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Formula 1 and its teams use AI to make fan experiences more tailored, interactive and accessible. Examples range from a race-strategy simulator that responds to a circuit a fan draws, to personalized newsletters, app features, multilingual audio and a planned F1.com companion for questions about the 2026 technical regulations. The key distinction is that not every feature is a recommendation engine: some adapt to a fan’s profile, while others respond to that person’s choices in the moment.
What does AI personalization mean in Formula 1?
In this context, personalization means adapting the information, interaction or content a supporter receives to their interests, behavior, language or input. It can happen before a race, during a weekend, or on demand. A tailored newsletter and a voice that reads an article in a chosen language are both examples, even though neither needs to predict a fan’s next click.
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Formula 1 is the championship and rights holder; individual teams also build fan-facing experiences with technology partners. Their systems serve different purposes. Championship-level services can draw on F1 content and fan relationships across the sport, while a team’s app or subscriber program can focus on that team’s audience. The examples publicly described so far are a mix of launched products, announced capabilities and partnership use cases—not one shared AI platform used by every team.
The underlying ingredients vary. They may include a person’s selected interests or interactions, historical race data, vehicle telemetry, editorial content, or information a fan supplies directly. AI can then help produce an explanation, select or adapt content, answer a question, generate an audio presentation, or make an interactive simulation respond to a scenario. The resulting experience is only as specific as its inputs and the product’s stated capabilities.
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How does the F1 Real-Time Race Track experience personalize strategy?
F1 and AWS launched Real-Time Race Track on 14 May 2025. Instead of choosing from a fixed set of circuits, a fan draws a track, selects a location and receives AI-generated analysis shaped by those choices. It is the clearest example of personalization through user-created input rather than a stored fan profile.
What a fan can do
- Draw a circuit. Use a mouse or touchscreen to create a track layout.
- Choose a location. The selected location changes the weather context used in the experience.
- Request analysis. The system provides estimated top speed and projected lap time, plus two strategy options. The options cover pit timing, tyre recommendations and adjustments for weather.
- Explore by voice or share the result. The experience supports voice consultation and can create a retro-style poster based on the circuit.
AWS says the experience uses Amazon Bedrock for personalized track creation and Amazon Nova Pro for analysis. Amazon Nova Sonic powers voice consultation, while Amazon Nova Canvas creates the poster. The important personalization mechanism is that track geometry, location and resulting weather context differ according to what each fan enters. It is a scenario tool: its output is a projection for a fan-created circuit, not a prediction of an actual race result.
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The experience also illustrates how live-sport data can support fan education without requiring the fan to inspect raw telemetry. Amazon Web Services said in 2025 that more than one million data points per second come from F1 cars. That is an organization-published figure about the sport’s data stream; it does not mean every fan-facing AI feature processes every point, or that Real-Time Race Track uses that full stream for each response.
How do F1’s newsletters and fan services use profile data?
F1’s Fan Personalisation Programme focuses on communications tailored to customer segments and preferences. An AWS case study describes F1 bringing data from multiple sources into an AWS data lake and connecting models to Salesforce Data Cloud. The stated uses include AI-written newsletters with dynamic content for different customer segments and subject lines generated according to preferences.
This is different from the circuit simulator. The simulator adapts to an input a fan creates for one session; a personalized newsletter uses information associated with a customer relationship to shape communication. The case study describes the intended and reported use cases, but it does not establish how every subscriber is segmented, how frequently each message changes, or how much those changes affect engagement.
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F1’s relationship with Salesforce has included unified fan profiles and personalized newsletters and communications since 2022, according to F1 and Salesforce. A unified profile can help organize information from multiple touchpoints around a fan, but public descriptions do not specify the identity-matching rules or the exact controls available to fans. It is therefore more accurate to say that F1 has described a unified-profile approach than to assume every interaction across F1 and its teams is joined into one record.
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F1 and Salesforce also announced an AI fan companion for F1.com in 2026. The announced companion is intended to answer questions about the 2026 technical regulations using trusted F1 sources, monitor trending questions and help shape future educational content. This is a distinct use from targeted marketing: its purpose is to explain the sport and help identify what fans are asking. The announcement describes the intended capabilities; it should not be read as evidence that every question receives a complete answer or that the companion is available in every market or language.
What are teams doing with AI and fan apps?
Team examples show that “personalized” can mean a customized digital product, targeted communications, easier access to content or more ways to participate. These programs are associated with a team’s own identity and audience rather than automatically being features of the F1 championship app.
Scuderia Ferrari HP: a data-led mobile experience
IBM became Scuderia Ferrari HP’s official Fan Engagement and Data Analytics Partner beginning in 2025. The partnership describes using current and historical data to create custom insights and personalized content, alongside a reimagined mobile app. Announced app features include Race Centre, Italian-language support, race summaries, fan messages and interactive polls; additional features were planned during 2025.
These features combine different kinds of participation. A Race Centre and summaries package race information; language support changes how it can be consumed; messages and polls let supporters contribute. The partnership announcement does not establish that every feature is generated by AI, so it is better to describe the broader data and fan-engagement program without labeling each app function an AI feature.
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McLaren: audience segments, app experiences and subscriber offers
McLaren’s Salesforce partnership describes audience segmentation, tailored marketing content, personalized app experiences, digital guest check-in and offers for McLaren Plus subscribers. This is a broader customer-experience example: some capabilities concern communications and content, while others concern event access or offers. The public description does not provide a detailed model of what data each feature uses or show that an offer is individually selected by an AI system.
Aston Martin: making articles easier to hear and access
Aston Martin describes Ai.lonso as functionality that reads selected articles in an AI voice of Fernando Alonso. At launch it supported English, Spanish and French, with more languages planned. This example makes personalization about presentation and language, not just what content a recommendation system chooses. The documented capability is article narration; it does not establish that the system can hold an open-ended conversation in Alonso’s voice.
How do these experiences differ?
The examples vary in their inputs, timing and purpose. The table separates the capabilities described publicly from what should not be inferred from them.
| Experience | Input or context | Interaction and output | Personalization type | Timing and evidence |
|---|---|---|---|---|
| F1 and AWS Real-Time Race Track | Fan-drawn track, selected location and resulting weather context | Track analysis, projected lap time and top speed, two strategy options, voice consultation and a shareable poster | Adapts analysis and creative output to a fan-created scenario | Launched 14 May 2025; on-demand interactive experience |
| F1 Fan Personalisation Programme | Data combined from multiple sources and customer segments or preferences | Dynamic newsletter content and generated subject lines | Tailors customer communications | Use cases described in an AWS case study; no public engagement-lift figure established |
| F1.com AI fan companion | Fan questions and trusted F1 sources | Answers about the 2026 technical regulations; trending-question monitoring | Adapts the response to a question and helps inform future educational content | Announced for 2026; availability and market coverage are not specified in the announcement summarized here |
| Scuderia Ferrari HP and IBM | Current and historical data, plus fan participation | Custom insights and personalized content; announced app features include summaries, polls and fan messages | Team-specific content and participation | Partnership began in 2025; more app features were planned during that year |
| McLaren and Salesforce | Audience segments and subscriber relationship | Tailored marketing, app experiences, digital guest check-in and McLaren Plus offers | Team audience and subscriber services | Partnership use cases; specific AI methods and feature availability are not detailed |
| Aston Martin Ai.lonso | Selected articles and chosen supported language | Article narration in an AI voice of Fernando Alonso | Changes language and audio presentation | At launch: English, Spanish and French; additional languages were planned |
One further figure helps put fan-facing features in context. AWS and Amazon reported 23 data-driven F1 Insights during broadcasts in 2025. Those broadcast insights are a separate way of translating data for viewers; their existence does not mean every viewer receives a uniquely personalized insight.
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Can AI explain race strategy or the 2026 regulations?
Yes, the documented examples point to two different kinds of explanation. Real-Time Race Track lets a fan explore how a chosen circuit and location affect projected performance and strategy. The announced F1.com companion is intended to answer questions about the 2026 technical regulations from trusted F1 sources. Neither example should be confused with an official race forecast or a substitute for the full regulation text.
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For strategy, the value is the ability to change a scenario and see the analysis respond. That can help a newcomer understand why circuit shape, weather, tyre choice and pit timing matter together. But the output remains a model-generated explanation of a hypothetical setup, and the public description does not provide a validation score or guarantee that a suggested strategy would be optimal in a real race.
For regulations, grounding answers in trusted F1 sources is important because technical rules are precise and can change. The 2026 companion announcement describes that grounding as part of the intended system. A reader relying on a specific rule for technical, sporting or professional purposes should still check the official regulation text and relevant updates; the announcement does not establish how the companion handles citations, ambiguous questions or changes after launch.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What are the limits of the public evidence?
Public partnership and product announcements show deployments or planned capabilities, but they do not amount to a controlled comparison of fan conversion, retention or satisfaction. No independent ROI statistic is established in the material summarized here. A vendor-reported audience or data-volume figure can indicate scale, but by itself it does not prove that a feature caused more engagement.
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Scan for outdated or missing drivers - takes under a minuteDriver Scan →Clear out junk files and repair common Windows errorsFree Scan →Fix the driver behind crashes, sound loss and screen glitchesFind Drivers →For example, Formula 1 and Salesforce reported more than 827 million global fans in 2026. This is an organization-published audience figure, not a count of people using an AI feature or a measure of how many fans have a unified profile. Similarly, the more than one million car data points per second reported by AWS and the 23 data-driven broadcast insights reported by AWS/Amazon in 2025 describe the data-rich setting in which fan products operate, not individual outcomes for every user.
It is also important to distinguish personalization from automation. A feature may use AI to generate or present content without selecting that content from a person’s history. Conversely, a newsletter may be tailored by customer segments without every sentence being written uniquely for one individual. The product descriptions available for each example do not always reveal exactly which step is automated or how the system chooses its output.
- Launch status can change. Some features are described as launched, while others were announced or planned. An announcement is not proof of availability in every country, app store or language.
- Team and championship identities are not necessarily one profile. F1 has described unified fan profiles for its own communications, and teams describe their own subscriber or audience programs. The public descriptions do not establish a universal identity layer connecting all teams with F1.
- Language support is feature-specific. Aston Martin’s initial three languages apply to Ai.lonso at launch; they do not establish the language availability of other F1 products.
- Performance claims need attribution. Figures published by F1, AWS, Amazon or Salesforce should be treated as their reported figures unless independently measured results are available.
What does this mean for F1 fans?
For fans, the practical change is more ways to meet the sport at the level of detail they want: test a strategy in a made-up circuit, get communications shaped to stated interests, follow team-specific app content, hear an article in another language, or ask questions about technical rules. The strongest examples are useful because they expose a choice or make a complex subject easier to approach, rather than simply adding the word “AI” to a digital service.
Today’s examples also show different degrees of personalization. Real-Time Race Track responds directly to the fan’s inputs; newsletters and subscriber services use segments or preferences; language and audio features personalize access; and a regulation companion is designed to tailor explanations to questions. What they do not demonstrate is a single AI that knows each supporter across every team and every F1 platform, or independently proven gains in loyalty or engagement.
Frequently Asked Questions
Does F1 use AI to predict who will win a race?
The fan-facing examples described here focus on explaining scenarios, tailoring communications and answering questions, not publishing a guaranteed winner prediction. A projected lap time for a fan-drawn circuit is a hypothetical output, not a forecast of an actual Grand Prix.
Can I use the F1 AI companion to check a technical rule?
The 2026 companion was announced as answering regulation questions from trusted F1 sources, but its exact availability and answer-handling details are not specified here. For a decision that depends on precise wording, consult the official regulation text and current updates.
Do F1 teams share one AI profile for every fan?
Public descriptions point to separate championship and team programs: F1 has discussed unified fan profiles for its communications, while teams describe their own audience and subscriber experiences. They do not establish a universal profile shared across every team and F1 service.
Can AI-generated F1 content be wrong?
Generated analysis and explanations can be incomplete or unsuitable for a real-world decision, particularly when the scenario is hypothetical or rules have changed. Check consequential claims against official race or regulation information rather than treating an AI response as authoritative.
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