The expression “AI First” is now so prevalent in discourse that, in one way or another, it is, if not a goal for businesses, at least a focus for reflection and questioning. But if we look at those who claim to be AI First and those who actually are, we discover two very different realities.
On the one hand, there are businesses designed from the outset around the intensive use of data, models, and fast cycles. On the other hand, there are established organizations seeking to reinterpret their operations in light of capabilities they had not previously considered.
Between the two, there is a real gap that is crucial to understanding what it really means to “become” AI First.
In short:
- The term “AI First” covers two realities: businesses that are natively structured around AI (AI natives) and those that seek to integrate it into an existing model.
- AI native businesses are designed from the outset to operate using data and machine learning, with AI being an organizational principle rather than an added technology.
- Conversely, established businesses have to contend with legacy systems, cultures, and processes that are often ill-suited to the agility and experimentation required by an AI First approach.
- Becoming AI First for these organizations involves a profound cultural and structural transformation that goes beyond simply adopting the technology.
- The question remains: can you really become AI First if you weren’t from the outset, or can you only adopt certain elements without matching the effectiveness of AI natives?
Born AI First
A truly AI First business, which I would rather call “AI native”, has not ‘adopted’ artificial intelligence. It is built first and foremost around data, and AI has naturally found its place as the “engine” of the organization. Its architecture stems from the initial assumption that every process can be instrumented by data, that every interaction can produce a usable signal, and that every operational loop will become more efficient if it is capable of learning as it is used. It is a way of designing the organization, not a technological orientation.
In these structures, flows take precedence over silos, which is a real issue for businesses that have been designed around different principles (Thinking of work as a flow: appealing, but is it realistic?). Data flows because it has been designed as an asset, not as a resource that we try to access as and when we need it. Decisions are made closer to the groundbecause a model does not have to wait for validation from multiple authorities before being readjusted. Teams work according to a product-based approach, where continuity prevails over a project-based approach. Rhythm becomes a key concept because iteration is a way of working that is embedded in the business’s DNA and the design of the work, rather than a one-off choice in response to a given situation. Everything contributes to placing AI at the center, not as a tool but as an organizing principle.
These businesses did not become AI First; they are AI First by definition. They were born AI First even before Google invented the concept (The AI-first company: the origins of an ambiguous concept that grew too quickly). This creates an advantage that is not only technological but above all structural, and I would even say cultural.
AI is not a project but a consequence
In AI-native businesses, AI has no special status. It does not require a steering committee, governance, transformation, or specific strategy. On the contrary, it is almost a commodity; it is everywhere, sometimes imperceptible, because the organization is based on choices that make it obvious and because it has always been there. The data is clean because it has to be for the products to work, the models evolve because the system is designed to learn and improve continuously, and governance is not light by choice but because there is a culture of data, operations, and AI that makes it everyone’s responsibility and not just a few people’s.
There has been no shift to AI, no disruption, no transformation program to implement. There is only the continuity of an operation designed to exploit machine learning as an ordinary capability. This is what makes these businesses difficult to imitate. What they do seems simple, but what enables them to do it is not simple at all and boils down to anything but technology.
Wanting to become AI First
Faced with these players that are natively structured around AI, established businesses are pursuing the same ambition but within a very different framework and with very different constraints. They have histories, businesses, systems, and habits that were built long before the emergence of advanced learning or processing capabilities. They are discovering AI after having built most of their operational models and must therefore integrate what others have been able to use as a foundation.
In doing so, they encounter constraints that have nothing to do with technology. The data exists, but its quality and governance leave much to be desired. Processes are well-controlled but rarely flexible and never 100% functional without compensation from employees (“work about work”: when the reality of work consists of making what doesn’t work work). Responsibilities are clearly divided but rarely aligned with what fast governance would require. Teams have solid expertise, built in an environment where stability, predictability, and risk control were the guiding principles. They have not been organized to operate in a cycle of fast testing, successive corrections, and constant adjustments, which is the natural logic of native AI First businesses. None of this is a flaw. These businesses are products of their time and have the natural attributes of organizations designed for stability, predictability, compliance, and risk control.
Wanting to become AI First in this context is like asking a structure designed for continuity to behave like a structure designed for fast adaptation. This requires difficult choices, often costly politically and organizationally, that cannot be summed up in a slogan.
Two asymmetrical trajectories
The idea that a business could “catch up” with another that was born AI First assumes that the two trajectories would simply be offset in time, which is not the case. One was designed to operate with AI as a structuring lever, while the other must reconfigure itself so that AI can initially be integratedand then become part of the organization’s DNA, which is more a cultural than a technological issue. In AI First businesses, adaptation mechanisms are part of the structure, whereas in property organizations, they must be introduced into an environment that is already organized around routines and stable structures.
The difficulty is not in adding a model or technical infrastructure. It lies in transforming decision-making mechanisms, the way activities are coordinated, the way performance is defined, the place given to experimentation, and more broadly, an organization’s ability to rethink its flows rather than reinforce its silos. The two worlds are not opposed, but they do not overlap either. They respond to different logics and constraints.
An open question
This naturally leads to a question: can you really become AI First if you weren’t born that way, or can you only get close to it without achieving what an architecture designed from the outset to exploit these capabilities allows? And if this ambition is realistic, what is the real cost for an organization designed for stability, not permanent plasticity?
These are questions that would be premature to answer, even though we already have some answers in terms of digital transformation, with businesses that were aware of their slow pace of transformation and created a subsidiary, a competitor, that was digitally native. From there, several outcomes were possible.
Either the subsidiary would establish itself and ultimately replace the parent company, which it could eventually buy out.
Or it could occupy the field to prevent a competitor from gaining ground, while the parent company caught up, and then be absorbed.
Or it could fail and be sold.
In any case, the principle that “if you don’t cannibalize your business, they will do it for you” will probably resurface in the race for AI First business.
Bottom Line
Distinguishing between AI native businesses and those that want to become AI native businesses helps us understand that AI does not behave in the same way in a system designed around it and in a system that needs to integrate it.
The former advance as a natural extension of their architectures, DNA, and culture, while the latter must reinterpret how they operate, sometimes at the cost of a profound revision of their organizational principles.
Between the two lies a space where most businesses find themselves today. A space where the question is not only how to use AI, but also understanding what needs to be transformed so that it can truly find its place.
Pour répondre à vos questions…
An AI First business is designed from the outset around data and continuous learning. AI is not a project, but a logical consequence of an architecture designed to instrument each process and promote flows rather than silos. Decisions are made close to the ground, and teams work in fast iteration cycles. This approach is as much about culture as it is about technology. For a decision-maker, this means considering AI as a structuring principle rather than an add-on tool.
Established organizations operate with imperfect data, rigid processes, and governance designed for stability. They are not prepared for the fast cycles of trial and error typical of AI-native businesses. Their culture values predictability over adaptation. Becoming AI First therefore requires a profound transformation that goes far beyond the question of technology. Decision-makers must anticipate a comprehensive reinterpretation of how the organization operates.
Native AI businesses have a structure where data flows naturally and models evolve continuously. AI is integrated organically, without heavy governance or cultural disruption. Their ability to iterate quickly creates an advantage that is difficult to imitate, as it is based on fundamental choices rather than tools. For executives, this shows that AI performance comes first and foremost from structure and culture.
That’s difficult to answer. A business can come close, but fully achieving the level of native AI remains uncertain, as it requires a thorough review of decision-making, coordination, and learning mechanisms. Some successes involve the creation of more agile subsidiaries, as in digital transformations. For a decision-maker, this means assessing the organizational and cultural cost of such an ambition.
Image credit: Image generated by artificial intelligence via ChatGPT (OpenAI)
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