AI Strategy

Case Study

Value Discovery & Portfolio

Portfolio governance for AI investments: structured intake, explicit value hypotheses, scale criteria, and stop rules so spend concentrates on initiatives that can reach production safely in a federated organization.

Public reference: 100+ AI use cases prioritized and scaled enterprise-wide

Executive Outcome

Ended up with a prioritized portfolio of 100+ initiatives, each with an explicit value hypothesis, constraints, and stop criteria, visible across every business unit and domain.

Leadership got real decision clarity on what to scale, pause, or retire — from legal search to field maintenance to customer voicebots — based on value, feasibility, risk, and operability instead of momentum.

The governed intake process, with clear decision rights and a review cadence, got adopted progressively across the business units that opted in.

Engagement focus

Portfolio governance for AI investments in a federated organization, structured intake, decision rights, and stop rules that scale with adoption.

What this covers

Intake and scoring (value hypothesis, feasibility, risk signals, production operability)
Portfolio governance (decision rights, review cadence, stop criteria, exit rules)
Scale readiness (security and operating constraints surfaced before expansion)

Context

A regulated energy group with federated business units was scaling AI from early experiments into an enterprise-wide program spanning 100+ use cases — legal contract search, field maintenance assistants, customer-facing voicebots, IoT-driven operational intelligence, internal productivity tools. Demand was outpacing the capacity to assess feasibility, security exposure, and operating cost. Pilots kept advancing with no repeatable path to production, low-signal initiatives were piling up, and scale decisions were reactive more often than not. I wasn't trying to slow anyone down — I needed a shared intake and decision framework that worked across business units with different priorities, risk profiles, and delivery maturity, so leadership got decision clarity without me becoming the approval bottleneck for every idea.

The Challenge

01100+ use case candidates across wildly different domains, inconsistent inputs, unclear ownership, and not much accountability for the outcomes.
02Pilots kept advancing without a repeatable path to production — sunk cost, duplicated effort, and reputational risk building up across business units.
03Feasibility constraints, security gaps, and operating costs surfaced late, which meant more delivery friction and scale decisions getting delayed.
04There was no shared way to compare initiatives across business units on value, risk, and operability — every domain judged its own opportunities by its own rules.

Approach

→Built a standardized intake and scoring framework — value hypothesis, feasibility constraints, risk signals, and production operability, assessed the same way across every business unit and use case domain.
→Segmented the portfolio by domain and maturity — customer-facing, employee productivity, operational intelligence, field operations — each with feasibility and risk criteria adapted to it.
→Added a pre-scale review to surface security and operating constraints early, with concrete remediation actions defined before anything expanded.
→Set explicit stop criteria, exit rules, and time horizons so low-signal initiatives couldn't keep consuming resources indefinitely.
→Defined decision rights and a review cadence so leadership got portfolio visibility without me having to sign off on every team's next step.
→Kept the evidence requirements for scaling lightweight — enough to support a real governance decision, not so much that it discouraged early experimentation.

Key Considerations

Speed of intake versus rigor of assessment — I optimized for fast triage and saved deeper review for the initiatives that made the shortlist.
Central oversight versus local innovation — ideation stayed federated, prioritization and decision rights didn't.
Risk and security became first-class scoring signals from the start, not something bolted on as a late-stage review item.
Lightweight evidence requirements balance governance against the risk of discouraging early-stage experimentation — that balance needed constant tuning, not a one-time decision.
Domain-specific feasibility criteria add real assessment complexity, but a one-size-fits-all score misses constraints that actually matter.

Alternatives Considered

✕Ad hoc funding decisions produce inconsistent outcomes and make initiatives impossible to compare against each other.
✕ROI-only ranking underweights feasibility, risk, security, and whether the thing can actually run in production.
✕Requiring central approval for every initiative creates a bottleneck and discourages the federated experimentation that was generating good ideas in the first place.

Representative Artifacts

01Use Case Portfolio Dashboard (cross-business-unit, cross-domain visibility)

02Feasibility, Risk, and Operability Scoring Matrix (with domain-specific criteria)

03Investment Decision Memo Template

04Stop Criteria and Exit Rules

05Decision Rights and Review Cadence Model

Acceptance Criteria

All initiatives assessed against a common taxonomy for value, feasibility, risk, security, and operability.

Investment allocation reflects explicit horizons and scaling conditions, not pilot momentum.

Low-signal initiatives deprioritized early with documented rationale, ownership, and next actions.

Decision rights and review cadence adopted across participating business units.

Portfolio segmentation by domain reflects adapted feasibility and risk criteria without fragmenting governance.

Continue Exploring

Value Discovery & Portfolio

Executive Outcome

Context

The Challenge

Approach

Key Considerations

Alternatives Considered

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