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One free scan finds every outdated or missing driver and matches the right update for your exact hardware.Free scan · exact hardware matchNASCAR teams use AI and related analytics to help engineers, crew chiefs, and drivers find useful signals in race records, measured parts, driver feedback, and—where series rules permit—live vehicle data. The systems can speed up analysis, flag potential issues, and inform setup or pit decisions. Published examples describe decision support, not an AI that independently chooses a winning setup or guarantees faster lap times.
What AI does—and does not do—in NASCAR analysis
“AI” can describe several different tasks, and the available NASCAR examples do not point to one standard system used by every team. Data collection captures information; analytics organizes and compares it; simulation estimates how a configuration may behave; and AI can help interpret or prioritize information, such as transcribing radio or identifying likely damage in photographs. These methods can work together, but they are not interchangeable.
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The practical goal is to help people make decisions with a large amount of information arriving from different sources. A system may surface a pattern or estimate, but engineers still interpret the evidence, and the driver, crew chief, and team decide what to do. The published accounts do not establish that AI autonomously sets up NASCAR cars.
It is also important to separate the examples by source and scope. Hendrick Motorsports has described specific engineering and preparation workflows; Microsoft has reported on selected technology deployments with NASCAR and Hendrick; and GM has described AI applications across its motorsports efforts. These examples illustrate possible uses, not a verified inventory of current AI practices across every NASCAR team.
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How the analysis loop works across an event
A useful way to understand the work is as a loop: prepare from previous information, compare a real car and driver with that baseline, then use new evidence to inform the next decision. The individual tools vary, and not every step is an AI application.
| Stage | Information used in documented examples | What the analysis can support | Who makes the decision |
|---|---|---|---|
| Before the event | Prior races at the track, lap times, speed, tire pressures and temperatures, vehicle wear, and driver comments on steering, throttle, and braking | A prepared plan and comparisons with earlier visits | Engineers and drivers interpret the reports together |
| Engineering preparation | Scanned part dimensions stored in files and a database, alongside simulation tools | Virtual assembly, configuration work, and investigation of outliers | Engineers choose what to examine and how to apply the findings |
| During competition | Radio, permitted car-to-pit telemetry, and photographs, in GM’s account | Faster review of driver comments, strategy estimates, or possible damage | Race personnel assess the output and decide whether to act |
| Across operations | Operational and engineering data represented in analytics and visualization systems | More timely access to information for setup and pit choices | Team staff use the information in context |
The stages are connected, but they answer different questions. A historical report helps establish a reference point; a part scan describes a physical component; simulation evaluates a virtual configuration; and a live estimate may help frame a race strategy choice. Combining them can give engineers a broader picture, but it does not make the underlying inputs identical or remove the need to check whether a comparison is relevant.
Before race weekend: building a track-specific baseline
In a reported Hendrick Motorsports workflow, engineers compile track and car reports before the event, then add observations from practice and qualifying. Microsoft says the reports include information from previous races at that track: lap times, speed, tire pressures and temperatures, vehicle wear, and driver feedback about steering, throttle, and braking. That combination matters because a lap-time number alone does not explain what the driver experienced or what condition the car was in.
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For example, a report that includes both a lap time and driver comments can help staff ask a more useful question than “Was this lap faster?” They can also consider whether the driver described a different steering, throttle, or braking response, and whether tire temperatures, pressures, or vehicle wear differed. The documented workflow does not prescribe a specific adjustment from any one reading; it supplies context for people deciding what to investigate and whether past experience applies.
Practice and qualifying observations then add information from the current event to the pre-race baseline. That does not mean a historical result is a recipe that can be copied unchanged. The value is in being able to compare the current car with earlier visits and discuss the differences, while engineers and drivers decide which similarities matter for the plan.
This is a documented team-specific example, not proof that every team uses the same report, stores the same fields, or follows an identical preparation process. Nor does the existence of an extensive history mean the past configuration is automatically right for the next race: the information supports comparison and planning, while the people involved judge its relevance.
Part measurement and simulation: analyzing the car itself
Not all useful engineering information comes from a car circulating on track. NASCAR’s February 2025 report described Hendrick Motorsports scanning parts to create three-dimensional models, exporting measurements into files, and ingesting those files into a database that feeds simulation. As quality control supervisor Roy Crump put it: “So the Excel file is ingested into our database, and that feeds our simulation.”
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Race engineers can select parts and virtually assemble a car using this workflow. The data can also help the team identify measurements that stand out for further investigation or suggest a part for a particular event. In other words, measurement and simulation can narrow what engineers need to inspect or evaluate; the report does not say that a model independently determines the final setup.
The sequence is useful to understand: physical parts are measured; measurements are saved in files; the files enter a database; and that information feeds simulation. Engineers can then use the simulation environment to select components and virtually assemble a car. This gives them a way to evaluate combinations without treating every possible assembly as a physical track test. The account does not quantify how many combinations are tested or how much time the process saves.
Measurements can also help identify an outlier: a value that merits a closer look rather than being accepted without question. An unusual measurement may prompt investigation, while distributions of measured parts can help the team decide which component may suit a particular event. That is a screening and engineering-support role, not proof that a flagged part is defective or that a suggested choice will improve performance.
NASCAR reported that 85% of the Next Gen vehicle was built from supplier-sourced parts. That figure is context for why quality control and measurement matter in the reported workflow. It is not an AI statistic, a measure of setup accuracy, or evidence of a performance gain.
These activities should not all be called AI. Scanning a part, storing measurements, and running a simulation are data and engineering processes. AI may be relevant to some analytical tasks, but the NASCAR report does not characterize the entire metrology-to-simulation workflow as an AI system. That distinction helps avoid crediting AI for work done by measurement equipment, databases, simulation software, and engineering judgment.
During a race: prioritizing information and supporting strategy
GM has described several AI applications in motorsports, including functions relevant to race operations. Its account says AI can transcribe driver radio despite noisy conditions, helping analysts follow multiple conversations and identify remarks about changing track conditions. The advantage is faster access to what drivers are saying; a transcript still needs interpretation in context.
Radio transcription is a prioritization aid: it can make relevant remarks easier to find while analysts are monitoring multiple conversations. The words alone may not answer what action to take. Staff still need to interpret what a driver means, relate the remark to other information, and decide whether it changes a setup or strategy discussion. GM describes the capability; its account does not establish that every NASCAR team uses it.
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GM says that in racing series where rules permit vehicle-to-pit telemetry, its systems monitor information such as fuel consumption and use simulations to predict tire wear. Those estimates can support pit-timing decisions. The qualification about rules is essential: this account does not establish that every NASCAR car transmits every engineering measurement continuously to its team, or that the same data access is available in every series.
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Telemetry is therefore not a blanket promise of unrestricted live access. Whether particular information can be transmitted depends on the rules for the series. Even where permitted, fuel consumption or a tire-wear estimate is an input to a decision rather than a command to stop. Race personnel must weigh the estimate against the situation and decide whether a pit call makes sense.
Photographs and possible damage
GM also says AI can tag photographs quickly and alert engineers to likely damage. That may help a team assess whether an incident warrants a stop—or avoid an unnecessary stop when an apparent incident caused no damage. The system is described as a way to direct attention, not as a guarantee that damage will always be detected or that a stop decision can be made without human review.
Image tagging addresses a different problem from telemetry: it helps sort visual evidence and flag images that may deserve attention. It can help distinguish an incident that appears serious from one where no damage is visible, but the alert is not a definitive inspection. An engineer still has to assess what the image shows and decide whether further action is warranted.
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These are capabilities GM describes across its motorsports efforts. The account does not establish that every NASCAR team uses every function or that these tools are deployed in the same way at every event.
Why speed of information matters to race decisions
In a race, an analysis is useful only if it reaches the people making a decision in time to matter. A Hendrick Motorsports race engineer, Taylor Moyer, expressed that point in Microsoft’s report: “If I can get the data out of the race car faster, I can make a decision faster.” The quote describes the value of timely information, not a measured improvement in lap time or results.
Microsoft’s NASCAR and Hendrick coverage describes Azure-based high-performance computing and real-time analytics for setup and pit choices, as well as Power BI for visualizing operational data. These examples illustrate how computing and visualization can make information available to team staff; they are vendor-published accounts of selected deployments, not a comprehensive or current list of every team’s systems.
High-performance computing and real-time analytics address the handling and analysis of substantial information flows; visualization helps staff inspect and communicate operational data. They are infrastructure and workflow components, not a setup recommendation by themselves. Microsoft’s coverage describes selected deployments and does not provide a complete inventory of the computing systems used throughout NASCAR.
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1Fix the driver behind crashes, sound loss and screen glitches2Clear out junk files and repair common Windows errors3Scan for outdated or missing drivers - takes under a minuteIn practical terms, analysis can help staff consider a setup or strategy question with more evidence at hand. It does not remove the need to judge whether the data is relevant, whether it is reliable, and whether an action makes sense for the particular race situation. A quick output is not automatically a correct decision.
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How these methods compare
The documented approaches are best compared by asking what information they use, when it is available, and what sort of output they provide. The comparison is about methods, not competing commercial products: the published accounts do not establish equivalent products that a reader can buy or compare feature by feature.
| Method | Main input | Typical timing in the examples | Useful output |
|---|---|---|---|
| Historical preparation | Prior race records, vehicle information, and driver feedback | Before an event, with practice and qualifying added | A track-specific baseline for discussion and planning |
| Part measurement and simulation | Scanned dimensions and selected components | Engineering preparation | Virtual assembly, comparison, and outlier investigation |
| AI-assisted radio and image review | Radio audio or photographs | During operations, as information arrives | Transcripts, tags, or alerts that help prioritize review |
| Permitted telemetry and estimates | Vehicle information such as fuel consumption | During competition where rules allow transmission | Estimates, such as predicted tire wear, that can inform strategy |
| Analytics and visualization infrastructure | Operational and engineering information | Across preparation and race operations | Analysis and visual access to support setup or pit choices |
Each method has a different human handoff. Historical reports inform a conversation; simulation lets engineers examine virtual configurations; a radio transcript or image alert points analysts toward material to review; and an estimate contributes to strategy deliberation. The output is not the same as the decision. That distinction is central to understanding what an AI-assisted workflow can—and cannot—claim.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What is established, and what remains unproven
The published examples support a clear but bounded conclusion: analytics and AI can help teams organize, interpret, and prioritize information, while simulation and measurement contribute to engineering evaluation. They do not establish a universal NASCAR AI workflow or prove a quantified performance benefit.
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- Established in the cited examples: Hendrick has reported track-specific preparation using historical records and driver feedback, and a separate workflow that scans parts, stores measurements, and feeds simulation.
- Described by GM: AI-assisted radio transcription, analysis involving fuel and tire estimates where telemetry rules allow it, and photographic triage for possible damage.
- Not established by these accounts: a lap-time improvement, setup-accuracy gain, finish-position effect, or win-probability increase attributable to AI.
- Not established: that all NASCAR teams use the same AI, have the same access to telemetry, or deploy every capability described by a manufacturer or technology provider.
NASCAR’s 2014 reporting on Hendrick’s CAD/CAM work and Siemens Teamcenter offers historical context for connecting engineering files, configurations, simulation, parts information, and track data. Because that account is from 2014, it should not be read as confirmation that the exact same systems remain in use today.
What the published examples do not tell us about cost or access
The accounts describe specialized team infrastructure—analytics and cloud computing, simulation, telemetry where permitted, and equipment for measuring parts—but do not publish a price for recreating these workflows. They also do not identify a specific consumer product that would reproduce a professional team’s setup analysis. Generic computers, sensors, or racing merchandise are not substitutes for the integrated systems and expertise described here.
For a team, access is not just a matter of acquiring software. The documented examples depend on relevant data, engineering tools, measurement processes, and staff who can interpret outputs. Telemetry adds the separate constraint of series rules. The published accounts do not provide a standardized equipment list, minimum budget, or universal technical requirement, so a precise cost or do-it-yourself specification cannot be responsibly inferred from them.
How to read claims about AI-powered NASCAR setups
When evaluating a claim that AI improved a car setup, ask what the system actually did and what evidence supports the result. “AI analyzed the data” could mean anything from transcribing a radio call to surfacing a pattern in past records. Those tasks differ from measuring parts, simulating a configuration, and deciding which setup to run.
- Identify the input: Was the system working from historic lap and vehicle records, driver comments, measured parts, images, radio, or permitted telemetry?
- Identify the output: Did it produce a transcript, flag an anomaly, estimate wear, visualize data, or evaluate a virtual assembly?
- Check the human role: Did engineers or race personnel review the result and decide what to do, or is the claim implying autonomous setup selection?
- Check the scope: Is the statement about one team or manufacturer’s described deployment, or does it claim to represent NASCAR as a whole?
- Look for a measured outcome: A system’s described capability is not the same thing as evidence that it improved lap time, setup accuracy, or race results.
Keeping those distinctions in view gives a more accurate picture than treating every digital engineering tool as AI. In the documented cases, the strongest description is that AI and analytics help racing professionals process information and make decisions; setup and strategy remain human responsibilities informed by data, simulation, and experience.
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Frequently Asked Questions
Does AI choose a NASCAR car’s setup?
The published examples describe AI and analytics as decision-support tools. They can help organize records, surface information, or support estimates, while engineers, drivers, and race personnel interpret the results and decide what to do. The accounts do not show AI independently choosing a winning setup.
What data do NASCAR teams use when preparing for a track?
In a Microsoft-reported Hendrick Motorsports workflow, track and car reports include prior races at that track, lap times, speed, tire pressures and temperatures, vehicle wear, and driver feedback about steering, throttle, and braking. Practice and qualifying observations are added as the event approaches.
Do NASCAR cars continuously send all their data to the team?
The documented GM account qualifies telemetry use by series rules. It describes monitoring information such as fuel consumption and predicting tire wear in series that permit vehicle-to-pit telemetry; it does not establish that every NASCAR car transmits every engineering measurement continuously.
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How does part scanning help with car setup?
NASCAR reported that Hendrick Motorsports scans parts into three-dimensional models, exports measurements into files, and feeds those files through a database into simulation. Engineers can select parts for virtual assembly, investigate outliers, and consider components for an event. This supports evaluation; it does not automatically determine a final setup.
Has AI been shown to make NASCAR cars faster?
The cited accounts do not provide a measured AI-attributable gain in lap time, setup accuracy, finish position, or win probability. They describe capabilities and selected workflows, not proof of a quantified performance improvement.
Can a fan buy the same AI setup system or reproduce it at home?
The published accounts describe specialized team infrastructure and do not identify a consumer product that reproduces the workflows. They also provide no standardized equipment list or price for recreating them.
The Bottom Line
NASCAR AI is best understood as decision support within a larger engineering process. Documented examples include historical reports and driver feedback, scanned parts feeding simulation, and GM-described tools for radio, image, fuel, and tire analysis. Rules constrain telemetry, the deployments are not universal, and no cited account proves a quantified performance gain. People still turn the information into setup and race decisions.
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