02.24.26 By Dominick Profico
This is Part 2 of our series exploring the future of Agile in the era of artificial intelligence. In Part 1, we examined the argument that AI challenges the foundations of Agile and demands a fundamental reinvention. Here, we take the counterpoint, exploring how AI can actually strengthen and advance Agile, becoming its greatest enabler rather than its undoing.
There’s a lot of noise out there about artificial intelligence “taking over” software development. Headlines talk about coders being replaced by machines, and the end of everything we’ve relied on. But if you listen past the panic, you’ll hear a different story, one of teamwork, not taking over.
Here’s the thing: Agile was always about adapting, about responding to the unknown. The problem is, for all our talk of adaptability, executing Agile has often gotten stuck in the mud, bogged down by endless estimation meetings, struggling to keep up with cluttered backlogs, and wrestling with chores that sap energy away from innovation.
So what happens when Artificial Intelligence enters the picture? It doesn’t erase what makes Agile great; it quietly removes the obstacles that keep us from living up to its promise. Generative AI solutions makes it possible for Agilists to do what we do best, ideate, create, innovate, and generate real value.
If you picture the Software Development Life Cycle (SDLC) as a relay race, AI isn’t just making runners faster, it’s reworking the track so every leg is smoother and smarter.
Ever spent an hour debating story points or estimating project complexity? You’re not alone. Now imagine having an AI agent that can comb through piles of past projects, spot patterns, and offer guidance in plain language. Your team is armed with clear, data-backed advice to start sprints with confidence.
Let’s be honest: backlogs can get out of control. Good ideas pile up, and priorities get lost. With AI tools, it’s possible to keep the backlog fresh and focused. Assistants can flag duplicates, suggest how to group related items, and even draft acceptance criteria based on what’s worked well before. Your teams will spend less time sifting through tickets and more time on building what matters.
Manual testing is slow. Code moves forward, and then everyone waits for feedback. AI-driven testing flips the script. Automated agents write unit tests alongside the feature code, predict where problems might pop up, and even fix minor issues on their own. Continuous Integration and Deployment (CI/CD) doesn’t just sound good, it becomes a reality, delivering greater trust in the end product.
Sometimes retrospectives get stuck on what’s most recent, skipping over underlying patterns. AI can pull together data from across the entire sprint, highlighting where things flowed well, where they didn’t, and even where team sentiment shifted. Instead of guessing what needs to improve, teams can see the data and have more productive conversations about real change.
One of the biggest fears around AI is that it will make skilled engineers obsolete, but that ignores a simple industry reality: even before AI, most developers spent only about 10–20% of their time actually writing code, while the remaining 80% went into understanding requirements, resolving ambiguities, reviewing code, debugging issues, collaborating across teams, and aligning technology to business goals. AI can dramatically accelerate the 10–20% by generating syntax and reducing boilerplate, but the real impact lies in how it reshapes the other 80%. That’s where judgment, system thinking, customer empathy, architectural trade-offs, and creative problem-solving live, areas machines can support but not replace. When routine tasks are automated, engineers are not diminished; they are elevated, freed to focus less on mechanical output and more on designing resilient systems and delivering meaningful business outcomes.
Collaboration is the lifeblood of Agile, and AI is quickly becoming its unsung hero. Need to kick off a call with teammates around the world? AI-powered translators and meeting assistants make sure ideas are heard and action items don’t slip between the cracks. Less stress, more spark, that’s what collaboration looks like when the busy work fades into the background.
True agility is about changing course gracefully, not just quickly. Shifting product strategy used to mean rewriting plans or refactoring entire codebases, not for the faint of heart. But with AI, the process can be far less painful. Machine insights show what a pivot would mean, model different outcomes, and help teams adjust priorities on the fly. Innovation comes not from luck, but from being able to test, adapt, and move confidently.
Is Agile on its way out? Far from it. What we’re seeing is evolution, Agile powered up by a new kind of teammate – AI. This is the age of Augmented Agility, where human imagination and machine intelligence converge to accelerate not just delivery, but digital realization, turning ideas into working, value-generating solutions faster and with greater precision.
Embracing this partnership means spending less time on routine, more time on impact. People get to do more of what they love, solving, building, and connecting. It’s not about machines replacing us, it’s about using new capabilities to bring Agile’s original promise to life.
So here’s the real question: Are you ready to let AI help you become even more Agile?
Join the conversation or request an Agile-AI assessment today to explore what’s possible for your teams.
