Based on our own experiences across different roles and projects, this blog post examines how AI is integrated in customer work today, what it changes in practice, and what clearly remains human responsibility.
AI as a tool, not a team member
In our work, AI is best understood as a tool. A powerful one, but still something that needs to be instructed, supervised, and evaluated.
While AI can generate code, support analysis, and help explore solutions, accountability stays with us. We remain responsible for what goes into production and how systems behave. AI reflects the quality of the input it is given. If intent, context, or constraints are unclear, the output will be too.
Used well, AI helps clarify thinking. It can surface assumptions, challenge initial ideas, and make gaps in understanding visible. Used carelessly, it can just as easily amplify confusion and chaos.
Where AI creates the biggest impact in customer projects
In our experience, AI creates the most value in situations where iteration speed, scale, or friction would otherwise slow teams down.
We use AI throughout software projects in different ways, depending on the context. It supports feature development and code changes, helps with testing and data generation, assists in debugging and issue investigation, and acts as a powerful tool for information retrieval and learning. It is also increasingly useful for documentation, explanations, early ideation, infrastructure-related changes, and UX or UI prototyping.
What matters most isn’t the individual use case, but the ability to iterate quickly. AI allows us to try ideas earlier, explore alternatives, and discard approaches that don’t work before too much effort has been invested. This makes it easier to focus on solutions that create value for our customers.
Faster iteration changes expectations
One of the clearest shifts AI brings to customer projects is pace. When ideas, prototypes, or implementations can be created much faster, expectations naturally change as well.
What used to be framed as “later” can quickly become “why not now?”. This makes expectation management even more important. While AI accelerates delivery, it doesn’t remove the need for architectural thinking, prioritisation, or deliberate trade-offs.
We have seen that sometimes the most valuable contribution is slowing things down on purpose. AI makes it easy to generate more, but not everything that can be built should be built. Helping customers decide what not to do becomes just as important as delivering quickly.
Quality and architecture matter more, not less
As generating code becomes easier, architecture and domain understanding become even more critical.
AI can produce large amounts of convincing-looking code, but it doesn’t automatically understand what is safe, maintainable, or appropriate in a specific system. Good outcomes depend on clear boundaries, a strong understanding of the domain, and conscious decisions about where AI is allowed to operate freely and where it must be constrained.
In practice, this often means designing systems in a modular way. Some parts can support rapid experimentation, while core components are handled with more care. This allows us to benefit from AI-driven speed without losing control of the overall system.
New ways of working, without losing team discussion
AI also changes how we collaborate. When generating and modifying code becomes easier, understanding what the system is doing and why becomes more important.
We have seen that this often leads to more discussion, not less. Alignment around intent, design decisions, and constraints helps ensure that fast iteration doesn’t come at the cost of stability or shared understanding. Traditional processes and ways of working don’t always fit perfectly anymore, and many teams are still learning what works best.
Rather than trying to control every detail, the focus shifts towards managing systems at a higher level of abstraction, supported by better validation, review, and shared context.
AI is nowhere near replacing people
Despite rapid technical progress, AI is nowhere near replacing people in software projects.
Understanding customer needs, translating those needs into technical solutions, making value-based trade-offs, and taking responsibility for long-term outcomes remain human tasks that can’t be outsourced to AI. Sometimes there is a gap between what a customer asks for and what they actually need, and bridging that gap requires judgement, communication, and experience.
AI can support better decisions, but it shouldn’t decide what matters in a given context. That responsibility stays with the people who understand the customer, the domain, and the broader impact of the system we are building.
Using AI well is a skill
Working effectively with AI is a skill that takes time to develop. Good results rarely come immediately. Learning to give clear direction, review outputs critically, and adjust based on feedback requires practice and patience.
In many ways, using AI well highlights the same fundamentals that have always mattered in software work: clarity of thinking, understanding of systems, and the ability to weigh trade-offs. AI doesn’t remove the need for these skills; it makes their absence more visible.
AI changes how software projects move forward, but it doesn’t change the need for thoughtful, responsible, and human-led work.
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