AI is now part of our daily work. Because of that, we think it’s important to explain how AI fits into our everyday work.
For us, this openness is part of being a partner that clients can rely on and have confidence in execution.
If AI helps our teams work more efficiently, we believe clients should know:
- where that efficiency comes from,
- what it changes in practice,
- how they can benefit from it.
That is why openness matters as an enabler of long-term success.
Why AI is changing the fundamentals of software delivery
The rapid advancement of generative AI in the context of software engineering is an observable and verifiable reality.
Both delight and awe arise from the possibilities of LLM and automation. At the same time, there is anxiety influencing strategic decision-making as we witness a disruptive technology that is causing massive ripple effects across the service industry.
AI is changing how software is designed, built, and maintained, and so are evolving client expectations. It has a profound impact on our value chain and how we should evolve our value proposition and operating principles.
Because of this, AI already changed how knowledge work is done, and this affects everything we do!
Often, these changes are not easy to see – the industry moves slowly, projects are still planned and priced in familiar ways, and the processes happen like they always do. But we already live in a different world inside the company.
What changes inside delivery teams
Shorter development cycles affect how projects move forward and decrease uncertainty. Over time, this will become an expectation of how software engineering is delivered – for example, we already develop PoC during client calls.
This does not mean every project team will be based on agents – software delivery still depends on complexity, risk, and change, where human judgment prevails.
What we see as a step forward towards improving value for clients and making teams focus on meaningful work and business understanding, the technical delivery is no longer a moat. The teams emphasize:
- faster feature development,
- rapid prototyping,
- reduced toil,
- much improved quality control,
- ability to test multiple scenarios.
What this means for projects and clients
AI helps engineering teams work faster, reduce toil, and stay focused. It opens possibilities for exploration, decreases product risks, and supports quicker value generation, but lets people still make the final call.
Over time, AI changes how technical delivery and project management are done across the entire project lifecycle. New tools and approaches optimize:
- Timelines,
- Scope,
- Quality,
- Trade-off discussion.
Increased efficiency already leads to:
- Less uncertainty and “cat in the bag” risk,
- More focus on business alignment,
- Earlier validation of where to invest engineering capacity,
- Turning engineering effort into business value earlier
Our internal work feeds directly into this approach. The patterns we test inside our own teams help us support clients in practical ways.
This is about making everyday AI use clear, safe, and easy to understand, and offering a more open approach to how we work with our clients and where value generated by AI becomes a client benefit.
What next?
This is only the first article in a broader series. In the coming pieces, we will share where AI truly adds value in our delivery, where it does not, how we make trade-offs, and what we recommend to clients based on real project experience.
But most importantly, we will continue to invest in building these capabilities internally, so that our teams keep learning and raising the bar before we bring new approaches to our clients.
