Data Science Leadership Series – Part 3: Building Great Products by Balancing Product and Engineering Mindsets

I wrote about why we need AI Product Management when building complex data products in a previous post in the Data Science Leadership Series, and I feel this has to be followed by a very important topic that almost every single software development team faces, especially in high growth startups where the speed of innovation and the rate of making sudden pivots to build the right product is very high – which is the critical balance between product and engineering teams.

Too much of product strategizing without concurrent engineering pulls back the engineering momentum and ends up adding unnecessary layers of bureaucracy – which I believe is the reason for bloating of teams and slow down in the pace of innovation at many large organizations. At the same time, too many ideas thrown around from all direction and actually starting to be built by engineers without a concrete product strategy and roadmap that’s built around actual customer and business need is the reason for a lot of unnecessary engineering chaos and burnout. 

So what’s the sweet spot where product and engineering meet and galvanize their vision to come together to build a strong successful product ? Anyone who is pondering over this question or whose team/ company is seeing conflicts emerge between their really smart product and engineering teams should listen to this podcast, where the CTO of Microsoft Kevin Scott interviews the CTO of OpenAI, Mira Murati. OpenAI’s ChatGPT, I believe, is a great example of how to build a complex AI product and make the product decisions that ultimately leads to a widely usable product that changes the way people, even who are not generally technologically driven, engage with your product. Mira previous worked at Tesla as an engineer and product manager and helped develop the Tesla X car, and then joined OpenAI in 2018 and has since then become the CTO of the company.

Some tidbits from the interview that struck a chord with me, as a person who is getting more and more interested in building successful AI products at scale:

1. “working with teams that had different backgrounds, domain expertise, figuring out how to design something that has never been done before, adopting new ideas, very quickly kind of killing old ideas and moving on to the next one and just, like, figuring out the right problem to work on at the right time”
2. “it’s key to innovating at scale past a certain size of the company. It’s difficult to innovate if you’re just throwing things over the wall and, you know, like bureaucracy can kick in or processes and, you know, but as they grow, companies can lose their vision and sort of stop pursuing new ideas…if you kind of cut through that and minimize sort of the layers of processes and things or hoops that you have to jump through to get something done or bring some new idea, then I think it’s much easier”
3. “incredibly important — stepping back and not — I mean, having the ability to be immersed in details and dig deep when you need to, but also stepping back and asking the right questions and having sort of this high degree of adaptability in the team, and tolerance for ambiguity”

It’s a really inspiring interview, and I highly recommend, if you are a new product leader or manager learning the ropes of product management, especially if you are working with stochastic data products with high degree of ambiguity.

Here are some strategies for bridging the gap, aligning goals, and fostering effective collaboration between product and engineering teams to build exceptional AI products:

1. Establish Clear Communication Channels:

Effective communication is the cornerstone of any successful collaboration. Creating an environment that encourages open and transparent communication between product and engineering teams is paramount. Establish regular forums such as stand-up meetings, sprint reviews, and retrospectives to facilitate cross-team communication. Encourage both teams to actively participate, share insights, ask questions, and provide feedback. By fostering open lines of communication, you promote a culture of collaboration, mutual respect, and shared ownership of the product’s success.

2. Develop Shared Goals and Objectives:

To find a balance between product and engineering teams, it is essential to align their goals and objectives. Collaboratively define key performance indicators (KPIs) that reflect both the product’s success and engineering excellence. Foster a shared understanding of how the product’s success is interlinked with engineering achievements. This alignment ensures that both teams work towards a common purpose and fosters a sense of camaraderie, eliminating potential silos and conflicts.

3. Embrace Cross-functional Collaboration:

Building a complex AI product requires the integration of various disciplines. Encourage cross-functional collaboration by promoting joint brainstorming sessions, workshops, and problem-solving exercises. Create opportunities for product managers and engineers to collaborate closely throughout the development lifecycle. By fostering cross-pollination of ideas and expertise, you unlock innovative solutions, prevent knowledge gaps, and enable the development of a cohesive product that addresses customer needs effectively.

4. Foster Empathy and Understanding:

Developing empathy and understanding between product and engineering teams is vital to finding a balanced collaboration. Encourage product managers to gain a deeper understanding of technical constraints and challenges faced by engineers. Likewise, promote technical literacy among product managers to facilitate effective communication and decision-making. By fostering empathy and understanding, both teams can appreciate the complexities of each other’s roles and work together towards realistic and achievable product outcomes.

5. Encourage Iterative and Agile Practices:

Complex AI products often require iterative and agile development processes. Embrace methodologies such as Agile, Scrum, or Kanban that emphasize frequent iterations and continuous feedback loops. Encourage the product team to work closely with engineering in defining and prioritizing product backlogs, ensuring that requirements are refined and validated iteratively. This iterative approach allows for early detection and resolution of issues, facilitates flexibility, and ensures that product development remains aligned with customer expectations.

6. Promote a Culture of Learning and Growth:

Building complex AI products demands a growth mindset from both product and engineering teams. Encourage continuous learning and knowledge sharing within the organization. Organize internal workshops, lunch-and-learns, or external speaker sessions to keep the teams abreast of emerging technologies and industry trends. By fostering a culture of learning, you empower individuals to expand their skill sets, stay motivated, and contribute to the overall growth of the product and the organization.

I’ll conclude with a quick thought on first principles framework – as product leader, when faced with conflicts or complex problems regarding balancing product and engineering mindsets, it helps to strip back to the core problem as to what do you want to solve, is what you are attempting to do, the most efficient way to solve that problem ? Answering this question will bring back the focus to what exactly is needed, and can guide the product and engineering teams to work towards the shared goal.