Abigail Tate
Business Development Manager – IBM Software | TD SYNNEX UK&I
Charting Innovation with Scuderia Ferrari and IBM
If I were to tell you about how Scuderia Ferrari built an AI-powered fan experience using IBM’s watsonx.ai, watsonx.data, and watsonx.governance (and I will in a minute), you’d probably think about speed. Fast cars. Rapid innovation. Lightning decisions.
And you’d be right, of course. But what you might not think about is what these organisations were up to in the 1950s – and how it still matters today.
While I admit I’m getting on a bit, I wasn’t around in 1950 for the first World Championship Formula One race, which put motorsport technology centre stage, or the launch of the IBM 701 in 1952, which promised to “shatter the time barrier confronting technicians.” I also wasn’t yet born when IBM researcher Hans Peter Luhn introduced the concept of Business Intelligence in 1958, defining it as “the ability to apprehend the interrelationships of presented facts in such a way as to guide action toward a desired goal.”
I was, however, continuing the spirit of Luhn’s work in the late ‘90s whilst getting my hands dirty as a DB2 DBA. By then, the concept of BI had developed and the focus was on clean datasets, well-designed warehouses, and reliable reporting so organisations could trust the dashboards guiding their decisions. Good data meant better insight.
Today, whilst the technology has changed almost beyond recognition, the challenges and desire to innovate remain. In the era of AI, not only do we need to report on the past, but we also need to train systems that can learn, act and predict the future.
Why AI projects stall
Every organisation is chasing an AI-driven advantage. Boards expect transformation, executives demand measurable returns, and technology leaders are under pressure to move from pilots to production-ready systems. Yet despite significant investment, many enterprise AI initiatives stall before delivering meaningful business impact.
The problem rarely lies with the models. Instead, it runs deeper within fragmented, low-trust, and poorly governed data foundations. In many industries, a common pattern is emerging: the performance of enterprise AI is limited not by the algorithms, but by the quality, structure, and availability of the data they use.
The need for a structured data strategy
Structural difficulties relating to data are common. This includes data dispersed across hybrid and multi-cloud settings, inconsistent quality and governance protocols, and limited lineage, observability, and explainability. This leads to vast amounts of unstructured data remaining unexploited and an environment where AI outputs are difficult to trust, operationalise, and scale effectively. With promising pilots left to falter, and production-grade systems not materialising, it’s clear that project failures aren’t a tooling problem – but an architectural and strategic one.
Re-thinking your data relationship for AI
Leading enterprises are fundamentally rethinking their relationship with data. Instead of viewing it as a back-office operational necessity, they design data foundations built specifically for AI – where governance, quality, security, and lineage are embedded from the start, not retrofitted.
In these environments, data becomes easily discoverable, inherently trustworthy, secure without slowing innovation, and ready for AI at scale. This shift allows organisations to move beyond experimentation and into trusted, production-grade AI systems that deliver real business value.
Scuderia Ferrari and its IBM-powered data foundation
An example of such a transformation is happening at Scuderia Ferrari. With IBM technology under the hood, they’re delivering AI-driven fan engagements based on trusted data.
Like many enterprises, Scuderia Ferrari has an abundance of data – from race telemetry and historical performance data to real-time fan engagement signals. The issue they faced was turning vast, complex data points into real-time, meaningful experiences at scale.
To solve the challenge, they chose to build a unified AI and data foundation using IBM’s enterprise-grade AI and data platform, including watsonx.ai; watsonx.data; watsonx.governance; and observability capabilities. As a result, Scuderia Ferrari developed a next-generation fan engagement platform delivering:
• Personalised content
• AI-driven race summaries
• Historical insights
• Real-time storytelling
The impact was immediate and measurable:
• 2× increase in daily active users
• 35% increase in average in-app time
By synthesising complex data into compelling narratives that fans can trust, Scuderia Ferrari transforms every race into a more immersive experience – powered by solid data and production-grade AI.
Preparing for pole position
What I love about this story is how it combines the rich heritage of two leaders in their respective fields and enables them to build upon those early foundations to deliver unexpected and new innovation without compromising that business fundamental: trust. I think Luhn would approve.
This article was written by Abigail Tate, Business Development Manager for IBM Software at TD SYNNEX. For more information, contact Abi by emailing her at abigail.tate@tdysynnex.com or by completing the form below.
