Teaming Principles for Innowaring
Structuring, empowering, and aligning Teams for AI-Native Innovation
This post is part of a series outlining my principles-first approach to AI-Native Product Innovation.
The first post describes the origin of the Innowaring idea after I published my book, Becoming a Software Company.
The second post describes the Innowaring Concepts, the operating model dimensions for AI-Native product innovation.
This post describes the principles for the Teaming dimension of Innowaring.
Great products come from great teams. That’s why the Innowaring starts with the Team, not the product or system.
The Team isn’t just engineers and product people. It spans levels and functions—marketing, business, and beyond. It is the overall Team-of-Teams structure that solves hard customer problems and delivers the best tech solutions.
Within the Innowaring Model, everyone builds. Teams build the product, while management builds the team. In fact, everything you want in the product must be embodied in the team—skills, knowledge, behaviors, and communication.
There’s no one-size-fits-all recipe for an optimal teaming structure. It must fit the unique context of the problem it’s tackling. This context now includes AI, which is transforming how teams ideate, design, and engineer products. Even so, following proven teaming principles, adapted for the AI era, can help forge winning teams.
Principle: Team-Building over Resourcing
Organizations and managers must shed the “resourcing” mindset for software product teams. Tech product innovation isn’t about optimizing headcount. It thrives on insights and collaboration. The focus must shift to teams, their roles, and how information flows across them.
Good teams don’t come together overnight. They form over time after the team members work together. Mutual trust builds as they tackle projects, and they go from good to great when they rally around a meaningful problem—their mission.
Teams must stay small to keep trust and mission intact. AI is upending traditional heuristics like Amazon’s “2-pizza rule.” Team sizes will shrink as prototypes become the starting point instead of mockups and documents. Even managerial oversight will reduce as product, design, and engineering roles blur together.
Scaling an organization will demand even more care in forming AI-native small teams and deciding areas of ownership.
Principle: Problem-Solving over Solution Delivery
In today’s tech landscape, there is a heavy solutions bias. Most innovation starts with a shiny fix in mind. Customer requirements are anchored in trends they have seen, not in needs they have felt. True innovation stalls with solution-led approaches.
To innovate, don’t assign solution requirements to your teams. Unpack those requirements to uncover the problem that your customers need to be solved. Pull in your teams to iterate and decide what’s worth solving—and understand why it matters and how to solve it.
A good product team understands customer problems deeply and has a mission-like focus on building the best possible solutions. Without that, you might call it a product team if you’d like, but it’s merely there to deliver features or projects as solutions.
Principle: Creative Ownership
When teams understand the problems to solve and are empowered to build the best possible solutions, the responsibility makes the work meaningful. Tech comps get hyped as talent magnets, but meaningful work isn’t celebrated that way. It is the best and most underrated source of creative motivation.
Helping teams grow until they are comfortable and equipped to build the best solutions is management’s responsibility. Managers must offer creative freedom while keeping chaos in check. Early on, new teams need more direction, less as they mature —maturity still varies across teams.
In modern complex software with many subsystems, each team must retain ownership of a meaningful chunk of work. Leaders must stay connected with teams and find ways to automate drudge work to ensure that a creative challenge remains for each team within the Team-of-Teams structure.
AI will amplify the value of truly creative work, making it critical to position your teams as problem-solving entities rather than order-taking units.
Principle: Impact over Effort
Effort to Impact Continuum (h/t Kent Beck and pragmaticengineer.com)
Your Team-of-Teams must build products and systems that deliver positive customer and business results. If you have identified the right problem and your teams can deliver the best solution, your internal or external customers must see them as better than any alternative—that’s the outcome and impact you are seeking.
Your teams’ effort and output must align with customer outcomes and business impact. Everyone grasps the effort-to-impact continuum intuitively, yet aligning them remains notoriously hard. Without specific problems to solve and team autonomy to build the best solutions iteratively, that alignment is impossible.
A broken link between effort-and-output and outcomes-and-impact creates underperformance. The gap widens when people who think about impact don’t sync with those who produce effort. Measure outcomes and impact—quantitatively and qualitatively—and align them with effort and output.
With AI, teams can iterate and learn faster, thereby shortening the translation cycle of effort into impact. With this speed, organizations must be super mindful about building alignment between effort and effect to avoid waste.
Principle: Cross-Functional Collaboration
When innovation is the goal, there is a high degree of uncertainty attached to the outcomes and impact. There are 4 big risks, whether your product is valuable, usable, feasible, and viable. Rarely can one person handle all the decisions to test and mitigate them.
It takes cross-functional and high-agency collaboration within and across your Team-of-Teams, where members feel comfortable contributing their best skills and experience to decision-making. The goal isn’t consensus but to make evidence-based decisions.
Personal conflicts are inevitable in group work. But can team members stay collaborative and laser-focused on product and business goals despite them? That’s the true test of collaboration. Another key is how much collaboration relies on implicit communication, not explicit documentation. Some documentation is essential—yet when everything must be written down, it signals a low-trust environment that stalls collaboration.
Good collaboration cuts the cycle time for solution iterations. In the AI era, shorter cycles are not just essential but critical to success: to tighten the feedback loop between what teams create and what customers and the market demand, to speed up information flow to mitigate value, usability, feasibility, and viability risks.
The Innowaring model rests on a premise: great products stem from building great teams. As AI reshapes software development, time-tested principles of teamwork grow even more vital, not less. By building trust within teams, empowering them to solve meaningful problems, enabling creative ownership, aligning effort with impact, and fostering cross-functional collaboration, organizations don’t just create functional teams—they forge engines of AI-native product innovation.
This post is part of a series outlining my principles-first approach to AI-Native Product Innovation. Links to the previous posts (Part 1, Part 2)
The next post describes the Strategy dimension of Innowaring.
References:
Sidhu, A. (2023). Becoming a Software Company: Accelerating Business Success through Software. Apress.
Silicon Valley Product Group (SVPG). (2024). Transformed: Moving to the Product Operating Model. Wiley.