Since the concept of “AI First” was born, it has become, if not a real trend in organizational transformation, then at least a real topic of discussion, especially for businesses that are not “AI Natives.” And while some new concepts, due to their cryptic names or apparent complexity, make it clear from the outset that we will need to make an effort to understand them in order to embrace them, this is not the case here: what could be clearer than “AI First”?
Well, it’s not that simple.
As I have already said, when the term first appeared, it referred more to an intention than a target vision, and this is clearly evident in the different approaches that can be observed in practice (The AI-first company: the origins of an ambiguous concept that grew too quickly). Everyone has therefore appropriated it in their own way, according to their own challenges and strengths. The result is alignment in terms of wording but not concept, with different orientations grouped under the same banner. Identifying these competing interpretations is essential to prevent the AI First ambition from becoming a formula with no operational significance.
In short:
- Although the concept of “AI First” seems clear, it is interpreted in different and sometimes contradictory ways by different businesses, which limits its operational scope without prior clarification.
- A first technological approach sees “AI First” as a priority investment in infrastructure, without immediate transformation of the organization, as illustrated by the case of Microsoft with Azure.
- A second, strategic interpretation uses AI as a lever for repositioning in markets and products, as exemplified by Google, which adopted this approach in 2016 without making any major changes to its organization.
- A third, organizational interpretation involves restructuring the business around AI, integrating models into operations and decision-making, as Uber has done since its inception.
- Finally, an experimental approach consists of multiplying trials and prototypes around AI, often without lasting transformation, as shown by the example of Airbus’s innovation laboratories.
A technological focus
For the first category of players, AI First means above all strengthening the technical capabilities of the business. The focus is on data infrastructure, computing capacity, models, and system modernization. The goal is to have a sufficiently robust foundation to accommodate a wide range of future uses. This interpretation emphasizes technological depth and treats AI as a priority investment area, without presupposing immediate organizational or strategic transformation.
Microsoft has clearly invested in AI as a foundation: Azure is described as an infrastructure optimized for AI workloads of all sizes, combining hardware, networking, and cloud services. The business has made clear its desire to make Azure the global “AI supercomputer” making it an example of a technological approach not accompanied by a profound organizational repositioning (How Microsoft quietly took over the AI infrastructure game and Azure AI infrastructure).A strategic orientation
For other businesses, AI First refers to a strategic repositioning. AI becomes a prism through which to re-examine target markets, product offerings, and business models. It is seen as a lever that can shift competitive balances, open up new segments, or transform value propositions. This interpretation emphasizes the direction to take rather than how to reorganize the business to get there.
This is how Google presented the expression in 2016, when Sundar Pichai announced the shift from “mobile first” to “AI first”. The ambition is clear and in line with the group’s positioning, but the concrete implications for the internal structure remain largely implicit (Google’s CEO is looking to the next big thing beyond smartphones).
An organizational orientation
A third interpretation considers AI First as a change in internal structure. The goal is no longer just to exploit AI, but to reorganize activities, flows, and responsibilities around it. The focus is on how decisions are made, how activities are coordinated, and how models fit into operations. This interpretation involves reviewing team formats, decision-making processes, and how data flows through the organization. It is closer to the logic of AI First businesses.
A significant example is Uber, whose operations have been based since the beginning on the integration of predictive models into its operations. The business publicly describes this logic, in which AI is a fundamental part of dispatching, dynamic pricing, and anomaly detection, perfectly illustrating this organizational interpretation of AI First (Engineering More Reliable Transportation with Machine Learning and AI at Uber).
An experimental approach
Finally, some organizations interpret AI First as an invitation to intensify experimentation. The goal is not yet to transform the business, but to increase the number of trials, test prototypes, explore runways, and understand what AI can do in the short term. This dynamic creates a visible volume of activity and maintains the idea of momentum, but it rarely leads to lasting transformation if it is not structured by a broader trajectory.
This experimental approach has been widely documented in initiatives led by industrial players such as Airbus, whose innovation laboratories have multiplied AI prototypes without these efforts yet constituting a profound transformation of the organization. Their public communication bears witness to this (Artificial intelligence – Capitalizing on the value of data).
Bottom Line
These four interpretations pursue different objectives and mobilize distinct levers. What they have in common is that they are based on the same expression while referring to realities that are sometimes incompatible. As long as this dispersion is not made explicit, AI First risks remaining a rallying cry on the surface but incapable of guiding structural decisions.Clarifying what is meant by AI First is therefore an essential prerequisite, and this work makes it possible to distinguish between intention, strategy, organization, and experimentation in order to turn it into a real benchmark rather than a mere slogan.
To answer your questions…
“AI First” seems clear, but in reality it refers to very different approaches. Depending on the business, it can be a general intention, a technological priority, a strategic repositioning, or an internal reorganization. This diversity creates confusion and dilutes the operational impact of the term. Clarifying what is meant by AI First therefore prevents it from becoming a slogan and helps guide decisions in a consistent manner. For decision-makers, this means explicitly defining the objective before launching initiatives.
The technological approach aims to strengthen data and AI infrastructure without immediately changing the organization. The goal is to build a robust foundation capable of supporting numerous future uses: computing capabilities, system modernization, advanced models. Microsoft is a good example of this approach, with Azure as its AI backbone. This approach prioritizes technical investment over internal transformation. For executives, this means aligning these investments with future priorities.
From a strategic perspective, AI is used to rethink markets, products, or business models. It becomes a lever for anticipating competitive developments and identifying new opportunities. Google has illustrated this vision with its shift from “mobile first” to “AI first”, expressing an ambition rather than an internal restructuring. For decision-makers, this orientation requires translating this ambition into concrete lines of action.
The organizational approach involves reorganizing activities, decisions, and flows around AI. It requires integrating models into operations and rethinking teams and data flows. Uber already operates according to this logic, with AI at the heart of dispatching, pricing, and anomaly detection. This approach requires in-depth work on internal processes. For decision-makers, this means reviewing structure and responsibilities.
The experimental approach aims to quickly increase the number of tests, prototypes, and trials in order to understand what AI can offer. It generates momentum but does not produce lasting transformation without a clear strategy. Airbus illustrates this case with numerous prototypes without any major structural changes. This approach is useful for learning quickly, but must be part of a broader trajectory to avoid remaining a mere accumulation of experiences. For a leader, this requires articulating experimentation and vision.
Image credit: Image generated by artificial intelligence via ChatGPT (OpenAI)
Related articles:
AI First: lots of debate and few answers
Serendipity doesn’t preclude control : a few rules to manage fate within informal networks
Innovation at Google : a model to follow ?
Did you find my strategy ? Lost a part of it on my way !
Trying to solve a business problem ? Don’t start with a social media plan !
What challenges for HR in 2012 ?
