How to unlock the real value of AI in software engineering

Published via GitHub Executive Insights | Authored by Bronte Van der Hoorn

Artificial intelligence is changing the software development lifecycle. From code completion to test generation and documentation, tools like GitHub Copilot are undeniably powerful. And so, amidst this technological acceleration, a quieter—and perhaps more enduring—question deserves attention:

Are we creating the right conditions for AI to achieve maximum impact?

It’s tempting to fixate on measuring progress in terms of usage or how many suggestions are being accepted. But the real question isn’t about your acceptance rate. You should be asking how well-aligned are your AI solutions and the work that matters most for your business. To make this concept actionable, consider this Venn diagram.

The three circles that matter

This diagram brings together three intersecting ideas:

1. Work that’s worth doing: High-impact work
In software engineering, this is the work that drives improved business outcomes. Think: increased reliability, solving customer pain points, enhanced security and privacy posture, and faster time to market. It’s not just what's on the to-do list—it’s what contributes to the business’s short-term and long-term success.

2. Work that benefits from AI: AI-tool capability
This includes many familiar tasks such as code generation, reviewing pull requests, identifying edge cases, and writing tests. These are areas where AI can accelerate, suggest, and assist, sometimes dramatically so.

3. Conditions that enable effective AI use: Success conditions
The third circle is about creating the conditions that make AI genuinely effective. As GitHub’s guidance on scaling Copilot shows, success with AI isn’t just about tool capability. It’s about organizational readiness. It’s about:

having clear intent and psychological safety around AI use.
delivering training that aligns with intent, is relevant to the team’s work, and shows what success looks like in practice.
incentivizing experimentation, not just efficiency.
having policies and procedures that guide responsible use.
updating engineering processes or responsibilities that account for changes introduced as part of the AI workflow.

In short, you need to do more than make AI available—you need to create the right conditions to maximize its impact.

The power zone: Where it all comes together

When all three circles overlap and we have all these elements—doing the right work, having the right tools, creating the right conditions—we enter what I call the AI Power Nexus.

Test case development is a great way to see the AI Power Nexus in action. It’s high-impact work. Check. AI is a capable partner. Check. To ensure we check success conditions:

we know the behaviours and edge cases that matter most.
our engineers know how to use AI to generate the test cases.
we know of any regulatory conditions that we must test for.
test cases are integrated into an automated CI/CD pipeline.

With all three circles checked, we’re in the AI Power Nexus.

The frustration areas: When a circle is missing

If you're not in the nexus, you're likely in frustration due to the misalignment between impact, tooling, and the conditions:

Tool frustration: You’ve got valuable work and the right conditions, but the tools fall short for your use case.
Conditions frustration: You have valuable work and powerful AI tools, but teams lack training, trust, or clarity regarding how the tools fit within other processes. This is sometimes a case of “Copilot is installed, but no one’s sure what to do with it” problem.
Impact frustration: The tools are working, and your organization has created the right conditions, but the team could be using the AI on higher-impact tasks. This is where efficiency gains might be measurable, but untapped potential remains.

The real opportunity for leaders

When it comes down to it, you need to adopt a mindset that prioritizes the creation of the conditions for your team to use AI for maximum impact.So ask yourself:

Are we enabling the right conditions, and not just deploying tools? (Success conditions)
Are teams clear on what work actually moves the needle? (Valuable work)
Are we using AI where it has the potential to meaningfully assist? (AI-tool capability)

And the sooner you direct energy toward creating those conditions, the better. This means deeper satisfaction and greater long-term impact realization from this wave of transformation. So consider today how you can create the conditions for your team to use AI to achieve maximum impact.

Want to learn more about the strategic role of AI and other innovations at GitHub? Explore Executive Insights for more thought leadership on the future of technology and business.