Today, a burning question is at the heart of nearly every executive strategy meeting: how can we effectively integrate artificial intelligence into the enterprise ?
Whether it’s to improve production processes, boost commercial strategies, or optimize internal support functions, the objective is clear: enhance operational efficiency and competitiveness. In this race to integrate AI, some business leaders are deploying significant resources and turning to top consulting firms, yet they sometimes risk making costly or misaligned decisions. And yet, one smart approach is to look to the past to better anticipate the future.
That’s exactly what we propose in this article: to look back at the rise of Digital Factories. Emerging in the late 2010s across nearly all CAC 40 companies and several mid-sized firms, they offer a valuable precedent to examine. Seven to eight years after their creation, it’s time to draw lessons, identify their limitations, and ask whether this model could inspire the shift toward AI. In other words, should we be building AI Factories ?
Why did Digital Factories come into being ?
In the 2010s, large corporations quickly realized that technology had become a vital strategic imperative, especially following Tesla’s spectacular arrival in the automotive industry. Innovation was no longer optional; it had become an urgent necessity to avoid falling behind.
Digital Factories were created in direct response to this urgency, bringing together a wide range of talent: developers, UX designers, data scientists, and strategy experts, often recruited from outside the company. Backed by top consulting firms like BCG or McKinsey, these cross-functional teams were designed to generate innovative ideas that could be rapidly implemented to maintain competitiveness.
👉 A symbolic turning point: In just a few years, Tesla redefined the standards of the industry, not only through electric powertrains, but above all through its deeply embedded tech DNA.
How did Digital Factories operate ?
Digital Factories were centralized, cross-functional teams operating much like an internal strategy consulting firm within the organization. Their primary role was to engage with various business stakeholders in order to identify or help generate innovative use cases, leveraging proven methodologies such as Open Innovation and Design Thinking.
These teams also had their own investment capacity. As highlighted by Serge Yoccoz, former Managing Director of Renault Digital, Renault Group’s Digital Factory initially benefited from a centralized corporate budget specifically allocated to funding innovative technology projects.
Aligned with the group’s strategy and validated by top management, these projects nonetheless enjoyed real financial and operational autonomy, enabling faster execution and implementation.
Why bypass the traditional IT department ?
Faced with the need for agility and innovation, the traditional IT department did not naturally emerge as the obvious choice in the eyes of executives. Often perceived primarily as a cost center focused on user support, and frequently distant from core business operational challenges, it was seen as poorly positioned to play a driving role.
By contrast, Digital Factories were viewed as more agile and dynamic structures, capable of rapidly identifying high value technology opportunities in direct connection with concrete business needs.
What’s the legacy of Digital Factories ?
Digital Factories brought greater speed, efficiency, and above all a stronger tech culture within organizations. The widespread adoption of cloud technologies and the growing awareness among leadership teams of the importance of data and AI are undeniable successes.
However, the experience was not without its flaws. The high cost of recruiting external talent and the heavy reliance on top-tier consultants limited short-term profitability.
That said, the cultural impact remains undeniable. Today, it’s nearly unthinkable for any executive to overlook the strategic importance of technology.
How are Digital Factories evolving today ?
In today’s tighter economic climate, Digital Factories are gradually being reintegrated into IT departments, resulting in reduced scope and smaller budgets. Business units now play a more active role in identifying technological opportunities, often conducting their own strategic watch. The role of Digital Factories and IT teams is increasingly focused on the industrialization of the solutions identified.
At the same time, the tech talent market is adjusting to this new economic reality. Salaries for previously in-demand roles, such as developers, data scientists, and data engineers are beginning to decline accordingly.
How can IT departments support the AI shift ?
Artificial intelligence represents a profound disruption, comparable to the industrialization of digital technologies in the 2010s. However, the organizational response cannot be the same.
At Hubadviser, we hold a clear conviction: AI cannot be effectively deployed through ultra-centralized models such as AI Factories. Instead, success lies in a distributed adoption across business units, supported and governed by a strong IT department.
Why do centralized models quickly reach their limits ?
Business teams are now on the front line :
- they understand the processes,
- they identify pain points,
- they spot quick wins and key performance levers.
With generative AI, the technological barrier has collapsed. A supply chain, legal, or finance executive no longer needs an intermediary to prototype a relevant use case. Replicating Digital Factory–style approaches, centralized teams, a single backlog, top-down prioritization, inevitably creates:
- frustration among business teams,
- slower execution,
- and a growing gap between AI strategy and real business value.
The real challenge for IT departments: moving from producer to AI system architect
Decentralization does not mean giving up governance. On the contrary, it requires a more strategic, more structuring, and more influential IT department. We identify five major responsibilities for IT leaders.
1. Establish AI governance focused on value, not just control
The risk is not experimentation. The real risk is invisible chaos: a proliferation of AI use cases that are unprioritized, non-scalable, and misaligned with the group’s strategy.
The IT department must define :
- clear principles (what is allowed / prohibited),
- value criteria (ROI, business impact, scalability),
- simple and fast arbitration mechanisms.
Enterprise Architects play a key role here: they must no longer only ensure IT landscape coherence, but also the consistency of the AI initiative portfolio.
2. Formally structure the Citizen Developer role
The rise of no-code, low-code, and AI is profoundly transforming IT production. A significant share of development is already taking place outside the IT department.
Ignoring this movement means being exposed to it. Structuring it turns it into a lever.
The IT department must :
- officially recognize the Citizen Developer role,
- define clear accountability rules,
- train teams on best practices (security, data, architecture, technical debt).
We are seeing IT organizations emerge that build internal communities, create internal certification paths, and even conduct periodic reviews of business-led AI use cases.
3. Fully assume responsibility for regulatory and ethical compliance
AI is not neutral. The AI Act, GDPR, data sovereignty, and technological dependency are now structurally critical issues.
The IT department must :
- build up expertise on these topics,
- work closely with legal, compliance, and security teams,
- embed regulation from the design phase onward (compliance by design).
In large organizations, the IT department’s role as a trusted foundation increasingly becomes a key accelerator rather than a constraint.
4. Build truly enabling technology platforms
Business autonomy is only possible if the IT department provides solid foundations :
- governed data platforms,
- secure and sovereign cloud environments,
- shared AI building blocks,
- clear architecture standards.
The issue is not the tool itself, but the ability to move from prototype to industrial scale. This strengthens the role of technical and enterprise architecture as a strategic discipline, at the very heart of the dialogue with business teams.
5. Lead by example in adopting AI within IT operations
An IT leader cannot promote AI without using it within their own organization. ITSM, support, operations, incident management, capacity planning, and service quality: AI use cases in these areas are now mature enough to deliver measurable gains.
The IT department must serve as an advanced experimentation ground, lead by example, and strengthen the credibility of the AI narrative with both the executive committee and business teams.
Conclusion
The challenge is not to recreate a new Digital Factory dedicated to AI. The real challenge is to redefine the role of the IT department in a world where technology is becoming accessible to everyone. The IT organizations that will succeed are those that know how to :
- allow business teams to innovate,
- while maintaining coherence, compliance, and scalability.
This is the vision of an IT department as architect, regulator, educator, and role model that we champion at Hubadviser.
About the author
Ismail has 15 years of experience in IT and digital consulting. He spent nearly 7 years at Gartner. He has supported innovative startups in their growth strategy and worked with CIOs of large groups on their digital transformation. In 2021, Ismail founded Hubadviser to help CIOs challenge their vision with top-level experts.