In the race to adopt generative AI, numerous organizations have encountered setbacks with pilot projects failing to yield substantial value. Now, the focus has shifted towards achieving measurable outcomes, prompting a need for a structured approach to designing successful AI systems. At Mistral AI, we collaborate with leading global firms to co-create customized AI solutions that address their most pressing challenges. Whether it’s enhancing customer experience with Cisco, developing smarter vehicles with Stellantis, or expediting product innovation with ASML, our strategy begins with open frontier models tailored to each company’s distinct objectives.
The cornerstone of our approach is the identification of an ‘iconic use case’—a pivotal element that lays the groundwork for AI transformation and establishes a blueprint for subsequent AI initiatives. Selecting the right use case is critical; it can mean the difference between achieving genuine transformation and merely engaging in endless testing. Mistral AI employs four criteria for a successful use case: it must be strategic, urgent, impactful, and feasible. A use case should provide significant value to core business processes, demanding buy-in from executive leadership. For instance, while an internal HR chatbot may offer convenience, it lacks the innovative edge of an externally facing banking assistant capable of executing transactions and generating revenue.
Additionally, the urgency of the use case is paramount; it should address a pressing business issue that justifies the investment of time and resources. Our goal is to ensure that prototypes are ready for real-world application, allowing for immediate feedback and iterative improvement. Feasibility is equally important, as projects that can show quick returns on investment help sustain momentum and encourage further AI advancements. By conducting workshops with stakeholders and subject-matter experts, Mistral AI helps organizations sift through potential projects to identify the most promising first use case. Once defined, a pilot phase follows, involving data exploration and mapping, infrastructure setup, and governance processes. This collaborative effort aims to empower organizations to independently innovate and scale their AI solutions, ultimately establishing a robust framework for ongoing AI transformation.
Source: The crucial first step for designing a successful enterprise AI system via MIT Technology Review