Product-Focused Teams: The Secret Behind Successful Generative AI Projects
Break Silos, Boost Accountability, and Transform How AI Teams Work
In generative AI projects, success is not just about the technology, but also how teams are structured. To be successful, those team structures must allow for maximum agility and adaptability. Traditional approaches often don’t define ownership, which leads to silos and inefficiencies. When “everyone” is responsible, no one feels responsibility. It’s the classic “us vs. them” thinking. But clarifying ownership and shaping responsibility across the organization is a key aspect of leadership. Leaders can increase accountability, agility, speed, and innovation by organizing teams around products instead of functions or projects. Let’s break it down…
Break Down Silos to Drive Results
I recently came across a short business column in The Guardian that talked practically about how to increase agility and adaptability across an organization. This is an increasingly important question for leaders to ask as generative AI experimentation becomes a top priority. One of the biggest challenges in generative AI projects is team structure. A common setup is to split responsibilities across various business functions—data scientists focus on models, product managers handle business alignment, and separate teams look for use cases. While this may seem logical, it often creates silos that delay progress and make cross-functional collaboration difficult.
In contrast, structuring teams around a product—an AI capability or a business solution—can significantly reduce the friction caused by functional silos. Instead of segmenting by skill set, product-based teams work across disciplines to drive the success of the entire solution. This approach removes unnecessary coordination, empowers teams to make decisions more independently, and speeds up the delivery of AI solutions.
By focusing on the entire product lifecycle rather than isolated tasks, teams can maintain a sharp focus on delivering business value quickly. This strategy eliminates confusion around responsibilities, allowing teams to work more efficiently toward a common goal.
Empower Teams to Own the Solution
A key to fostering agility is establishing clear accountability. In many generative AI projects, accountability becomes blurred when teams are organized by function. It’s easy for different teams to shift blame when problems arise, leading to delays and miscommunication.
The solution lies in empowering teams to fully own the product they’re developing. This means they’re responsible for completing specific tasks and the entire product lifecycle—from identifying generative AI opportunities through model development to final deployment. When teams have clear ownership, they’re more likely to take the initiative, seek help when needed, and drive the project forward with minimal intervention from leadership.
By organizing teams around products, businesses also reduce the need for constant coordination between different departments. Guardrails, rather than rigid approval processes, can ensure teams stay aligned with broader business objectives while allowing for autonomy in decision-making. This approach fosters trust within teams, encouraging them to take ownership and innovate without excessive oversight.
Use Guardrails, Not Tollgates
For generative AI teams to truly thrive, autonomy is essential. When teams are structured around a product, they have a direct connection to the result of their work, which fosters a sense of ownership and fuels innovation. Rather than waiting for approval at every stage—often referred to as tollgates—teams should be provided with clear goals and expectations but given the freedom to decide how to meet them.
Consider the development of machine learning models. In a traditional setup, one team might prepare data, another for building models, and a third for deployment. This handoff-heavy approach slows progress and creates unnecessary dependencies. On the other hand, a product-focused team would be responsible for the entire process, from identifying the generative AI need to delivering the model in production. This structure eliminates delays, reduces bureaucratic overhead, and empowers teams to innovate faster.
Establishing this kind of autonomy requires trust from leadership. When teams are free to make real-time decisions within a defined framework, they can move faster and produce better results. Clear goals and minimal interference allow teams to thrive, driving speed and creativity in generative AI projects.
Check out the business column by Tom Godden that inspired this article: What can I do to help my organization be more agile and adaptive?, and follow Tom on LinkedIn.
Summary
In AI, structuring teams around products instead of functions or projects can transform how organizations approach generative AI innovation. This approach breaks down silos, clarifies accountability, and empowers teams to own the entire process. By focusing on small, autonomous teams with clear goals and guardrails, businesses can drive generative AI solutions to market faster and more effectively.
Embracing these principles—accountability, autonomy, and guardrails—enables AI teams to operate at their full potential, ensuring that technology catalyzes innovation and strategic advantage.
Are your current team structures promoting accountability and agility, or are they creating more silos? Consider how a product-focused approach could revolutionize your generative AI initiatives.
Thank you, #AWS, for partnering with me on this one. #Sponsored #2024gtlkolp
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