Seven years after the phrase first emerged, cloud-native is the desired destination for the majority of organizations embarking on a digital transformation journey.

But seven years is a lifetime in technology and it would be extremely naive to think that cloud-native is the end of the story, any more than the fall of the Berlin Wall was the end of history.

As Container Solutions COO Pini Reznik explains, it’s important to remember that “Cloud-native is a set of enabling technologies. It allows you to go faster, but it’s not the goal.”

So, what is the goal? When it comes to helping companies achieve their cloud-native ambitions, one of the most valuable tools to have is the Cloud Native Maturity Matrix . 

We use this to help organizations establish precisely where they are in their own journey toward cloud-native, whether their starting point is waterfall, Agile or even no process at all.

The objective is to help them draw a map for their development and evolution. That’s because we believe there are many elements involved in successfully evolving toward cloud-native beyond the technology and infrastructure choices. Adopting microservices and continuous delivery is part of the process, but that’s going to have little impact if changes to products or services must still be agreed upon by a board that meets monthly or even quarterly. Likewise, you can lift and shift a monolithic application onto bare metal in the cloud, but it’s still going to pose the same maintenance and resilience problems that it did when it was sweating away in your own data center.

Put another way, the Cloud Native Maturity Matrix helps answer the questions, “How far is our destination?” and “Are we there yet?” It helps us at Container Solutions create the maps that companies need to make sense of a fast-evolving business and technology landscape. This allows them—and us—to make intelligent choices and monitor progress along their unique path to the cloud.

And after working with hundreds of stakeholders at dozens of organizations, we can begin to trace the outline of the technologies and practices that will shape the near future of cloud-native and technology and business more generally, and usher in the next phase of machine-led development.

The first axis on the index is culture, and it’s perhaps no surprise that cloud-native maturity is associated with a collaborative culture. As organizations face up to today’s fast-moving, unpredictable world, it’s essential to embrace learning, strive for consistent, continuous improvement and reward self-education, experimentation and research.

So, we predict that the next step will be the fully-fledged experimental organization, where people are encouraged to try new ideas a small scale, learning from their failures and scaling up their successes.

When it comes to product and service design, we believe cloud-native maturity means a data-driven design process where the final decision on what products, improvements or features to work on is not the result of a long design process but is based on data collected from real users. Think A/B or multi-variate testing.

So, let’s stick our neck out and suggest that the next step is to cut humans out of large parts of this process altogether. The test data gathered in the field should be feeding AI-driven systems which will make evolutionary tweaks and run tests themselves, with little developer interaction.

That doesn’t mean humans are out of the loop entirely. They still make up the teams that constitute the organization as a whole. But how these individuals and teams interact will also continue to change. The shift from waterfall to Agile to cloud-native has meant less hierarchy and more autonomy for both teams and individuals, in parallel with the breakup of monolithic applications into distributed systems based on microservices.

The next step, we believe, is internal supply chains, where each service is a separate product, with the team bearing full tech and business responsibility for what they manage or produce. This isn’t such a stretch—it’s how many e-commerce teams have operated for the last decade.

It also parallels the changes we expect in how organizations execute. Cloud-native maturity has been associated with a process that brings together design thinking, together with Agile and lean techniques, which work together to de-risk large and complex projects. This works particularly well when organizations are faced with high levels of uncertainty or rapidly changing technology.

So, the logical next step is for self-organizing, highly experimental systems with individuals or small teams generating ideas that are then iterated and improved on in the field, by the platform, automatically.

This will be mirrored at an architecture level. We already know microservices form the foundation of mature cloud-native architectures—in parallel with all the other factors we’ve covered, of course. When architected correctly, this approach makes it easy to scale up the system. Indeed, it should be a fully automated process.

So, a logical evolution of this is a move toward functions or serverless architectures where there is no need to provision infrastructure, operations tasks are removed and everything becomes a pay-as-you-go process.

Similarly, when it comes to maintenance, the observability and self-healing that characterizes cloud-native maturity will evolve into preventative machine learning and artificial intelligence—as with product design. Systems will be able to prevent failures, for instance, by scaling up capacity. This will be faster, more secure and more reliable.

And automation will underpin the transition of delivery from continuous delivery to continuous deployment, with manual approvals processes removed, and automatic rollback of features when key metrics take a hit.</p

Automation, in the form of serverless architectures running on cloud platforms, also represents the next stage for provisioning.

When it comes to infrastructure, the default for mature cloud-native organizations is containers and hybrid clouds, which enable a high degree of automation. But the coming evolution is toward edge computing, where workloads are run locally at the edge where appropriate and where enough data is available.

Does this mean that a forward-thinking organization needs to already be switching investment to AI and edge computing to eliminate the need for containers and even cloud developers in the future?

Not necessarily. As we said, the tech and business landscape is in constant flux. The biggest insight of cloud-native was to recognize this and evolve a methodology that doesn’t try to eliminate or control change but accepts that change is inevitable. This means organizations need to simultaneously invest in sustaining innovation and continue investing in disruptive innovation.

“It’s not about just jumping to the next disruptive innovation,” says Reznik. “It’s about changing your organization in such a way that the transition from one to another and from that to another and then again is part of life. It’s a non-issue.”

So, if we had to suggest you pay attention to just one of the above axes, head right back to the top line and look at culture. Ask yourself where you fit. Because the future is more uncertain now than it’s ever been. But if you and your organization are willing to try new ideas on a smaller scale, learn from your mistakes and be prepared to scale up your successes, you’re going to stand a much better chance of thriving in the future. Wherever the other axes end up pointing.

Join us for KubeCon + CloudNativeCon Europe 2022  in Valencia, Spain (and virtual) from May 16-20—the first in-person European event in three years!

Jamie Dobson is co-founder and CEO of Container Solutions, a professional services consultancy specializing in cloud migration.

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