How to Prioritise AI Use Cases: A Value-vs-Feasibility Framework

Many organisations have more AI ideas than they can sensibly pursue.

They have a prioritisation problem.

Once teams begin exploring AI, the opportunity list grows quickly: customer service, research, sales support, document processing, forecasting, reporting, internal knowledge, quality control, coding, workflow automation and dozens of smaller tasks.

The wrong response is to choose the most impressive demo.

The better approach is to prioritise AI use cases according to business value, feasibility, risk and learning value.

Why simple value-vs-effort matrices are not enough

A traditional two-by-two matrix is useful, but AI introduces additional questions.

A use case can look valuable and technically easy while still being a poor first investment because:

• its data is sensitive;
• mistakes create significant customer or regulatory risk;
• there is no reliable way to measure the outcome;
• the workflow is poorly understood;
• adoption depends on behaviour change nobody owns;
• the organisation could learn more from a smaller adjacent experiment.

That is why Abrasim uses a four-part prioritisation model:

Value × Feasibility × Risk × Learning

The objective is not to find the use case with the highest theoretical score. It is to build a defensible portfolio of what to do now, what to enable, what to test and what not to pursue.

Step 1: Start with the business problem

Do not begin with a list of AI capabilities.

Start with operational or commercial problems such as:

• customer queries take too long to resolve;
• sales teams spend too much time researching accounts;
• management reporting is manual and slow;
• employees struggle to find trusted internal knowledge;
• large document sets require repetitive review;
• marketing teams cannot produce enough useful variations for testing;
• analysts spend too much time preparing data before analysis.

Then ask whether AI is the best intervention.

Sometimes the answer will be workflow redesign, systems integration, better data, a rules engine or conventional automation.

That is a good outcome. The purpose of the exercise is business improvement, not finding somewhere to insert a model.

Step 2: Score business value

Value should be tied to a measurable business outcome.

Useful categories include:

Revenue

Could the intervention improve conversion, retention, cross-sell, speed to market or sales capacity?

Cost

Could it reduce manual effort, rework, external spend or operational cycle time?

Customer

Could it improve response time, personalisation, consistency or service quality?

Risk

Could it identify errors, anomalies or policy breaches earlier?

Capacity

Could skilled employees spend more time on judgement and less on repetitive preparation?

A good value score uses current evidence where possible.

For example:

The workflow handles 8,000 cases per month, consumes 1,600 staff hours and has a 9% rework rate.

That is substantially more useful than:

AI could make this more efficient.

Step 3: Score feasibility

Feasibility is not simply “can a model do this?”

Assess at least five things.

Workflow clarity

Is the current process understood well enough to redesign it?

Data availability

Are the required inputs available, accessible and good enough?

Integration

Can the AI capability connect to the systems where work actually happens?

Operating ownership

Is there a team able to own, monitor and improve it?

Measurement

Can the organisation detect whether the intervention creates value?

A use case may be technically possible but operationally unready.

That distinction prevents a large percentage of AI theatre.

Step 4: Score risk

Risk should influence both priority and design.

Consider:

• impact of an incorrect output;
• personal or confidential information;
• financial or legal consequences;
• customer-facing autonomy;
• reputational sensitivity;
• explainability requirements;
• model uncertainty;
• supplier dependency;
• security exposure.

NIST’s AI Risk Management Framework is useful because it encourages organisations to manage AI risk throughout the lifecycle rather than adding governance at the end.

A high-risk use case is not automatically a bad use case. It may simply require stronger controls, human review and a slower path to autonomy.

Step 5: Score learning value

This is the dimension most prioritisation frameworks miss.

An early AI project should not only create value. It can also build reusable knowledge.

Ask whether the use case will teach the organisation about:

• integration patterns;
• data access;
• model evaluation;
• employee adoption;
• governance controls;
• vendor performance;
• monitoring;
• cost at scale.

A moderate-value project can be an excellent first use case if it creates capabilities that unlock several higher-value opportunities later.

The Abrasim opportunity matrix

After scoring the four dimensions, place use cases into one of four action groups.

1. Act now

High value, feasible, manageable risk.

These are candidates for a controlled pilot or implementation.

2. Enable first

High value, but blocked by data, workflow, integration or governance.

Do not discard these. Identify the enabling work explicitly.

3. Learn cheaply

Moderate value but high learning potential.

Use these to develop reusable capability without exposing the organisation to disproportionate risk.

4. Park

Low value, poor feasibility or unjustified risk.

An AI strategy becomes credible partly because it says no to things.

Example: prioritising four AI use cases

Imagine a mid-sized services company considering four opportunities.

Internal knowledge assistant

Value: Medium-high
Feasibility: High if documents are organised
Risk: Medium
Learning: High

Potentially a strong early candidate because it builds retrieval, governance and employee-adoption capability.

Fully autonomous customer complaint resolution

Value: High
Feasibility: Medium
Risk: High
Learning: High

Worth exploring, but probably not as a fully autonomous first deployment. Start with agent assistance and human approval.

AI-generated weekly management report

Value: Medium
Feasibility: Medium-high
Risk: Low-medium
Learning: Medium

Useful if the reporting baseline is known, but first check whether the real bottleneck is data preparation rather than narrative generation.

Automated contract risk review

Value: High
Feasibility: Medium
Risk: High
Learning: High

Potentially valuable, but requires strong evaluation, domain ownership, confidentiality controls and clear limits on reliance.

The matrix turns “all four sound useful” into a sequencing decision.

Do not score use cases in isolation

Portfolio effects matter.

Several use cases may depend on the same capability:

• identity and access management;
• enterprise search;
• document ingestion;
• customer data;
• workflow orchestration;
• model monitoring;
• human approval patterns.

If three high-value use cases require the same foundation, that shared dependency may deserve investment before any one application is optimised.

This is where prioritisation starts becoming strategy.

Use ranges, not false precision

A score of 73.4 does not make an uncertain business case scientific.

For early prioritisation, simple scales are enough:

• Low;
• Medium;
• High;
• Very high.

The discussion and evidence behind the score matter more than decimal places.

As a use case moves toward funding, replace assumptions with stronger evidence.

Build the baseline before the pilot

For each priority use case, define:

• current performance;
• desired outcome;
• leading indicators;
• guardrail metrics;
• evaluation method;
• owner;
• decision date.

For example, a service-assistance pilot might measure:

• average handling time;
• first-contact resolution;
• escalation rate;
• factual error rate;
• customer satisfaction;
• employee adoption.

The pilot should answer a business question, not simply prove that the AI can generate an output.

How many use cases should you start with?

There is no universal number.

A useful first portfolio is usually small enough that leadership can learn from it and large enough to avoid drawing conclusions from one unusual workflow.

The limiting factors should be:

• operating capacity;
• risk-management capacity;
• integration effort;
• quality of measurement;
• ability to act on what is learned.

Ten simultaneous pilots with no common evaluation model are often less useful than three well-chosen experiments.

How this connects to AI readiness

Prioritisation and readiness should happen together.

Start with the six AI readiness dimensions, then assess which opportunities deserve deeper evaluation.

A high-value use case with low readiness is not a contradiction. It is a roadmap item.

The assessment should tell leadership what must change before that opportunity becomes feasible.

The decision leadership needs

At the end of prioritisation, leadership should be able to see:

• which AI opportunities matter commercially;
• which are ready now;
• which require enabling work;
• which can create useful learning;
• which should be stopped;
• which shared capabilities deserve investment.

That is considerably more useful than an “AI use case catalogue” containing 150 possibilities nobody has agreed to own.

For organisations needing a structured baseline, an Automation & Technology Readiness Assessment combines workflow readiness, opportunity discovery, prioritisation and roadmap design.

Sources and further reading

• NIST AI Risk Management Framework
• OECD: The Adoption of Artificial Intelligence in Firms
• OECD: Fostering an inclusive digital transformation as AI spreads among firms