to‑operate minimums for this class and how they will be met pre‑launch (policy‑as‑code gates where possible).
Pre‑launch reviews required and SLAs (privacy, security, model risk, content safety for GenAI).
Exceptions or waivers requested, with mitigations and expiry dates.
Incident playbook (detection, escalation, rollback, communication), owners, and drill date.

10) Operating Model and Roles

Decision rights (intake, data access, model approval, deployment, rollback, funding).
Team roster for the stage: product, data science, ML/LLM engineering, platform, analytics engineering, design, evaluators, risk, change.
Ways of working: cadence (stand‑ups, design reviews, risk checkpoints), definition of done (controls, documentation, adoption).
Post‑launch ownership (on‑call rota, SLOs, ticket flows).

11) Adoption and Change Plan

Stakeholder map (executives, managers, frontline) and messages tailored to each.
Enablement plan (training, job aids, in‑app guidance), rollout waves, and completion targets.
Workflow redesign and responsibility changes; alignment with incentives and performance measures.
Adoption metrics: target usage, time‑on‑task change, satisfaction signals, and review cadence.

12) Ecosystem and Vendors

Build‑buy‑partner rationale; shortlisted vendors and evaluation status.
Commercial terms sought (IP/data rights, training opt‑out, SLAs, credits/price protections, termination/exit).
Integration requirements (APIs, observability, guardrails) and test plan.
Portability and exit criteria; portability drill definition for this use case.

13) Timeline and Critical Path

Milestones by stage with dates; critical path items called out (e.g., data access, approvals, platform features).
Risks to timeline with owners and mitigations; escalation ladder.

14) Stage‑Gate Acceptance Tests

Discovery → Pilot: owner named; baseline locked; risk class assigned; instrumentation plan complete; DPIA decision; data access SLO confirmed; acceptance tests drafted.
Pilot → Production: pre‑launch reviews passed; task‑quality gates met (for GenAI: retrieval precision/recall, grounded answer rate, safety pass rate); rollback drill completed; adoption plan ready; cost‑per‑task within target range at planned volume.
Production → Scale: value realized vs. plan for two intervals; SLOs met with low variance; incident handling tested; unit economics stable or improving; portability drill executed in lower environment; training and adoption targets achieved.
For each gate: list the exact evidence you will present (links to dashboards, logs, approvals, drill records).

15) Monitoring and Operations

Live metrics and owners: reliability (availability, latency p95), quality (model/task), safety (content filters, incident counts), data (freshness, drift), cost (per task, per 1K tokens, retrieval), adoption (usage by role).
Alert thresholds and who is paged; automated rollback conditions.
Runbooks for common failure modes; post‑incident review cadence and playbook update path.

16) Appendices (links only)

Business case workbook, measurement plan, instrumentation spec.
Architecture diagram, CI/CD definition, test plans, rollback runbook.
Data catalog entries and SLAs; DPIA (if required).
Responsible‑AI checklist, model card/data sheet template.
Vendor evaluations, draft term sheets, risk assessments.

Completion checklist (must be true before you ask for money)

Named business owners, KPI baseline, and risk class are documented.
Measurement plan and instrumentation events/fields are defined and owned.
Benefits and cost models include unit economics and sensitivity to volume/model choice.
Data owners and access paths with SLO are confirmed; any DPIA requirement is decided and scheduled.
Technical approach, SLOs, and rollback strategy are explicit; evaluation harness exists.
Safe‑to‑operate minimums and pre‑launch reviews are scheduled with SLAs.
Adoption plan and operating model (decision rights, on‑call, definition of done) are in place.
Funding ask is tied to the next gate’s acceptance tests; exit and portability are addressed.

Quality bar and red flags

Numbers without owners, or owners without time allocated.
“We will measure X” without an instrumentation plan.
No rollback drill but a plan to ship externally facing content.
GenAI values cases without retrieval quality targets or content safety thresholds.
Platform spend with no link to lead‑time reduction or unit‑cost improvement.
Exceptions with no expiry date or mitigation.

One‑page executive summary (use this format for readouts)

What it is and why now (one sentence each).
Owner, KPI, baseline, target range, time to first value.
Risk class and safe‑to‑operate minimums.
Economics headline (benefits range, cost per task target, funding ask to next gate).
Top dependencies and their owners/dates.
Gate acceptance tests and decisions required.
Two biggest risks and mitigations; one portability/exit note.

Submission and review workflow

Attach this template to the portfolio system of record; link all artifacts.
Route concurrently to data, platform, privacy, security, and model risk for scoped reviews.
Hold a single decision forum with pre‑reads 24 hours in advance; record decisions and dissent in the decision log.
On approval, convert acceptance tests into backlog items with dates and owners; schedule the next gate now.

Complete this template with discipline and you will have everything required to fund, govern, and deliver the value case at speed—anchored in economics, guarded by responsible use, and wired for adoption.

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