DORA 2025, Part 7. How Value Stream Management (VSM) helps IT teams achieve sustainable growth and use AI strategically

All organizations are expected to move faster today.

Everyone is adopting AI, automating processes, building platforms, and shipping new features at breakneck speed.

But are we actually getting better?

Or are we just building features with no value faster, burning out faster, and increasing complexity faster?

The main risk now is not falling behind, but investing heavily in chaotic activity that produces no result.

For more than a decade, DORA research has rested on a key idea: the most effective organizations do not stop at adopting tools; they become experts in the value delivery system.

They know how to "get better at getting better": understand their workflow, see the real bottlenecks, and direct resources deliberately. This year we confirmed that the ability to analyze and manage the value stream is exactly what separates chaotic activity from focused improvement.

According to a 2025 study, teams that focus on understanding their value stream spend significantly more time on work that creates real value. And most importantly:

Value Stream Management (VSM) is the practice of visualizing, analyzing, and improving the movement of work from idea to customer.

It is not a complicated bureaucratic process, but a set of four principles that help create clarity and focus improvements where they really matter.

A detailed step-by-step guide to value stream mapping is available in the DORA Value Stream Management guide.

It is hard to understand a complex system.

It is impossible to keep all the details in your head all the time, and that makes it harder to see the big picture.

When the team visualizes the system together, all those details move out of individual heads and into a shared space.

The system structure, and the hidden patterns, become visible.

This makes it easier to discuss what works and what does not.

The practice is to describe the entire software delivery lifecycle, from idea to customer.

It covers everything: product discovery, design, development, testing, deployment, and operations.

A shared visualization helps the team build a collective understanding of the process and spot bottlenecks and inefficiencies faster.

The goal is to map the entire system from concept to customer.

The key is to start where the impact will be greatest.

Before you start, look at the process as a whole and identify the main limiting factor so you do not optimize a part that is not the real bottleneck.

If your team's main challenge is at the product discovery stage, that may be the right place to start.

A good starting point, used by DORA for many years, is the path from commit to production.

It is the easiest to standardize and optimize.

Even more important, this is where teams have the greatest opportunity for fast, visible improvement.

This differs from discovery stages, where the main goal is to improve effectiveness, not speed.

Successfully improving the core process creates quick wins, builds trust, and creates momentum for changes in broader areas such as product research and user feedback collection.

Example of a value stream map: the path of a task from backlog to production.

Once the flow map is built, the key task is to ensure predictable, smooth movement of work.

To do that, stop optimizing isolated parts and start improving the system as a whole.

Track the metrics: lead time, process time, and the share of value relative to waiting time.

These metrics show the system's real constraints and provide a baseline for understanding whether improvements are working.

This systems view helps identify where new tools or technologies will have the greatest impact. For example, a team may see on the map that code review is the bottleneck.

In that case, it makes sense to use AI to speed up that process rather than to generate more code, which would only increase overload.

The real gain is not speeding up one step, but removing the actual bottleneck.

That is the point of focusing on flow: solve the problem for the whole system, not just speed up one stage. "And then there is the complexity of the process itself: how much work will it take? how much time? how do you even estimate it?"

There is always uncertainty and a feeling of having no control.

Will we be able to make the needed changes quickly, as we go?

Or do we have to go through a two- or three-week process every time just to, say, change a firewall rule? That sort of thing.

There are too many ways this process could slow down our work.

I think the more experienced employees especially understood this well, because they had already encountered it at other large companies."

For many years, DORA has recommended using VSM practices to enable fast and stable flow of work.

But the question remains: is this approach still relevant now that AI is being adopted everywhere? This year, our goal was to test our long-standing hypotheses about the value of VSM.

The results confirm that organizations that adopt VSM principles achieve meaningful, measurable improvements.

Key findings of the study: VSM improves team effectiveness. Teams that regularly analyze and improve their value stream show significantly better performance. VSM increases the share of "valuable" work.

Such teams spend much more time on tasks that truly matter to the business and customers. VSM improves product quality.

Focusing on the value stream leads to better product outcomes, which is ultimately what matters most.

The data shows a simple truth: teams that work together to understand their value stream start spending more time on work that delivers results.

A shared understanding of the process helps focus effort on what matters and turn clarity into concrete improvements.

This clarity is the key to unlocking the value of new technologies, including AI. VSM enables strategic action: instead of local optimizations, you remove the system's real bottleneck.

Our analysis confirms this hypothesis.

While AI adoption on its own has a moderate effect, that effect becomes much stronger in organizations with a mature VSM practice.

This confirms that VSM is a critical mechanism for realizing a real return on AI investment.

When gains in productivity at the team and individual level are directed at key system constraints, VSM turns local improvements into meaningful organizational outcomes.

This study is not just an observation, but a challenge.

The first step out of the cycle of chaotic activity is simple, but not easy: ask your team, "Can we draw our software delivery value stream on a whiteboard?"

If the answer is no, or the map raises more questions than it answers, you have found your starting point. That conversation is where the path begins to getting better at getting better.

To understand why AI's impact on teams differs so much from expectations and from company to company, it is important to look not at the tools, but at the system in which they operate.

The next chapter explains this key principle: AI acts as a mirror of organizational processes, amplifying strengths and exposing weaknesses.

We examine what actually determines AI's effect and which conditions turn isolated acceleration into a meaningful shift in the whole system.