At this year’s SuperNova, Projective Group’s BE Head of Data, Bart Claeys, brought together senior data and AI leaders from Belgian financial services and insurance organisations. Bart guided the session with a simple question: as the technology matures, are financial institutions truly scaling AI, or still experimenting around the edges? Their discussion offered a grounded view of what is working, what is not, and what will matter most in the years to come.
Our panel members:
Across the panel there was clear consensus: AI is no longer a side project. It underpins key operations. One speaker noted that if AI disappeared overnight, “a lot of things would cease to exist”, because volumes handled by AI now match those handled by humans in areas such as contact centres.
AI is no longer a side project. It underpins key operations.
Large institutions are already seeing tangible returns. BNP Paribas Fortis, for example, measures the financial contribution of all AI solutions in production, generating significant annual recurring value from use cases that support employees, clients and internal processes. As Jonathan Neubourg observed, this scale only became possible after shifting away from proofs of concept toward value‑driven delivery: “If you don’t get the other side of the balance under control, it becomes more proof of technology than creator of value.”
A similar evolution is happening in insurance. Vanbreda, for instance, moved from basic automation towards step‑by‑step AI augmentation across large‑volume processes. Their decision to remove Interactive Voice Responses entirely reflects a broader belief that AI will reshape how organisations design customer journeys. The industry is therefore not stuck. It is transitioning – unevenly – from experimentation to operational reliance.
The industry is therefore not stuck. It is transitioning – unevenly – from experimentation to operational reliance.
Despite the progress, the panel was clear that the biggest barriers to scale are not algorithms. They are data foundations, process maturity and people. Frank Fripon emphasised process discipline strongly: “Bad processes with data mean bad data. And if you don’t have good data, you will not do anything with AI.” When human workarounds prop up broken workflows, automation exposes the underlying inconsistencies rather than masking them. AI forces organisations to redesign processes instead of digitising paper‑based habits.
Bad processes with data mean bad data. And if you don’t have good data, you will not do anything with AI.
Jonathan Neubourg brought the people dimension to life. He described how employees often struggle to adopt tools that remove everyday friction, noting that “the human is always the weakest link”. For many staff, AI still feels like a threat rather than a productivity gain. Cultural readiness therefore becomes as important as technical capability.
Anthony Belpaire pointed to architecture as a decisive factor. AI tends to “surface which company has its data and tech in order”, especially as more unstructured data enters the picture. Knowledge management, documentation and consistency between languages can quickly become limiting factors if the organisation has not invested early. In short, most obstacles arise long before the model is deployed.
The panel touched on two major sources of value. The first lies in large, labour‑intensive processes. Contact centres, KYC, lending, claims and onboarding all carry high volumes and repeatable tasks. Automating these steps with conversational interfaces, classification models or agentic orchestration can fundamentally change the cost‑to‑serve. As one speaker put it, “If you have a way to fundamentally impact how these processes run, you will have a great outcome.”
The second source is individual productivity. Jonathan Neubourg described it simply: every employee carries a few hours of repetitive administrative work each week, and “if you can automate these four hours, it probably creates more efficiency than the biggest pockets you can find.” Small, self‑built automations can free up capacity across thousands of people and create a cultural shift that makes AI feel accessible rather than intimidating.
This is why some institutions are introducing “agent marketplaces” – controlled environments where employees can build and reuse simple agents without specialist skills. It helps organisations scale AI from both the top down and the bottom up.
Financial services organisations are no longer debating whether AI creates value. They are figuring out how to scale it responsibly and consistently. The panel’s closing message was clear: progress depends far more on people, processes and data foundations than on model sophistication.
AI is ten per cent tech and ninety per cent bringing people along.
As one panellist summarised the mindset needed for the next few years: “AI is ten per cent tech and ninety per cent bringing people along.” At Projective Group, we see the same reality across our client work. Scaling AI requires clarity, collaboration and pragmatic execution. Whether shaping strategy, redesigning processes or building long‑term capability, we work side by side with organisations to turn emerging AI opportunities into trusted, tangible impact.
Established in 2006, Projective Group is a leading financial services consultancy. We are recognised across the European industry for turning complex challenges and emerging themes into clear, pragmatic solutions. With deep roots and trusted relationships in financial services, we bring hands‑on expertise across key domains. We support the full journey of change: shaping strategy, delivering complex transformation or building long‑term capability through managed services, staffing and training. Our purpose is simple: to empower financial services to shape the future of wellbeing, prosperity and innovation.
