Many organizations rushed towards generic 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 create tailored AI solutions that solve their toughest problems. Whether it’s increasing CX productivity ciscoBuilding a more intelligent car with Stellantisor accelerating product innovation asmlWe start with the Open Frontier model and customize AI systems to make an impact on each company’s unique challenges and goals.
Our methodology begins by identifying an iconic use case, the foundation of AI transformation that forms the blueprint for future AI solutions. Choosing the right use case can mean the difference between real change and endless tinkering and testing.
Identifying an Iconic Use Case
Mistral AI has four criteria we look at for use cases: strategic, urgent, impactful, and feasible.
First, the use case must be strategically valuable, addressing a core business process or a transformational new capability. It needs to be more than optimization; It needs to be a game changer. The use case must be strategic enough to excite an organization’s C-suite and board of directors.
For example, use cases like internal-facing HR chatbots are great, but they are easy to solve and they aren’t enabling any new innovations or opportunities. At the other end of the spectrum, imagine an externally facing banking assistant who can not only answer questions, but also help take actions like blocking cards, placing trades, and suggesting upsell/cross-sell opportunities. This is how a customer-support chatbot turns into a strategic revenue-generating asset.
Second, the best use case to pursue must be highly urgent and solve a business-critical problem that people care about right now. This project will take time from people’s time—it must be important enough to justify the investment of that time. And it needs to help business users solve urgent problems.
Third, the use case must be practical and impactful. From day one, our shared goal with our customers is to deploy solutions in real-world production environments to enable us to test solutions with real users and gather feedback. Many AI prototypes end up in the graveyard of fancy demos that aren’t good enough to put in front of customers, and without any scaffolding for evaluation and improvement. We work with customers to ensure that prototypes are stable enough for release and have the necessary support and governance framework.
Ultimately, find the best use case possible. There may be many urgent projects, but choosing one that can deliver a quick return on investment helps maintain the momentum needed to continue and scale.
This means looking for a project that can go into production within three months – and have a prototype go live within a few weeks. To make sure the project is on track and moving forward as needed, it’s important to get a prototype in front of end users as soon as possible to get feedback.
Where use cases fall short
Enterprises are complex, and the way forward is usually not clear. To explore all the possibilities and uncover the right first use case, Mistral AI will run workshops with our customers, along with subject matter experts and end users.
Representatives from different functions will demonstrate their processes and discuss business cases that could be candidates for the first use case – and together we agree on a winner. Here are some examples of types of projects that are not eligible.
moon: Ambitious bets that excite leadership but lack a path to quick ROI. Although these projects may be strategic and urgent, they rarely meet feasibility and impact requirements.
future investments: Long-term games that can wait. Although these projects may be strategic and feasible, they rarely meet the urgency and impact requirements.
strategic reform: Firefighting projects that address immediate pain but don’t move the needle. Although these matters may be urgent and feasible, they rarely meet strategy and impact requirements.
quick win: Useful for building momentum, but not transformational. Although they may be effective and feasible, they rarely meet the strategy and immediate needs.
blue sky views: These projects are gamechangers, but they require maturity to be viable. Although they may be strategic and influential, they rarely meet the urgency and feasibility requirements.
Hero Projects: These are high-pressure initiatives that lack executive sponsorship or realistic timelines. Although they may be urgent and impactful, they rarely meet strategy and feasibility requirements.
Moving from use case to deployment
Once a clearly defined and strategic use case ready for development has been identified, it is time to move to the validation phase. This means performing initial data exploration and data mapping, identifying a pilot infrastructure, and choosing a target deployment environment.
This step also includes agreeing on a draft pilot scope, identifying who will participate in the proof of concept, and establishing a governance process.
Once this is complete, it is time to move on to the building phase. Companies that partner with Mistral work with our in-house applied AI scientists to create our frontier models. We work together to design, build and deploy the first solution.
During this phase, we focus on co-creation, so that we can transfer knowledge and skills to the organizations we are partnering with. This way, they can be self-reliant in the future. The output of this phase is a deployed AI solution with empowered teams capable of independent operation and innovation.
first step is everything
After the first win, it is imperative to use the momentum and learnings from the iconic use case to identify more high-value AI solutions. Success happens when we have a scalable AI transformation blueprint with multiple high-value solutions across the organization.
But none of this could have happened without successfully identifying that first iconic use case. This first step isn’t 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’s 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 practical enough to deliver..
This content was created by Mistral AI. It was not written by the editorial staff of MIT Technology Review.
