CASE STUDY
Collaboration is one of the most transformative impacts we have seen. Traditionally, AI initiatives were largely driven by technical teams, with limited business involvement beyond defining initial requirements. This often created gaps between model outputs and real business expectations.
With Dataiku, we established a shared platform where both technical and non-technical stakeholders can actively contribute throughout the AI lifecycle. This aligns perfectly with our internal model of‘ AI ambassadors’, business representatives embedded within each domain who work closely with data teams.
Through this approach, business teams are no longer passive stakeholders. They participate in defining use cases, validating models and ensuring outputs are aligned with real operational needs. At the same time, technical teams benefit from clearer requirements and faster feedback loops.
The result is a true human-AI collaboration model, where AI becomes a shared organisational capability rather than a centralised function. This has significantly improved adoption, trust and the overall effectiveness of our AI initiatives across the bank.
Can you quantify the impact the new platform has had on development speed and the time required to operationalise new use cases?
Another key challenge was the disconnect between business and technical teams. While strong models were being developed, translating them into sustainable, production-ready solutions was often slow and complex. This limited our ability to scale AI beyond isolated use cases.
Transitioning to a unified platform like Dataiku fundamentally changed this dynamic. It provided a centralised environment that integrates data, development, deployment and governance in one place. This allowed us to standardise processes, ensure consistency and significantly improve model lifecycle management.
Most importantly, it enabled us to shift from fragmented experimentation to a structured, end-to-end AI operating model, where solutions are built, governed and scaled with business impact in mind.
How has the adoption of Dataiku improved collaboration between technical and business teams across the bank?
While exact figures may vary by use case, the impact on speed and efficiency has been substantial. We have significantly reduced the time required to move from idea to production by streamlining development, testing and deployment processes within a single platform.
Previously, operationalising a model required coordination across multiple environments, manual integrations and extended validation cycles. Today, with a unified platform, these steps are integrated and automated, enabling faster iteration and quicker deployment.
In practical terms, this has allowed us to accelerate our AI time-to-market and scale multiple use cases in parallel rather than sequentially. More importantly, it has enabled us to focus less on technical overhead and more on delivering measurable business value.
This acceleration is critical in banking, where responsiveness to customer needs, market changes and regulatory requirements can directly impact competitiveness. Ultimately, the platform has transformed AI from a slow, project-based activity into a continuous, scalable capability. www. intelligentcio. com
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