Agile teams are often created to respond more flexibly to market changes. Their advantages are undeniable if they are implemented correctly. But let’s be honest: in many large companies, setting up agility correctly is no easy task:

There is a shortage of senior backend specialists.
People are not replaceable.
The Product Owner doesn’t have time to sit down with the team.
We resist rework if we don’t get it right the first time.

People are reluctant to change, and no wonder—they have spent years gathering experience, and agile approaches change many things.

AI is now entering this situation, helping to transform the economics of work in development and product teams. But it’s not just about generating code or tests. It’s also about changing the very structure of teams, their competencies, and the way value is created.
It’s about a structural change in agile teams.

Below are five changes we are seeing today in teams that are integrating AI systematically, not just as personal assistants to individuals.

1. Specialization ceases to be a barrier (I-shape → T/M-shape)

In traditional development, we have long built teams based on the principle of specialization (known as I-shape). The larger the company, the more specializations and narrow roles there are. Typical I-shape specializations include:

Backend developer
Frontend developer
QA engineer
Architect

With the advent of agile frameworks, the I-shape gradually began to change to a T-shape (deep expertise + minor overlaps into other crafts). This helps a lot with planning team activities and staff substitutability. With more versatile teams, you plan capacity as a whole, not as individual specialists.

AI is dramatically accelerating this trend. Today, a “builder,” i.e., a person working on development with AI support, deals with:

product specifications
frontend and backend
code quality
test generation
documentation

Of course, a lot depends on where you deploy AI and where your team works. But one thing is clear: AI expands the scope of action for every team member. That doesn’t mean specialization will disappear. It just means that specialization will no longer be a barrier where AI is used in development. Companies that understand this are moving from role-based structures to more flexible working models. And that has a fundamental impact on team adaptability and productivity.

Here’s a question to ponder:
How many of your roles today exist only because technology previously did not allow for a broader scope?

2. Cross-functional teams free of barriers

High specialization and organization around various crafts created a number of external dependencies in traditional companies. So-called silos were created. These teams or departments must collaborate on various projects, but they usually have their own goals and priorities. And it is precisely because of their own priorities that silos often limit collaboration with surrounding teams/departments. They do not have common goals as a team across the entire company.

In contrast, agile teams are structured as cross-functional. One team includes people from multiple departments: development, business, QA, and sometimes even a lawyer or marketing. This reduces external dependencies and barriers to collaboration. However, in large companies, you will often encounter a number of challenges in setting up teams:

you only need part of a specialist’s capacity
the person is shared between teams
departments need key people for themselves

AI in agile teams also represents another shift, reducing barriers between roles. A team is no longer cross-functional just because it has different roles. It is also cross-functional because it can cover a wider range of activities without waiting for the “right specialist.”

For example, AI helps with:

with initial text analysis
with the generation of marketing variants
with architectural design proposals
with test scenarios

Of course, it always depends on where you want to have control and where you let AI take over. But the reality is that some tasks that would otherwise take weeks can now be handled by a team with AI support in a matter of hours.

Cross-functional is no longer just an organizational ambition. It is becoming a technological effect.

3. The substitutability of people is increasing

One of the biggest problems with traditional teams was the so-called bus factor – dependence on a specific senior member.

Everything was in one person’s head.
Documentation was minimal.
And if the senior member was not at work, the team came to a standstill.

Agile approaches seek to build substitutability in teams through T-shape and knowledge sharing. People in teams train their colleagues, who can then help with simpler tasks with varying degrees of supervision from more senior colleagues.

AI further accelerates this process significantly. Today, AI also:

explains existing code
analyzes change history
generates documentation
helps with onboarding

Loading a complex application is a matter of minutes for AI. For humans, it used to take weeks. But again, this does not mean that the experience of seniors will disappear. It means that their knowledge is no longer the only source of truth. Teams are thus becoming more self-sufficient.
And from a leadership perspective, this is a fundamental shift in risk management.

And again, one question:

Is your organization prepared for the fact that know-how may no longer be the main bargaining chip for individuals?

4. End-to-end product teams are becoming realistic

In the world of traditional development, teams used to be organized according to systems, known as component teams. This meant there was a strong need to manage dependencies in order to deliver the entire project.

Agile approaches brought about a change, the concept of end-to-end (E2E) teams. These are teams composed of all roles that can deliver value to your product from A to Z.

This principle is easy to implement in smaller companies, but in large corporations, this approach encounters a number of necessary compromises.
And in some large corporations, an E2E team is a difficult ideal to achieve in the short term.

Today, AI makes it possible to cover a larger part of the value stream directly within a single team:

frontend development
backend development
test generation
ongoing quality control

If you are willing to use AI across systems, some of the historical dependencies disappear. The E2E team ceases to be an organizational revolution. It becomes a technological possibility.

And that is a fundamental change for large companies.

5. Rework is reduced thanks to shorter feedback loops

Traditional project management usually assumed a limited number of change requests for projects. And in the best case scenario, reserves were prepared for this in the plan.

Agile approaches, on the other hand, accept change as a natural part of the journey. They focus on living with constant change. The goal is to realize that we are going in the wrong direction in time, before the change costs us too much. That is why this way of working is often associated with reworking what has already been done. In my experience, companies often fear rework because it makes development more expensive. In reality, however, rework focused on increasing value for customers can save many times more than it costs to implement.

Even with the use of AI development, it is clear that rework will never completely disappear. But AI reduces the effort required to address rework. This makes it easier for teams to experiment and find the right direction that your customers, and ultimately you, will appreciate.

Using AI for rework means:

less costly repairs
higher quality output
better team predictability

And most importantly: greater courage to experiment. Companies that systematically combine agile approaches and AI often find that rework is no longer a specter. It is a controlled quality tool.

What does this imply?

Most companies today are experimenting with AI individually:

people use AI assistants
generate parts of code
test automatic code reviews

Excellent! That’s a good start. But the real advantage comes when AI becomes part of the team structure and not just a personal tool for individuals.

So the question today is not, “Are we using AI?”

But rather:

Has AI changed the structure of our agile teams?
Has it increased substitutability and autonomy?
Has it reduced dependencies between roles?
Have we adjusted our metrics and governance?

Because if the way we work changes, the way we manage must change too.

If you are dealing with this now

If you are faced with the question of how to systematically integrate AI into product and development teams in a way that leads to real performance gains and not just experiments, it makes sense to go through your specific context.

In 30 minutes, we can go through:

how your teams work today,
where AI brings the greatest leverage,
where, on the contrary, it creates a false sense of productivity,
and how to adjust the structure of work so that AI actually increases performance.

Sometimes it’s enough to change the structure of the team. Not the tool.