The AI-Enhanced Team of the Future

Do we still need development teams now that AI agents can write, test, and deploy code? The answer is more surprising than you might expect. And not a simple yes or no.

Vibe programming and agentic AI let a single engineer coordinate dozens of specialized code agents to ship production features (→ Claude Flow).

Even those new to the field can achieve impressive results. For example, my daughter, a design student with no previous coding experience, built a complete Mac desktop app in just two weeks—including the build setup, version control, and GitHub page.

Critics might argue that code quality is lacking and that a junior developer may overlook important details. I heard similar concerns from C developers when I began using managed code runtimes like Java or .Net. However, the impact and scale of this shift with AI are even greater—there is no doubt about that.

So the question is natural: if AI can build most things, what is the role of a development team?

Widening the Value Chain with AI

Organizing in close groups of 7 ± 2 people is a pattern that most likely can be dated back to early Homo sapiens. A team of this size provides significant advantages, particularly when navigating uncertainty and complex challenges.

• Cognitive load / communication overhead: Every extra team member adds more potential communication lines (if everyone interacts with everyone). With too many members, coordinating and keeping everyone aligned becomes harder.
• Accountability & motivation: In smaller teams, each person’s input is more visible; as teams grow large, individuals may feel less accountable (social loafing).
• Skill coverage vs. coordination cost: With fewer than ~5 people you may miss skills or people become overloaded; with more than ~9 you may get diminishing returns because the overhead of coordination overwhelms the added capacity.
• Working memory / chunking metaphor: The idea of “7 ± 2” from Miller is about how many separate things a person can keep in mind; by analogy, a team of ~7 is still manageable in terms of keeping track of members, tasks, roles, interaction patterns.
• Decision-making & interaction efficiency: Smaller teams can meet, decide, adapt faster; larger ones may slow down because scheduling, alignment, consensus become harder.

GenAI accelerates the evolution of products and services. However, previous tools and technologies have also driven similar changes. Yet, none of them fundamentally altered the social pattern of the team. So, why should AI change this now?

One argument is that, for many, AI appears almost human. However, anyone who has worked with LLMs or diffusion models quickly sees that, while these systems are impressive at predicting human behavior, they have clear limitations. They do not replace human counterparts; instead, we use them as tools to deliver products and services for people.

When we want products and services to change, it is because humans desire it. A system that mimics our behavior can support this, but it is always guided by human intent. AI helps simplify tasks to a remarkable extent, but it remains a tool directed by people.

Looking back at past technological revolutions, we can observe that whenever we created tools to simplify work, we immediately used them to tackle greater complexity. AI is just one such tool. It increases throughput and knowledge access, thereby expanding the scope of the value chain that a team can manage.

Naturally, an Enterprise seeks to manage all stages of the evolution flow, either by outsourcing to suppliers or by delegating to smaller social subsystems. The size and structure of these smaller social systems depend on their specific purpose, as humans tend to organize themselves accordingly. AME3 is designed to reflect this natural order for human systems:

• Medium Enterprise (50–5,000 people): covers the entire flow from genesis to commodity. An Enterprise provides commodity services to an Arena and delegates everything that is not commodity to the Arena.
• Arena (7–250 people): a highly independent business unit. It controls the value chain to deliver converging services and products to customers. It delegates genesis and emerging tasks to Teams and provides the necessary support for them.
• Team (5–9 people): primarily focused on genesis to custom/emerging tasks. Teams discover new ways to build and deliver to customers while establishing an initial stable process. They utilize co-creative approaches like pairing.
• Pair (2 people): focuses on genesis and concept work, especially when tasks are highly novel or urgent.

Teams are fundamental social systems. AI won’t change this.

So ultimately, it is the Teams who drive progress and deliver value in an Enterprise. This is unlikely to change, as long as enterprises continue to seek progress and deliver value. However, the composition of teams will inevitably evolve.

How Teams Are Evolving

The pattern of team evolution over the decades is clear: fewer handoffs, faster feedback, and broader responsibility. AI will accelerate the next stage of this transformation. Let’s walk through this evolution to understand it more clearly.

Era I — Component Teams

Enabled by the first programming languages.

• Work focus: isolated components and libraries; many handoffs to integrators.
• Constraints: limited tooling and automation; high coordination cost; long feedback cycles.
• Outcomes: quality inside components, but brittle integration.

Era II — Full‑Stack Teams

Enabled by XP, integrated dev environments, and test automation.

• Work focus: vertical slices from UI to persistence; trunk‑based flow; continuous testing.
• Constraints lifted: faster feedback; tighter product–engineering loop; fewer handoffs.
• Outcomes: shorter cycle time; improved responsibility and product fit.

Era III — DevOps / HW&SW Teams

Enabled by cloud computing and platformization.

• Work focus: build and run; infrastructure as code; observability; security by design.
• Constraints lifted: provisioning and deployment become utilities; scale and reliability increase.
• Outcomes: continuous delivery and on‑call responsibility, including hardware where relevant.

Era IV — AI-Enhanced Teams

Enabled by vibe programming and agentic AI.

• Work focus: end‑to‑end business outcomes across customer demand, design, system architecture, service improvemnts, operations, and market growth.
• Constraints lifted: generation, integration, and orchestration shift to AI agents; humans focus on intent, constraints, and verification.
• Outcomes: teams are responsible for complete business capabilities and micro‑enterprises, with AI as force multipliers.

The most appropriate term for these teams is still discussed. We suggest AI-Enhanced Team as an initial option. Alternatively, Full Business Teams may be a more consistent description. We welcome your suggestions if you think there is a better term.

In pure software and IT-Areas, these teams will become Full Business Teams, as they can easily achieve many end-to-end business outcomes within a single cadence. In other domains, even with AI, this level of integration may not be possible. For example, mass production and product development might still require separate teams. However, fewer specialized areas will be necessary, handoffs will be reduced, and cycle times will be shortened.

Full Business Teams existed even before the rise of AI. For example, the team I led as Product Owner for seven years operated in this manner. We were a single-Team company, running very lean and outsourcing most commodity functions. Working in this way required special and radical conditions, making Full Business Teams quite rare—especially in larger organizations. Our hypothesis is that this model will soon become the new normal.

New AI Products and Services

You may have noticed that the above team evolution model does not fully address a key aspect of GenAI: its potential to enable entirely new products and services, or to drive major enhancements to existing offerings. We examine this important dimension in greater depth here: Explore how GenAI unlocks product and service innovation.

What Changes for Humans?

This is likely the biggest question your employees, colleagues, and you are asking right now. I am asking it myself as well: What changes for us as humans? As with any new technology, it is difficult to answer this in detail right away.

Reflect on your experience from previous technology revolutions. GenAI has the potential to create an impact as significant as the internet (IP protocol, WWW, etc.). We can expect many changes to our current beliefs and ways of working.

What we know so far:

• Intent and constraints: We will focus more on defining what to build and what to avoid.
• Taste and ethics: We will set standards for quality and ethical boundaries.
• Evaluation and acceptance: We will increasingly define tests, guardrails, and meaningful measures.

The craft is moving up the stack. People will spend less time on integration and more time shaping impactful outcomes.

The Transition Playbook with AME3

A practical flow to move from today’s model of work to AI‑enhanced teams:

1. Map the landscape using Wardley Mapping to identify the next goal you want to achieve, leveraging the potential of GenAI. Start with asking one of the Questions in Developing a Strategy for the GenAI Era.
2. Begin with a single Team or even a pair of specialists. Run experiments to validate your hypothesis.
3. If you decide to continue with your goal, continue with the same team but be prepared to change its composition. Strengthen your data and guardrails. Enhance data quality, observability, and develop security policies to ensure AI agents can operate safely.
4. Pilot a single product or service capability with a strong emphasis on delivering customer value. Choose a focused scope—such as a specific feature or internal service—leveraging an agentic toolchain.
5. Measure value delivered and customer signals.
6. Scale beyond the team. Leverage your team’s expertise in GenAI technologies, customer experience, and modern collaboration methods. Expand to multiple teams within an Arena, or even launch a new Arena that operates highly independently from the rest of the organization to scale faster and adapt more swiftly to changing markets and AI advancements.

All of this must take place within a lean and adaptable governance structure at the enterprise level. Ensure that new teams are integrated into the strategic empirical control loop of AME3.

Conclusion

So, what is the answer to the question, “Do we still need development teams now that AI agents can write, test, and deploy code?”

If you define development as coding, then yes, we will see these types of teams disappear. If you define development as creating next-generation products and services, then this has always been—and will continue to be—the domain of teams.

AI does not remove the need for teams. It changes what teams take responsibility for and how they deliver.

Focusing only on software-based products and services—such as coding and administration—where the most visible changes are occurring, would be a major mistake. AI now offers the opportunity to break down long-standing barriers between R&D, IT, engineering, and business functions. Failing to embrace this shift will put your organization at a significant competitive disadvantage.

In AME3 terms, teams widen their scope across the value chain and become responsible for more end‑to‑end business capabilities. Humans set direction and standards. Agents accelerate delivery and integration.

The future team is not smaller. It is broader, more empirical, and closer to the customer—and it wins by learning faster than the market changes.