Prioritizing AI Use Cases with a Value–Feasibility–Risk Matrix

Hello. In the previous lesson, you turned the enterprise intelligence platform from a broad AI idea into a measurable promise: help Strategic Operations staff assemble faster, more traceable evidence for portfolio recommendations, without replacing human judgment.

That outcome now becomes a decision criterion. This lesson shows how to compare several plausible AI use cases and select the one that deserves initial investment. By the end, you will be able to score candidate use cases consistently across value, feasibility, and risk; visualize the result; and make a defensible “accelerate, research, incubate, or shelve” recommendation.

---

Prioritization is a portfolio decision, not an AI popularity contest

Most organizations can generate more AI ideas than they can responsibly fund or deliver. The hard part is not identifying an attractive capability such as chat, summarization, prediction, or an agent. It is deciding which specific user problem in a specific context should be addressed first.

A candidate use case should therefore be written as a bounded proposition:

For [user], use [AI capability] to improve [specific task or decision], using [approved data], while retaining [human control or safeguard].

Consider five candidates for the enterprise intelligence platform:

1. Cited evidence assistant
   Authorized Strategic Operations users ask questions across approved portfolio sources and receive a grounded answer with citations.

2. Evidence-pack drafting assistant
   The platform creates a structured first draft of a recommendation pack from approved evidence; a human author reviews and edits it.

3. Funding recommendation engine
   The platform ranks initiatives and recommends “fund,” “defer,” or “stop.”

4. Portfolio-risk forecast
   The platform predicts which initiatives are likely to miss milestones or exceed budget.

5. Executive-meeting summarizer
   The platform summarizes portfolio meeting transcripts and extracts actions.

All five may use AI. They do not deserve equal priority.

The first two directly support the problem defined in the previous lesson: assembling permissioned evidence for a defensible human recommendation. The third appears strategically powerful, but it moves much closer to a consequential decision. The fourth depends on reliable historic outcome data. The fifth may be straightforward to build, yet could be only marginally connected to the platform’s primary outcome.

Microsoft’s Business, Experience, Technology framework provides a useful starting structure: assess strategic and business viability, user desirability, and technical feasibility before comparing use cases.

---

Build a value–feasibility–risk matrix

The Microsoft framework combines business impact, user experience, and technical feasibility. For an executive investment decision, make the risk dimension explicit rather than letting it disappear inside a broad “technical feasibility” score.

Use three separate scores on a consistent 1–5 scale:

• Value: How much strategic, business, and user value could this use case create?
• Feasibility: Can the organization deliver and operate it credibly in the intended initial scope?
• Risk: What harmful or unacceptable outcomes might remain after reasonable controls are applied?

Keep the direction of each scale clear:

Dimension	Score 1	Score 3	Score 5
Value	Weak strategic connection; negligible or unmeasured user benefit	Meaningful benefit for a defined user group	Directly advances strategy and has a material, measurable business and user outcome
Feasibility	Critical data, access, skills, or integration gaps	Key dependencies exist but are manageable through a defined pilot	Required data, permissions, technology, operating ownership, and delivery path are substantially ready
Risk	Low-impact use with controllable residual risk	Material risks require explicit safeguards and human oversight	Severe or unacceptable residual risk; deployment should not proceed until it is reduced

The goal is not numerical perfection. A score is a compact expression of current evidence and assumptions. A high score supported only by enthusiasm should be treated as low-confidence evidence, not as a fact.

For a practical first pass, calculate value and feasibility from three components each:

$$V=\frac{s+b+u}{3}$$

where:

• $s$ is strategic fit: connection to the organization’s goals and the platform’s defined problem;
• $b$ is business outcome potential: expected magnitude of measurable value;
• $u$ is user desirability: strength and frequency of the user problem, plus likelihood of adoption.

$$F=\frac{d+a+o}{3}$$

where:

• $d$ is data and access readiness: suitable data exists and can be used under the right permissions;
• $a$ is AI and architecture fit: AI is genuinely appropriate, and the integration path is credible;
• $o$ is operating readiness: skills, sponsor support, workflow ownership, evaluation capability, and change capacity exist.

A risk score should not simply be averaged from a long list of concerns. One unacceptable risk can be hidden by many low-risk items if you use an average. Instead, identify each significant risk, assess its residual exposure after planned safeguards, and let the most serious unresolved exposure constrain the decision:

$$R=\max (r_1,r_2,\dots ,r_n)$$

This is deliberately conservative. A platform that saves analysts time but exposes restricted strategy documents to unauthorized users is not a successful “average-risk” initiative.

---

Score evidence, not intentions

A useful scoring workshop involves the product owner, business sponsor, representative users, data owner, architect, security or privacy lead, and responsible-AI or legal partner where appropriate. No individual has enough visibility to score the whole system alone.

For every score, record three things:

1. Score — the 1–5 assessment.
2. Evidence — interviews, baseline data, architecture review, data inventory, pilot results, or policy guidance supporting it.
3. Assumption or dependency — what must remain true for that score to hold.

For example, consider the cited evidence assistant:

Criterion	Initial score	Evidence and dependency
Strategic fit	5	It directly supports the defined decision-support problem: gathering fragmented evidence for portfolio recommendations.
Business outcome potential	4	The prior lesson’s value hypothesis predicts lower preparation effort and stronger claim traceability; final baseline collection remains a dependency.
User desirability	5	Directors and analysts repeatedly search multiple sources to create review-ready material.
Data and access readiness	3	Several relevant document sources exist, but permissions must be preserved through ingestion and retrieval.
AI and architecture fit	4	Retrieval with grounded generation and citations is appropriate for evidence discovery; evaluation and source-quality controls are needed.
Operating readiness	4	A narrowly scoped pilot can be owned by Strategic Operations, assuming a data-access sponsor is assigned.
Residual risk	3	Hallucinated synthesis, stale evidence, and access-control errors require citation requirements, retrieval filters, human review, and audit logging.

This use case may not be “easy,” but it is bounded. It lets the team prove value and establish the safety, authorization, retrieval, and evaluation foundations that later features will need.

By contrast, a funding recommendation engine might receive a high value score because portfolio choices matter. But its feasibility and risk profile are substantially different. It requires a defensible definition of historical “success,” suitable data across varied initiatives, controls against biased or spurious rankings, explanation of recommendations, and careful human governance. A ranking that appears quantitative can create false authority even when its inputs are incomplete.

---

Risk is a gate, not a footnote

A value–feasibility matrix helps allocate development capacity. AI risk management determines whether, and under what constraints, the organization should proceed at all.

NIST’s AI Risk Management Framework emphasizes that risk must be assessed in the context of intended use, affected people, potential benefits and harms, organizational risk tolerance, and available resources. It does not require eliminating every risk. It does require that high or unacceptable residual risks receive the most urgent attention, and that development or deployment cease safely when risks cannot be sufficiently managed.

For this capstone, examine at least these risk categories:

Risk category	Example for the enterprise intelligence platform	Typical early control
Authorization and privacy	A user sees a source or excerpt they would not be allowed to access directly.	Enforce authorization at retrieval time; test permissions; log access decisions.
Grounding and accuracy	The model makes an unsupported claim or misrepresents a cited source.	Require citations; evaluate claim support; show source excerpts; retain human review.
Staleness and provenance	A recommendation relies on an old plan when a newer approved version exists.	Store source version and date; define freshness rules; expose provenance.
Prompt injection and malicious content	A retrieved document instructs the model to ignore system rules or expose data.	Treat retrieved content as untrusted; isolate instructions; filter tools and output.
Decision automation	Users treat a generated recommendation as an authoritative funding decision.	Position output as decision support; use uncertainty cues; require accountable approval.
Vendor and operational dependency	A model provider outage, pricing change, or policy change disrupts the workflow.	Define fallbacks, monitoring, cost limits, and an exit strategy.

The presence of a risk does not necessarily mean “do not build.” It may mean:

• narrow the scope;
• use a lower-risk interaction pattern;
• add a human approval point;
• create a research spike to reduce uncertainty;
• defer the use case until controls or data are available; or
• stop the proposal if residual risk exceeds the organization’s tolerance.

This distinction matters particularly for AI: a highly valuable use case can still be inappropriate for autonomous deployment.

---

Plot the candidates and make a decision

Use value as the vertical axis and feasibility as the horizontal axis. Then use the risk score as a visible label, color, or gate. Microsoft’s BXT method uses a related approach: strategic business impact combines strategic fit and business impact, while executional fit combines user desirability and technical feasibility.

The quadrant suggests the next learning and investment action:

Value and feasibility position	Default decision	What the decision means
High value, high feasibility	Accelerate to MVP	Fund a bounded pilot or minimum viable product with explicit success metrics and risk controls.
High value, low feasibility	Research	Fund uncertainty reduction: data discovery, user research, proof of concept, architecture spike, or governance design. Do not promise full delivery yet.
Lower value, high feasibility	Incubate	Run a small experiment only if it can clarify demand, create reusable capability, or become strategically stronger. Avoid mistaking technical ease for value.
Lower value, low feasibility	Shelve	Record the idea and its assumptions, but do not consume near-term delivery capacity. Revisit only if strategy, data, technology, or regulation changes.

Risk modifies every quadrant. A candidate in “Accelerate to MVP” is approved only if its residual risk is acceptable for a tightly defined pilot. A candidate with risk level 5 is not accelerated merely because it is valuable and technically possible.

Here is an illustrative first-pass register for the capstone. These are hypotheses, not final scores for a real organization.

Candidate use case	Value $V$	Feasibility $F$	Risk $R$	Recommended action
Cited evidence assistant	4.7	3.8	3	Accelerate to MVP, with authorization-aware retrieval, citations, evaluation, and human decision ownership
Evidence-pack drafting assistant	4.4	3.0	3	Research the structured source model, output schema, and claim-support evaluation before broad drafting
Funding recommendation engine	4.1	2.3	5	Research only; do not automate recommendations until data validity, explainability, fairness, and governance issues are resolved
Portfolio-risk forecast	3.6	2.2	4	Research data coverage, target definitions, and the consequences of false alerts or missed risks
Executive-meeting summarizer	2.5	4.2	2	Incubate as a small, controlled workflow experiment if it can reuse platform controls or test adoption
Generic enterprise chatbot	1.8	3.1	4	Shelve because its user problem, strategic value, grounding model, and governance boundary are too vague

The first selection should be the cited evidence assistant, not because it has no risks, but because it creates a practical path to the measurable outcomes already defined:

• reduced effort to assemble evidence;
• improved traceability of material claims;
• maintained human accountability for portfolio decisions.

It also creates reusable platform capabilities: document ingestion, permissions-aware retrieval, citations, evaluation, observability, and an approval-aware workflow. Those capabilities reduce uncertainty for future use cases, but they are not the reason to exaggerate the initial scope.

---

Run a short prioritization workshop

To create the first version of your own matrix, time-box the activity to roughly 60–90 minutes with the relevant decision-makers. The output should be a one-page Use Case Prioritization Register, not a large slide deck.

1. List three to six bounded use cases.
   Write each in the user–task–data–safeguard format. Reject vague labels such as “AI chatbot” or “use generative AI.”

2. Restate the primary outcome.
   Bring forward the baseline, target, horizon, and guardrail from the previous lesson. A use case with no connection to a measurable outcome cannot earn a high value score.

3. Score value and feasibility independently.
   Have stakeholders score first, then discuss differences. Large score disagreements often reveal an untested assumption about users, data, ownership, or architecture.

4. Identify the highest residual risk.
   State the potential harm, affected party, likelihood, severity, existing controls, proposed controls, accountable owner, and remaining exposure.

5. Assign a decision category and a next action.
   “Research” must include the question to resolve. “Accelerate” must include pilot boundaries, measurable success criteria, and non-negotiable controls. “Shelve” must identify the condition that would justify reconsideration.

6. Record confidence.
   Add high, medium, or low confidence to each score. Low confidence is not a failure; it is a work item. For example, “Feasibility = 3, confidence low: permission mappings from the document repository have not yet been tested.”

A concise executive recommendation for the capstone could read:

Decision requested: Approve a time-boxed MVP of an authorization-aware cited evidence assistant for Strategic Operations.
Why now: It is the strongest direct match to the measured objective of faster, more traceable evidence-pack preparation.
Scope boundary: It retrieves and synthesizes approved evidence; it does not make funding decisions or take consequential actions.
Conditions: Preserve source permissions, provide verifiable citations, evaluate groundedness and access-control behavior, and require a human owner for every recommendation.
Next decision point: Expand, revise, or stop after the pilot is assessed against preparation-time, traceability, adoption, and safety guardrails.

For a concise strategic perspective on balancing transformative initiatives and confidence-building quick wins, watch this short overview.

---

Key takeaways

A use case is not a technology label. It is a bounded proposition connecting a user, task, AI capability, approved data, expected outcome, and safeguard.

To prioritize AI use cases responsibly:

• score value through strategic fit, measurable business benefit, and user desirability;
• score feasibility through data and access readiness, AI and architecture fit, and operating readiness;
• assess risk separately, based on the most serious residual exposure after plausible controls;
• use the value–feasibility matrix to choose an action: accelerate, research, incubate, or shelve;
• treat unacceptable risk as a gate, not as a deduction hidden inside an average score;
• document evidence, assumptions, confidence, ownership, and the next decision point.

For the enterprise intelligence platform, the recommended initial use case is an authorization-aware, cited evidence assistant. It is sufficiently valuable and feasible for a controlled MVP while preserving a clear boundary: the platform supports a human portfolio decision; it does not make that decision.

Next, you will map the stakeholders who influence funding, data access, adoption, and governance—turning this prioritization decision into a viable cross-functional delivery effort.