The same core of change, a new operating environment
AI adoption gradually became part of our work. We began experimenting with whether it could help us structure large volumes of material, speed up the creation of first drafts, and leave more time for thinking, collaboration, and understanding customer needs. As the experiments progressed, our focus quickly shifted from individual tools to how the work itself is done.
We have learned that business value does not come from adopting AI alone. It emerges only when processes, responsibilities, capabilities, and the flow of information are examined at the same time. Otherwise, the new technology easily becomes just another tool or speeds up a work phase that did not create enough value in the first place.
Agile Coach capabilities do not disappear
As Agile Coaches, we have never focused solely on methods or new ceremonies. Our work has meant supporting people and teams: clarifying goals and responsibilities, removing obstacles, improving collaboration, and creating space for learning and experimentation.
As AI Coaches, we continue essentially the same work in a new operating environment. We still return to the fundamental questions: What problem are we solving? What value are we aiming for? How do we move towards a safe experiment? And how does a proven approach become part of everyday work? AI introduces new questions about data, information security, risks, and the human role, but the core of change remains the same.
We start with the work, not the tool
In an organisation enthusiastic about AI, there is usually no shortage of ideas. That is why we do not begin by selecting a tool or compiling a long list of use cases. We first look at everyday work: Where is information searched for across several sources? What is compiled manually? Where do delays occur? And where is too much expert time being spent unnecessarily?
We have used AI, for example, to define problems, create user stories and acceptance criteria, break down requirements, structure materials, and produce reports and summaries. When AI brings the key events of a project or working week together in a ready-made summary, the benefit is not simply faster writing. Less time is spent searching for information, reporting becomes more consistent, and experts have more time to interpret the information and prepare the next decisions.
A practical experiment quickly reveals both opportunities and limitations. If AI makes the work easier, improves quality, or shortens lead time, we continue developing the solution. If the source information is insufficient or the task requires strong expert judgement, that is also a valuable result. A limited test can prevent a major investment in a solution whose benefits would remain marginal.
AI should not be layered on top of old ways of working
One of our most important lessons has been that speeding up a single work phase does not necessarily improve the whole. We may produce a draft quickly, but if information does not move forward, responsibilities are unclear, or the same content is reviewed repeatedly, we have simply added a new layer on top of the old process.
This is particularly clear in product development. When we use agents for definition, specification, or moving work into the Jira process, we do not focus only on what the agent can do. We build a shared knowledge base and agree on what the agent does, where people apply judgement, and how information and decisions move through the process. The same applies to product information: if content is scattered and metadata, context, or ownership is missing, AI will not fix the foundation for us.
This is why an AI initiative is often also an exercise in developing processes and information management. When the information lifecycle, responsibilities, and structures are in place, AI can be used more reliably in areas such as expert work, product development, technical support, and customer service.
From individual success to shared capability
A good prompt can produce a good answer, but it does not yet change how an organisation operates. We have collected effective prompts, examples, and materials for shared use so that everyone does not have to build the same solutions from scratch. At the same time, we have noticed that the most important thing is not to create the most complex prompt possible. It is more important to describe the objective, the source information to be used, the desired outcome, and the boundaries of what AI must not infer.
Shared ways of working help turn individual experiments into organisational capability. The AI Coach’s role is to bring business, technology, information security, risk management, and the people who do the work into the same conversation. This helps avoid a situation in which a solution works technically but does not solve the right problem or find its place in everyday work.
Success is not measured by the number of agents
For us, success is not measured by a solution that works in a demo or by the number of agents built. Success is visible when time is released for value-adding work, manual steps decrease, information is easier to find, quality improves, and the new way of working remains in use after the experiment.
Not every experiment needs to reach production. The key is to define three things in advance: the starting point, the intended benefit, and the evaluation criteria. The results then show whether to continue, modify, or discontinue the solution. This way, AI development is based on learning and verified business value, not just enthusiasm for technology.
The same journey, the next step
For us, moving from Agile Coach to AI Coach is more than a change of title. It naturally continues work centred on learning, collaboration, and renewal. Our goal is not to give customers as much AI as possible. Instead, we look for the points where AI creates genuine value: a better customer experience, smoother work, or clearer decision-making.
The best outcome is a customer with the capability and confidence to keep developing independently. They can identify new opportunities, experiment safely, assess results critically, and scale effective solutions into everyday work. This is where the AI Coach adds the most value. AI becomes useful only when technology, people, and ways of working develop together.
“We do not start with AI. We start with the work that needs to become smoother, higher-quality, or more impactful.”
Are you wondering where to begin with AI?
Let’s discuss where AI could create genuine value in your work and what would be worth trying first.
Contact us: contact@taskmill.fi