The former offers a dynamic, agile way to innovate through testing, feedback and data analysis, while the latter represents a more structured, predictable system aimed at streamlining product strategy and management.
When it comes to achieving meaningful and sustained growth, experimentation should not only coexist with the product operating model - it should drive it. We take a look at why companies should embrace the power of experimentation while still valuing a strong operational framework.
Experimentation gives organisations the opportunity to test ideas in real-time, leveraging data to understand what truly works. A/B testing, for example, allows for rapid iteration and refinement of product features, messaging, or customer experiences, ensuring that every decision is backed by real insights, not gut feelings. It allows teams to move away from traditional planning cycles that often take months and instead focus on continuous learning.
With data, product managers, software engineers, and experience designers can collaborate more effectively, breaking down silos and aligning around shared goals. As we increasingly shift toward data-driven environments, businesses that rely too heavily on intuition or outdated operating models risk falling behind. Experimentation empowers teams to pivot quickly, course-correct, seize emerging opportunities and differentiate against competitors.
Traditional product operating models emphasize stability, predictability, and process optimisation. This can lead to more efficient workflows in the short term, but it can also cause a lack of responsiveness when markets shift, customer preferences change, or new competitors emerge. On the other hand, experimentation offers flexibility — allowing businesses to adapt in real-time based on feedback and performance metrics.
While rigid product operating models help maintain consistency, the flexibility that experimentation provides is crucial for staying competitive in dynamic environments. Instead of sticking to a pre-set roadmap that may no longer align with user needs or emerging trends, companies can make incremental changes, ensuring that the product is always evolving in the right direction.
One of the key pitfalls of traditional product operating models is the idea that innovation follows a linear, sequential process. Planning is done upfront, assumptions are made about market needs, and products are developed according to a fixed timeline. The reality is much messier. Products often evolve through trial and error, with teams discovering new opportunities and challenges along the way.
Experimentation creates an environment where innovation is expected to be non-linear. It acknowledges that failure is a natural part of progress and encourages teams to learn quickly from their mistakes.
In contrast to a model that demands everything be perfect before launch, experimentation fosters an environment where iteration and rapid learning become central to a team’s culture.
Experimentation thrives in environments where cross-functional teams are empowered to make decisions based on data. It blurs the lines between different roles—product managers, engineers, designers, and marketers—who can collaborate more effectively when they have access to real-time testing data. The result is more ownership, accountability, and alignment across teams.
In contrast, rigid operating models sometimes limit the autonomy of individuals within those teams. A hierarchical decision-making process, coupled with a fixed roadmap, can create friction and delays as teams wait for approval or clarification from leadership. Experimentation, on the other hand, unlocks the potential of teams to make decisions and adapt on their own, without constantly waiting for top-down directives.
While product operating models might be effective for establishing consistency and scaling once product-market fit is achieved, experimentation is essential for identifying and validating that fit in the first place. Without continuous testing, feedback loops, and data validation, companies risk scaling a product that doesn’t truly meet customer needs.
Experimentation helps teams uncover unexpected insights, refine their value proposition, and improve key performance metrics. Without a strong testing foundation, companies might scale products that only address a small subset of user needs or, worse, fail to differentiate themselves from competitors.
While product operating models are necessary for maintaining order, consistency, and operational efficiency, experimentation should be the engine driving innovation. By focusing on data-driven decisions, embracing flexibility, and empowering cross-functional teams, experimentation opens up opportunities to not only improve existing products but also discover groundbreaking ideas and features.
Rather than viewing experimentation and product operating models as competing approaches, businesses should think of them as complementary forces. The strength of any product development strategy lies in balancing rigorous operational structures with the flexibility and creativity of experimentation. When combined effectively, they can drive both sustained growth and meaningful innovation.
Khemistry has an experienced cross-functional team that has helped our clients establish a data-driven approach to product development and optimisation. We have also worked with clients to enhance their product operating models. If you are looking to get impartial advice on either aspect for your business please get in touch with us today.