Many organizations have jumped into generative AI, Only to see pilots fail to deliver value. Now, companies want measurable results – but how do you design for success?
At Mistral AI, we partner with global industry leaders to co-design AI solutions that solve their toughest problems. Even if it increases CX productivity Ciscocreating a more intelligent car with stellantisor accelerating product innovation asmlwe start with open-frontier models and customize AI systems to deliver impact for each company’s unique challenges and goals.
Our methodology begins by identifying a popular use case, the foundation of AI transformation that sets the blueprint for future AI solutions. Choosing the right use case can mean the difference between real change and endless tinkering and testing.
Identifying a popular use case
Mistral AI has four criteria we look for in a use case: strategic, immediate, effective, and actionable.
First, the use case must be strategically valuable, addressing a core business process or transformative new capability. It needs to be more than reform. It needs to be a game changer. Use cases need to be strategic enough to excite an organization’s C-suite and board of directors.
For example, use cases like internal-facing HR chatbots are good, but they’re easy to solve and aren’t enabling any new innovation or opportunities. At the other end of the spectrum, imagine an externally facing banking assistant who can not only answer questions, but also assist with steps like blocking cards, making trades, and suggesting appeal/cross-sell opportunities. Thus, a customer support chatbot is transformed into a strategic revenue generating asset.
Second, the best use case for moving forward must be highly relevant and solve a business-critical problem that people care about right now. The project will take people days – it has to be important enough to justify the time investment. And it needs to help business customers solve immediate pain points.
Third, the use case should be practical and impactful. From day one, our shared goal with our customers is to deploy the solution in 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 hard enough to put in front of users, and without any scaffolding to evaluate and improve. We work closely with customers to ensure that the prototype is stable enough for release, and that they have the necessary support and governance frameworks in place.
Finally, the best use case is possible. There may be many immediate projects, but choosing one that can provide a quick return on investment helps maintain the momentum needed to continue and scale.
This means looking for a project that will be in production within three months. And a prototype can be live in a few weeks. It’s important to get a prototype in front of end users to ensure the project is on track, and pivot as needed.
Where the use cases fall short
Businesses are complex, and the path forward is often unclear. To eliminate all possibilities and uncover the right use case first, Mystral AI will conduct workshops with our customers, hand in hand with subject matter experts and end users.
Representatives from different functions will break down their processes and discuss business cases that may be candidates for the first use case – and together we agree on a winner. Here are some examples of plans that don’t qualify.
Moon shots: Ambitious bets that excite leadership but don’t pave the way for immediate ROI. While these projects may be strategic and urgent, they rarely meet the requirements of feasibility and impact.
Investing in the future: Long term plays that can wait. While these plans may be strategic and feasible, they rarely meet the needs of immediacy and impact.
Tactical improvements: Firefighting projects that immediately resolve the pain but don’t move the needle. Although these cases may be immediate and possible, they rarely meet the needs of strategy and impact.
Instant win: Useful for speed pacing, but not conversion. Although they may be effective and feasible, they hardly meet the needs of strategy and urgency.
Blue Sky Ideas: These projects are game changers, but they need maturity to be viable. Although they can be strategic and effective, they rarely meet the needs of immediacy and feasibility.
Hero Projects: These are high-pressure initiatives that lack executive sponsorship or realistic timelines. Although they can be quick and effective, they rarely meet the needs of strategy and feasibility.
Moving from use case to deployment
Once a clearly defined and strategic use case is prepared for development, it is time to move into the validation phase. This means conducting initial data research and data mapping, identifying pilot infrastructure, and selecting a target deployment environment.
This initiative includes agreeing on a draft pilot scope, identifying who will participate in the proof of concept, and establishing a governance process.
Once that’s done, it’s time to move on to the building phase. The companies that partner with our domestic misfits are the AI ​​scientists who develop 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 expertise to the organizations we are partnering with. Thus, they can be self-sufficient in future. The output of this phase is a deployed AI solution with the ability to operate independently and innovate with empowered teams.
The first step is everything
After the first win, it is important to use the momentum and learning from the case to identify the most valuable AI solutions. use Success happens when we have a scalable AI transformation blueprint across the organization with multiple high-value solutions.
But none of this can be done without successfully identifying that first famous use case. This first step isn’t just about choosing a project — it’s about laying the foundation for your entire AI transformation.
It’s the difference between fragmented experiences and a strategic, scalable journey toward impact. At Mistral AI, we’ve seen how this approach unlocks the value of measurement, aligns stakeholders, and creates momentum for what’s to come.
The path to AI success begins with a single, chosen use case: one that is bold enough to inspire, compelling enough to demand action, and practical enough to deliver..
This content was created by Mistral AI. It was not written by the editorial staff of MIT Technology Review.