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Modern software teams are already moving faster because individual engineers use AI. Yet the real gains are still ahead. The biggest improvements do not come from speeding up coding. They come from speeding up the work that happens between people. That is where most of the time is lost, and where AI has the greatest leverage when applied at the level of the team.

A software engineer using AI increases their coding speed by 30 to 75 percent. But coding is only 30 percent of the job. The remaining 70 percent is the work that makes coding possible, safe, and correct. This work is shared, and it is deeply tied to the rest of the team.

1. Requirements, clarification and planning (15 to 20 percent)
2. Meetings and coordination (10 to 15 percent)
3. Code review (10 to 15 percent)
4. Debugging, testing, and validation (15 to 20 percent)
5. DevOps, tooling, and environment work (5 to 10 percent)
6. Documentation and knowledge work (5 to 10 percent)

These figures come from McKinsey, GitHub, Stripe, and Harris Poll. They show that most of an engineer’s time is spent on team‑level activities.

Modern Software is delivered by Teams

These twelve activities shape team throughput. Every delivery team performs them, and they determine how quickly and safely software moves from idea to production.

These twelve activities define how modern software is delivered. Every engineer contributes to them, but not in equal measure. To understand where AI creates leverage, we need to look at how an engineer’s time maps onto this system. That is what the next section describes.

What an Engineer Does

The work of an engineer is given in the Engineer Time column, their work feeding into the team activities described in column two.

The two hghlighted rows show the "coding" step, that is predominantly done by the software engineer alone.

Coding is the final expression of a much larger collaborative effort. The other 70 percent of the role ensures that what is coded is the right thing, built the right way, that is safe to run in production.

Software Engineer Adoption of AI is Individual

Developers are adopting AI tools on their own, at scale, and ahead of their organisations. JetBrains reports that 90 percent of developers now use at least one AI tool at work, and 74 percent have adopted specialised assistants independently. GitHub finds the same pattern: engineers use AI to improve their own speed and reduce cognitive load, not to change team workflows.

The result is a widening gap between personal productivity and the unchanged delivery system that the individuals operate within.

Accelerate One, Accelerate Many

When AI speeds up one engineer, it speeds up the interactions around them: reviews, iteration loops, testing throughput, coordination, and decision making. These effects compound across the delivery system.

Yet individual AI only improves the local interactions that depend on that engineer. Team level AI improves the global interactions that depend on shared context, shared artefacts, and shared decision making.

A team benefits from individual uplift, but several categories of work cannot be improved by individual tools alone.

A team cannot reach the next level of performance without AI that operates on the shared system, not just the individuals within it.

When every member of the delivery team becomes faster and clearer in their part of the system, the throughput of the whole team increases non linearly.

Team Throughput

Team throughput is shaped by the slowest interaction in the workflow. Delivery moves when shared activities move: reviews, fixes, integration, decisions, documentation, coordination, and onboarding.

Onboarding shows this clearly. A new engineer becomes productive when they understand the system, the domain, the architecture, the conventions, and the team’s way of working. These are team level artefacts. AI helps only when the team applies it to the shared knowledge and processes that support this learning.

AI Acceleration

AI can speed up every shared activity listed above. These activities are constraints that the whole team depends on. When they move, the system moves. The effect is non linear because software delivery is dominated by interaction rather than individual effort.

Faster reviews, clearer decisions, and quicker coordination reduce the waiting time between people, which shortens the entire cycle.

Example: How reduced waiting shortens the cycle

Imagine a team working on a small feature. The work passes through five steps:

1. Write the change
2. Wait for review
3. Apply fixes
4. Wait for approval
5. Merge and test

Without team level AI

• Writing the change: 3 hours
• Waiting for review: 1 day
• Fixing comments: 1 hour
• Waiting for approval: half a day
• Merging and testing: 2 hours

The total time is not the 6 hours of work. It is the 1.5 days of waiting wrapped around it.

Team level AI reduces waiting

Team level AI helps the reviewer by summarising the change, checking for risks, and drafting comments. It helps the author by preparing fixes and clarifications, and by coordinating activity through the five stages.

The waiting times drop:

• Writing the change: 3 hours
• Waiting for review: 2 hours
• Fixing comments: 30 minutes
• Waiting for approval: 1 hour
• Merging and testing: 2 hours

The work is still roughly 6 hours, but the waiting has fallen from 1.5 days to about 5 hours. With an 8 hour day, the cycle drops from 18 hours to 11.

Reducing idle time is key

The work has not changed. The gain comes from removing the idle time between people. Reducing waiting shortens the whole cycle. This is where team level AI has its strongest effect. It acts on the delays that dominate delivery, not the small pockets of individual effort.

When these delays shrink, the system moves more quickly. Reviews happen sooner, decisions are clearer, fixes flow more easily, and work spends less time sitting in queues. The improvements are non linear because the team is no longer held back by the slowest interaction.

AI Benefits at the Team Level

The gains that matter most cannot be achieved through individual AI use alone. Individual uplift improves personal speed, but it does not change the structure of the team’s workflow or the quality of the shared artefacts that the team relies on.

Team level performance improves only when AI is applied directly to the collective work: shaping requirements, coordinating plans, reviewing code, integrating changes, resolving ambiguity, documenting decisions, and keeping flow steady.

These activities form the delivery system. Improving them requires AI that operates at the level of the team rather than the individual.

Why Team AI is Necessary

Individual uplift improves the outputs that flow into team interactions. It does not improve the interactions themselves. The main bottlenecks in delivery are the points where people must work together: clarifying requirements, resolving ambiguity, negotiating trade offs, coordinating across roles, and maintaining shared understanding.

Individual AI helps a person contribute more quickly. Team level AI improves the clarity, accuracy, and speed of the shared work that binds the team together. This is where the real gains lie.

Team level AI

A team level AI agent can work on the shared system:

• rewrite requirements for clarity
• maintain architecture knowledge
• surface risks
• detect ambiguity
• summarise decisions
• generate consistent patterns
• keep the team aligned
• handle coordination and scheduling

Individual AI cannot do this because it has no view of the team’s shared context.

Individual AI cannot coordinate across roles

A delivery team includes product, design, QA, DevOps, security, architecture, and delivery management. Each role uses different tools and produces different artefacts. Individual AI tools do not coordinate across these boundaries.

A team level AI agent can maintain shared context, track dependencies, surface risks, ensure consistency, support the Agile process, and reduce coordination friction.

Team level uplift is a multiplier

Individual uplift is additive. It makes each person faster, but it does not change the structure of the system. Team level uplift is multiplicative. It changes the structure of the system, reduces shared constraints, collapses waiting time, improves flow, and increases throughput across the whole team.

This is why team level AI is required to unlock the full return on investment.

Conclusion

The shift to AI in software engineering will not be won through individual adoption alone. Teams already feel the lift from faster coding and quicker local tasks, but the real gains come when AI is applied to the shared work that governs how delivery actually happens. The constraints that slow teams down are collective, and so the improvements that matter must be collective as well.

The organisations that move first will be the ones that treat AI as part of their delivery system, not as a personal tool. They will use it to keep work flowing, reduce waiting, maintain shared understanding, and support the decisions that shape the product. Once AI is embedded at this level, the team’s throughput changes in a way that individual uplift can never reach.

The opportunity is simple. Teams that adopt AI together will outpace those that adopt it alone. The sooner a team treats AI as part of its operating model, the sooner it sees the return that individual tools cannot deliver.

Related Work

• Individual AI delivers diminishing returns; meaningful improvement comes from strengthening the collective workflow.
• AI systems behave differently from traditional software and require layered safety, strong governance, observability, and architectural discipline to operate reliably and sustainably.
• AI lowers the cost of code, not the cost of thinking. Clarity and judgement, not speed, determine whether teams build what truly matters.