Artificial intelligence (AI) can be a hero or an anti-hero in software development, depending on your implementation approach. According to the 2024 DORA Accelerate State of DevOps Report, AI adoption shows promise with 75% of respondents reporting positive productivity gains, but also reveals unexpected challenges, including a 7.2% reduction in software delivery stability.

The key is to position AI as a supportive partner. AI should be utilized to enhance human expertise rather than replace it.

AI Is Promising, but Let’s Stay Suspicious

AI has been rapidly infiltrating software development for almost two years now. Coding assistants, automated testing tools, and AI-powered analytics promise to transform how we build applications, and some early adopters have shown impressive results. When AI first entered development workflows, it tackled well-defined problems such as code completion, simple test generation, commenting, and draft documentation with reasonable success.

But that initial enthusiasm has given way to more complex realities. Modern software development involves intricate systems, multi-cloud deployments, complex security requirements, and accelerating delivery expectations. AI has shown both remarkable capabilities and concerning limitations in this environment.

After hearing customer stories about dozens of AI implementations, I’ve found the story isn’t as binary as “hero” versus “villain”—it’s more like “hero” versus “erratically-troubled-if-ultimately-victorious anti-hero.” The reality of AI is nuanced—and much more interesting, depending very much on the implementation approach, governance scheme, and where organizations choose to apply AI technologies.

The Toil Problem Is Real and Measurable

Every developer knows the frustration of “toil”—those manual, repetitive tasks that consume time without adding much value. Toil isn’t just a minor annoyance. The 2024 DORA report confirms that this challenge remains persistent, with developers continuing to struggle with time allocation between valuable work and repetitive tasks.

Recently, I had the chance to discuss AI heroism and anti-heroism with the Chief Product Officer of CloudBees, Shawn Ahmed. CloudBees is an enterprise DevOps solutions provider and was co-founded by Jenkins creator Kohsuke Kawaguchi, so it has been in a good position to observe the thousand ways AI has been injected into software development. Their internal research indicates developers spend about half of their time triaging test failures, waiting on build times or addressing security issues—and as little as 20–30% of their time writing code. As Ahmed pointed out to me, this inefficiency seems like it wouldn’t be tolerated in most other industries; it is like accepting a manufacturing line that’s only 20–30% effective.

An example of CloudBees’ approach to incorporating AI productively and helpfully is their continuous integration (CI) capability. It analyzes historical pipeline data to identify patterns in successful deployments and uses AI to provide suggestions for future deployments. This eliminates hours of configuration work, thanks to AI’s ability to base pattern recognition on thousands, if not millions, of data points.

From CloudBees’ perspective, AI in software development isn’t a replacement technology in dev but rather what Ahmed characterizes as a “companion.” AI’s best role is handling routine tasks to preserve and expand the human developer’s time performing creative and strategic work.

Three Implementation Models, Three Different Outcomes

It’s clear that not all AI implementations are created equal. I see three distinct approaches emerging, each with dramatically different results:

1. The Replacers: Some organizations approach AI to reduce headcount by automating development tasks. This model typically delivers poor results, fundamentally misunderstanding both AI capabilities and the creative nature of software development. The DORA research supports this concern, showing that while AI adoption is widespread, 39.2% of respondents reported having little or no trust in AI-generated code, indicating that successful implementations require human oversight. 
2. Tools First!: These organizations adopt individual AI tools without a cohesive strategy or governance framework. This approach delivers mixed results—productivity improves in specific areas, but in many cases at the cost of quality, security, or long-term maintainability. The DORA findings highlight this challenge, showing that despite AI’s positive impact on individual productivity and code quality, it can lead to decreased software delivery performance.
3. The Augmenters: The most successful approach treats AI as an amplifier of human capabilities rather than a replacement. The DORA research validates this model, showing that AI adoption leads to meaningful improvements: when AI adoption increases by 25%, individual productivity increases by 2.1%, flow improves by 2.6%, and job satisfaction rises by 2.2%.

CloudBees is built on this augmentation philosophy. According to Ahmed, AI handles pattern recognition and repetitive tasks while humans make decisions on strategic matters. Their DevSecOps solution demonstrates this approach by using AI to identify security vulnerabilities while preserving human authority over remediation strategies. CloudBees’ Value Stream Analytics uses AI to spot workflow inefficiencies while leaving process improvement decisions to the teams who understand the organizational context.

Making AI the Hero of Your App Dev Story

Here are five critical success factors that determine whether AI becomes a hero or an anti-hero for you:

1. Target high-toil activities first. Successful implementations start with well-defined, repetitive tasks where AI can deliver immediate value. CloudBees’ recommended targets include test generation, pipeline optimization, and code reviews, including areas where AI can significantly reduce manual effort.
2. Establish governance first, deploy AI second. Organizations seeing positive results implement governance frameworks before widespread AI adoption, defining, for example, appropriate use cases, verification requirements, or clear boundaries for AI autonomy.
3. Invest in developer education. Contrary to some narratives, successful AI implementation requires more skilled developers. Top-performing organizations invest in training programs that help developers understand both AI capabilities and limitations.
4. Integrate rather than isolate. Rather than adopting disconnected point solutions, effective organizations implement AI capabilities that integrate throughout their delivery pipeline. CloudBees’ platform takes this approach, connecting AI capabilities from requirements analysis through production monitoring.
5. Measure meaningful outcomes. The most effective implementations have moved beyond measuring code generation metrics. Instead, they focus on quality improvements, security posture enhancements, and business impact indicators that reflect the true value of AI adoption.

AI Is Your Enthusiastic Partner, Not Your Replacement

Successful AI implementations in app dev seem to share the common characteristic of using it as a complementary force instead of as a replacement. AI is adept at the mundane, repetitive tasks that drain motivation and attention among developers and increase their toil. It lets humans focus on innovation, customer experience, and ultimately, business value.

According to Ahmed, CloudBees envisions a future where AI becomes increasingly personalized to individual developers. He predicts that within two years, developers will likely work with multiple AI companions tailored to their specific preferences and working styles. This personalization aspect represents an important evolution beyond today’s more standardized AI tools. 

A particularly noteworthy perspective I got from research and vendor discussions is the relationship between AI adoption and team scaling. While some organizations initially approach AI to reduce headcount, the data suggests the opposite effect. As Ahmed noted in our discussion, the more effective approach isn’t using AI to reduce a 10-person team to 5, but rather leveraging AI to assist that 10-person team in achieving what would have previously required 15 people. This is the proactive, business-building “scale up, not down” perspective that can help propel an organization to its goals.

I’ve concluded that today, the hero of software development isn’t AI. Not AI alone, but it is no longer purely human expertise either. The true hero is the partnership between them, working in concert to create outcomes neither could achieve independently.