Why AI Hasn’t Made Your Software Teams Move Faster (Yet)

And what it takes to turn AI-assisted coding into real, measurable delivery gains.

Teams Aren’t Always Ready For the Tools

AI is transforming software engineering faster than almost any other field. There is a good reason for this: it’s a verifiable domain with tons of training data available across public and private code repositories.

And coding agents keep getting better, even as progress with LLMs levels off. Each new version includes more advanced scaffolding, giving it greater autonomy to handle ever more complex tasks.” ‘Vibe coding’ was once dismissed as a layman’s hack — now it’s a fast, legitimate way for professionals to create viable tools and automation.

Yet, many technology leaders say they still don’t see the promised productivity gains materialize across their organizations. It’s an all-too-familiar story that goes like this: Leadership enthusiastically buys into the idea of AI for software delivery. The early pilots look very promising as small teams report much faster output. Licenses are then rolled out across the company in quick fashion.

But when productivity levels are measured at the organizational level, the results disappoint. So, what’s the catch?

For many organizations, the problem isn’t the technology: it’s that the supporting processes, training, and measurement haven’t kept up. AI adoption is often driven by hype or FOMO, with little thought to whether the organization is ready for it. The truth is, early success simply doesn’t scale unless the proper fundamentals are in place.

Your Process Can’t Keep Up

It sounds counterintuitive, but faster coding doesn’t necessarily mean faster delivery.

Sure, AI tools accelerate individual performance. But teams often lack the necessary process maturity and visibility to turn local gains into system-wide improvements. There are a few common reasons for this:

• Inconsistent training for specific roles and use cases
• Individual resistance to AI-enhanced tools
• A lack of reliable metrics to track AI’s real impact
• Outdated processes preventing efficiency at scale

When requirements, testing, or pipeline deployment can’t keep pace with new developments, the process itself becomes the bottleneck.

Even if teams ramp up their coding throughput, nothing will improve if those gains pile up at the QA or deployment stage. That’s why good processes make bottlenecks impossible to ignore — and great teams use retrospectives to fix them fast.

Let’s Measure What Really Matters

Why AI isn’t one-size-fits-all

Every team’s AI journey will look different because the value AI delivers depends mostly on where and how it’s applied. It’s not just about tech stacks or team structure — the type of project and the developer’s level of seniority also play a big role.
Say a junior dev is tasked with setting up boilerplate code on a greenfield build. They’ll probably see immediate productivity gains from AI assistance. But what about a senior engineer working deep inside a complex, legacy codebase? They might find the tools far less helpful. In fact, it might just get in the way of a workflow that’s already fine-tuned.

To prove it’s working, measure it

To know whether AI is truly working, organizations need objective performance metrics – not just gut feeling. Personal impressions have their place, but only when paired with hard data.

Frameworks like DORA and SPACE provide a solid foundation for measuring engineering productivity. They cover critical metrics across productivity, quality, and team health — the same ones we’re tracking below.

But to understand the impact of AI adoption, we need to go a step further. AI usage stats add a missing layer — so you can tie actual usage to real outcomes.

A practical metrics framework

• Team productivity – cycle time, lead time, throughput
• Quality – defect density, escape rate, reopen rate
• Stability – change rate failure, MTTR
• AI Usage Stats – % AI-generated code, AI acceptance rate, individual usage statistics from IDE providers

Your gut might ring a few alarm bells – but only data can confirm your suspicions.

Adapt as you go

Experimenting adds real value here. Teams should continuously apply AI tools to specific use cases, and then measure its effect on real delivery metrics. And it’s just as important to monitor individual usage patterns. Identify those struggling with adoption and give them targeted support.

No two teams will implement AI the same way — and that’s OK. What matters is their ability to test, measure, and adapt the way they use it for real outcomes.

Your AI Results Depend on Training

AI doesn’t provide cover for skill gaps — it just makes them more obvious. That’s why real adoption starts with solid training in both AI tools and core role skills.

• Different projects, different strategies: AI strategies also vary depending on the type of project. Legacy modernization isn’t the same as greenfield development — and both differ from application support.
• Legacy modernization often means analyzing large, outdated codebases with little documentation. Projects may suffer from poor design and a lack of in-house expertise in legacy technologies. Success hinges on your ability to extract structure and insight from messy systems.
• Greenfield development starts with a lot of boilerplate code, having fewer dependencies, a manageable code base size and usually includes popular frameworks and languages.
• Application support revolves around navigating large codebases, existing documentation, and ticketing systems – while handling ongoing incidents. This includes writing RCAs, updating documentation, and ensuring operational stability and reliability.

Role-specific training matters:

Each role interacts with AI in different ways — and that means using distinct tools and prompts. For example:

• Engineers may use AI to write, refactor, or debug code faster.
• SDETs and QA specialists can generate test cases or automate repetitive validation.
• Product owners might use AI to summarize feature requests or customer feedback.
• Support engineers rely on AI for fast log analysis, RCA drafts, and ticket response suggestions.
• DevOps and SREs may benefit from AI-assisted config tuning, deployment scripts, or incident summaries.
• Scrum masters or project managers can speed up meeting notes, backlog grooming, and sprint reporting.
• Ultimately, training must be relevant and role-specific if you want to multiply the value of every AI license purchased.

The Human Factor

AI challenges more than your code:

Change is hard — especially so when it challenges habits shaped over many years.

Most engineers have developed a highly individualized approach to coding and reviewing. AI upsets that balance with fewer manual tasks, more time spent reviewing, and far more collaboration between human and machine. That can feel very uncomfortable and difficult to adapt to for some team members.

But good leaders have a plan:

• Making AI adoption a clear priority through consistent messaging
• Identifying champions whose success stories will inspire others
• Integrating AI adoption into performance management so it becomes part of everyday accountability.
• Hiring with AI fluency in mind, so that new talent can carry the momentum.

In the end, AI adoption is as much about people and trust as it is about the tools and data. It means watching how individuals use the tools — and stepping in when they get stuck. Everyone should be moving forward together.

Not All Projects Play by the Same Rules

Even with strong leadership, training, and measurement, outcomes will differ from team to team. That’s because AI performance depends heavily on the nature of the project itself. For example:

• Refactoring a legacy Cobol system won’t yield the same gains as building a new service in JavaScript. That’s because LLMs have seen far more JavaScript in their training data — and greenfield builds tend to be easier to automate than working with deeply-entrenched legacy code.
• Large codebases (over a million lines of code) are harder for AI to navigate effectively compared to smaller ones.
• Robust unit and automation tests speed up AI-driven work — but can hardcode bugs if poorly designed or unchecked.

Scale What Works

AI can only start to scale once the fundamentals are in place: process, measurement, training, and leadership. Then teams can begin to identify patterns, refine their workflows, and feed insights back into their pipelines.

Over time, these isolated wins will translate into repeatable improvements in consistency, speed, and quality of output across the entire organization.

A Reality Check In AI Adoption

AI adoption in software development isn’t a plug-and-play upgrade. It’s a complex change management journey that tests process maturity, leadership alignment, and organizational readiness.

Who’s ahead – and who’s not:

Companies invested in strong engineering practices, measurable metrics, and who are able to adapt will see faster, visible results.

For everyone else, AI will simply accelerate what’s already broken.