By 2025, the majority of enterprises will have already integrated—or will be in the process of integrating—artificial intelligence into their operations, utilizing it as a tool to automate tasks, optimize workflows, make informed decisions, and enhance operational efficiency. But this approach is only the first step of a deeper evolution, one that leads toward the AI-first paradigm.
To truly evolve, companies should—with timelines and methods that vary from one organization to another—abandon the classic incremental view of innovation and embrace a transformative one, where AI becomes the foundational principle around which everything is redesigned: strategy, operations, and organizational structure.
This is where the idea of an AIfirst model is born. It starts from a data-driven paradigm but has an even more ambitious goal: not merely to enhance the traditional enterprise, but to reinvent its very functioning, placing AI at the heart of its strategic vision, while still keeping professionals not only in the loop but front and center.
Being AI-first doesn’t just mean having AI projects underway or having automated complex, regulated processes. Rather, it means redesigning the enterprise on a new foundation, integrating artificial intelligence as a decision-making, operational, and organizational engine capable of guiding strategy, adapting processes, and co-governing growth.
AIfirst is a native evolution of the data-driven paradigm, which explains why it’s so hard to materialize. In a previous analysis, we highlighted that—even though every company holds massive amounts of data—only a few can truly be called data-driven, and those are the ones that built their business around data. Today, faced with an incomplete data-driven evolution, thinking about an even more radical paradigm shift may seem almost unrealizable. Yet, it is the only winning path to build predictive, adaptive companies capable of responding in real time to market complexity.
Like “datadriven,” the term “AIfirst” can be used so loosely that it ends up describing companies simply investing in AI projects in one of its many forms. In reality, the concept isn’t directly tied to the level of investment, but rather to the vision and positioning of AI within the organization, the business, and the company’s mission. Let’s look at some key elements of the new paradigm.
Some elements of this new paradigm aren’t revolutionary—at most, they’ll be more pervasive than before. For example, AI support in strategic and operational decision-making processes is already a reality in many business contexts, especially where digital ecosystems generate large volumes of (high-quality) data. This is the starting point.
The first real breakthroughs concern the structure and composition of the workforce, based on continuous synergy between people and virtual assistants (AI agents): the latter not only perform most low-value tasks but can also support more complex ones and, in some cases, even help professionals design them.
This is the true transformation: it’s no longer about adopting the mantra of “freeing professionals from repetitive activities so they can focus on strategic or creative functions”, but about developing a tandem that works synergistically on all activities, including those strategic ones that generate market value and differentiation. Thus begins talk—albeit with a certain dose of future projection—of AI as corporate citizens: autonomous (or semi-autonomous) entities capable of perceiving contexts and acting within workflows shared with human colleagues.
It’s a quiet but powerful flipping of the traditional organizational model: workflows are no longer designed only to be later optimized by technology, but co-designed by intelligent systems and humans in an adaptive, evolutionary, and dynamic logic. This is a medium to long-term vision, still under evaluation even in the most advanced organizations. But the idea is clear: organizations will increasingly rely on a hybrid network of human professionals and artificial agents, with activities distributed according to different skills, capabilities, and levels of responsibility.
The third pillar of the AIfirst paradigm is organizational architecture. In a traditional view, companies lean on fixed, compartmentalized structures that are often slow to evolve. By contrast, an AI-first organization is built on an adaptive, AInative architecture capable of changing dynamically over time.
We might call adaptive a structure designed to learn, modify itself, and optimize in real time based on new data, market signals, and user behaviors. Both human professionals and AI agents collect and interpret these inputs: a hybrid network in which every component—human or artificial—contributes to the collective intelligence of the enterprise.
In the AI-first paradigm, artificial intelligence is an active and constant presence in corporate life, involved in decision-making processes and the definition of operational flows.
If business models, architectures, and even workforce composition are evolving, the governance overseeing them cannot remain static. On the contrary, evolving toward an AI-first company requires the design of a robust, multi-level, proactive framework that keeps AI aligned with business objectives, job protection, ethical principles, and regulatory compliance.
An effective framework should integrate at least three main pillars:
In a context where AI can adapt processes, make decisions, and even suggest interventions in organizational balances, governance cannot be left to improvisation—lest it amplify risks of all kinds, from reputational to ethical and regulatory.