# AI Opportunity Portfolio > Identifies, scores (5-dimensional model: Business Impact, Technical Feasibility, Data Readiness, Strategic Alignment, Speed to Value/Risk), and prioritizes AI use cases with a 2x2 matrix (Quick Wins / Strategic Bets / Deprioritize / Hard-Low Value) — and classifies incremental and transformative opportunities separately. - Skill: `pilot2service/ai-opportunity-portfolio` (Agent Skill) - Install (CLI): `npx skillmds@latest add pilot2service/ai-opportunity-portfolio` - Raw SKILL.md: https://api.skillmd.com/api/skills/pilot2service/ai-opportunity-portfolio/raw - Safety review: pending - Works with: Claude Code, Claude.ai, OpenAI Codex - Category: AI & ML - Author: Pilot2Service (https://skillmd.com/u/pilot2service) - Updated: 2026-09-17 - Page: https://skillmd.com/skills/pilot2service/ai-opportunity-portfolio --- # AI Opportunity Portfolio ## Purpose Turns a raw list of AI use-case candidates (typically 20–100 items) into an objectively scored, prioritized portfolio, from which the 3–5 highest- value, lowest-risk items are selected to move forward. The skill deliberately separates two different opportunity types — **incremental efficiency gains** (making an existing process faster/ cheaper) and **transformative innovation** (new business that wasn't possible before current AI capabilities) — because they're assessed partly against different criteria. ## Anchored in research - LinkedIn Skills on the Rise 2026 — AI Business Strategy - Market research: open "Senior AI Business Designer"-type job postings - A research report supplied by the user, "AI Business Designer in the Age of AI" (2026) — identifying AI opportunities at the strategic level (the original problem-type/data/flywheel/agentic-ness triage, now folded into point 4 below) - Research digest "Methods, Frameworks, and Competencies for Identifying AI Opportunities and Capacity in Business" (2026) — the 5-dimensional scoring model (a synthesis of several industry AI capability reports), the 2x2 prioritization matrix, the Value Play taxonomy for transformative opportunities, the Deploy-Reshape-Invent taxonomy ## Method 1. **Assemble the raw list of candidates.** Start from existing friction points and value-chain bottlenecks — not from technology. Two complementary ways to assemble the raw list: - **Bottom-up** (if the process is already precisely described): use `../task-level-decomposition-and-automation-fit/SKILL.md` — its Automate/Augment-classified tasks are grouped here into larger opportunities. - **Top-down** (a fast first pass before a detailed process description): use `../ai-capability-pattern-matching/SKILL.md` — it poses the client the diagnostic questions of a ready-made capability pattern library and produces a validated raw list. If neither has been used, collect the list directly from stakeholders. 2. **Sort every candidate into one of two lanes before scoring:** - **Incremental efficiency gain** — the current process is done faster/cheaper. Cost-saving- and speed-driven (bottom-line impact). - **Transformative innovation** — a new business, product, or revenue stream that wasn't possible before current AI capabilities. Growth-driven (top-line impact). Check every candidate claimed as transformative against the Value Play taxonomy (point 3) — if it doesn't fit any of the three architectures, it's probably actually an incremental efficiency gain disguised as a big idea. 3. **For transformative candidates: check against the Value Play taxonomy.** Three known architectures for creating new AI value: - **Zero-Marginal-Cost Expertise** — packaging complex specialist expertise (legal, technical, medical) into a real-time, scalable service. - **Hyper-Personalization at Scale** — the product/service becomes dynamic for every user individually (e.g. tailored learning paths, financial products). - **Outcome-Based / Agentic Business** — moving from seat-based licensing/access pricing to outcome-based pricing (e.g. billing only for a resolved ticket or a closed deal). If a candidate doesn't fit any of these and isn't clearly a combination of them, reconsider whether it belongs in the transformative lane. 4. **Score every candidate on five dimensions (1–5 per dimension, max 25 total):** - **Business Impact** — measurable euro or time value (ROI, hours saved, new revenue, churn impact). - **Technical Feasibility & AI Fit** — is the problem probabilistic or deterministic in nature? Does current LLM/AI technology fit the task without unreasonable hallucination risk? (Use the SML assessment from `../task-level-decomposition-and-automation-fit/SKILL.md` here if available — the problem type prediction/classification/ generation also belongs in this dimension.) - **Data Readiness** — is the needed data available, in structured form, high quality, and interfaceable? Also assess **data flywheel potential**: does the solution generate unique data in use that improves the model over time and reinforces competitive advantage, or is it one-off data with no self-reinforcing loop? For a deeper diagnosis (the role of data, quality/bias, validating a flywheel claim), see `../../../data-strategy-and-literacy/skills/data-role-diagnosis/SKILL.md` and `../../../data-strategy-and-literacy/skills/data-ai-strategy-design-and-prioritization/SKILL.md`. - **Strategic Alignment** — does the target support the organization's 1–3-year core strategy, or is it a stand-alone experiment? - **Speed to Value & Governance/Risk** — implementation time as well as regulatory risk profile (e.g. EU AI Act classification: prohibited, high risk, low risk — see `../responsible-ai-and-governance-check/SKILL.md`). Also include **the degree of agentic-ness** here: is traditional rule-based automation enough, or does the opportunity require agentic, independent decision-making in unpredictable situations — an agentic solution is more expensive to build and govern, which slows down the Speed to Value score and should show up in it. 5. **Place every candidate on a 2x2 prioritization matrix** (vertical axis: Business Impact, horizontal axis: Technical Feasibility — use the point-4 scores): - **Quick Wins** (high impact, high feasibility) — low cost, fast implementation. Active piloting candidates. - **Strategic Bets** (high impact, low feasibility) — often transformative, require significant data/architecture investment before they're worth starting. - **Hard / Low Value** (low impact, low feasibility) — high technical bar, small ROI. Avoid. - **Deprioritize** (low impact, high feasibility) — easy to do but not worth it; low value doesn't justify the resources even when implementation would be easy. 6. **Also classify the selected Quick Wins and Strategic Bets items using BCG's Deploy-Reshape-Invent taxonomy** — this is a DIFFERENT question from the point-5 matrix: the matrix answers "is this worth doing and is it easy," Deploy-Reshape-Invent answers "what kind of change does this require of the organization": - **Deploy** — rolling out ready-made AI tools (e.g. copilots) for point tasks. Doesn't require process redesign. - **Reshape** — redesigning core functions and end-to-end processes around AI. Requires process change. - **Invent** — creating entirely new business models, products, and revenue streams. Requires building new business. **Don't confuse this with `../ai-capability-roadmap/SKILL.md`'s Horizon 1/2/3 breakdown** — Deploy-Reshape-Invent describes THE NATURE OF THE CHANGE (how deeply it touches the organization), Horizon 1/2/3 describes THE TIMELINE (when it's done). The same Reshape-level opportunity can land in any horizon depending on resources and dependencies. 7. **Produce the final output: a prioritized AI Opportunity Portfolio / Backlog** — for every selected item: name, lane (incremental/ transformative; if transformative, which Value Play), 5D scores and total score, 2x2 position, Deploy/Reshape/Invent class. Move the 3–5 highest-priority items into `../../../business-case-and-analysis/skills/business-case-builder/SKILL.md` for a deeper business case. 8. Validate the result with stakeholders or your own experience-based checklist. Make sure in particular that opportunities aren't assessed as an isolated silo but in relation to the organization's existing strategic goals. ## What this skill does NOT do - Doesn't make the final decision for you — it produces a structured draft to support a human decision. - Doesn't confirm figures, market data, or competitor data from memory — it uses the inputs you provide, or marks an assumption clearly (`[assumption — verify]`). - Doesn't assess technical feasibility in depth — the Technical Feasibility dimension here is a rough 1–5 rating, not technical due diligence. For a deeper assessment, see `../ai-use-case-feasibility-and-poc-scoping/SKILL.md`. - Doesn't do the task-level decomposition itself — if the raw list hasn't been assembled at the task level yet, use `../task-level-decomposition-and-automation-fit/SKILL.md` first. - Doesn't replace `../ai-capability-roadmap/SKILL.md` for scheduling — it produces a prioritized list, not a scheduled roadmap. ## Refinement notes Areas to keep deepening with real practice: - your own rules of thumb and heuristics for this technique — e.g. which dimensions carry the most practical weight in different industries - concrete templates (into `../../references/`, e.g. a 5D scoring table template) - reference cases / your own examples - what this skill deliberately does *not* do (guardrails, common mistakes) — add to the list above Once this section is filled in and validated in practice, update the `maturity` field in `skills_index.json` to `draft`, `validated`, or `canonical` (see `../../../meta/maturity_levels.md`). **Don't add new fields to the frontmatter** — `name` and `description` are the only ones allowed (see `../../../meta/frontmatter_schema.md`). ## Continue from here - Preceding skill in this pack (if a raw list doesn't exist yet): `../task-level-decomposition-and-automation-fit/SKILL.md` (bottom-up) or `../ai-capability-pattern-matching/SKILL.md` (top-down) - Next in this pack (business model design): `../ai-native-business-model-canvas/SKILL.md` — designs the transition from an AI-enhanced business to an AI-native business model using an extended Business Model Canvas. - Next in this pack (technical validation): `../ai-use-case-feasibility-and-poc-scoping/SKILL.md` — determines the technical boundary conditions of an AI use case and scopes the PoC phase. - Next in this pack (scheduling): `../ai-capability-roadmap/SKILL.md` — places the selected items on a Horizon 1/2/3 timeline (a different question from this skill's Deploy/Reshape/Invent classification, see point 6). - Related skill in another pack: `../../../opportunity-recognition/skills/opportunity-value-assessment/SKILL.md` — a more general, non-AI-specific opportunity assessment model. - If the whole process is run as a paid consulting engagement: `../ai-discovery-engagement-design/SKILL.md` - If the client is a public-sector or non-profit body: pre-screen with `../../../specialisation-packs/public-sector-ai-service-design/skills/ps-ai-opportunity-screening-for-public-value/SKILL.md` before or alongside this skill — public-value fit and mandate alignment change how the Business Impact dimension should be weighted. - A ready-made skill chain for this situation: see `../../../playbooks/` - This pack's shared guardrails: `../../CLAUDE.md` ## References - `../../references/` — the pack's shared background material - `../../CLAUDE.md` — the pack's shared guardrails