Introduction: Why We Need Hard Numbers
Most product leaders have experienced the pitch. A conference keynote, a consulting deck, or an internal champion declares that the "Product Operating Model" is the future of building technology powered products. Cross-functional, empowered product teams, product managers with a skill-set matching that of a proverbial "mini CEOs," continuous discovery, rapid iteration and an enabling and coaching (rather than commanding) leadership style. The principles are compelling. But when it comes time to justify the investment required to get there, leaders face an equally familiar and uncomfortable question from the boardroom: "What's the return?"
It's a fair question. For an incumbent organization, transforming 1 to a product operating model isn't a trivial feat. It entails changing how you build and ship (product delivery), how you solve problems (product discovery), how you decide which problems to solve (product strategy) and how you lead and manage people and teams (leadership, culture). See our article "The Product Operating Model" for a breakdown on what is changing within the product development organization.
At its core, this transformation means moving away from a world where building technology is a cost serving the business, to one where the product is the business. A transformation will therefore naturally, as second order effects 2, put change pressure on adjacent functions such as Finance (funding structures, governance), HR (talent acquisition, -development and -retention, culture), and Revenue (sales no longer "places orders", GTM more integrated in the product development cycle). The investment is real, so the evidence needs to be as well.
In December 2023, McKinsey Digital published research that provides quantitative evidence (Chawla et al., 2023). Their study of over 400 publicly traded companies, using a purpose-built Operating Model Index (OMI), found a direct and consistent correlation between operating model maturity and financial performance. Even though OMI doesn't cover the full Product Operating Model, the overlap is enough to show strong performance correlations. In other words, even a subset of these practices shows a strong link to performance, which strengthens the case for the broader model 3. This article examines that evidence, what relevance it has for our definition of the Product Operating Model, and explores what it means for executives weighing this transformation.
The Evidence: McKinsey's Operating Model Index
McKinsey's research is grounded in the OMI, a composite score measuring an organization's maturity across five core areas of the product and platform operating model. The survey was conducted among senior leaders (C-suite executives, VPs of product and engineering) at public companies with over $1 billion in revenue. The sample spans roughly 30% North America, 40% EU, 25% Asia-Pacific, and 5% South America, across sectors including technology, financial services, consumer goods, and advanced industries.
The Five Pillars of the OMI
The OMI measures maturity across five areas, each broken down into specific dimensions:
| OMI Area | What It Measures | Examples of Key Dimensions |
|---|---|---|
| Structure | How teams are organized and composed | Product taxonomy, team alignment, persistent teams, role ratios, co-location |
| Strategy & Governance | How priorities are set and funded | Portfolio planning, OKR maturity, backlog prioritization, funding processes, accountability structure |
| Ways of Working | How teams execute and collaborate | Cross-team dependencies, PDLC roles & responsibilities, PM and engineering practices |
| Culture & Talent Management | How organizations build and retain capability | Psychological safety, talent value proposition, career paths, upskilling programs |
| Tooling | The platforms that enable delivery | Planning and collaboration tools, developer tooling, DevSecOps and CI/CD pipelines |
The Financial Headlines
The headline findings are striking. Companies in the top half of OMI maturity, compared to those in the bottom half, show:
| Metric | Performance Gap | Source |
|---|---|---|
| Total Returns to Shareholders | 60% higher | 2021 financial data, n=380 |
| Operating Margin | 16% higher | 2021 financial data, n=380 |
| Customer Engagement | 38% higher | OMI survey, top vs. bottom quartile |
| Brand Awareness | 37% higher | OMI survey, top vs. bottom quartile |
| Overall OMI vs. Business Outcomes | Pearson r = 0.64 | Full survey sample, n=400+ |
By Cohen's widely used conventions for the behavioral sciences, where r = 0.50 marks a large, or even very large, effect (Cohen, 1988), a correlation of 0.64 represents a strong signal. To put that in perspective: the widely studied link between employee engagement and composite business unit performance, which underpins billions of dollars in annual corporate investment, shows an observed Pearson correlation of 0.30 (Harter et al., 2020). The correlation between operating model maturity and business outcomes is more than twice as strong.
What Drives Performance: Unpacking the Correlations
Not all five pillars of the OMI contribute equally. The research reveals a clear hierarchy in terms of impact on business outcomes, and understanding this hierarchy is essential for prioritizing investment.
"Ways of Working": The Strongest Predictor
Among the five areas, "ways of working" shows the highest correlation with overall business performance (r = 0.60). This pillar covers how teams execute: level of autonomous delivery, managing cross-product dependencies, defining clear roles and responsibilities across the product development life cycle, and adopting modern product management and engineering practices such as product discovery, evidence-based product strategy, and rapid experimentation through MVPs and A/B testing.
This is an important finding for executives because it reframes the transformation from a structural exercise into a behavioral one. You can redesign your org chart, but if teams still operate in siloed handoff models with product managers writing requirements documents that get "thrown over the wall" to engineering, the structural change achieves little. What matters is how people work together day to day: shared direction, joint prioritization and trade-offs, clear decision boundaries, and rapid feedback loops.
Paradoxically, McKinsey's data also shows that "ways of working" has among the lowest maturity scores across sectors. This means it is simultaneously the most impactful area and the one where most organizations are weakest; this should indicate a significant opportunity for executives aspiring to make an impact.
Culture, Talent, and Tooling: The Innovation Engine
When isolating innovation as the outcome variable, the correlations shift. Culture and talent management (r = 0.64) and tooling (r = 0.70) become the dominant predictors. This is the highest correlation in the entire dataset and it tells a clear story: innovation thrives where people feel psychologically safe to experiment and where the tool chain makes experimentation cheap and fast.
This finding aligns with broader research on innovation, notably Amy Edmondson's work on psychological safety (Edmondson, 2018). The ability to run experiments, fail quickly, and recover without blame is consistently identified as a prerequisite for breakthrough product development. Organizations that combine this cultural foundation with modern DevSecOps pipelines, feature flags, automated rollbacks, and generative AI-augmented development create an environment where innovation becomes systematic rather than accidental.
Where the Gaps Are Widest
McKinsey's research also identifies where top-quartile and bottom-quartile performers diverge the most. The five areas with the largest gaps are: the interaction model between product, engineering, and operations (61%), backlog prioritization (58%), funding (57%), technical debt management (56%), and product management practices maturity (55%).
What's striking about this list is how fundamental it is. These are not advanced or exotic capabilities. They are basics: how do teams interact, what opportunities do we explore next, how do we fund it, how do we keep the codebase healthy, and are our product managers genuinely empowered. The research suggests that the greatest differentiation between top and bottom performers comes not from sophisticated practices, but from getting these fundamentals right at scale.
A Critical Lens: Correlation, Causality, and Confidence
Strong correlations are valuable indicators, but they are not proof of causation. Any rigorous evaluation of this research must address three fundamental questions about the nature of the relationship being observed.
1. Does the Model Drive Performance, or Vice Versa?
The McKinsey research implies a directional relationship: adopting a mature product operating model leads to better business outcomes. The logic is intuitive; faster feedback loops, decentralized decision-making, and relentless customer focus allow teams to quickly and cheaply evaluate many ideas before committing to building them, hence substantially increasing their "hit ratio" of high-value outcomes. This "active management of value" should logically improve margins and shareholder returns.
However, a skeptic could argue for reverse causality. Perhaps highly profitable companies simply have the resources to invest in expensive organizational transformations, better tooling, and top-tier talent. Under this reading, the operating model is a consequence of financial success, not a cause. Companies that are already doing well can afford the luxury of adopting modern practices.
This is a legitimate concern, and the cross-sectional nature of the OMI data (measuring maturity and financial performance at roughly the same point in time) cannot fully resolve it. Longitudinal research (tracking companies before and after adopting the model) would be needed to establish directionality with confidence.
2. The Third Variable Problem
Is there a confounding variable, such as "leadership quality" or "strategic clarity", that independently drives both the willingness to adopt a modern operating model and strong financial performance? It is entirely plausible that companies with visionary, execution-oriented leadership would both adopt product-centric models and outperform peers, regardless of the operating model itself.
This is a common limitation in organizational research, and it should temper how confidently we attribute outcomes to the operating model alone. The OMI research does not include controls for leadership quality, strategic positioning, or market dynamics.
3. A Longitudinal Counter-Point
The McKinsey article does include a case study of a retail brand that provides a partial answer to the causality question. This company implemented the product operating model and subsequently observed a 60% improvement in innovation cycle times, a 20% improvement in self-reported customer centricity, and a 30 basis-point improvement in employee satisfaction scores. The initial pilot products were on track to deliver tens of millions of dollars in combined revenue uplift and cost savings.
While a single case study cannot substitute for rigorous experimental design, it provides a longitudinal data point: performance improved after the model was implemented, which is consistent with the operating model acting as a catalyst. The organization's subsequent decision to establish a transformation office and scale the approach via a playbook further suggests that leadership attributed the improvements to the model itself.
4. The Weight of the Evidence
Our assessment is this: the McKinsey research establishes a strong, consistent correlation across 400+ companies, multiple geographies, and multiple sectors. It does not, and, given its design, cannot, establish causation in the strict scientific sense. However, the consistency of the signal, the logical mechanisms that connect the model to performance, and the supporting case evidence together make a compelling case that the product operating model is, at minimum, a critical enabler of strong performance.
For decision-making purposes, this level of evidence is strong. Executives rarely have the luxury of randomized controlled trials for organizational strategy. The combination of correlational data at scale, a coherent theoretical framework, and case-level longitudinal evidence constitutes a strong basis for action.
Implications for Executive Decision-Making
What should senior leaders take from this evidence? Three implications stand out.
Prioritize "Ways of Working" Over Structure
The data is clear: how teams work together matters more than how the org chart is drawn. If you are early in your transformation, resist the temptation to spend months redesigning reporting lines before addressing the behavioral changes that drive results. Start with the interaction model: define roles across the product development life cycle, establish shared backlogs, and create forums for cross-functional teams to resolve dependencies together. Structure should follow ways of working, not precede them.
Invest in the Innovation Enablers
If innovation is a strategic priority, and for most companies in software-driven markets, it must be, the evidence points to two areas of investment: culture and tooling. Building psychological safety is not a soft aspiration; it is the single cultural factor most correlated with innovation outcomes. Combine this with modern developer tooling, CI/CD pipelines, feature flags, and the strategic adoption of generative AI coding tools, and you create an environment where the cost of experimentation drops and the velocity of learning increases.
Start with the Fundamentals
The five capability areas with the largest gaps between top and bottom performers (interaction model, backlog prioritization, funding, tech debt management, and PM practices) are not exotic or novel. They are fundamental disciplines. The research suggests that the greatest value often comes not from advanced practices, but from getting the basics right at scale. Before investing in sophisticated tooling or advanced analytics, ensure your teams have clear roles, alignment, autonomy, outcome-based funding, and the time and incentives to manage their technical debt.
Conclusion: A Necessary Bet
The McKinsey Operating Model Index research provides some of the most compelling quantitative evidence available that the product operating model is not just a trend, but is directly associated with meaningfully better financial and business outcomes. The 60% gap in shareholder returns, the 16 percent higher in operating margins, and the strong correlations with customer engagement, brand awareness, and innovation represent a level of signal that executives cannot afford to ignore.
Is it proof of causation? No. But in the real world of organizational strategy, where controlled experiments are impossible and the cost of inaction is measured in competitive decline, the weight of the evidence points clearly in one direction. In a software-driven market, operating like a digital native is no longer a strategic option; it is a prerequisite for maintaining competitive margins.
The product operating model is not a guaranteed formula for success. It is, however, the approach most consistently associated with the business outcomes that matter. This is also our experience from working with organizations going through this shift. When we at Better Product Work partner with a client to facilitate a transformation to the product operating model, we don't guarantee a particular business outcome. However, we guarantee that the organization drastically improves their chances to deliver tech powered products that customers love, and that works with their business. For leaders willing to commit to the organizational development required, the evidence says the investment is worth it.
Footnotes
1 We experience some pushback for using the word "transformation"; clients have asked us to "dial down" the implied magnitude of the word as to referring to our engagements, and people from the "Agile" community tell us it's frowned upon. After some consideration, we have decided to keep using it, since it's the best word we have to describe the, usually profound, change most organizations need to go through, if the target state is a modern, product model organization.
2 This is why some refer to working in a product model organization as being "Product-led". We find the term misleading for two reasons. First, it is surprisingly often confused with the established customer acquisition strategy Product Led Growth, which is really mixing apples and pears. Second, framing a transformation as a power shift to an existing function is neither pragmatically advantageous nor factually correct. In reality, the shift is driven by changes in customer behaviour and technology, not an internal power struggle.
3 The OMI measures five areas of operational maturity: Structure, Strategy & Governance, Ways of Working, Culture & Talent, and Tooling. There is strong overlap with the Product Operating Model, particularly in product management practices (empowered PMs with end-to-end accountability, user research, data-driven decisions) and culture (psychological safety, cross-functional collaboration). Three examples of areas where our definition of the Product Operating Model goes further:
First, the role of leadership. The OMI accounts for accountability structures, but the Product Operating Model requires a fundamental shift in what leaders do. In a mature product organization, leaders build, coach, and develop teams to increase their ability to solve problems autonomously. This is a departure from conventional leadership where directing product decisions and overseeing delivery are the core activities.
Second, the team as the foundational unit. The Product Operating Model places the empowered cross-functional team at the center of the organization. Incentives, support mechanisms, and performance structures are built around teams, not individuals. In most conventional organizations, the opposite is true.
Third, organizational coherence across teams. Individual empowered teams are necessary but not sufficient. The Product Operating Model emphasizes a "team of teams" structure, where groups of teams (for example a "Tribe") operate within a cohesive context with aligned leadership, shared purpose, and clear mechanisms for coordination and collaboration.
References
Chawla, A., Harrysson, M., Mayer, H., & Sinha, M. (2023). "The bottom-line benefit of the product operating model." McKinsey Digital, December 2023.
Cohen, J. (1988). Statistical Power Analysis for the Behavioral Sciences (2nd ed.). Lawrence Erlbaum Associates.
Edmondson, A. C. (2018). The Fearless Organization: Creating Psychological Safety in the Workplace for Learning, Innovation, and Growth. Hoboken, NJ: Wiley.
Harter, J. K., Schmidt, F. L., Agrawal, S., Plowman, S. K., & Blue, A. (2020). The Relationship Between Engagement at Work and Organizational Outcomes: 2020 Q12 Meta-Analysis. Gallup.