As artificial intelligence becomes ever more central to enterprise technology, leaders with deep operational experience are setting the pace for innovation. One such leader is Cloudera’s subject matter expert Adrien Chenailler, who brings his AI experience and expertise in banking to Cloudera, helping financial institutions modernise and scale.
His practical approach, honed over years within the high-stakes banking environment, is shaping how technology teams can build smarter, more effective solutions.
So, you need a strong data platform that breaks down the silos and gives you good observability.
Adrien Chenailler
“First, you don’t need a large team to do AI now,” Adrien explained. “Your AI developers are empowered with a lot of AI tools, including the one we have built in Cloudera called SQL assistant and coding assistant.”
He explained that this can help a lot and a good team with “a couple of smart and dedicated people” can get a bank started on AI.” In his previous role he was able to go from ideation to development to production in a matter of days instead of months because “…we were doing everything on Cloudera (platform).”
This overturns the myth that artificial intelligence demands huge investments or expansive headcounts.
Adrien’s method is defined by pragmatism in use case selection – to never be swayed by flashy ideas alone. “As a data science leader in a bank, you are probably being bombarded by AI use cases. We need to focus on what is going to be the ROI of the use case – are we bringing in revenue, saving cost, or mitigating risk?
“That’s the only three things I can do in a bank.”
Data volume and why banks need platforms
Does a bank need a modern data platform to deploy meaningful AI and get meaningful adoption?
During his tenure with a leading bank, Adrien saw first-hand how vital platform modernisation is to achieving scale.“The reality of banks is that they have hundreds of use cases, and they need (a platform) to scale on it. You cannot rebuild an entire platform from scratch for every single use case you have.
“So, you need a strong data platform that breaks down the silos and gives you good observability.”
This incremental data integration strategy delivered concrete results. “When the AI journey started, there was about 40% of this source data that was ingested in the cloud…when I left, I think 75 or 80% of this data were in Cloudera.”
This had included data from systems like payments, risk management, fraud management, CRM, the mobile app, and “…anything that you may need.”
All of these were integrated and were flowing data into Cloudera either in real-time or in batches. “This was the foundation,” Adrien pointed out.
Governance, too, is at the heart of Adrien’s approach. “AI governance is part of the AI building process, and many banks are starting to realise this.”
He highlighted tools like AI Studio for observability and mentioned a partnership with Galileo for real-time guardrails. He described, “When there’s an LLM response or an AI agent response, it can block the response if it feels like you are leaking some personal data on the chatbot that you should not be leaking.”
In essence, Cloudera’s Galileo works as an AI observability and monitoring platform that ensures enterprise AI workflows remain trustworthy, transparent, and high-performing throughout their lifecycle. It is designed to provide a closed loop for trusted AI deployment within Cloudera’s ecosystem.
