9MAY JUNE 2024AI Team: Towards a Lovable AI ProductCentralized large teams that could not efficiently devolve knowledge to business units because of focus on centralized technical resources gave way to a more enlightened focus on core outcomes of producing a lovable AI product. This was possible by abstracting or sublimating technology into an enabling role, which allowed the AI team's focus to be set on tangible outcomes, measuring success through user satisfaction and impact. Shifting the spotlight away from technological intricacies and towards user experience, the team empowers itself to craft solutions that truly resonate with audiences. Balancing swift iteration and thoughtful refinement is imperative, ensuring timely execution. Moreover, nurturing a continuous state of `flow' within the team cultivates a creative and efficient atmosphere, fostering innovation and collaboration.The `Secret' to AI Team Structure & FunctionAn emerging open secret of successful AI teams is that data scientists work in sync with products to support data-driven decision-making for customers. This is enabled by seasoned data scientists embracing the entire process continuum from understanding the business problem to deploying a model pipeline. Github repo to API. This is currently possible because of the many available end-to-end Machine Learning platforms, such as Databricks, Azure, AWS, and others.The new reality emphasizes the empowerment of data scientists to take care of much of the CI/CD pipeline with plan, building, testing, releasing, monitoring, and focusing on AI learning patterns implicitly, while data engineers, who implement rules explicitly, have a minimized role of operating the deployed models.Resiliency: Outliving the Hype Cycle of an AI TeamSince AI teams take advantage of novel technologies, it follows their rise and fall with the technology hype cycle. Embracing generative AI, we have to think of team resiliency in planning and executing with the goal of making it past the `Peak of Inflated Expectations' cycle of 2-3 years. What the hype cycle does not show is the productivity yield, and that productivity must align with initial expectations. Unsurprisingly, the ultimate goal is delivering productivity throughout the `honeymoon' period. Reaching the singularity point, where productivity equals expectations, is the pivotal moment of team achievement when AI products resonate with stakeholders, offering tangible and quantifiable business value. Outliving the hype cycle and surpassing the transient phase of technological hype means delivering on AI team productivity from day 1.Promoting Team FlowManaging a thriving AI team lies in the seamless progression of talent, resources, ideas, and results. Cultivating an environment that promotes the concept of team `flow,' the team experiences evolution. To endure over time, the team must embrace systems thinking such as, for example, the design principles of Constructal Law. This involves the intricate orchestration of teamwork processes - a dynamic in which the pursuit of greater access to resources is pivotal for the team's longevity. As the configuration of the team's flow changes, it drives evolution. This continuous adaptation ensures the AI team's vibrancy and progress, setting the stage for enduring success.Avoiding PitfallsConstantin Brancusi, the founder of modern sculpture, wrote, "The challenge lies not in the act of creation itself, but in cultivating the right mindset for it." Similarly, navigating the path of AI team success requires vigilance against common pitfalls. Striking a balance between team learning and execution is crucial. If the AI team spends most of its time training, then it doesn't have the required talent and needs new hires with requisite capabilities ­ instead of the ultimate learning team, remain open to augmenting talent. A blend of direct and contingent hires adds versatility. To stave off complacency, team diversity is paramount. Also, avoid blind adherence to Agile dogma that might lead to the deceptive `Busyness Trap.' Agile methodologies should empower rather than mask genuine progress. There's no grace period; productivity commences from Day 1. Finally, early showcases of successful results should take precedence over delays. These principles serve as guiding beacons to a resilient and accomplished AI team journey toward business value. Generative AI is set to touch every aspect of financial industry operations, from sales to customer service
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