Until very recently, artificial intelligence in a company was nothing more than an experiment tucked away in a spreadsheet. A pilot here, a production model there… and, with a little luck, something that could be shown in a presentation.
But this has become history as AI is now present in every corner of our company, from marketing, operations, human resources, and customer service. Whether we like it or not, this is a game-changer.
As is often the case, when something is not organized from above, the company organizes itself. What is the result of this? As a general rule, scattered tools, risks, and costs that no one sees until they arrive…
But the time has come to take this seriously.
What is really changing?
Three key things have turned the traditional organization chart upside down:
AI is no longer a project; it is a transversal capability.
It affects decisions, productivity, customer experience… and nobody governs it? Bad business. Like any other critical asset, it needs accountability, standards, and a clear vision.From experiment to execution.
Many companies get stuck in the pilot phase. And it’s not because the technology doesn’t work, but because the organization doesn’t follow suit: without data, without priorities, without a clear roadmap, it’s impossible to move forward.The “Shadow AI”.
Yes, like the “shadow IT” of years ago, but multiplied by ten. Employees using ChatGPT, Copilot, or Midjourney without anyone knowing, with sensitive data, without policy or supervision. A minefield and a huge risk for companies…
Who should lead this?
This is where it gets interesting. It’s not about creating an “AI department” as if it were an accounting department. It’s about giving AI a chair on the steering committee. And that implies new roles (or at least new responsibilities):
CAIO (Chief AI Officer): the “orchestra conductor” who connects strategy, use cases, technology platform, governance, risk, and adoption.
CTO/CIO: infrastructure, integration and technical security.
CDO (Chief Data Officer): without quality data, there is no useful AI.
CISO and Legal: risks, compliance, intellectual property.
CHRO (Human Resources): training, job redesign, culture of change.
The important thing is not the title, but that someone has the mandate to unite value, risk, and execution in a single agenda.
Do you really need a CAIO?
It depends.
Not every company needs a CAIO with its own office and a budget of millions.
Yes, you do:
You are a large or multinational company.
You have high regulatory or reputational exposure.
You rely heavily on data and automation.
AI is strategic, not just “an extra”.
You probably won’t need it if:
You are an SME and the CTO or CDO can take on this role with a lean team.
You are only exploring a few specific use cases.
In the end, the trick is to have a small but powerful “AI Office”.
How do you organize this without going crazy?
The model that makes the most sense (and is most often repeated) is thehub-and-spoke:
A central hub (AI Office) defines standards, platform, priority cases.
Spokes in each business area adapt AI to their reality.
This allows you to scale without losing control. It avoids duplication, manages risks and accelerates results.
A clear example: SOFTSWISS appointed a CAIO and established a shared AI platform. This way, they connect data, avoid chaos, and ensure safety and efficiency. Instead of having 30 loose “mini-IAs”, they build a common highway with lanes, rules, and tolls.
Emerging teams
A company that takes this seriously ends up riding (although the names vary):
AI Platform/Enablement: approved tools, integrations, catalog of reusable components.
AI Governance: policies, risk control, model inventory, data and supplier rules.
AI Product / Use Case Squads: mixed teams that prioritize cases by real impact, not hype.
Adoption and change: role-specific training, process redesign, measuring real impact.
How to avoid "Shadow AI" without killing innovation
No prohibitions are valid here. If you block tools without offering alternatives, people will look for hidden paths.
What works:
Offer approved tools (easy and useful).
Simple policies: this yes, this no.
Practical training, not theory.
Monitor usage and expenditure.
Periodic audits.
Gartner is already warning that by 2030, many companies will suffer incidents due to poor governance of their AI. Better safe than sorry.
The "minimum viable organization chart" for 2026
If you want something agile and functional, without inventing an AI Ministry, here’s a recipe:
C-Level Sponsor: CAIO, CTO or CIO with clear authority.
AI Office small but with muscle.
AI Platform (internal or with partners).
Champions in each business area.
5-10 KPIs: time saved, quality, cost avoided, risk, adoption…
With this you can climb without losing the north.
And in your case, do you already have a plan for governing AI in your company? Who is really in charge? Is your company already considering a role such as CAIO or an AI Office?
Leave me your comments and tell me about your experience, I’d love to read about it.
Have a good week!
