Imagine having a powerful tool like AI by your side in daily development tasks. Code flows faster, repetitive tasks are automated, and devs deliver more. Sounds perfect, right? But, as with everything, it’s not that simple. This individual speed boost from AI comes with a downside for the team: more instability and less predictability in delivery.
Sounds contradictory? Well, that’s exactly what the DORA Report, a top reference in software engineering, points out: while AI boosts dev productivity, it often leads to larger “batches” of changes, which goes against best practices for stability. The takeaway? Individual gains don’t always lead to positive results for the team.
In this article, we’ll dive into the delicate balance between individual productivity and team stability. More importantly, we’ll look at how teams can tweak their routines and processes so that this powerful tool becomes a true ally for everyone.
Is AI helping or hurting the team’s rhythm?
AI has become a major asset for devs, simplifying repetitive tasks, improving code quality, and speeding up output. It all seems like a blessing: faster deliveries, less time spent on manual tasks. But when you broaden the scope to the whole team, the story shifts.
Here’s the catch. According to the DORA Report, AI’s benefits come with challenges for engineering teams. The speed it gives devs has led to bigger code batches, which clashes with one of the main recommendations for stability: keeping batch sizes small.
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The “Super Dev” paradox and team instability
AI is turning developers into “super devs,” boosting speed and helping them accomplish more. But this increased productivity has a price: bigger volumes of changes impact the entire team. When a dev produces more in less time, it creates big batches of changes, which are harder to integrate and test. It’s a snowball effect — the faster these changes come, the more effort it takes to keep things stable and organized.
This “super dev,” meant to be a team asset, ends up creating a paradox. The team gets stuck in a cycle of rework and adjustments to handle the pace and complexity of changes. Instead of making things easier, individual speed becomes an extra weight for the team, which now has to manage and stabilize each new batch of changes.
When individual speed clashes with collective stability
The 2024 DORA Report reveals an interesting paradox: while AI enhances individual productivity, it can also disrupt team stability. The idea of “small batch sizes,” promoted by DORA, aims to keep code changes predictable and manageable, allowing the team to spot and solve issues quickly. But with AI accelerating code production, these “batches” end up large and complex, making integration harder and stability riskier.
When devs use AI for speed, they generate bigger, more substantial changes in less time, which means more work for the team in reviewing, testing, and integrating. This dynamic affects delivery stability the most, with stability dropping 7.2% for every 25% increase in AI use. Throughput, or the frequency of deployments, also takes a slight hit (-1.5%). So, even if devs are moving fast, the team has to invest more time to keep up, slowing down the overall flow.
In the end, what seems like an individual productivity gain turns into a team-wide challenge. The team has to put in extra effort to ensure that these larger, faster batches of code don’t compromise product stability. So while AI brings lots of benefits on a personal level, it requires teams to rethink practices and routines to balance speed with security in deliveries.
AI: Tool or source of complexity for engineering teams?
AI definitely brings a ton of possibilities, helping devs speed up tasks and solve complex issues in less time. But here’s the thing: even with all these advantages, it’s not a miracle solution. Without well-defined processes, AI can sometimes do more harm than good. Picture it this way: AI allows devs to write code faster, but without continuous integration and structured feedback, these fast changes turn into a puzzle for the team.
Complexity rises because AI often boosts individual productivity without giving the same support for collaborative work. Tools that only focus on individual dev efficiency don’t help teams manage the flow of work or tackle integration, testing, and stability challenges. It takes extra effort from everyone to fit these “batches” of changes into the team’s process and prevent new changes from breaking or slowing down the product.
So, what makes a difference? AI tools that consider team workflow, not just individual devs, tend to have a more positive impact. They help balance speed and stability by fitting into team practices like continuous integration and frequent feedback cycles. The key is making sure AI complements and simplifies the group’s workflow, not just for individual productivity but to keep the whole team on track.
Individual productivity vs. team stability
For AI to truly benefit the team, the challenge is finding that sweet spot between individual productivity and team stability. It’s not just about making devs faster; it’s about making sure that individual speed doesn’t mess with the team’s flow. This requires process adjustments, turning personal gains into team benefits. Practices like continuous integration and automated deployments are great allies in striking that balance, as they absorb changes more smoothly, reduce risks, and keep the pace steady.
Collaboration is what turns individual productivity into tangible team results. Boosting one dev’s performance is awesome, but without integrating with team practices, that extra productivity can get lost along the way, creating more rework and instability. AI tools that support team-wide productivity, not just isolated dev performance, help a lot in preventing bottlenecks and keeping the flow smooth.
In the end, productivity and stability aren’t separate goals. In a well-aligned team, individual speed only becomes a real gain when it fits the collective flow. Adjusting routines to bring in AI’s advancements without losing sight of stability allows the team to hit a balance where everyone benefits: faster devs, more predictable deliveries, and an efficient, integrated team.
The future of teams with AI in software engineering
AI is here to stay, but it still needs some fine-tuning to fit in with teamwork. If the priority is maintaining stability and delivery pace, teams need a more strategic view to balance individual gains without losing collective equilibrium.
The future for engineering teams will involve adapting practices and processes to embrace AI’s power without sacrificing predictability. Finding the right balance between “super dev” and “super team” is what will turn AI into a real ally instead of an added complexity.
