The key to AI adoption? Your team’s tech skills

What does it take to successfully roll out AI within a company? That was the central question explored during Le Wagon’s round table with:

• Agustina Rosenfeld, Data & AI Governance Leader at BNP Paribas

• Alexa Kehailia, Data Strategist at MGEN

• Juan Berdah, Head of SEO & AI Lead at THOM

Together, they shared concrete examples and hard-earned lessons from inside large organizations at different stages of their AI journey.

The consensus? Successfully adopting AI across an organization takes more than tools and technical talent. It requires a unified cultural shift, inclusive upskilling strategies, and cross-functional collaboration between business and data teams. From building trust in AI to identifying impactful use cases, the speakers shared practical insights on what it truly takes to scale AI within large organizations.

Below, we break down the key themes from the discussion, and what they mean for companies navigating AI transformation.

1. AI transformation must be business-led, not just tech-led

While data and IT teams are often the first to experiment with AI, real impact only happens when business units are empowered to integrate AI into their workflows. The speakers agreed: business teams need to be the primary drivers of AI adoption.

This shift requires changing how organizations think about AI—moving from technical experimentation to real business use cases. That includes giving non-technical teams both the confidence and skills to identify opportunities and collaborate with data teams to implement them.

“Training business teams is key because once you give people an expertise, they can talk about it, spread the word and share the importance.” – Alexa Kehailia, MGEN

2. A unified learning culture is essential

AI adoption doesn’t happen in pockets, it needs to be part of a company’s culture. The speakers highlighted the risk of only training isolated teams or a handful of AI ambassadors. Instead, AI learning should be inclusive and organization-wide.

At BNP Paribas, this meant creating a unified framework for upskilling and ensuring that learning wasn’t confined to the most tech-savvy employees.

“It starts with culture, and I think it’s important to say that it has to be a unified culture. No one must be left behind in this AI transformation.” – Agustina Rosenfeld, BNP Paribas

3. Practical, business-relevant training drives adoption

Generic AI training isn’t enough. What’s needed is practical, role-specific learning that helps business teams apply AI to their actual problems. This includes workshops, use case discovery, and co-learning experiences that bring data and business profiles together.

For example, MGEN’s internal training programs connect employees from different departments so they can explore real use cases collaboratively, sparking new ideas.

4. AI can boost efficiency as much as innovation

While AI often gets linked to innovation, the speakers stressed that some of its most immediate value comes from optimizing existing processes. Whether it’s reducing dependency on costly external services or automating repetitive tasks, AI can unlock significant operational efficiency.

“Using AI, we can spare money by not using some external services that cost lots, or by gaining in terms of quality.” – Juan Berdah, THOM

At THOM, for instance, Juan shared how AI is already helping their teams save time and budget while improving output quality, especially in areas like SEO and content generation.

5. Internal alignment is as important as technical readiness

Finally, successful AI adoption depends just as much on alignment across departments as it does on technical maturity. That means securing buy-in from leadership, involving HR and L&D in training strategies, and ensuring teams share a common language around AI.

The speakers agreed that when AI is treated as a shared initiative—owned by product, business, and data teams together, it becomes far easier to scale.

Final thoughts

Rolling out AI across an organization doesn’t start with models or tools, it starts with people. As this round table made clear, equipping teams with the right mindset and skills is essential to any successful AI strategy.

If you or your company is looking to build AI capabilities, explore how Le Wagon can help!