AI Capabilities Assessment for Developers
About Course
AI Capabilities Assessment
AI can help software teams move faster, but speed alone is not the goal. The real question is whether your team can deliver meaningful change faster, with less rework, stronger feedback, and lower risk of technical debt.
AI Capabilities Assessment (ACA) gives leaders a practical way to assess, improve, and measure their team’s ability to build high-quality software with AI.
You will gain a clearer picture of where your team is today, what is getting in the way, and what kind of support will help them build better software with more confidence.
Great software teams do not simply produce more code.
They create shared understanding. They work in small steps. They build feedback into the system. They make behavior straightforward to verify. They refactor when the design starts to reveal itself. They protect the learning embedded in their codebase instead of treating software as disposable text.
That is why assessments matter.
Many leaders know AI is important, but they do not yet know what to measure, how to interpret what they see, or how to tell whether AI is genuinely improving development. They see uneven adoption, unclear practices, extra review burden, and a growing pressure to move faster. But moving faster in the wrong direction is not progress.
This program gives you a grounded way to look at your team.
We do not measure developer productivity by counting lines of code or pretending story points are comparable from sprint to sprint. Instead, we look at whether the team is becoming more capable of delivering valuable change with clarity, quality, and confidence.
AI Capabilities Assessment pairs three quantitative signals with three qualitative measures:
Quantitative signals:
– Time to Value: how long it takes to move from request to validated, working software
– Review and Rework Friction: how much effort is spent reviewing, correcting, reopening, or fixing work
– Changeability Signal: how small, understandable, and reasonable changes are, including commit frequency, PR size, and complexity hotspots
Qualitative measures:
– Developer Confidence: whether developers can use AI to produce code they understand and can change
– Testability Confidence: whether important behavior is straightforward to verify
– Team Coherence: whether the team has a shared approach for using AI in development
The assessment is designed to be useful, not burdensome. The goal is to collect enough evidence to create insight, guide improvement, and tell a credible before-and-after story.
This offering can stand alone as a focused assessment or serve as the measurement foundation for a deeper AI Collaboration Lab or AI Developer Essentials engagement.
About Your Guide
David Scott Bernstein is a software developer, author, trainer, and founder of The Passionate Programmer and To Be Agile. He has trained more than 10,000 professional developers in Extreme Programming, test-first development, domain modeling, design patterns, refactoring, and the practices that make software easier to understand and change.
David is the author of Beyond Legacy Code and co-author of Prompt Engineering for Everyone with ChatGPT. His current work helps developers, teams, and leaders use AI to build better software without abandoning the craft, feedback, and human judgment that make great development possible.
About AI Collaboration Labs
AI Collaboration Labs are experiential learning sessions where teams work directly with AI and each other to explore new ways of thinking, creating, and collaborating.
The Labs help people move beyond prompt tips into shared language, practical judgment, and real capability. AI Capabilities Transformation can be used before, during, or after a Lab to help teams understand where they are, what is improving, and what support will create the most value.
Next Steps
If you want your team to get real value from AI, start by learning what is actually happening.
AI Capabilities Assessment gives leaders a grounded assessment, developers a clearer path forward, and teams a practical way to improve how they build software with AI.
Let’s have a conversation to see if this is a good fit for you. You can schedule a call directly with me and we can explore if this offering is right for your. team. Your first consultation is always free and at the least you’ll walk away with valuable insights on how the best teams are leveraging their development skills.
Schedule a CallWhat Will You Learn?
- Identify which signals actually matter when assessing AI-assisted software development.
- Measure progress without relying on misleading productivity metrics such as lines of code or raw task counts.
- Evaluate whether AI is reducing friction or creating additional review and rework burden.
- Connect technical practices such as testing, refactoring, and complexity reduction to business outcomes.
- Understand how great teams build software by preserving learning, improving feedback, and keeping future change affordable.
- Use a simple before-and-after scorecard to guide team improvement and communicate results to leadership.
- Decide which level of support will best help your team build durable AI development capability.