As organizations begin adopting and implementing agentic AI into their software engineering processes, many tend to start with technology. A platform like AAVA™ can introduce new ways of working, open the door to meaningful productivity gains, and reshape the software development lifecycle. But the real determining factor in whether this transformation succeeds is the change that must take place across people, processes, and the operating model.
After months working closely with enterprises through pilots and proof-of-concepts, one thing stands out: agentic transformation is as much a human and organizational journey as it is a technological one. Companies are navigating unfamiliar ground, and they need practical guidance on how to align their teams and systems around this shift. Technology moves quickly. Enterprises, by design, operate within established rhythms. Unless human and organizational changes move in step, progress can slow before it has a chance to build.
Making Human and AI Collaboration Real Inside the SDLC
Business leaders often say they understand human and AI collaboration. They’re aware of the concept and they appreciate why it matters. But that understanding is often at a high level. What’s harder to picture is how it translates into the day-to-day work of engineering teams.
As soon as we move from concepts to real engineering scenarios—testing, coding, requirements, design—the detailed questions start coming:
- How many agents can one engineer create and manage?
- What does agentic output validation look like?
- What parts of the current process stay the same, and what changes?
These are healthy questions, and they show teams are trying to imagine how this fits into their own environments. That’s where a detailed, customized playbook becomes important. It helps leaders and engineers visualize the operating model in a tangible way: how work shifts, where humans stay firmly at the wheel, and how agents step in across different phases of the SDLC. Without this grounding, organizations stay at the conceptual level and adoption becomes uneven.
Helping Engineers Navigate a Changing Landscape
Across global delivery centers, engineering hubs, and long-tenured teams, people are thinking carefully about what this shift means for their careers. That’s natural. Many individuals have strong experience in specific roles and want to understand how agentic AI fits into their path forward.
Leaders play a key role in helping teams navigate this change with clarity and confidence. Engineers can grow into new roles as agentic engineers. Some may choose to be strong consumers of agentic AI, highly productive testers or developers who use agents to amplify their work. Others may choose to become creators, designing agents and workflows.
To support this, people need training: how the agentic SDLC operates, how the new model works in practice, and how to use platforms like AAVA. Some individuals will choose to stay close to the work they’ve always done, enhanced by new tools. Others will explore deeper paths. And just like moving from horses to cars, the shift can feel dramatic until people see the new possibilities ahead of them. What’s most important, is that they see the opportunities ahead of them and understand how to prepare for those opportunities.
The Enterprise Barriers That Make Change Harder
Many enterprises have employees who have been with the company for more than a decade. They’re deeply experienced in a particular way of working and want to see how this new operating model fits into their responsibilities.
At the same time, these organizations are balancing revenue pressures, legacy systems, and the day-to-day work that keeps operations moving. Introducing a new engineering model into that environment requires thoughtful planning. In many cases, it’s like trying to pull a piece out of a sandcastle without collapsing the whole structure. People worry about disrupting what’s already working, even if inefficiencies are well understood.
This is why change management must be a core part of any agentic transformation. Companies benefit from having dedicated change leaders working alongside engineering teams to help people understand what’s shifting, how they participate, and how to move from uncertainty to active involvement. Change management has become its own track, because helping people through the transition is as important as designing the future process itself.
How Leadership Mindsets Influence Agentic Transformation
Across many engagements, we see a consistent pattern in how leaders respond to agentic transformation. There are leaders fully in from the start, who see the potential and want to move quickly. Others are more cautious; they want to understand the details, the risks, and the implications for their teams. And then there’s the large group in the middle: leaders who want to believe, who see the potential, but who also want training, mentorship, and reassurance before they introduce a significant shift to their operating model.
Supporting this middle group requires time, transparency, and a thoughtful approach. Some leaders respond well to envisioning the future. Others want deep dives into the process and methodologies. Many want to understand the wins and the challenges—what went wrong, how it was resolved, and what we learned. These “battle scars,” as we often call them, help leaders build conviction based on real-world evidence.
There’s no single approach that works for everyone. Each leader needs something different. But bringing this middle group along is essential for broad, sustained transformation.
Where Early Value Appears: High-Impact Agentic Candidates
A key part of the early phase of agentic transformation is value discovery. Organizations want to understand where agentic AI can make a noticeable difference, so they start scanning their SDLC for the right entry points, places where teams are stuck performing many manual steps, older processes, or repetitive tasks. The idea is to find a few areas that are ready for change, run small but meaningful tests, and learn quickly from what comes out of them.
Through early value discovery, two functions often emerge as strong candidates for agentic workflows amongst others: legacy modernization and quality engineering. These areas tend to rely on long-standing processes and manual workarounds, which naturally accumulate inefficiencies over time. When agentic approaches are introduced, the lift in productivity is typically the most visible here. Those early signals help leaders understand where to begin, because they can see the impact with their own eyes.
These early wins matter more than they seem. They give teams confidence, help leaders understand what’s possible, and create internal champions who have lived through the improvement and can speak to it in a real way.
The Three Indicators That Show Agentic Change Is Working
Once an organization commits to agentic AI, the real question becomes: is the change actually taking hold across the company? These three elements are key to assessing whether an agentic transformation is taking root.
- Adoption across teams: Adoption is the first signal. Are engineers using the agentic process? Are departmentsparticipating? This isn’t a “use it if you want to” situation. For the operating model to work, adoption needs to be consistent, supported, and reinforced across the organization. Teams often need hands-on help to make the switch, and that support makes a significant difference.
- Career progression into new roles: A second measure is whether people aretruly moving into new roles. Are engineers growing into agentic engineers? Are testers becoming power users or even creators of workflows? If everyone is trained but no one is progressing, something is off. Seeing people move into the next version of their role is a strong indicator that the change is working.
- Value realization: The third measure is whether the organization is seeing the benefits it set out to achieve. Are software releases moving faster? Are teams delivering more with the same resources? Are costs coming down where they should? If teams are adopting the model, but outcomes aren’t improving, the process needs to be refined. Value realization is how we know the transformation is doing what it’s meant to do.
Keeping Humans at the Wheel
As agentic systems become more capable, one principle remains steady: software engineering is still driven by people. Humans guide the operating model, shape workflows, validate outputs, and decide how systems evolve. Technology doesn’t install itself and run; we’re nowhere near that world. Agents expand what teams can accomplish, but people determine how the work comes together.
That’s where the idea of “humans at the wheel” becomes clear. It reflects the steady role people play in directing the work, making decisions, and helping agentic systems operate effectively inside the SDLC. In the end, while the technology can be impressive, it’s the people who make transformation successful by guiding it, shaping it, and making it part of how the enterprise actually works.
About Author
Prakash Balasubramanian is Executive Vice President & Global Head of Engineering Practices & Delivery at Ascendion, leading global teams that build AI-powered products, platforms, and transformative customer experiences. He oversees the development and delivery of AAVA, ensuring clients achieve measurable gains in engineering velocity and business impact. With more than two decades of experience at Ascendion, Cognizant, and Tata Consultancy Services, Prakash has led some of the world’s most complex technology transformations across modernization, customer experience, and platform engineering. A recognized voice in the technology community, he is dedicated to shaping the future of AI-driven software engineering and mentoring the next generation of engineers.
