AI Adoption Is a Business Transformation. It Starts Where Deployment Ends

Who should own AI adoption inside ao rganization?

RM: Primarily the business — whoever owns the outcome and can change how the work is done. One of the biggest mistakes is treating AI adoption as the deployment of another tool. You can complete the implementation and still achieve very little.

The starting point should be the outcome. Do we want to improve quality, shorten delivery time, increase productivity or make a process easier for employees? That objective should determine what needs to change in the process, and only then which technology can support it.

AI adoption should therefore be managed as a business transformation, with clear ownership, process changes and measurable results. What makes AI different is that some activities previously seen as exclusively human can now move partly to technology. That changes not only the process, but also people’s roles and responsibilities.

Why do employees sometimes reject AI even when the technology works?

RM: I see three recurring reasons.

First, the company approaches AI as a technology rollout rather than a change in how work is done. People get a tool but no picture of where it fits their work, so they cannot see how it would help them.

Second, the ambition never gets translated into how teams actually work. Middle management often lacks the mandate or capacity to make that happen, so you end up with two parallel worlds: some people adopt the new way of working, while others continue as before.

Third, expectations are too high. If leadership promises a dramatic improvement and the first results fall short, trust can disappear very quickly. AI adoption requires people to learn where the technology works well, where it does not and where human judgment still needs to stay in the process.

And sometimes the rejection is right. For some tasks AI genuinely does not help yet, and a team that says so is giving you accurate information rather than resisting change. A use case needs an exit as well as a start and someone has to be able to say this one is not worth it, without that counting as a failure of adoption.

Does leadership also need to accept a short-term cost of that change?

RM: Yes. Transformation requires capacity. Teams need time to learn, experiment, share experience between teams and redesign parts of their processes, and that can temporarily reduce their immediate output.

The problem comes when organizations say AI adoption is a priority but keep every existing target unchanged. Then people are effectively expected to transform the way they work on top of their normal workload.

Leadership has to treat that capacity as an investment. You cannot demand a new operating model while funding only the old one.

AI scales when the team changes

If senior leadership sets the ambition, who makes AI part of everyday work?

RM: That is where middle management becomes critical. Senior leadership sets the direction, but managers are the ones who turn that ambition into the way teams actually operate. AI cannot remain an optional layer next to the existing process. Managers need to decide where it belongs in the workflow, what people should do differently and which successful practices should become standard across the team.

Otherwise, individual enthusiasts may get value from AI while the organization itself continues to work in essentially the same way.

Where do you already see that shift happening in software delivery?

RM: Across the development process. Analysts use AI to structure customer input, identify missing information and prepare questions. Developers use it for code generation, debugging and problem analysis. Testers use it for automation as well as for designing testing scenarios.

But the more important shift happens when those roles stop optimizing only their own work.

We are seeing analysts and testers improve shared artifacts together. A tester might identify that a different structure in the analysis will make downstream testing more effective. AI then starts improving not just individual tasks, but the handovers between roles.

That is a much more meaningful level of adoption. AI stops being a personal productivity tool and starts changing how the team works as a system.

How do you turn those individual gains into something the organization can reuse?

RM: That is where standardization comes in. The first stage is usually very individual. People build their own prompts, tools and small solutions. The next step is to turn what works into shared workflows, artifacts and practices.

This is also part of the thinking behind Trask Vela, Trask’s framework for AI-supported software delivery. It brings together technology with a shared delivery methodology, turning individual AI-supported activities into a more consistent way of working.

Trask Vela can support the full delivery process or individual activities such as testing or bug fixing. The goal is to make AI know-how systematic and repeatable across teams, rather than leaving it with individual users.

How do you standardize AI without creating another centralized bottleneck?

RM: You need a common foundation, but the business still has to own the problem and the outcome.

A central competence center can provide the parts that should not be reinvented by every team: access management, monitoring, regulatory requirements, shared components, standards and knowledge about solutions that already exist.

Without that, five teams can easily solve the same problem five different ways.

Centralization should remove duplication, not move ownership away from the business. The central capability provides the secure, reusable foundation that lets teams move faster without losing control.

The real test of AI adoption

What does mature AI adoption actually look like?

RM: It is not when everyone has access to an AI tool. The stronger signal is that the role of people in the process starts to change.

You can already see this in software delivery. As models take over more of the mechanical execution, engineers spend more time defining how the solution should work, providing the right context and validating the quality of the result. The value shifts toward design, judgment, control and decision-making.

The same principle applies more broadly. Repetitive work can increasingly move to technology, while people remain responsible for intent, quality and the decisions that shape the final outcome.

That is a much more meaningful measure of adoption than the number of tools deployed.

What separates companies that actually reach that point?

RM: The cleanest separator is whether the company named its outcome first, in the terms as I described earlier. Success has to be measured against the outcome the organization wanted to achieve in the first place.

But the companies that move faster usually have one thing in common: people are already used to questioning how work gets done. They look for a better or more efficient way instead of treating the current process as fixed.

That appetite for change is not enough on its own. People still need the knowledge to use AI well, the trust to rely on it where appropriate, the mandate to change how they work and the controls that make responsible use possible.

That is when AI stops being an additional tool and starts becoming part of how the organization operates.

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[.infobox][.infobox-heading]Five decisions that move AI beyond pilots[.infobox-heading]
‍1. Fund the change, not just the technology
Teams need capacity to learn and redesign how work gets done. AI adoption cannot simply be added on top of unchanged delivery targets.

2. Give middle management a real mandate
Executive ambition only becomes operational when managers can change processes, responsibilities and team routines.

3. Stop optimizing individuals. Redesign the workflow
Personal productivity gains are useful, but real scale starts when teams improve shared artifacts and the handovers between roles.

4. Centralize what teams should not reinvent
Access, monitoring, governance, reusable components and AI know-how should form a common foundation, so five teams do not solve the same problem five different ways.

5. Measure the operating model, not tool usage
The strongest sign of adoption is not how many people use AI. It is whether work has changed, with repetitive execution increasingly moving to technology while people focus more on judgment, control, design and decisions.[.infobox]

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About Roman Marek

Roman Marek is an AI Architect and AI expert at Trask with more than 10 years of experience in enterprise IT, team leadership and delivery. His expertise spans integration platforms, DevSecOps automation, developer experience and product ownership. Today, he focuses on practical AI adoption in IT, particularly productivity, code quality, governance and the integration of AI into secure and reliable delivery processes.