Companies around the globe are in hot pursuit of the best use cases for generative AI. Given the technology is so new, there’s little information to guide the use case selection process, much less prototype design and final implementation. Much rests on the outcomes of trial-and-error pilot projects.
Some companies are passing on this haphazard approach entirely and electing to hire guides to walk them through at least their first generative AI project. The aim is to come up to speed on talent development and strategic plans before too much money is at stake either in investment or in the market.
There are a variety of ways to glean valuable information from vendor guides, consultants, and partners. The important thing is to match their expertise and teaching methods to how your organization thinks and works – and the goals it wishes to achieve.
The three stages
There are three stages that make good starting points when working with a third party on generative AI efforts: deep-dive education, use case identification, and prototype design.
The first stage is a deep and broad education on generative AI to bring your team and organization up to speed quickly on what the tool can do and how to make it work. The provider should go beyond the basic, high-level tips that require little working knowledge of the tool and provide little deep value to an innovation-driven organization.
The second stage is use case identification. Many companies try to achieve a deep-dive education and use case identification simultaneously. That sometimes works. But often it’s just awkward, confusing, and unproductive. However, honing the ability to correctly assess use cases and the data stores that support them is critical to the success of any generative AI project.
The third stage is prototype design. This stage is vital as it enables discovery of the feasibility, scalability, impact, and business alignment of any given use case.
Some partners, like Quantiphi, offer all three stages, allowing companies to take full advantage of the entire series or engage at the stage that most suits their AI readiness.
The hands-on experience
The third stage, prototype design, is offered by third parties in numerous ways: maybe as a technology or blend of technologies to predict the outcomes of prototype designs currently under consideration; maybe as a pre-trained AI or ML model that actually creates the prototype design based on success potential.
But a better way to handle prototype design is to use a mix of human and AI capabilities to design a minimum viable prototype (MVP) that draws on the strength of AWS expertise and also affords your talent hands-on experience throughout the process. This results in a working and success-primed prototype of a generative AI solution for your actual use case and valuable training for your team.