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Motorsport has always had adrenaline. What it hasn’t always had is accessibility, especially for fans who crave more than just lap times and checkered flags. In April 2025, Infosys and the ABB FIA Formula E World Championship unveiled an AI-powered statistics platform designed to decode the sport for a new generation of digitally native fans. This isn’t just a tech upgrade—it’s a transformation in how an entire league communicates its drama, in real time, across screens.
At a time when traditional motorsport is still wrestling with energy transitions and digital staleness, with Infosys, Formula E is doubling down on its identity as the innovation-first series. The championship, already celebrated for racing electric vehicles through global city circuits, is now setting the pace in digital, launching a platform that makes energy usage, attack mode strategy, regen behavior, and overtaking patterns part of the mainstream viewing narrative.
Infosys, stepping in as the Official Digital Innovation Partner, brings Infosys Topaz to the table, an AI-first set of services, solutions, and platforms using generative AI technologies, real-time analytics capabilities, and a commitment to explainable AI. The platform uses machine learning to surface in-season and off-season race telemetry in human-understandable formats—any stellar performance of the driver and team isn’t just “what happened,” it’s now “why it happened.” These insights are delivered via dynamic stat cards, natural language narratives, and a domain-tuned AI Companion trained on ten seasons of race data and storyline nuance.
But this isn’t just another digital skin. According to the Greyhound Global CX and Fan Engagement Tracker 2025, 68% of global CIOs are under board-level pressure to prove ROI on engagement platforms within 12 months, while 73% of Gen Z fans abandon platforms that feel static, one-dimensional, or impersonal. The challenge isn’t launching AI. It’s proving that it matters. This partnership is a live-fire test of whether generative intelligence can truly move the needle on minutes spent, clicks earned, and loyalty retained.
Greyhound Fieldnotes from sports-tech engagements confirm that the bar for fan engagement has been permanently raised—stats must now entertain, stories must personalise, and AI must earn attention, not just automate it. In that light, this partnership is more than a feature—it’s a business signal.
Greyhound Research believes this collaboration sets a new standard, not just for motor sport, but for every live sport and digital business grappling with the new physics of attention. In today’s race, the real winners aren’t the ones with the fastest laps. They’re the ones who make every moment explainable, emotional, and worth coming back for.
Market Backdrop – Understanding the Business and Tech Landscape
Sports leagues around the world are in the middle of a structural reckoning. The triple threat is real: fragmented fan attention, collapsing linear viewership models, and a generation of fans who demand immersive, interactive experiences as the default, not a delight. In this landscape, content alone no longer wins. Context does.
Motorsport is no exception. Formula One may remain the legacy titan, but its vast trove of data often remains locked behind paywalls or buried in broadcast overlays. Formula E, by contrast, is leaning into data democratization—not by diluting technicality, but by decoding it. And in it signals a very different vision of what the future of racing fandom might look like.
The average Formula E race lasts just under 45 minutes. It isn’t brute speed that decides the winner—it’s energy strategy, regen behavior, and deployment discipline. Fans who tune in without a telemetry decoder are left chasing shadows. And until now, much of that story—lap-level power usage, attack mode gambits, optimal overtaking windows—was confined to engineering war rooms or buried in post-race debriefs.
Infosys answered this challenge with a cloud-native AI architecture capable of ingesting high-frequency telemetry, combining it with race metadata like driver history and track conditions, and applying predictive machine learning to simulate race dynamics in real time. The stats website would be aided by the creation of human-like automated text from Formula E results, which would allow fans to be more connected to drivers, teams, and the sport itself.
According to Greyhound CIO Pulse 2025, 68% of global sports CXOs rank ‘live explainable AI for fan interfaces’ as their top innovation priority. Fieldnotes from league leaders across European football, American basketball, and Indian cricket echo a similar tension: sponsors are demanding measurable engagement, not just impression metrics. Static dashboards no longer cut it. Engagement must be responsive, narrative-rich, and monetizable.
This is where Formula E’s approach diverges from the rest of the pack. While many leagues treat AI as a back-end tool for analytics or ops support, Infosys and Formula E have made a bold architectural bet—positioning AI as the product itself. This is not back-end insight wrapped in a marketing veneer. This is a front-end experience designed for exploration, curiosity, and interaction.
It’s also what makes this initiative a bellwether. Greyhound Research believes that sports-tech investments are undergoing a structural pivot. They’re no longer about supporting operations in the background. They’re about owning the digital fan relationship at the edge, across apps, screens, and moments that matter. Because in a world where every second of attention is contested, what you serve—when, how, and why—is the strategy.
Partnership Architecture – Roles, Delivery, and Governance
This isn’t your standard vendor-client playbook. The Infosys–Formula E partnership was structured from day one as a co-creation engine, with cross-embedded teams, shared delivery sprints, and intentional design rituals. This wasn’t about delivering a product—it was about building a new kind of experience layer, one that fans could touch, explore, and learn from in real time.
At the core of the platform is Infosys Topaz, an AI-first set of services, solutions, and platforms using generative AI technologies. But this wasn’t a lift-and-shift implementation. Infosys analysed over ten seasons of proprietary race data—driver telemetry, team performance history, track conditions, and contextual race dynamics. That gave Infosys Topaz the power that most fan tools lack: muscle memory. It doesn’t just retrieve stats—it knows what they mean in the language of Formula E.
The platform architecture follows a clear three-layer model:
1/ Ingestion Layer: Pulls in high-frequency telemetry, driver stats, and race metadata—everything from car performance to environmental conditions.
2/ Inference Layer: Uses machine learning to derive interesting insights that can engage a fan through live position updates during race, automated race summary post-race, key performance milestones of the drivers/teams, or circuit record information of race location.
3/ Experience Layer: Translates all of that into digestible, real-time insights served as interactive stat cards, narrative prompts, and the conversational AI Companion.
And that Companion? It’s not a skin-deep GPT wrapper. It’s a domain-tuned agent trained on the linguistic, strategic, and emotional nuances of electric motorsport. Formula E is a growing race sport, and there are fans always eager to learn and understand the race more – that’s where the Companion helps narrate and bring the history of interesting records and achievements of the past 10 seasons. It’ll give you a story—framing the result in the context of career arcs, rivalries, and historical form. This is not analytics. This is AI-powered narration.
Behind the scenes, Infosys and Formula E structured the build using agile rituals with cross-functional squads—bringing together data scientists, race engineers, digital content teams, and fan engagement strategists into shared sprints. Greyhound Fieldnotes from other sports-tech transformations show that this kind of multi-disciplinary pod model dramatically shortens delivery feedback loops and reduces misalignment between technology and content.
Crucially, governance wasn’t bolted on. From the outset, Infosys baked in observability frameworks, attribution controls, and explainability protocols. That means every fan-facing insight can be traced to source data, audited for compliance, and tuned over time as new inputs emerge. According to the Greyhound CIO Pulse 2025, 64% of global enterprises say shared delivery models accelerate time-to-value in complex platform rollouts, and this initiative reflects that blueprint with precision.
Greyhound Research believes that this is what true partnership architecture looks like in the generative AI age: shared execution responsibility, design-forward thinking, and a governance spine strong enough to support global scale. It’s not just about delivering digital. It’s about delivering differently, with modularity, narrative logic, and contextual trust built in from the start.
Measured Outcomes – Commercial and Strategic Benefits
What began as a Stats Centre is quickly proving to be a commerce engine in disguise. Since its launch, Formula E’s digital properties have reported an increase in average session time and a 17% boost in content sharing across social media platforms during live race broadcasts. These aren’t vanity spikes. They’re structural signals that fans are not just watching—they’re engaging, exploring, and staying longer.
More than just an experience enhancer, the platform is rewriting Formula E’s monetisation playbook. Sponsors are no longer limited to logo placements or broadcast mentions. Now, they can anchor themselves to dynamic, AI-generated insights—“The ABB Predictive Driver Insight,” “Julius Baer Energy Efficiency Moment,” or “Heineken Overtake of the Race.” These branded micro-moments are immersive, contextual, and entirely new inventory for revenue generation.
Internally, Formula E’s content and digital marketing teams are using the platform not just to publish, but to learn. A/B tests on narrative formats, predictive content performance models, and session-level behavior analytics are shaping how content is surfaced, when it’s delivered, and which fans see what first. The league is no longer relying on retrospective engagement data. It’s forecasting fan behavior and tuning content in near-real time.
Crucially, this isn’t just about breadth—it’s about depth. Formula E now operates a first-party data engine, capable of tracking not just clicks, but curiosity: how long fans interact with stat cards, what kind of insights they request, and which moments create emotional lift. These aren’t soft signals. They form the foundation for predictive engagement, content recommendations, and personalised fan journeys.
According to Greyhound Fieldnotes, many elite leagues struggle to justify their AI investments beyond ops and uptime. But this partnership flips that equation. Infosys has helped Formula E turn insights into inventory—each interaction a potential monetizable unit. This is platform intelligence as business leverage, not just performance glue.
Greyhound CIO Pulse 2025 further supports this shift: 56% of global CIOs now say fan platforms must deliver measurable ROI within 12 months, and 71% believe that engagement, not just availability, is the new success metric. Infosys didn’t just hit those benchmarks. It helped redefine them.
Greyhound Research believes this is the real litmus test for transformation: when AI isn’t just a layer beneath your system, but a surface your business can stand on. And when every moment of intelligence is also a moment of monetisation.
Implementation Insights – What Got Challenged and Fixed
Digital products that sit inside live events are rarely implemented cleanly, and this was no exception. For all its AI muscle and engineering elegance, the first hurdles Infosys faced weren’t technical. They were human.
The earliest challenge came from within: internal narrative control. Some stakeholders worried that too much AI might dilute the soul of the sport, making Formula E feel robotic or alienating to purist fans. Others feared that surfacing too much data could hijack the storytelling, burying the drama of the race beneath a mountain of metrics.
Infosys anticipated this resistance and tackled it not with tech, but with empathy. The team co-created a set of data storytelling guardrails—predefined narrative templates that translated storytelling into emotionally resonant, human-understandable language that is dynamic as per the time of the race season. Fans are presented with dynamic stats cards and information that changes as per the season – be it offseason, live race, or during the season. The cards narrate the story of the race and key achievements powered by automated AI content.
The second challenge was architectural: handling real-time concurrency. During race-day peaks—when millions of fans log in simultaneously across time zones, devices, and platforms—any latency could erode trust. Infosys implemented cloud burst scaling, paired with a multi-region CDN architecture, to ensure the platform could stretch and flex without breaking stride. Greyhound Fieldnotes from similar deployments in global tournaments confirm that this kind of burst-readiness is rarely designed in from day one. Here, it was.
However, the most persistent challenge lay in the middle—adoption. Even the most beautifully engineered platform can stumble if the people meant to use it don’t know how, or worse, don’t trust it. Within Formula E’s own digital, content, and fan operations teams, there was natural hesitancy. What did this mean for their roles? Was AI going to replace editorial judgment? Or was it just another tool with a steep learning curve?
Infosys addressed this head-on by embedding functional change leads within each business team, not as trainers, but as translators. These were cross-skilled liaisons who helped contextualise the Companion, field objections in real time, and maintain a feedback loop between developers and daily users.
Greyhound Fieldnotes from global sports transformations confirm that the biggest barriers aren’t tooling gaps—they’re comfort gaps. Teams need to feel ownership of the product, not just access to it. And that only happens when design is treated as a cultural alignment exercise—not a tech drop.
Greyhound Research believes Infosys got this right, not because the Companion was flawless from day one, but because the implementation team made space for resistance, listened to hesitation, and treated early scepticism as a data point, not a defect. In transformation journeys, that’s the real difference between a pilot that launches and one that lasts.
The CXO Playbook – Ten Replicable Strategic Takeaways
Designing a generative AI experience that actually works in the wild—during live events, at scale, with real emotional stakes—is a rare achievement. For CIOs, CMOs, and platform leaders looking to replicate the Infosys–Formula E formula, here are ten lessons drawn from the architecture, narrative, and cultural DNA of this project.
1/ Narrative-first AI wins hearts and minds. Don’t just build systems that process stats—build ones that tell a story. Fans don’t remember numbers. They remember meaning. Infosys succeeded because it translated telemetry into tension, metrics into moments.
2/ Treat fans as data partners, not targets. The Companion didn’t just deliver answers—it invited curiosity. The best AI systems give users something they can act on, react to, or share. That’s how data becomes dialogue.
3/ Latency kills experience—design for burst load. Don’t optimise for averages. Optimise for spikes. Infosys engineered its platform to stretch under peak race-day traffic using cloud burst scaling and multi-region CDNs. That’s not ops insurance—it’s experience insurance.
4/ Govern explainability as a product requirement. Compliance is only half the story. Trust is the real moat. Infosys built observability frameworks into the Companion, ensuring fans and regulators alike could trace outputs to data lineage—no black boxes, no blind spots.
5/ Don’t separate tech and editorial teams. Infosys embedded digital marketers, race strategists, and UX designers into sprint teams. When engineers and storytellers co-own the roadmap, the product doesn’t just function—it feels right.
6/ Pilot in sprints, not monoliths. The platform launched in phased rollouts, starting with stat cards, adding narrative overlays, and then AI-powered insightful race summaries and race achievements. This sprint-based release model built feedback into the design cycle and avoided overbuild.
7/ Use AI for both pre-race and post-race loops. Most platforms over-index on the live moment. But true engagement is cyclical. Infosys used AI for race previews and narrative recaps, driving lifecycle retention—not just real-time stickiness.
8/ Create engagement SLAs alongside uptime SLAs. It’s not enough for the platform to stay live. It has to stay emotionally resonant. Infosys and Formula E track attention-based metrics—session time, interaction depth, live race footprints—as seriously as latency and error logs.
9/ Design for modular reusability. What Infosys built wasn’t a one-off—it was a portable engagement engine. Every component—stat card, narrative insight, Companion query—can scale across mobile, broadcast, OTT, and even non-sport use cases like insurance or education.
10/ Internal buy-in is a product feature. The platform didn’t win because of flash. It won because internal teams—from digital ops to media—felt like co-authors, not custodians. Infosys treated adoption as a performance metric. And it showed.
Greyhound Research believes successful platforms aren’t defined by the brilliance of their code, but by the breadth of conviction they command across disciplines. When design, delivery, and culture evolve in lockstep, transformation stops being theoretical and starts scaling.
Forward View – A Partnership That’s Still Evolving
What we’re seeing is just lap one.
Infosys and Formula E aren’t treating the Stats Centre as a finished product—they’re treating it as a launchpad. Plans are already underway to expand the platform with predictive overlays, gamified telemetry-driven challenges, and driver-AI co-commentary modules that surface insights from past races while narrating the live moment in real time.
The goal? To build a fan experience that doesn’t just inform—but learns, adapts, and improves with every race, every query, every moment of curiosity.
And the implications don’t stop at motorsport. Greyhound Fieldnotes indicate that other leagues and live-event ecosystems—from tennis and basketball to music festivals and OTT platforms—are now exploring similar architectures. The demand is clear: platforms that surface data with meaning, at speed, and at scale. Not just analytics. Narrative intelligence.
Infosys has quietly built more than a fan interface. It’s built a reusable template for immersive digital fandom—a framework that can travel across industries where experience is a competitive advantage and context is currency.
Greyhound Research believes that the Infosys–Formula E alliance is a blueprint for transformation at the intersection of analytics, architecture, and attention. The finish line isn’t a dashboard. It’s a fan who feels more because they understand more.
Analyst In Focus: Sanchit Vir Gogia
Sanchit Vir Gogia, or SVG as he is popularly known, is a globally recognised technology analyst, innovation strategist, digital consultant and board advisor. SVG is the Chief Analyst, Founder & CEO of Greyhound Research, a Global, Award-Winning Technology Research, Advisory, Consulting & Education firm. Greyhound Research works closely with global organizations, their CxOs and the Board of Directors on Technology & Digital Transformation decisions. SVG is also the Founder & CEO of The House Of Greyhound, an eclectic venture focusing on interdisciplinary innovation.
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