The crucial first step for designing a successful enterprise AI system

For CIOs and transformation leaders, the real management decision is not whether to “do AI” but how to choose the first use case that can justify wider investment. The article’s core message is that a first win should be strategic, but the harder question is who owns that judgment: business leaders who feel the pain, technology leaders who understand feasibility, or a joint steering group that can arbitrate trade-offs. Without clear decision rights, AI programmes drift toward whichever proposal is loudest rather than whichever one is most valuable.

The practical implication is that AI portfolio governance needs a stronger filter than enthusiasm or technical novelty. A useful first-case screen should balance value, urgency, execution risk and organisational readiness, then force an explicit choice about what will not be funded yet. That matters because “quick win” and “moonshot” projects can both consume scarce talent, data access and executive attention while delivering very different levels of learning. Leaders should ask whether a candidate use case creates a reusable operating model, not just a one-off demo.

The next management questions are about scale, not experimentation. Before committing, teams should test whether the use case has named process owners, a credible benefits baseline, a path to production support, and a plan for measuring adoption after launch. They should also decide what governance must exist on day one versus what can be added after the first release. The hidden trade-off is speed against control: moving fast is useful, but only if the organisation can absorb the change and repeat the pattern across a portfolio. The first use case is therefore a funding, accountability and change-management decision as much as a technology one.

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Many organizations rushed into generative AI, only to see pilots fail to deliver value. Now, companies want measurable outcomes—but how do you design for success?

At Mistral AI, we partner with global industry leaders to co-design tailored AI solutions that solve their most difficult problems. Whether it’s increasing CX productivity with Cisco, building a more intelligent car with Stellantis, or accelerating product innovation with Original Postress-releases/2025/asml-mistral-ai-enter-strategic-partnership" target="_blank">ASML, we start with open frontier models and customize AI systems to deliver impact for each company’s unique challenges and goals.

Our methodology starts by identifying an iconic use case, the foundation for AI transformation that sets the blueprint for future AI solutions. Choosing the right use case can mean the difference between true transformation and endless tinkering and testing.

Identifying an iconic use case

Mistral AI has four criteria that we look for in a use case: strategic, urgent, impactful, and feasible.

First, the use case must be strategically valuable, addressing a core business process or a transformative new capability. It needs to be more than an optimization; it needs to be a gamechanger. The use case needs to be strategic enough to excite an organization’s C-suite and board of directors.

For example, use cases like an internal-facing HR chatbot are nice to have, but they are easy to solve and are not enabling any new innovation or opportunities. On the other end of the spectrum, imagine an externally facing banking assistant that can not only answer questions, but also help take actions like blocking a card, placing trades, and suggesting upsell/cross-sell opportunities. This is how a customer-support chatbot is turned into a strategic revenue-generating asset.

Second, the best use case to move forward with should be highly urgent and solve a business-critical problem that people care about right now. This project will take time out of people’s days—it needs to be important enough to justify that time investment. And it needs to help business users solve immediate pain points.

Third, the use case should be pragmatic and impactful. From day one, our shared goal with our customers is to deploy into a real-world production environment to enable testing the solution with real users and gather feedback. Many AI prototypes end up in the graveyard of fancy demos that are not good enough to put in front of customers, and without any scaffolding to evaluate and improve. We work with customers to ensure prototypes are stable enough to release, and that they have the necessary support and governance frameworks.

Finally, the best use case is feasible. There may be several urgent projects, but choosing one that can deliver a quick return on investment helps to maintain the momentum needed to continue and scale.

This means looking for a project that can be in production within three months—and a prototype can be live within a few weeks. It’s important to get a prototype in front of end users as fast as possible to get feedback to make sure the project is on track, and pivot as needed.

Where use cases fall short

Enterprises are complex, and the path forward is not usually obvious. To weed through all the possibilities and uncover the right first use case, Mistral AI will run workshops with our customers, hand-in-hand with subject-matter experts and end users.

Representatives from different functions will demo their processes and discuss business cases that could be candidates for a first use case—and together we agree on a winner. Here are some examples of types of projects that don’t qualify.

Moonshots: Ambitious bets that excite leadership but lack a path to quick ROI. While these projects can be strategic and urgent, they rarely meet the feasibility and impact requirements.

Future investments: Long-term plays that can wait. While these projects can be strategic and feasible, they rarely meet the urgency and impact requirements.

Tactical fixes: Firefighting projects that solve immediate pain but don’t move the needle. While these cases can be urgent and feasible, they rarely meet the strategy and impact requirements.

Quick wins: Useful for building momentum, but not transformative. While they can be impactful and feasible, they rarely meet the strategy and urgency requirements.

Blue sky ideas: These projects are gamechangers, but they need maturity to be viable. While they can be strategic and impactful, they rarely meet the urgency and feasibility requirements.

Hero projects: These are high-pressure initiatives that lack executive sponsorship or realistic timelines. While they can be urgent and impactful, they rarely meet the strategy and feasibility requirements.

Moving from use case to deployment

Once a clearly defined and strategic use case ready for development is identified, it’s time to move into the validation phase. This means doing an initial data exploration and data mapping, identifying a pilot infrastructure, and choosing a target deployment environment.

This step also involves agreeing on a draft pilot scope, identifying who will participate in the proof of concept, and setting up a governance process.

Once this is complete, it’s time to move into the building phase. Companies that partner with Mistral work with our in-house applied AI scientists who build our frontier models. We work together to design, build, and deploy the first solution.

During this phase, we focus on co-creation, so we can transfer knowledge and skills to the organizations we’re partnering with. That way, they can be self-sufficient far into the future. The output of this phase is a deployed AI solution with empowered teams capable of independent operation and innovation.

The first step is everything

After the first win, it’s imperative to use the momentum and learnings from the iconic use case to identify more high-value AI solutions to roll out. Success is when we have a scalable AI transformation blueprint with multiple high-value solutions across the organization.

But none of this could happen without successfully identifying that first iconic use case. This first step is not just about selecting a project—it’s about setting the foundation for your entire AI transformation.

It’s the difference between scattered experiments and a strategic, scalable journey toward impact. At Mistral AI, we’ve seen how this approach unlocks measurable value, aligns stakeholders, and builds momentum for what comes next.

The path to AI success starts with a single, well-chosen use case: one that is bold enough to inspire, urgent enough to demand action, and pragmatic enough to deliver.

This content was produced by Mistral AI. It was not written by MIT Technology Review’s editorial staff.