After the promises of productivity, companies must now learn to measure the real effects of the agentic SDLC on their teams, their delivery and their performance.
In a previous column, we talked about the emergence of spec-driven development and the appearance of an agentic SDLC capable of profoundly transforming the way in which digital products are designed and developed. We then wondered about the future of product teams facing the emergence of augmented micro-squads, before exploring the contours of a new operational model adapted to this reality.
However, one question remains largely underestimated in current debates: how to manage this transformation over time?
Because if organizations are starting to better understand the impacts of agentic SDLC on their working methods, their teams and their operating methods, few of them today have a structured framework allowing them to measure the real effects of this transformation. However, like any business transformation, the agentic SDLC cannot be managed sustainably without a suitable measurement system.
From the productivity debate to transformation management
Discussions around agentic SDLC are often dominated by the question of productivity gains. Publishers, analysts and feedback regularly highlight sometimes spectacular gains on certain development activities.
These figures have the merit of providing food for thought but also present an important limitation. They generally arise from targeted experiments, carried out in specific contexts and over restricted areas. They make it possible to assess the potential of a technology but do not constitute a management system at the scale of an organization.
The issue for an IT department is not whether a developer produces code faster with an AI assistant. The challenge is to understand whether the organization as a whole becomes more efficient, more responsive, more robust and more capable of delivering business value.
The real subject is therefore no longer that of individual productivity. It is that of collective performance.
What businesses measure today…and what they should measure
Most of the transformation programs we see start with relatively simple metrics: number of licenses deployed, number of registered users or volume of tool usage.
These indicators remain useful but they do not answer the essential question: does the transformation really produce the expected effects?
An organization may have several thousand licenses deployed and see very low utilization. Conversely, a smaller population can develop intensive uses and generate significant profits.
This distinction is essential because it leads to differentiating between several levels of measurement. The first concerns the adoption of new tools. The second concerns their actual use. The third concerns the impact observed on the development chain. The last is interested in the value actually created for the company.
It is the combination of these dimensions that makes it possible to construct a faithful vision of the progression of an agentic SDLC program.
A pragmatic measurement framework to drive transformation
Rather than multiplying indicators, we observe that a limited number of KPIs already allows us to have a robust vision of the situation.
The first family of indicators concerns adoption. It aims to understand whether the teams are really embarking on the transformation. The user activation rate, the percentage of weekly active users as well as the intensity of use make it possible to distinguish an equipped organization from an organization truly engaged in the transformation.
The second family concerns operational performance. The metrics resulting from DORA work constitute a particularly relevant basis in this regard. The evolution of Lead Time, Throughput, the production failure rate or even the average incident resolution time makes it possible to evaluate whether the new practices actually contribute to improving the performance of the SDLC.
Finally, the third family of indicators concerns the value created. This is probably the most delicate subject. Few organizations today are capable of establishing a direct link between an agentic SDLC program and perfectly measured economic gain. A pragmatic approach then consists of measuring the time saved as perceived by the teams themselves. Although imperfect, this measurement offers a particularly interesting signal for monitoring the evolution of the benefits experienced and estimating the additional capacity gradually released.
Measure adoption before measuring gains
One of the most common mistakes is trying to demonstrate productivity gains before even reaching a significant level of adoption.
This situation is reminiscent of many agile transformations observed over the last twenty years. The organizations that performed best were not necessarily those that deployed new practices the fastest. They were often those who invested the most in supporting change, increasing skills and adopting these new practices.
The agentic SDLC follows a comparable trajectory. The benefits do not only depend on the intrinsic capabilities of the tools but also on the ability of teams to integrate them into their daily practices, to evolve their modes of collaboration and to adapt their decision-making processes.
Measuring adoption therefore becomes a prerequisite for any attempt to measure gains.
The next challenge for product organizations
In recent months, debates have largely focused on the technologies, tools and new organizational models made possible by AI agents. These subjects obviously remain fundamental. However, they are only part of the equation.
The next stage of maturity will probably consist of industrializing the management of these transformations. The companies that will get the most out of the agentic SDLC will not necessarily be those that deploy the new tools available most quickly. They will be those who will be able to objectively measure the results obtained, identify value-creating uses and constantly adjust their trajectory.
After rethinking development methods, after starting to redefine product teams and operating models, the real challenge now becomes that of measurement. Because in an environment where technologies evolve every month, the ability to learn from your own results could well become the main competitive advantage.
