Your answers suggest limited visibility into your engineering teams’ current AI practices. This is a common finding, especially outside of engineering leadership.

This isn’t a failing result, rather a signal that visibility itself may be one of the first things worth establishing, before you decide what to prioritize next.

Where should you start?

Share your email to get practical next steps for improving visibility into your team's AI practices.

Take the test again

Once you’ve got more visibility into your team’s AI practices, take the test again and see where you stand in terms of AI readiness.

Your engineering teams are at the beginning stages of translating AI tool use into business value. They’re typically performing informal, individual experimentation with a handful of AI tools, without structured training in cost management and safe use cases. Deep agentic AI use is limited, and there’s little team-wide consistency in how AI is used.

At this level, AI use by your engineering teams can accelerate output without improving it. While teams may be shipping more code at a faster rate, they’re doing it without the governance training in place to keep quality and risk in check. In the worst cases, existing issues in the SDLC are amplified by AI, not reduced.

The good news?

These are well-understood, addressable gaps. Organizations at this stage see the fastest, most noticeable gains from undertaking a tailored upskilling program for their engineers.

Learn more about increasing your AI readiness

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Your engineering teams have moved past early experimentation, but there are still gaps getting in the way of hitting full ROI from AI adoption. While there are some training, governance, and consistent practices, it’s unevenly applied rather than standardized.

The good news?

You’re past the foundational stage, so now it’s time to adopt strong agentic engineering knowledge and principles in your SDLC. Running a tailored upskilling program for your engineering teams is a great way to shift from moderate to advanced use.

Learn more about increasing your AI readiness

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Your engineering teams show strong AI fundamentals. Training, governance, and consistent practices are in place, and you're measuring success with real verification methods rather than self-reporting or usage volume alone. Agentic engineering principles are being codified into the SDLC moving forward if they haven’t been already.

Now it’s time to fill any remaining gaps you might have missed. This means creating and maintaining a learning culture where your teams stay up to date with the latest AI models, product releases, and tooling features. Offer secure virtual spaces such as sandboxes to your engineers to experiment with the latest AI tools if you haven’t already, and set aside protected learning time for them.

Learn more about increasing your engineering teams’ AI readiness

Share your email to get recommendations tailored to your adoption level.