In the ever-evolving world of artificial intelligence, Generative AI has taken center stage, promising new horizons of productivity and innovation. As we venture into the eighth week of our Generative AI Derisking Program, we've gleaned essential AI insights that can significantly benefit teams embarking on a similar journey. These lessons transcend the technical aspects, delving into the fundamental principles that underpin the success of Generative AI projects.

AI Insight 1) It's Not Just About the Technology

One of the paradoxes that frequently surfaces in Generative AI innovation projects is the tendency to emphasize the technology itself. Often, projects are named after the solution, such as the "AI Project Team" at a company. However, it's crucial to remember that Generative AI is ultimately a tool for enhancing productivity. For our teams, this realization meant delving deep into their practice areas and data to uncover systemic financial inefficiencies that could be mitigated through automation.

In essence, it's not enough to be enamored with the technology – project teams must focus on how Generative AI can address practical, real-world challenges and improve the bottom line.

AI Insight 2) It Will Never Be Perfect

Generative AI projects are often initiated in sectors like law, medicine, and engineering, where precision and accuracy are paramount. Professionals in these fields are trained to prevent errors and provide infallible expertise. However, these traits can sometimes hinder progress in Generative AI endeavors.

The critical lesson here is embracing imperfection. Progress requires making informed decisions even when complete certainty is elusive. Waiting for absolute assurance before proceeding is a surefire path to stagnation. Project teams must be willing to embark on the journey, even if they don't have all the answers at the outset. And to be successful, teams must be willing to admit – at least among the team members - all that they don’t know (yet). The latter is not an easy feat for professionals, who are paid to provide solid answers.

AI Insight 3) Start Small

When teams approach us with their Generative AI ambitions, they often present grandiose plans, such as predicting court case outcomes or revolutionizing entire industries. While these aspirations are commendable, they don't offer the most pragmatic starting points. Most law firms, for example, lack the infrastructure and expertise needed for such large-scale projects, and overambition can lead to disappointment.

We advocate for a more incremental approach. Teams should begin by identifying smaller, pressing issues that can be efficiently addressed with Generative AI. This approach allows for more manageable progress, faster learning, and a smoother path to delivering tangible results.

Starting small doesn't mean thinking small; it means being strategic and laying a solid foundation for growth. What is a real pain point, when it comes to mundane tasks that clients are not willing to pay for? Those are tasks and work processes that are prime targets for (generative) AI.

In the end, the results matter, not the ideas.