European companies are poised to invest €150 billion in artificial intelligence over the next two years, but many projects remain underwhelming in their ability to deliver tangible value, according to insights from Mistral AI. The findings were presented by Guillaume Bour, head of enterprise Europe at Mistral AI, during the ALFI Global Asset Management Conference held in Luxembourg on March 24–25, 2026.
Bour highlighted that the challenges faced by firms stem from structural issues rather than insufficient funding. He emphasized that many organizations are integrating AI into their operations without gaining adequate oversight of how these systems function within core business processes. “AI is going to be at the very core of all the internal and business processes of your organisation,” Bour stated. “Most models are black boxes today.”
This lack of transparency presents significant compliance and governance concerns, especially for financial institutions, where it is crucial that decision-making processes are explainable and auditable. Bour pointed out that firms must not only analyze outputs but also comprehend the underlying rationale behind those results.
The Challenge of General Models
Bour further elaborated on the limitations of general-purpose AI models, particularly in the finance sector, where performance often hinges on access to proprietary data. “General AI do not integrate domain-specific intelligence,” he noted. Models that are trained on broadly similar public data tend to exhibit akin behaviors, thus constraining their capacity to produce varied outcomes. To enhance performance, it is vital to incorporate institution-specific data that accurately reflects the dynamics of financial markets and operations.
As the industry transitions from experimentation to execution, Bour remarked that the challenge is evolving from identifying potential use cases to scaling them effectively. “The real question is not, can we build a use case,” he said. “The real question is, how do we bring those dozens or hundreds of tests… to production at scale?”
To address these challenges, Mistral AI has structured its financial services offerings around three key components: sector-specific models trained with proprietary data, agentic systems that can operate within defined workflows, and infrastructure that maintains firm control over deployment. “We partner with financial services organisations to train some models with that specific data,” Bour explained, “to bring domain-specific knowledge so the models are way smarter when it comes to talking about finance.”
This specialized approach is being implemented across various sectors, including banking, insurance, and asset management, targeting areas such as know-your-customer checks, anti-money laundering processes, and reporting. Bour described agentic systems as models capable of taking actions within workflows, rather than merely responding to prompts. He also underscored the potential for significant value generation through time-series modeling for market forecasting, where even slight improvements in accuracy can be impactful.
Customer-facing systems are also advancing, with expectations that models will evolve to execute transactions on behalf of users, moving beyond merely addressing queries.
Ensuring Control Over AI Deployments
Bour emphasized the importance of “sovereign” AI, which refers to systems deployed in a manner that preserves control over data and architecture. “The only way to have that is to deploy where the data is,” he said, advocating for institutions to develop internal capabilities to operate AI systems effectively. “You have to build your platform,” Bour insisted. “You have to hire the people who actually can help you build and operate your AI workflows at scale.”
Instead of pursuing a comprehensive transformation, Bour advised firms to concentrate on a limited number of high-impact use cases. “An iconic use case is a use case that is strategically valuable, that is urgent,” he remarked. “Identify the top 10 iconic use cases and bring them at scale.”
The ALFI Global Asset Management Conference this year has placed considerable emphasis on the operational implications of artificial intelligence, alongside discussions on regulation and market structure. Mistral’s session reflects a broader trend in the industry as financial institutions shift from pilot projects to scaled deployments, all while striving to retain control over data, models, and decision-making processes.
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