When the tyre industry speaks today about digitalisation, virtual validation and sustainability, it often does so in abstract terms – models, data sets, algorithms and computing power. Yet, at its core, tyre development remains an intrinsically human endeavour. Grip, stability, steering feel and ride comfort are ultimately experienced by people, not machines. Bridging that divide between digital precision and human perception has become one of the defining challenges of modern tyre R&D.

Few companies sit more squarely at that intersection than Ansible Motion. Known globally for its high-fidelity Driver-in-the-Loop (DIL) simulators, the company has, over the past decade and a half, quietly reshaped how vehicle manufacturers, motorsport teams and – most notably – tyre makers think about simulation-led development.

At the centre of this evolution is Salman Safdar, Executive Director at Ansible Motion, whose perspective is shaped not only by technological ambition but also by a deep understanding of how tyres influence the driving experience in ways that no other vehicle component can.

ORIGINS ROOTED IN FIRST PRINCIPLES

Although Ansible Motion is frequently associated with motorsport and advanced vehicle simulation, its origin story is less about racing glamour and more about questioning inherited assumptions. When the company was founded in 2009, the dominant simulator architectures used in motorsport had been adapted from aerospace applications – an approach that Safdar and his colleagues believed was fundamentally flawed.

“When we started the company in 2009, it was to provide an alternative to aerospace-derived simulator architectures that were beginning to make their way into motorsport applications. At the time, many high-level racing teams were investing in technologies that were, from a first principles perspective, better suited to simulating aircraft than ground vehicles,” Safdar explains.

Aircraft and cars, after all, interact with their environments in profoundly different ways. Aerodynamic forces act over long distances and gentle arcs, while tyres generate immediate, localised forces through a constantly changing contact patch. Subtle road surface irregularities, rapid directional changes and short-range visual cues define the driving experience on the ground.“We intentionally departed from the popular, but limited, hexapod – or Stewart platform – and invented a novel, six-degree-of-freedom motion system built in logical layers corresponding to primary ground vehicle axes. The intention was that it would be linear, agile and highly dynamic – and that it would be much better suited to simulating ground vehicles than anything else,” Safdar explains.

Tyres, he notes, were central to that architectural rethink from the very beginning. “Tyres are one of the fundamental reasons why ground vehicle simulators need to be architecturally different from aerospace simulators. Directional changes are immediate with tyres… subtle disturbances that result from pavement irregularities are ever-present… human sensory experiences regarding vehicle control and stability are fundamentally different,” he says.

In that sense, tyre performance was embedded in Ansible Motion’s DNA long before the tyre industry itself became a direct customer.

FROM VEHICLE OEMS TO TYRE MANUFACTURERS

For much of its early life, Ansible Motion’s simulators were deployed primarily by vehicle manufacturers and elite motorsport teams. The tyre industry, traditionally more conservative in its adoption of immersive simulation, took longer to engage directly. That has now changed decisively.

“Today, the tyre industry is a core strategic pillar in our simulation R&D and sales pipeline, alongside OEM vehicle development, advanced mobility research programmes and motorsport. Currently, Michelin, Continental, Nexen, and most recently, Kumho Tire are trusting Ansible Motion driving simulators to develop their next generation of tyres,” Safdar says.

This shift reflects broader pressures reshaping tyre R&D. Development cycles are shortening, sustainability targets are tightening and the cost of physical testing – both financial and environmental – is under intense scrutiny. At the same time, the rise of electric vehicles has introduced new performance trade-offs, forcing tyre engineers to balance rolling resistance, noise, durability and grip in unfamiliar combinations.

Against this backdrop, Driver-in-the-Loop simulation has emerged as a powerful complement to conventional modelling and laboratory testing.

WHY DRIVER-IN-THE-LOOP MATTERS

At its simplest, DIL simulation places a human driver inside a virtual vehicle, interacting in real time with simulated tyres, roads and vehicle systems. For Safdar, the value lies precisely in that human presence.

“The key aspect of Driver-in-the-Loop simulation is the human element. Unlike other simulation and lab testing approaches, DIL simulation invites – in fact, it requires – human participation,” he says.

Modern tyre development depends on a complex interplay between objective metrics and subjective perception. Measurements of braking distance, lateral force or rolling resistance must ultimately align with how a tyre feels to a driver – how it communicates grip, how it responds on centre, how it rides over imperfect surfaces.

DIL simulators allow these subjective attributes to be explored much earlier in the development cycle and more frequently than is possible with physical prototypes alone. Crucially, this happens in parallel with traditional simulation and modelling work, not in isolation.

“This allows critical decisions to be made early enough to avoid delays and unexpected expenses in later stages of programmes. It also reduces costs and environmental impacts due to reduced prototyping,” Safdar notes.

Beyond efficiency gains, Safdar emphasises a less tangible but equally important benefit: collaboration. DIL simulators function as hubs where engineers, test drivers and decision-makers can converge around a shared experience.

“In a sense it enables tyre engineers to be engineers – so they can be more creative in a lower-risk environment,” he says.

THE KUMHO TIRE CASE STUDY

The partnership with Kumho Tire provides a clear illustration of how these principles translate into practice. Framed under the banner ‘Driving the Future with Digital Tyres’, the collaboration reflects a shared ambition to accelerate tyre development through digitalisation while embedding subjective assessment earlier in the design process.

“Both Kumho Tire and Ansible have a shared ambition to accelerate tyre development through digitalisation and to inject subjective assessments into earlier tyre design stages,” Safdar says.

Achieving that ambition requires more than just motion hardware. High-fidelity sensory cueing – perfect synchronisation between motion, visuals and steering feedback – is essential if drivers are to trust what they feel in the simulator. Equally important is process optimisation: a computational environment that integrates multiple modelling tools seamlessly and allows engineers to run tests efficiently and extract meaningful data.

Modern tyre development depends on a complex interplay between objective metrics and subjective perception. Measurements of braking distance, lateral force or rolling resistance must ultimately align with how a tyre feels to a driver – how it communicates grip, how it responds on centre, how it rides over imperfect surfaces.

Safdar believes Ansible Motion’s strength lies in precisely that integration capability. “We believe that Kumho Tire, in part, selected Ansible Motion due to our expertise in integrating advanced tyre models with other HIL, MIL, SIL software and hardware elements,” he explains, referencing hardware-, model- and software-in-the-loop methodologies. High-fidelity digital road surfaces, developed by Ansible Motion’s sister company rFpro, also play a key role.

There is also a market reality underpinning the partnership. “Within a highly competitive space, Ansible Motion supplies over 50 percent of engineering-grade DIL simulators to the marketplace. So perhaps there is some confidence in working with us,” Safdar notes.

FROM ASPIRATIONS TO MEASURABLE OUTCOMES

Digital transformation initiatives often falter at the point where aspiration meets execution. Safdar is candid about the need for clear targets and measurable outcomes if DIL simulation is to deliver real value.

“It’s important to have the aspirations in the first place. But it’s important to clearly identify targets and be able to measure achievements towards them,” he says.

He illustrates this using the concept of multi-attribute spider – or radar – charts, commonly used by tyre engineers to visualise trade-offs. For electric vehicle tyres, key attributes might include rolling resistance, durability, noise, wet and dry traction, load capacity and material sustainability. Improvements in one area often come at the expense of another.

“The end goal is to create a tyre that strikes an acceptable balance for a particular vehicle application,” Safdar explains.

The same logic applies to high-performance tyres, albeit with a different set of priorities: dry braking, wet handling, comfort, on-centre feel and tread wear, among others.

“Designing a tyre is a complex process. The utility of DIL simulation lies in its ability to keep real people involved with conceptual – digital – explorations of all the above trade-offs,” he says.

In practical terms, success can be measured in several ways. How much time was saved in reaching a design decision? How many prototype tyres were avoided? Did virtual prototyping improve alignment between objective data and subjective perception?

In some cases, entirely new metrics emerge, such as improved communication between tyre suppliers and vehicle OEMs during fitment programmes.

REPLICATING TYRE-ROAD INTERACTION

A recurring scepticism surrounding simulation is whether virtual environments can ever replicate the complexity of real-world tyre-road interaction with sufficient fidelity. Safdar’s response is clear: the fidelity depends less on the simulator itself and more on the quality of the models it integrates.

“DIL simulation – except for the human participant – is indeed a virtual environment. This means that human-experienced ‘tyres’ and ‘roadways’ and ‘vehicles’ are computer representations,” he says.

Ansible Motion does not develop tyre, road or vehicle models in-house. Instead, it provides an open, scalable co-simulation architecture – the Distributed Data Bus (DDB) – that connects industry-leading third-party models and customer-developed tools in real time.

“This gives our customers an engineering sandbox where they can use and combine different models that come from trusted third-party simulation providers as well as models that they might develop in-house,” Safdar explains.

The result is a test environment where subjective and objective assessments are conducted much as they would be on a proving ground – except that changes are made with keystrokes rather than tools, and hundreds of evaluations can be run without interrupting a driver’s mental state.

Safdar cites a recent example from Ansible Motion’s UK R&D centre, where a customer ran parallel DIL sessions on opposite sides of the globe. Within four hours, the teams gathered sufficient data to inform the next phase of tyre development. The equivalent physical testing, used as a correlation benchmark, had taken two weeks.

“Test drivers were scoring physical tyres against virtual tyres and seeking correlation within five percent – which they achieved,” he says.

THE DELTA S3 ECOSYSTEM

Central to many of these applications is Ansible Motion’s Delta S3 class of DIL simulators, including variants such as the Delta S3 Spin and S3 Thrust. Safdar is careful to describe them not merely as platforms but as complete ecosystems.

“They are turn-key DIL ecosystems that include all aspects of sensory cueing, including high-fidelity motion, visuals, steering feedback, haptics and audio,” he says.

Correlation with real-world data, he argues, is primarily a function of model quality rather than simulator mechanics. The simulator’s role is to deliver sensory cues accurately and collect driver inputs faithfully, while the DDB ensures synchronised execution across all models.

“If a simulator session and its supporting models are set up correctly… correlation is typically not an issue,” Safdar says. Deviations, when they occur, are often treated as valuable insights that help refine the models themselves.

WHERE SIMULATION DELIVERS THE GREATEST VALUE

From a tyre engineer’s perspective, the greatest benefits of simulation-based validation emerge early in the development cycle, when design freedom is at its highest.

“Simulation allows quick sanity checks on the numerous models and directs attention towards focused refinements of the selected few that show promise. This allows significant cost and time saving,” Safdar explains.

Further downstream, DIL simulation can eliminate entire rounds of prototype iterations, particularly in OEM fitment programmes. The return on investment is often easy for tyre manufacturers to quantify. Safdar points to Continental’s estimate that its simulator usage eliminates around 10,000 sets of test tyres per year, along with roughly 100,000 kilometres of physical driving.

MEETING THE EV CHALLENGE

Electric vehicles have intensified the demands placed on tyres. Higher torque loads, increased vehicle mass, stricter noise requirements and heightened sensitivity to rolling resistance all converge in ways that challenge traditional development approaches.

“Ansible Motion simulators can replicate a wide range of EV-specific scenarios, enabling engineers to tune vehicle performance by testing high torque behaviour, instantaneous load changes, lane changes, high-speed cornering and braking, while also modelling NVH and cabin noise more accurately,” Safdar says.

With lightweight vehicle structures limiting the use of sound-deadening materials, tyres play an increasingly prominent role in overall NVH performance. DIL simulators also allow safe exploration of energy efficiency, regenerative braking strategies and charge-deplete cycles.

Crucially, they enable engineers to explore rolling resistance optimisation in the context of competing trade-offs, such as reinforced constructions required to handle battery weight and torque.

DEFINING THE DIGITAL TYRE

Safdar defines a digital tyre as “a validated virtual representation of a real tyre which considers material properties, compound, tread design, tyre profile, contact patch information, aerodynamic and thermodynamic properties.”

Commercial viability depends on establishing strong correlation between digital and physical tyres, often through close collaboration with vehicle OEMs. When implemented effectively, virtual validation reduces reliance on early prototypes – saving time, cost and environmental impact.

“DIL simulation, in particular by incorporating the test driver’s subjective feedback at the early design phase, can inject insights that would otherwise not be discovered, thus avoiding costly late changes,” Safdar notes.

EXPANDING THE GLOBAL FOOTPRINT

Beyond established partnerships with Kumho, Continental and Michelin, Ansible Motion sees growing demand for digital R&D infrastructure across regions, particularly in Asia. OEM-driven virtual development programmes are increasingly mandating simulator use among suppliers.

Emerging markets and new entrants, especially in China’s rapidly expanding EV sector, represent a further growth opportunity. For these companies, simulation offers a way to compete with established brands on speed, cost and measurable ROI.

“Speed, reasonable cost and measurable ROI are key to success. And we’re happy that this falls within the core competencies of Ansible Motion’s products and solutions,” Safdar says.

LOOKING AHEAD

Over the next 5–10 years, Safdar expects tyre development to be shaped increasingly by digital twins and AI-generated models incorporating new compounds and manufacturing processes. Validation demands will rise, as will regulatory scrutiny, making simulation indispensable not only for development but also for homologation.

“Subjective driver evaluation remains a critical cornerstone of the driving experience and brand identity,” he says. Sustainability pressures will further accelerate the shift towards virtual validation.

“If we can help reduce environmental impacts and reliance on physical prototypes, we are happy to be a part of it,” Safdar concludes. “We would like to think that Ansible Motion is positioned as a key enabler of digital, data-driven tyre innovations.”

As generative AI transforms the way consumers and businesses discover products, tyre manufacturers face a fundamental shift in digital marketing. Roshan Mohan, Co-Founder and CMO at FlowBlinq, and Founder of PCG argues that the next battle will no longer be fought on search engine rankings but on whether AI systems choose to recommend a brand in the first place.

For more than two decades, tyre manufacturers have refined their digital strategies around a familiar formula: optimise websites for search engines, invest in paid advertising, strengthen dealer networks and build visibility through reviews and comparison platforms. Success depended largely on securing a prominent position on Google’s search results.

That formula, however, is beginning to change.

The rapid adoption of generative artificial intelligence (AI) platforms such as ChatGPT, Gemini and Claude is reshaping how consumers search for information, compare products and make purchasing decisions. Rather than browsing multiple websites, customers are increasingly asking AI assistants to recommend the most suitable product based on their specific requirements.

For tyre manufacturers, this represents far more than another digital marketing trend. It fundamentally changes how products are discovered.

According to Roshan Mohan, Co-Founder and CMO at FlowBlinq, and Founder of PCG, companies that continue treating AI as simply another marketing channel risk missing a much larger transformation. “The customer journey for tyres has already started bending around AI, and the change over the next three to five years won’t be a redesign of the funnel; it’ll be a shift in where the funnel begins,” he says.

FROM SEARCH ENGINES TO AI CONVERSATIONS

Historically, buying tyres has been an information-intensive process. Consumers often compare technical specifications, dealer recommendations, user reviews, pricing and compatibility before making a purchase. Search engines have traditionally served as the starting point for that journey.

Generative AI is simplifying this process dramatically.  Instead of opening multiple browser tabs and manually comparing products, motorists can simply ask an AI assistant for recommendations based on vehicle type, budget, driving conditions and performance priorities. The AI then synthesises information from numerous sources into a single response. “What changes for the buyer is effort, not intent. They still want the right tyre for their car and budget, but instead of researching options and making the final comparison themselves, they are increasingly describing their needs to AI and letting it identify the best solution,” Mohan explains.

This shift effectively transfers much of the research process from the consumer to the AI model.

Industry forecasts suggest this transition is already underway. Gartner predicts traditional search engine volume will decline by 25 percent by 2026 as generative AI absorbs many queries that previously began with conventional search engines. Meanwhile, Checkout.com’s research indicates that consumers are embracing AI-assisted purchasing faster than many businesses are preparing for.

For tyre companies, the implication is profound: visibility may increasingly depend not on appearing first in search results but on being recommended within AI-generated answers.

THE RISE OF AI VISIBILITY

Search engine optimisation (SEO) has long centred on improving rankings through keywords, backlinks and domain authority. AI discoverability, Mohan argues, follows a very different logic.

“Traditional SEO was about ranking, winning a position on a page of 10 blue links. AI visibility, or Generative Engine Optimisation (GEO), is about being the answer rather than a link to the answer,” he says.

Unlike traditional search engines, large language models evaluate whether product information is sufficiently trustworthy, structured and complete before referencing it. If they cannot confidently interpret a manufacturer’s data, the brand may simply disappear from the recommendation altogether.

This places far greater importance on machine-readable product information than on conventional search optimisation. At the same time, AI systems are looking beyond a company’s own website to understand whether a brand is trustworthy. This makes it important for brands to have a presence across credible, independent sources, where editorial PR and genuine reviews can play a key role, rather than advertisements or advertorials. AI systems bring together these trust signals from multiple sources and present them to users in one place. This means the decision-making journey is increasingly shifting to the AI chat, where consumers can get a more comprehensive view before making a choice. Brands that build credibility across trusted sources will therefore be better placed to influence how AI systems recommend them.

Structured specifications, consistent product descriptions, schema markup and clearly organised technical information become essential because AI systems rely on these elements when generating responses.

FlowBlinq has developed what it describes as 17 Generative Engine Optimisation pillars to assess whether brands are sufficiently prepared for AI discovery. These include structured data quality, technical completeness and AI crawlability.

Perhaps more significantly, Mohan believes many companies have little understanding of how frequently AI platforms mention their products – or whether they are mentioned at all.

“Our citation tool runs a brand across ChatGPT, Claude and Gemini and shows, prompt by prompt, whether the brand gets cited, where it loses out to a competitor and where it’s simply absent from the answer altogether,” he adds.

TECHNICAL ACCURACY BECOMES A COMPETITIVE ASSET

Tyres differ from many consumer products because purchasing decisions depend heavily on technical specifications. Load index, speed rating, rolling resistance, wet grip, tread pattern and vehicle compatibility all influence suitability. Inaccurate recommendations can have genuine safety implications.

Mohan believes this makes structured product information particularly important for the tyre industry. “When product data is thin, a model doesn’t refuse to answer; it defaults to the brand it has the most confident, well-structured information about,” he adds. He warns that this tendency naturally favours manufacturers with richer digital product catalogues rather than necessarily those with superior products.

FlowBlinq’s research suggests considerable room for improvement. According to the company’s findings, 62 percent of Indian brand websites provide product descriptions that are insufficiently detailed for AI systems, while more than half lack product codes needed for accurate identification.

For tyre manufacturers, the solution is relatively straightforward but frequently overlooked.

Rather than embedding specifications within downloadable PDF brochures or image-based catalogues, companies should publish technical information directly on webpages in formats that AI systems can easily interpret.

Equally important is the broader digital reputation surrounding a brand. Mohan notes that AI systems increasingly rely on trusted third-party sources – including established news publications, Wikipedia and community platforms – to validate manufacturer claims before making recommendations.

AI ENTERS FLEET PROCUREMENT

The implications extend well beyond retail consumers. Business purchasing decisions often involve lengthy comparisons of performance, lifecycle costs, regulatory compliance and operational efficiency – precisely the type of structured analysis that generative AI performs well.

According to Mohan, procurement teams, fleet operators and original equipment manufacturers (OEMs) may adopt AI-supported purchasing even faster than retail buyers. “B2B tyre buying was never going to be immune to this, and it may move faster than consumer purchasing because procurement teams are exactly the audience generative AI tools were built to serve,” he says.

A fleet manager could ask AI to compare total cost of ownership across several tyre brands. An OEM purchasing team might request suppliers meeting specified rolling resistance or durability thresholds.

In such scenarios, manufacturers lacking accessible technical documentation risk exclusion before human procurement teams even begin formal evaluation. “The practical response isn’t a new sales deck. It’s making sure spec sheets, compliance documentation and comparative data exist in formats a model can read and trust,” Mohan says.

AI WILL ADVISE, BUT HUMANS WILL STILL DECIDE

While AI is poised to transform product discovery, Mohan believes the actual purchase decision will remain firmly in human hands – at least for high-value, safety-critical products such as tyres.

“I’d separate ‘AI helping me decide’ from ‘AI deciding for me’, because consumers still are the final decision makers,” he says.

Recent consumer research supports this view. While surveys indicate growing confidence in AI agents handling routine shopping tasks, willingness declines sharply when AI is expected to complete purchases autonomously. Most consumers remain comfortable with AI conducting research, comparing alternatives and shortlisting products but prefer to approve the final transaction themselves.

Tyres, Mohan argues, naturally fall into the category where human oversight will continue to matter.

“It’s a purchase people make infrequently, it carries real safety implications, and it typically involves a meaningful amount of money,” he says.

Consequently, AI is likely to dominate the research phase – evaluating specifications, warranty terms, prices and dealer options – while the final purchase decision remains with the customer.

However, one area where agentic commerce could quickly gain traction is in connecting customers directly with dealers. Rather than merely recommending a tyre, future AI assistants may also identify nearby retailers with available stock and book installation appointments automatically.

BECOMING AI-READY STARTS WITH THE BASICS

One of the most striking aspects of Mohan’s assessment is that the industry’s biggest challenge is not technological sophistication but digital housekeeping.

“It’s data, overwhelmingly, and it’s more basic than most companies expect,” he says.

FlowBlinq’s audits suggest that many corporate websites still lack the fundamental structure AI systems require. According to the company’s research, 91 percent of audited websites failed to provide clear information explaining their product catalogues in a way that AI could

understand. Even more concerning, nearly half were unintentionally preventing ChatGPT’s web crawler from accessing their websites because of security settings or plugin configurations.

“These aren’t strategic gaps; they’re operational oversights, and they’re fixable in weeks, not years,” Mohan claims.

For tyre manufacturers, this means that substantial improvements may not necessarily require major investments in new technology. Instead, they require a systematic review of how product information is organised, published and made accessible to AI systems.

Mohan also believes the next phase of digital readiness will involve preparing websites for agentic commerce by enabling real-time inventory visibility and ensuring AI systems can interact directly with product databases.

MEASURING RETURN BEYOND TRADITIONAL SEO

Digital marketing budgets have historically focused on search advertising, social media campaigns and marketplace optimisation. As AI-driven referrals grow, Mohan argues that businesses should begin allocating dedicated budgets towards AI discoverability.

“Yes, and the case for it is measurable rather than speculative now,” he says. Rather than relying solely on website traffic or keyword rankings, he believes organisations should monitor a different set of performance indicators.

Among the most important are how frequently AI systems cite a brand when responding to relevant queries, whether those citations are accurate and whether visitors arriving through AI recommendations convert differently from those originating through conventional digital channels.

Adobe’s Digital Insights research suggests AI-generated referrals are not only increasing rapidly but also producing stronger conversion rates than traditional referral sources. According to Mohan, this reflects the higher purchase intent of consumers who have already completed much of their evaluation through AI before visiting a manufacturer’s website.

TRUST WILL DETERMINE INFLUENCE

The emergence of AI recommendations inevitably raises questions about transparency. If AI systems become influential in shaping purchasing decisions, how can brands improve visibility without manipulating results?

For Mohan, the answer lies in accuracy rather than optimisation. “The honest answer is that AI-powered recommendations only work for a brand in the long run if they’re accurate, because these systems increasingly get checked,” he explains.

He believes manufacturers should resist the temptation to game AI systems through exaggerated marketing claims.

Instead, success will depend upon providing complete, verifiable product information that allows AI to make fair comparisons based on genuine performance characteristics.

“So the lever isn’t gaming a model into over-recommending you. It’s making sure that when a model compares your tyre honestly against a competitor on wet grip, rolling resistance or price, your data is complete enough that you win the comparisons you’re actually built to win,” he says.

In his view, transparency is not a constraint on AI marketing but its most durable competitive advantage.

FROM RECOMMENDATIONS TO TRANSACTIONS

The next evolution extends beyond recommendations. Emerging protocols are enabling AI systems to communicate directly with commerce platforms, inventory databases and pricing systems, allowing them to perform increasingly sophisticated purchasing tasks.

According to Mohan, this represents a significant opportunity for tyre manufacturers and dealers.

“The interesting shift is that AI agents are starting to interact with commerce systems directly... rather than just reading a webpage and stopping there,” Mohan says.

Once connected to live inventory systems, AI assistants could recommend the exact tyre that fits a customer’s vehicle, confirm stock availability at nearby dealers and compare prices in real time. Now, it can also make purchases directly from the chat window. This is something FlowBlinq is uniquely positioned to address as well.

Rather than generic recommendations based on previous purchasing patterns, personalisation could become highly contextual – considering vehicle compatibility, driving conditions, current inventory and even maintenance priorities.

However, Mohan cautions that these benefits will only be realised by organisations whose internal systems can support such interactions. Manufacturers and retailers will need modern, connected back-end infrastructure capable of sharing real-time inventory and pricing information with AI platforms.

ENGINEERING PRODUCTS – AND ENGINEERING DISCOVERABILITY

Looking ahead, Mohan does not believe AI will replace product quality as the defining competitive factor. Instead, he sees AI readiness becoming an equally important complement to engineering excellence.

“Product quality will always be table stakes; nobody wins on AI visibility with a mediocre tyre,” Mohan says. Yet he argues that superior products alone may no longer guarantee commercial success.

As purchasing journeys increasingly begin with AI conversations rather than search engines, brands that fail to present their technical information in formats AI systems can retrieve and trust may simply disappear from consideration.

“The winners will be the manufacturers who treated AI readiness as seriously as they treat product engineering,” Mohan says. He returns to Gartner’s prediction of declining traditional search volumes not as a warning but as an indication of how rapidly digital discovery is evolving.

“The discovery layer is moving to AI faster than most manufacturers’ data infrastructure is moving with it,” he says.

His concluding observation perhaps best captures the industry’s emerging challenge.

“A brand can make the best tyre in its category and still lose the sale simply because it was invisible in the one conversation the buyer had before deciding. That’s a genuinely new way to lose, and avoiding it is now a core marketing responsibility, not a technical footnote,” Mohan says.

AI mobile data capture company Anyline has released the latest version of TireBuddy, a smartphone-based system for automotive tyre inspections. Version 1.8 introduces fully automated sidewall capture that removes human variability from data collection. The tool has already helped service teams achieve faster, more uniform inspections over the past year, leading to increased tyre sales and stronger customer trust.

The automated mechanism uses on-device guidance that evaluates each image against four criteria: full sidewall detection, sharpness, proper distance and angle and overall clarity. This real-time feedback minimises redo scans by guiding technicians to capture optimal images immediately. The system addresses common challenges in busy service bays where accuracy often suffers due to varying experience levels.

Standardisation of inspection quality is a primary benefit, as consistent results are achieved regardless of who holds the phone. This removes dependency on technician skill or training duration. New or seasonal staff can perform scans confidently from day one without extensive instruction. The automated capture now serves as the standard protocol for all inspections across locations and shifts.

Additional features include tyre mismatch alerts that flag size discrepancies, automated email reports to back-office systems and a redesigned results screen consolidating sidewall information and tread measurements. With hundreds of thousands of annual inspections, this update reinforces TireBuddy's role in modernising tyre service operations.

Lukas Kinigadner, CRO, Anyline, said, “A shop is only as consistent as its least experienced inspector. Automated sidewall capture gets every scan to the same standard, so teams can stop treating inspection quality as a variable.”

Epson, a global leader in SCARA robot manufacturing, has unveiled its next-generation industrial robotics portfolio at Automation Expo Mumbai 2026. The newly introduced lineup features the high-end CX-A Series 6-axis robots, the LS-C Series SCARA robots, the RC+ 8.0 programming software and the advanced SafeSense safety technology, all designed to address diverse manufacturing applications such as pick-and-place, precision assembly, parts transfer and material handling.

The new offerings significantly expand Epson’s existing industrial robotics family, which already includes the 6-axis C-Series and SCARA T-Series and LS-Series models with payloads ranging from 3 to 20 kilogrammes. With the addition of the CX-A and LS-C Series, manufacturers across various sectors can achieve heightened productivity, flexibility and operational efficiency. The CX-A Series is engineered for complex tasks with a payload capacity of up to seven kilogrammes and a reach of 900 millimetres, available in IP67, cleanroom and ESD variants, while the LS-C Series provides a compact SCARA platform with a 50-kilogramme payload, a 1,000-millimetre reach and cycle times as fast as 0.298 seconds.

Complementing the hardware, the RC+ 8.0 software offers an integrated environment for programming, simulation and system management, facilitating faster automation deployment with support for Visual Studio and C++ development. Additional efficiency features include enhanced diagnostics, OPC UA, GUI builder and safety functions, alongside co-creation tools like Library Builder and RC+ Extension. Meanwhile, the SafeSense technology promotes safer human-robot collaboration by incorporating Safety Limited Speed and Safety Limited Position functions, which can potentially reduce the need for extensive safety fencing and thereby increase operational flexibility.

With over four decades of industrial robotics expertise and more than 200,000 robotic arms deployed globally, Epson continues to drive operational excellence for businesses. Attendees at Automation Expo Mumbai 2026 have the opportunity to view live demonstrations of these solutions and consult with Epson specialists about transforming their manufacturing operations.

Siva Kumar, Sr General Manager – Sales and Marketing, Epson India, said, "India is rapidly emerging as a global manufacturing hub, and automation will play a pivotal role in shaping its future. With our new industrial robot lineup and RC+ 8.0 platform, Epson is delivering the speed, precision and intelligence manufacturers need to compete in an increasingly dynamic marketplace. We remain committed to enabling businesses to accelerate automation adoption and build smarter, more agile and globally competitive manufacturing operations."

A consortium of Fraunhofer institutes has reached a key milestone in the Technology Platform for Tire Abrasion and the Identification of its Emissions in Road Traffic (TERIS) project, moving closer to establishing standardised laboratory methods for generating, analysing and predicting tyre wear.

The project, led by the Fraunhofer Institute for Structural Durability and System Reliability LBF, together with Fraunhofer ICT, Fraunhofer IGD and Fraunhofer IWM, aims to provide the tyre industry, testing organisations and environmental agencies with reliable and practical laboratory procedures for assessing tyre abrasion emissions.

The first project milestone has been completed following a successful review by an advisory board comprising industry experts.

The consortium has developed reference methods for tyre abrasion, particle analysis, tribological modelling, artificial intelligence-based surface analysis, a laboratory test bench concept, accelerated ageing techniques and volatile organic compound (VOC) detection.

According to the consortium, combining different particle collection and measurement techniques enables more precise analysis of both airborne and deposited tyre wear particles. At the same time, tribological models have been developed to better understand the relationship between loading conditions, material properties, surface structures and particle formation, allowing real-world tyre wear processes to be replicated under laboratory conditions.

Researchers have also developed a specialised test chamber for accelerated ageing, enabling tyre samples to be exposed to controlled environmental conditions before evaluating their abrasion behaviour.

Another development is an optical detection system that uses artificial intelligence to identify and classify surface structures. The system has been validated using substitute materials and is expected to be applied to rubber samples during the next phase of the project.

The consortium has also designed a laboratory test bench that combines multiaxial loading, controlled generation of tyre wear particles, targeted particle collection and integrated optical sensors within a single testing platform.

In addition, the project combines accelerated weathering with chemical analysis of volatile organic compounds released from tyre abrasion to assess the environmental impact of tyre wear particles.

The researchers said the work will provide the foundation for faster and more practical laboratory evaluation of new rubber compounds. The resulting methods are intended to help tyre manufacturers reduce emissions, accelerate product development and support compliance with the requirements of the Euro 7 standard.

At Fraunhofer IWM, researchers focused on refining tribological wear models and friction surface concepts to simulate particle formation under controlled laboratory conditions. The institute designed a parameterisable wear test that studies friction between plate materials and model surfaces with different structures, enabling researchers to investigate the mechanisms responsible for particle generation.

Initial findings indicate that tyre wear results from multiple interacting mechanisms rather than a simple relationship between particle emissions and factors such as speed, contact force or temperature. The researchers collected and analysed particles across a wide range of sizes during the study.