‘Becoming AI-native also means taking joint responsibility for results.’

AI has integrated itself into our tools, our products, and our organizations. It accelerates, it automates, it simplifies. But above all, it changes the nature of what creates value.

When the cost of iteration decreases, value is no longer measured by the resources used or the volume of deliverables produced. It is measured by the results obtained. By what the product truly enables, the experience it delivers, and the decisions it helps an organization to make.

It is in this context that Source.paris becomes "AI-native".

The shift is not technological, it is operational

Many teams approach AI as an overlay. They add a tool. They adjust a process. They hope to save time.
The issue runs deeper than that. AI is changing the economics of digital production. Where building, testing and iterating used to require time, difficult trade-offs and significant resources, it is now possible to explore earlier, more often and further.
This does not make design any less important. It makes direction more important.
As speed increases, so does the risk of producing ‘standard functionality’. Noise increases. Artefacts accumulate. And the illusion becomes commonplace: confusing acceptable output with useful, distinctive and sustainable results.

AI-native at Source: a simple definition

For us, being AI-native means integrating AI into every stage of our workflow and deliverables, without compromising on standards, in order to explore further and faster.
But this comes with one condition: maintaining control.
AI is not blind delegation. It is a lever for amplification. It accelerates production. It does not decide what deserves to exist. It does not protect a brand. It does not bear responsibility for an experience.
This is precisely where the difference lies between an organisation that adopts AI and an organisation that truly becomes AI-native.

From service provider to strategic partner: shared responsibility as a consequence

If AI makes production more accessible, then it is no longer ‘doing’ that sets us apart. It is ‘doing it right’. And "doing it right" is never neutral: it involves trade-offs, sacrifices, profound choices, quality choices, consistency choices.

Embracing AI-native at Source therefore leads us to take a more engaging stance: that of a strategic partner, jointly responsible for results, not just resources.

Jointly responsible for product KPIs, when the framework allows: conversion, activation, retention, adoption. And jointly responsible for perceived quality: consistency, finish, clarity, and the level of confidence that an experience inspires.
This stance is demanding, both for us and for our clients. But it corresponds to what AI makes possible: shifting the focus from deliverables to results.

What AI really changes: the ability to confront intention with reality

One of the historical biases of product design is to confuse the representation of a product with the product itself.
For years, this was a necessity. "Real" prototypes were expensive. Design was used to specify, frame and limit risk before committing to development.
This paradigm is changing. It is now possible to prototype in real conditions earlier on and to present a functional product to users, decision-makers and real technical constraints.
This transforms the role of design. Value no longer lies solely in the ability to produce specifications, but in the ability to reduce the gap between intention and delivered product.
When working in the real world, missing states become apparent. Frictions become visible. Promises clash with usage. And the discussion shifts: we no longer debate opinions. We arbitrate on facts.

Two field situations: explore quickly, deliver better

We observe this shift in two very concrete contexts.

The first is from 0 to 1. In this context, AI allows for rapid exploration without losing control, provided we work within a clear technical framework. The goal is not to "do quickly", but to validate hypotheses as closely as possible to reality, without waiting months of building to discover what was not right.

The second is intervention on a product already in production. In this context, the issue is not speed, but quality. The ability to refine an experience, to address implementation details, to stabilize micro-interactions, to enhance overall coherence.
We are currently observing this at a major public player. In this type of environment, AI does not replace teams. It reduces blind spots, accelerates validation loops, and makes what pertains to perceived quality more tangible. It brings design closer to implementation, thus to reality.

And it's also where our stance of co-responsibility makes perfect sense. At one point in the work, an initial output generated by AI, although functional, did not meet the expected level of uniqueness for the brand. We chose not to use it as a basis and to take control at that specific point. Not to slow down “as a principle,” but because acceleration only has value if it serves the right result.

The framework that makes AI useful: measurable objectives and delivery standards

AI does not create value on its own. It amplifies a system.
This system begins before production, with clarity. Explicit, measurable, and shared business goals. Without this intention, AI produces volume, not value.
It continues with standards. A way to contribute, to review, to validate. Quality rules. Conventions. An organization of responsibilities. And, in constrained environments, a clear separation between exploration and production.
It is at this level that AI-native is truly played out. Not in the choice of a tool, but in the ability to industrialize a framework of execution that maintains requirements, protects the brand, and secures production.

Figma, code, and the question of the source of truth

It would be absurd to oppose Figma and code. Figma remains today the most robust and collaborative tool for designing a fine, industrializable UI, ready to fit into a design system.
What changes is its role.

Figma is no longer meant to be the source of truth to maintain at all costs. The product lives in the code. The experience is judged in reality. Figma becomes a complementary tool, used at the right moment, to work on form, signature, and the level of craft where it is decisive.

Similarly, we now use Cursor to prototype and work directly with the product. But the tool matters less than the ability to change it. What we seek to build are standards and rules that make this mode of production robust, regardless of the IDE.

What this implies for us

Becoming AI-native is not a slogan. It is a discipline.
This implies training designers capable of working as closely as possible to the finished product. It also implies training developers who can embrace this movement, define a framework for contribution, and accept that the boundary between design and delivery evolves.
Finally, it implies adopting a more engaging stance towards our clients. If AI accelerates production, we choose to invest this acceleration in quality, clarity, and impact.
Ultimately, this is the most important promise of this shift: not to do more, but to do better, and to stand by it until the result.

Recruitment

This shift also transforms the profiles we are looking for. We recruit complete product designers, capable of holding intention, craft, and impact, with the desire to go further in contact with the finished product.