Many organizations rushed into generative AI, only to see pilots fail to deliver value. Now, corporations want measurable outcomes—but how do you design for fulfillment?
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, constructing a more intelligent automotive with Stellantis, or accelerating product innovation with ASML, we start with open frontier models and customize AI systems to deliver impact for every company’s unique challenges and goals.
Our methodology starts by identifying an iconic use case, the muse for AI transformation that sets the blueprint for future AI solutions. Selecting the suitable use case can mean the difference between true transformation and countless tinkering and testing.
Identifying an iconic use case
Mistral AI has 4 criteria that we search for in a use case: strategic, urgent, impactful, and feasible.
First, the use case should be strategically worthwhile, addressing a core business process or a transformative recent capability. It must be greater than an optimization; it must be a gamechanger. The use case must be strategic enough to excite a company’s C-suite and board of directors.
For instance, use cases like an internal-facing HR chatbot are nice to have, but they’re easy to resolve and are usually not enabling any recent innovation or opportunities. On the opposite end of the spectrum, imagine an externally facing banking assistant that can’t only answer questions, but in addition help take actions like blocking a card, placing trades, and suggesting upsell/cross-sell opportunities. That is how a customer-support chatbot is changed into a strategic revenue-generating asset.
Second, the very best use case to maneuver forward with must be highly urgent and solve a business-critical problem that individuals care about at once. This project will take outing of individuals’s days—it must be vital enough to justify that point investment. And it needs to assist business users solve immediate pain points.
Third, the use case must be pragmatic and impactful. From day one, our shared goal with our customers is to deploy right into a real-world production environment to enable testing the answer with real users and gather feedback. Many AI prototypes find yourself within the graveyard of fancy demos that are usually not adequate to place in front of shoppers, and with none scaffolding to guage and improve. We work with customers to make sure prototypes are stable enough to release, and that they’ve the mandatory support and governance frameworks.
Finally, the very best use case is possible. There could also be several urgent projects, but selecting one which can deliver a fast return on investment helps to keep up the momentum needed to proceed and scale.
This implies on the lookout for a project that may be in production inside three months—and a prototype may be live inside a couple of weeks. It’s vital to get a prototype in front of end users as fast as possible to get feedback to ensure that the project is heading in the right direction, and pivot as needed.
Where use cases fall short
Enterprises are complex, and the trail forward is just not often obvious. To weed through all the probabilities and uncover the suitable 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 possibly be candidates for a primary use case—and together we agree on a winner. Listed here are some examples of forms of projects that don’t qualify.
Moonshots: Ambitious bets that excite leadership but lack a path to quick ROI. While these projects may be strategic and urgent, they rarely meet the feasibility and impact requirements.
Future investments: Long-term plays that may wait. While these projects may 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 may be urgent and feasible, they rarely meet the strategy and impact requirements.
Quick wins: Useful for constructing momentum, but not transformative. While they may 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 may 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 may 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 maneuver into the validation phase. This implies doing an initial data exploration and data mapping, identifying a pilot infrastructure, and selecting a goal deployment environment.
This step also involves agreeing on a draft pilot scope, identifying who will take part in the proof of concept, and establishing a governance process.
Once that is complete, it’s time to maneuver into the constructing phase. Firms that partner with Mistral work with our in-house applied AI scientists who construct our frontier models. We work together to design, construct, and deploy the primary solution.
During this phase, we concentrate on co-creation, so we will transfer knowledge and skills to the organizations we’re partnering with. That way, they may be self-sufficient far into the long run. The output of this phase is a deployed AI solution with empowered teams able to independent operation and innovation.
Step one is every part
After the primary win, it’s imperative to make use of the momentum and learnings from the long-lasting use case to discover more high-value AI solutions to roll out. Success is when we’ve got a scalable AI transformation blueprint with multiple high-value solutions across the organization.
But none of this might occur without successfully identifying that first iconic use case. This primary step is just not nearly choosing a project—it’s about setting the muse in 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 trail to AI success starts with a single, well-chosen use case: one which is daring enough to encourage, urgent enough to demand motion, and pragmatic enough to deliver.
