How to prioritize artificial intelligence use cases by impact, feasibility and risk?

To prioritize artificial intelligence use cases, assess each initiative by its business impact, data quality, technical feasibility, cost, risk and ease of adoption. First rule out the ones that fail on data or compliance, and start with a quick win you can measure.

Choosing the wrong artificial intelligence initiatives has a real cost: you solve a problem that wasn’t a priority, you can’t measure the contribution, and leadership loses the sponsorship to keep investing. At GeneXus Consulting, we prioritize before we build. This guide covers criteria, weighting, quick wins and the roadmap.

Which artificial intelligence use cases are usually worth it?

Of all the ways to apply artificial intelligence in business, it pays to look first at work whose output is a file, or a conclusion drawn from files. Four use cases fit that pattern:

• Document review: checking whether documents comply with a regulation or a process, or whether content is missing.
• Data entry and extraction: automating both tasks.
• Internal or external customer service: filtering questions with accumulated knowledge, so fewer tickets reach the help desk.
• Capturing internal knowledge: documenting how things, processes and jobs work.

According to McKinsey’s The State of AI 2026 (1,719 participants, August 2026), companies cut costs with AI mainly in supply chain management, service operations and manufacturing. Revenue gains are concentrated in marketing and sales.

What criteria should you use to prioritize artificial intelligence use cases?

Six criteria make it possible to compare AI opportunities: impact, data, feasibility, cost, risks and adoption.

1. What is the business impact?

Impact is assessed outward, on the business, and inward, on operations: how often the process happens, how many people it involves and how many it frees up for other tasks.

Estimating impact before implementation requires a baseline, that is, how long the process takes today. If you are improving an existing process, you compare time saved, the human error rate and the cost of the hours replaced. If you are creating something new, such as a bot that explains how to use a tool to sell more, you measure how much comes in through that channel.

2. How good is the data?

If the data isn’t organized for what you want to do, the project takes on the extra work of cleaning it up and presenting it in a usable way, something that starts with data governance.

3. Is it technically feasible and compliant?

Technically, almost anything can be done, and the deciding factor is the cost of implementing it. Before moving forward, you need to confirm that there is technical capacity, integration with current systems, prepared data and people to operate it. It also matters whether regulations and compliance allow AI to be used as intended.

4. How much does it cost, and how much effort does it take?

Cost includes building the solution and running it: in McKinsey’s The State of AI 2026, about 20% of participants say AI operating costs, including tokens, constrain their use. Before comparing ideas, it helps to define the tools, the cost of what needs to be built and how the process will improve.

5. What risks does it involve?

You need to review technical, operational, regulatory, reputational, security, privacy and vendor dependency risks. The ones we emphasize most are regulatory compliance and exposing data externally, an area addressed through sound cybersecurity risk management.

6. Can people adopt it?

If you build something for a person’s daily work and that person can’t adopt it, the initiative fails.

How do you weight an artificial intelligence initiative?

The weighting isn’t fixed: each business decides how much each criterion counts based on its priorities. Score each initiative on each criterion and combine the scores, according to each criterion’s weight, into a single number. The initiatives with the best scores are the ones to tackle first.

How do you identify quick wins and more complex projects?

A quick win meets four conditions: it has impact, it can be measured, the data is ready and people adopt the tool quickly.

The table summarizes how to handle each type of initiative.

Type of initiative	Main signal	How to handle it
Quick wins	Measurable impact and fast adoption	They go first on the roadmap, as an MVP (minimum version in use), to sustain sponsorship.
Strategic	High potential and leadership support	They are prioritized with the leadership team and incubated.
High complexity	Significant dependencies	They are split into small deliveries, and hypotheses are validated before investing heavily.
Not ready	Unresolved data, integrations or compliance	They are postponed and reassessed.

In one case from our experience, the financial forecast looked like a good priority, because freeing people from that calculation would let them analyze other issues. But the ERP, outsourced in the cloud, wasn’t ready for AI, and the data had to be pulled out by hand to feed it to the AI agents, which broke the process.

How do you go from prioritization to an implementation roadmap?

Our recommendation, which we applied with a recent client, is to build the roadmap around the three main initiatives, one after the other, and not go beyond two or three months. With the current pace of AI, a one- to three-year roadmap is hard to sustain.

A well-organized roadmap follows five steps:

1. Select the three priorities and make explicit why they are better prepared than the alternatives.
2. Resolve the prerequisites: data, tools, system integration, infrastructure and compliance.
3. Define AI governance: who is accountable for the AI agents, who anonymizes the data, who trains people and who runs the new process.
4. Put a focused MVP into use, with metrics tied to the baseline.
5. Measure, learn and reprioritize before expanding the scope.

Each initiative also requires redesigning its process with AI built in, rather than copying the steps a person does today.

Frequently asked questions

1. What should you do if there are many AI ideas and it isn’t clear which to prioritize?

Assess them with the same criteria and start with something safe and measurable. The only way to improve the organization with AI is to get started and build up knowledge, one initiative on top of another, to keep rethinking the organization.

2. What information is needed before recommending where to start?

In our strategic exploration process, the information is gathered through a digital assessment, in-depth interviews and an AI-augmented innovation diagnosis. It covers business models, strategic priorities and the level of technology and AI adoption.

3. Is it better to do a proof of concept or an MVP?

We recommend an MVP: something that works and can already be used in the company. In our experience, going from a proof of concept to something real almost never succeeds.

4. How is prioritizing AI use cases different from a simple list of applications?

Prioritization starts from what the organization has today and avoids unfeasible proposals. A list of applications, or an attractive proof of concept built on data that isn’t real, can turn out to be impossible to implement once you talk to IT and infrastructure.

How can we help you prioritize your AI use cases?

Prioritizing well means assessing each case with the same criteria and ruling out early the ones that fail on data or compliance. At GeneXus Consulting, we support the prioritization of AI use cases through NOVA, a four-stage cycle: Navigate (where to innovate), Originate (proposals and hypotheses), Validate (experimenting with an MVP) and Activate (integrating into operations). To identify and prioritize initiatives in the Navigate stage, we offer two strategic exploration services:

The next step is to connect the prioritized initiatives with the tools and platforms that make them viable, through our AI Solutions for Business.