The New Auto Buyer: How AI is reshaping the journey from Discovery to Decision | Autocar India

Executive summary

AI is adding another layer to an already complex car-buying journey rather than making it shorter. Buyers now research for months, often arrive at dealerships with a shortlist and substantial prior knowledge, and use the showroom mainly to validate choices and experience the car. For automakers, this shifts the task from simply generating awareness to staying relevant across digital research, family decision-making, dealership interactions, and ownership.

The discussion’s central business implication: product differentiation and brand trust still matter, but success increasingly depends on having accurate, current information and credible recommendations available wherever buyers search.

Key consumer and journey insights

• The journey is getting longer: Google–Kantar research cited in the discussion found an average of 7.8 touchpoints from starting a car search to purchase, up from 6.9 in earlier research. AI surfaces appear to have added an information layer rather than reduced the number of touchpoints.
• AI use is widespread: About 90% of auto buyers reportedly used AI surfaces in some form during the buying journey. Search is becoming more conversational, with questions framed around a buyer’s circumstances, budget, and family needs.
• Research increasingly happens before the showroom: One panelist estimated that the portion of decision-making completed before a showroom visit had risen from roughly 10% to nearly 70%. The dealership’s role is therefore shifting from discovery and persuasion to confirmation, advice, and hands-on product experience.
• The purchase is a family decision: The study found that 53% of buyers had a spouse or partner acting as a “co-pilot” across brand, variant, and budget choices. Children and younger family members also influence technology and feature preferences.
• Aspirations remain important: The moderator cited findings that 40% of buyers were motivated by matching the standing of their social circle, and 40% by road trips or weekend travel. Panelists described cars as sources of pride and personal expression, as well as practical purchases.
• Video is central to research and discovery:
  • About three in four buyers casually explore automotive content on video.
  • 77% were reportedly influenced by trusted voices.
  • About 70% of auto buyers watch connected TV; the panel also cited connected TV as a major influencer.
  • One speaker said 93% of TVs shipped since 2023 were connected TVs.
• Trust and relevance shape influence: Creators, expert reviewers, brand channels, regional-language voices, and owner communities help buyers understand products before visiting a showroom. Video can also explain topics such as safety ratings in accessible terms.

Business frameworks, processes, and playbooks

Adapt the funnel to the buyer’s journey

• Early exploration: Use short-form content to build brand awareness, curiosity, and desirability—even among people who may not buy for several years.
• Active evaluation: Provide useful comparisons and credible medium- and long-form reviews.
• Pre-purchase validation: Make detailed product, ownership, and financial information easy to find; equip showroom teams to respond to questions buyers have already formed.
• Test drive and delivery: Preserve the in-person experience as the point where digital research is validated against the actual product.

Move from “share of voice” toward “share of recommendation”

• Visibility and media spend alone are not enough if AI-driven tools are synthesizing answers from multiple sources.
• Automakers should monitor whether their products are being recommended in relevant AI answers and ensure accurate information is available on brand sites, independent reviews, and customer communities.

Build content for different stages and formats

• Short videos can generate interest.
• Five-to-ten-minute reviews can support comparison.
• Podcasts and longer ownership-focused content can serve buyers seeking deeper detail.
• Cover real-world ownership, costs, family use, and product performance—not just launch information.

Make product information AI-ready

• Keep website and product data current, complete, and structured enough to support conversational queries.
• The panel discussed retrieval-augmented generation (RAG) as a way to ground AI responses in current, reliable information and reduce inaccurate answers.
• Treat a product launch as the beginning of the content journey; ongoing reviews and ownership experiences contribute to what AI systems and buyers encounter later.

Use customer insights to inform planning and showroom interactions

• Conversational search can reveal needs earlier and in greater detail, including price range, fuel type, EV range, location, and family requirements.
• Panelists described using website-interaction insights to help sales teams prepare for customer needs and guide buyers toward suitable, available variants.
• Some brands are testing personalized AI video responses on WhatsApp.

Connect marketing, sales, and ownership

• Buyers experience one continuous ownership journey, not separate marketing, sales, customer-experience, and service functions.
• Brand desirability helps a company enter the shortlist; accurate information, recommendations, and the showroom experience help convert that interest.

Product, operations, and management implications

• Features must solve a customer need, not just add to a specification sheet. Buyers increasingly ask what a feature does for them. For example, a 360-degree camera may be understood as a safety aid for tight parking, not merely a technology feature.
• Different family members can determine the choice. A panelist described a buyer whose daughter insisted on a car with a third screen. Such requirements can drive model or variant selection even when they do not determine brand preference.
• Offerings must balance broad appeal with commercial and operational complexity. Customers have varied priorities—safety, infotainment, driving ease, ADAS, and comfort—but automakers cannot customize every vehicle to each buyer. Feature packaging and variant decisions remain a difficult trade-off among customer choice, production, sales, and marketing.
• Use connected-car data carefully. Mahindra’s representative said nearly 70% of its cars were connected, creating data on which features are used. That can inform product and variant planning, but low usage does not necessarily mean a feature is dispensable: a panoramic sunroof was given as an example of something customers may value even if they rarely use it.
• Demand forecasting remains difficult. The panel described variant forecasting as highly sensitive to consumer preferences, competitor actions, offers, and policy changes. One speaker said the company could get the price-value equation right about 50% of the time. AI may analyze current conditions, but changing assumptions limit forecast reliability.
• Demand sensing is an ongoing process. Mahindra’s representative said the company had been working for two years on getting demand signals to the supply chain sooner. Models must be refreshed as market conditions change; there is no permanently accurate forecast.
• The Indian market is maturing and fragmenting. Buyers are becoming more specific about how a car should drive and which features it should have. Panelists pointed to growing interest in city cars, road-trip vehicles, and seven-seaters, with the possibility of more multi-car households over time.

Concrete examples discussed

• Škoda Slavia: The discussion contrasted feature-counting with customer value. The addition of an 8-speed automatic prompted questions about how it improved driving quality; a 360-degree camera was also added to address a competitive gap.
• Family-driven feature choice: The “third screen” example illustrated how one family member’s preference can sway model or variant selection.
• Feature reframing: A 360-degree camera can shift from being marketed as convenience or technology to being understood as a safety feature for narrow parking spaces.
• AI-personalized video: Škoda was described as testing personalized videos through WhatsApp in response to customer queries.
• Personalized conversational search: The panel cited queries such as a buyer explaining they have older parents, a young child, and a spouse, then asking which car would suit the family—including which cars are easier for older parents to enter and exit.

Timelines and metrics to track

• Journey length: 7.8 average touchpoints, up from 6.9 in earlier research.
• AI adoption: Around 90% of auto buyers used AI surfaces during the journey.
• Pre-showroom decision-making: Estimated at nearly 70%, compared with roughly 10% previously.
• Family influence: 53% reportedly had a spouse or partner acting as a co-pilot.
• Video influence: Three in four casually explored automotive video; 77% were influenced by trusted voices; about 70% watched connected TV.
• Search behavior: Long-query searches were described as growing about 40% year on year, roughly twice the growth rate of the baseline cited. The speaker said this measure had been doubling or tripling each year.
• Connected-car data: Nearly 70% of Mahindra cars were described as connected.
• Forecasting: One panelist put price-value forecasting accuracy at about 50%.
• Near-term AI shift: Google’s representative expected more agentic AI use over the next 12–18 months, with agents handling some basic tasks. The implication is that product information and digital commerce experiences should be ready for conversational, agent-assisted interactions.
• Financial metrics: No company revenue, margin, CAC, LTV, or churn figures were provided.

Actionable recommendations

1. Audit what AI systems and search surfaces say about your products. Correct outdated specifications, pricing, availability, and comparisons across official and third-party sources.
2. Build content around buyer questions and real-life use cases. Include ownership costs, family needs, safety explanations, EV range, and variant comparisons.
3. Create a format strategy by purchase stage. Use short-form content for discovery, credible reviews for evaluation, and deeper ownership content for final consideration.
4. Train dealership teams to validate, not restart, the decision. Equip them with the product knowledge and tools to address buyers’ existing hypotheses and questions.
5. Use digital intent signals in planning and sales preparation. Feed detailed, early customer questions into product, variant, inventory, and showroom processes, while recognizing that demand models need frequent updates.
6. Measure recommendation presence as well as awareness. Track whether the brand is surfaced and recommended in relevant AI answers, not only impressions, clicks, or conventional search visibility.
7. Keep the physical experience strong. Digital research may narrow the shortlist, but the panel expects the test drive, product feel, and dealership interaction to remain decisive.

Presenters and sources

• Hormazd Sorabjee — Editor, Autocar India; moderator
• Nalinikanth Gollagunta — CEO, Automotive Division, Mahindra & Mahindra
• Siddharth Shekhar — Google India
• Ashish Gupta — Brand Director, Škoda Auto India
• Vivek Srivatsa — Chief Commercial Officer, Tata Passenger Electric Mobility
• Research source discussed: Google–Kantar study on automotive consumer behavior, as described by the panel.