Maturity models don’t work. And I’m not saying that to be provocative. I’m saying that because I believe it—and for good reason.
To put it into perspective, picture this familiar scenario: Today you find yourself in yet another transformation board meeting. You’re ready to record ideas and key points for how your company needs to change. At the head of the table, a team is showcasing a consultancy’s strategy for innovation. The classic maturity model appears on the screen—and surprise! The company is rated as immature, against all indicators.
While maturity models have been held in high regard for years as proven templates for continuous improvement, it’s becoming quite evident that their structure is actually their greatest flaw—far too static, a snapshot, a single perspective and a solution path unable to keep up with an ever-changing world. After all, the vast majority of maturity models are sales tools created to market a consistent buyer’s path, rather than a dynamic, context-specific, or customized outcome that a company wishes to achieve.
Because of that, not only should assertions of maturity—or immaturity— be questioned, but the entire concept should be challenged. And it only takes a quick look at history to exemplify why.
Origins of The Maturity Model
The Capability Maturity Model was originally developed in the late 1980s and early 1990s as a means for the US Department of Defense (DoD) to evaluate its vendors’ ability to effectively deliver on software development projects. Essentially, it was a supply chain management technique that helped mitigate the risk of project failure from things like a vendor not having a well-defined quality assurance process.
As a framework, it was simple. The model defined five progressive stages of maturity, starting with Level One (least mature) and ending with Level Five (most mature). Then, it described each level based on its respective maturity characteristics—similar to how you might describe the different stages of fetal development from fertilization through birth.
Using this model, the DoD could easily classify each vendor with a maturity score based on the stage that most closely matched their processes. They could then use these scores to influence resourcing decisions, engage in supply-chain improvement activities, and more.
Since its inception, the Capability Maturity Model has been widely adopted by the business world, not just as a means for qualifying and optimizing software engineering, but as a generic technique for organizational evaluation. A way to profile oneself, one’s industry, one’s clients, or one’s perceived competition against an idealized and sometimes futuristic state of best practices.
In the wake of the Capability Maturity Model’s success, many others have been developed in attempts to capitalize on its methodology, such as the Digital Transformation Maturity Model, Social Media Marketing Maturity Model, Supply Chain Maturity Model, and the Scaled Agile Framework (SAFe) Maturity Model (one of my more detested models), among others.
.Why Shouldn’t I Use It?
So you may be asking if it’s been so widely used and expanded upon over the years, what’s the problem? Unfortunately, maturity models come with some significant issues when we look at the big picture:
- It’s merely a snapshot in time. As a model, it doesn’t look toward the future. It looks at past stages and current statuses rather than being predictive and aligning for progress.
- Maturity, or more specifically higher performance, doesn’t have an end state. While you work toward your initial objective, you learn. Sometimes, your objective even shifts. You might learn that the path you’ve been taking isn’t productive or efficient. In either case, a maturity model does not allow for these shifts and changes.
- Maturity models focus on only one person’s experience, opinion, or narrative of what “better” is for the company instead of defining and unifying business objectives team- or organization-wide.
- It doesn’t evolve at the same pace as the changes in your market, customer, or technology—and applying a static model to dynamic variables rarely meets expectations.
- Maturity models are not scientific models and don’t follow the scientific method—yet the methodology and results are often trusted as if they are.
- How a company moves through each stage is completely arbitrary, as there is no “best practice”. In fact, today’s mature practices can easily become tomorrow’s obsolete ones.
- Maturity models are notoriously packed with the “filter bubble” phenomenon. You will only see what you want to see—and miss out on knowledge that could be game-changing for your company.
OK. I’m listening. If maturity models don’t work, then where do we go from here?
A Better Approach:
Regardless of your industry or vertical, there is no one-size-fits-all model for growing your business. At least, not in the context of what I’ve outlined above. But that’s not to say that there aren’t successful and enduring strategies to compensate for the regular changes that affect your customers, market, or business over time.
In contrast to the maturity model, it’s better to:
- Stick to critical thinking paired with the scientific method to create a model customized to your needs, desired outcomes, and the capabilities to create to get there.
- Identify and communicate quantifiable, organization-wide objectives to align your people, while setting outcome-based metrics for your teams to provide a clear scope of progress.
- Create hypotheses in relation to your objectives and test them. By testing, recording, and adapting to the information you gather, you will more accurately identify what will and won’t be viable paths for achieving those outcomes.
- Repeat. Continue formulating hypotheses for growth, testing your assumptions, and gathering evidence of what works and doesn’t for your context, circumstances, and challenges.
- Just because you’ve found a problem-solution fit for the time being doesn’t mean it will remain a solution in the future—you’ll learn, unlearn, and relearn along the way.
- Compare and contrast your assumptions and assertions with other ideas over time to identify further gaps in your thinking and testing.
- Your objectives and path will adapt over time alongside changes to your customers, technology options, and your market. Plot it, map it, review it, and reevaluate it.
- Sharing your lessons learned along the way is the greatest gift to help others on their journey, inspiring action and debunking the myth that it’s impossible to innovate in your highly regulated, bureaucratic business domain.
Over the years, these methods have created a significant body of proof that progress is possible by taking an evidence-based approach to innovation, and there’s no better example than the case study of Capital One’s Talent Transformation from Chapter 10 of Unlearn.
The Big Problem: Capital One is highly competitive and was designed in such a way where employees worked against each other for individual short-term achievements rather than working together to move the organization toward their larger, long-term, and shared objectives.
Drew Firment (former Technology Director of Cloud Engineering at Capital One) had a lofty objective: Drive Capital One’s cloud adoption and talent transformation by achieving certain organizational-level objectives such as faster delivery, less expensive operations, and increased product innovation.
They needed to migrate the organization’s systems from on-location data centers to the cloud as quickly and efficiently as possible while keeping costs down and maintaining security.
The Hypothesis: Rather than using a maturity model to measure Capital One’s performance toward their overall objective of cloud adoption, they needed to be specific about what success looks like. With that in mind, Firment hypothesized that by providing a clear destination, they would be able to achieve the breakthrough that the organization was looking for.
To put the plan into action, Firment partnered with a developer to create the Cloudometer (below)—a system that measured the metrics relevant to their objective: the speed, quality, and cost of cloud migrations. Thus, it gave the teams a clear path to plot, map, review, and reevaluate their position in relation to the organizational desired outcomes.
Success: Through trial and error—or more accurately, hypothesizing and adapting—Capital One was able to meet its objectives.
They determined that in order to meet their objectives and truly innovate their technology, they would need to focus on complete talent transformation—identifying specific new skills and capabilities to build, along with customized coaching, mentoring and training. By shifting their KPIs, they were able to change employee behavior and incentivize the dramatic transformation Capital One needed to grow from a company with an IT shop to a technology-led organization.
As you can see, by defining an objective and allowing teams to set their own outcomes to be achieved relative to that objective, as well as how they might get there, they were able to measure the true distance they needed to cover—and where they were in relation to it.
Making that visible across the entire department, while uncomfortable, gave the company a true picture of where they really were in relation to their desired objectives, what outcomes each team was achieving, and what (if any) support would be needed to help people move.
A maturity model would never have given that real-time insight and accuracy of information.
Of course, this is only one example. For more insight into Capital One’s journey, alongside in-depth analysis of how and why plenty of other organizations have found success through this methodology, get your copy of Unlearn here.
Next time you’re tempted to use a maturity model, try this instead:
- Make your objective clear.
- Measure hard to measure outcomes.
- Map where you are in relation to your objective.
- Encourage teams to identify what is best for them.
- Create a hypothesis narrative for how you might get to where you want to be.
- Test and monitor your progress.
- Adapt as needed, and repeat.