Watch: How can AI turn data overload into smarter decision-making?
In November at SiGMA Central Europe in Rome, industry leaders gathered for a high-level panel powered by CEVRO titled AI in Decision-Making: From Data Overload to Strategic Insights. The discussion was moderated by Krzysztof Szyszkiewicz, Co-Founder & Partner at Valueships, a boutique pricing consultancy working extensively with AI-driven strategies.
Joining him on stage were Michael Cutajar, Founder & CEO of Accora, Chaim Heber, Founder & CEO of Cevro AI, Mex Emini, Managing Director of SiGMA Play and Chavdar Trendafilov, Technical Director at Cloud Office. Together, they explored how organisations can move past raw data accumulation toward real, actionable intelligence.
Data to insight
The session opened with a reality check. “We have 90% more data year after year,” Szyszkiewicz noted, asking the audience how many felt they were “sitting on a pile of data,” with nearly every hand in the audience going up.
For Heber, the problem is not collection but more so clarity. “Collecting the data is not the key. You have to know the outcome you’re trying to achieve,” he said, emphasising that decision-making starts with intent, not dashboards.
Trendafilov echoed this, pointing to fragmented systems as a major blocker. “Very often we have silos of data in different places without connecting and using them,” he explained, advocating for unified data strategies and treating data as a product rather than a byproduct.
Structuring chaos
In finance and accounting, precision is non-negotiable. Cutajar highlighted the challenge of transforming messy inputs into structured intelligence. “Turning unstructured data into structured data is the number one challenge,” he said, noting that AI still benefits from human expertise when context is unclear.
For personalisation-driven AI, context becomes even more critical. “If you don’t feed it the right context, the AI agent can’t act appropriately,” Heber explained. Selecting which historical data matters, and which does not, is what separates helpful automation from noise.
Measuring what matters
Underpinning all of this is infrastructure. Trendafilov outlined three pillars including architecture, automation and governance. “Even if the data is in silos, you need to know where it resides and how to use it,” he said, stressing that resilience and scalability are strategic advantages.
When it comes to performance, Emini urged companies to rethink metrics. “Uptime doesn’t move you forward, it just keeps you afloat,” he said, reframing KPIs as health indicators rather than growth drivers. The panel agreed that true success lies in measurable business impact.
Where AI drives profit
AI’s role in profitability sparked some of the strongest discussion. Emini shared how AI-powered insights helped SiGMA Play pivot quickly in the Brazilian market. “Before AI, you would have spent $2 million on a consulting project to learn,” Szyszkiewicz remarked.
On the revenue side, Heber pointed to hyper-personalised player engagement. “We’re preventing churn and increasing revenue with tailored offers,” he said. Meanwhile, Cutajar quantified efficiency gains, tasks that took over a minute manually are now completed in under 15 seconds with AI.
AI in decision-making
“Focus on the problem first, then the technology,” Trendafilov advised. While hyper-personalisation and AI-assisted strategy will continue to scale, the consensus was clear that humans must remain in control, especially in high-stakes decisions.
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