Key Takeaways:
- Development teams adopt AI faster through peer-driven learning than through top-down mandates. Shared pilot stories create attraction, not pressure.
- New development paradigms where teams formulate clear intent and acceptance criteria before using AI to generate options are emerging as a high-quality workflow for AI-powered software development.
- AI does not replace software engineers, but rather expands the opportunities to participate in the build, accelerates feedback loops and creates new opportunities for cross-functional collaboration.
As SVP of Development, I’ve seen a lot of platform changes, but AI feels different. It’s an added benefit that shows up everywhere: how we clarify requirements, how we explore designs, how we prototype, how we write and review code, and how quickly we navigate through ambiguity.
Early on, we kept coming back to one simple question:
If there were five of you and time wasn’t an issue, what would you build? What would you fix? What would you finally make possible?
This was the year of dreams. The change came quickly, but not all at once. Looking back, I see three themes that took AI from curiosity to real transformation for our teams: inspiration, pilots and evolution Roll.
Table of Contents
1. Inspiration: How we built an AI-first engineering culture, without mandates
Our change did not begin with a directive. It started with engineers being curious and experimenting. A very experienced team member told me he was skeptical at first. He assumed that AI would only be useful for generating brand new code. So he pointed out a point on our roadmap and fully expected the AI to fail. Instead, he was impressed by how much it delivered.
Our teams love winning and learning, so those early stories created the right appeal: “You’ll fall behind if you don’t at least try.”
This dynamic—curiosity leads to a result, a result leads to a story, a story spreads throughout the team—proved more impactful than any top-down AI initiative could have been.
2. Pilots: What 20 AI Pilots Taught Us About Software Development at Scale
Next, we encouraged teams to test spec-driven development: start by clearly articulating intent and acceptance criteria, then use AI to generate options (design approaches, frameworks, testing, and first-run implementations) before committing to a direction. The idea is to advance thinking so that AI reinforces clear human intent.
We assumed we would do three to five pilots. In the end we got closer to 20.
It was a bit chaotic – and that was okay. We set up a weekly sync to share insights and give someone the opportunity to tell a specific story: a problem they solved faster, how they made AI work more effectively, or a lesson they learned the hard way so others didn’t have to.
No one has all the answers, but together we learn quickly and scale what works.
When thinking about how to structure AI adoption within your own software team, our biggest takeaway was giving people permission to fail publicly – and making it easy to share their insights on the other side.
3. What is evolving: How AI is changing roles in software development and who is allowed to develop
One of the most encouraging changes has been how much AI has democratized the work of software development and how quickly the roles are mixing. More and more people outside of engineering are getting used to developing prototypes and validating ideas sooner. This leads to better communication, faster feedback loops and better decisions.
At Precisely, this looked like product managers creating rough prototypes to pressure test a concept before it reaches a developer. It looked like data teams were building internal tools that they previously had to wait months to prioritize. The bar for “I can build something to test this idea” has been significantly lowered, and that’s a good thing.
People took the challenge to “dream big” and ran with it. From tackling large re-architecture projects to developing new product concepts in record time to developing internal tools that save hours every week. In future posts we will introduce some of these teams and share what they learned along the way.
What this means for the future of software engineering
AI does not replace the craft of software development, but rather transforms the impact we have when we apply that craft. My goal is to ensure we use this leverage to build better products, create more opportunities for our teams, and stay focused on the results that matter.
The engineers and builders who thrive in this environment bring clear thinking, creativity, strong judgment and a willingness to share their discoveries. This is the type of team we are building at Precisely.
If you are on a similar journey, I would love to compare my experiences.
Frequently asked questions About AI and software development
How is AI changing the way software development teams work?
AI in software development is shifting teams from linear, sequential workflows to more exploratory, iterative workflows. Instead of writing all the requirements upfront, teams can now use AI to quickly generate design options, test implementations, and create scaffolds—and then evaluate and refine them. The biggest cultural shift is that learning occurs faster and spreads more easily when teams openly share pilot results.
What is specification-driven development with AI?
Spec-driven development is a workflow in which engineers establish clear intent and acceptance criteria before deploying AI tools. By defining the goal first, teams gain more useful AI-generated options – be it code frameworks, test cases, or alternative design approaches – and can make better decisions about which direction to pursue.
How do software leaders drive AI adoption without top-down guidance?
The most effective AI adoption typically begins with voluntary pilots and peer storytelling. When an engineer shares a result that surprised them, others want to try it. Leaders can accelerate this by creating structured forums—a weekly sync, a shared channel, a recurring spotlight—where teams share what they’ve learned, including what didn’t work.
https://www.precisely.com/blog/engineering/from-skepticism-to-momentum-how-ai-is-transforming-our-approach-to-software-development/
