AI-Powered Customer Research for Automotive: Faster Insights for OEMs, Dealers, and Mobility Teams

The automotive industry runs on customer assumptions — what buyers will pay for, why they switch brands, how they shop, why EV intenders convert (or don't). Traditional research can't keep up with how fast those assumptions are changing in 2026: 26% of new-vehicle shoppers now say they are "very likely" to consider an EV, up 3 points in a single month, according to J.D. Power's April 2026 EV Consideration Study. Sixty percent of car buyers rely exclusively on digital channels for research and configuration before a dealer ever sees them, McKinsey reports. And the global automotive market hit $2.33 trillion in 2026 — every percentage point of buyer understanding is worth a lot.

AI-powered customer research lets automotive teams interview hundreds of buyers, owners, dealer customers, and lapsed prospects in the time a traditional firm would have scheduled the kickoff call. This guide shows OEMs, dealer groups, finance arms, mobility startups, and aftermarket brands how to run modern AI research with Koji.

Why automotive needs continuous customer research

Auto buying is a high-consideration, multi-touch journey. Cox Automotive's 2025 Car Buyer Journey Study found that buyers who lean into AI-powered online tools report 84% satisfaction — a record high — while affordability concerns and tariff-accelerated decisions reshaped the funnel in real time. Yet most OEMs still rely on tracker studies that field quarterly and deliver topline findings months later.

Meanwhile, dealer-side data shows the cost of not listening. The average dealership only connects on about 65% of inbound service calls; 43.2% of sales leads are mishandled and 65% of return website visitors hear nothing back within 24 hours. Forty-six percent of customers say they'd switch OEMs if a competitor delivered better experiences. Closing the loop with the people who actually called, browsed, test-drove, charged, serviced, or churned is the highest-leverage research an automotive team can run — and AI interviewing makes it operationally cheap enough to do every week.

The automotive research stack today

Most automotive insights orgs juggle three disconnected layers:

1. Syndicated trackers (J.D. Power, Cox, Deloitte) — slow, expensive, broad.
2. Custom qual (focus groups, clinics, IDIs) — high signal, but a 30-person IDI study runs $10K–$30K and 6–10 weeks.
3. CX surveys at the dealer or aftersales level — fast but shallow, with declining response rates.

AI customer research with Koji collapses the second and third layers: you publish a structured AI-moderated interview link, recruit from your CRM or dealer DMS, and read a synthesized themed report within days. The result is a continuous discovery loop instead of a quarterly study cadence.

7 automotive research playbooks you can run this quarter

1. EV consideration and barrier deep-dives

The barriers to BEV adoption are well-quantified: 46% cite charging availability, 44% charging time, and 42% purchase price. But aggregated stats hide segment-level decision logic. Run a Koji AI interview with current ICE owners who recently shopped EVs and ask what specifically would tip them: home charging install support? A guaranteed minimum range in cold weather? A specific tax-credit structure? Voice mode and adaptive probing surface drivers a checkbox survey never captures.

2. Lost-shopper interviews

Pipe leads who pinged the website, requested a quote, or test-drove but didn't buy into an always-on cancel-flow-style intercept. Koji's always-on AI moderator runs 24/7, so a buyer who walked off the lot at 9 p.m. on a Saturday can be in the data by Sunday morning.

3. Service-visit Voice of Customer

With maintenance visits at dealers taking 1.6–2.5 hours and 84% of service customers saying a bad experience would stop them from buying their next car from that brand, the service write-up is where loyalty is won or lost. Replace the static CSAT with a 4-minute AI-moderated interview that asks why the rating, what the customer would change, and whether they considered switching.

4. EV ownership and charging experience

J.D. Power found 96% of new BEV owners would buy another, the highest level since 2021. Capture why: which features carry them through the rough edges, which fall flat, which would they swap for ICE features. Use Koji's structured questions (single_choice for "what's your home charging setup", scale for charging-anxiety, open_ended for "describe your worst charging experience this year") to mix structured and qualitative in one interview.

5. Dealer experience benchmarking

The average dealer service satisfaction score is 868, up 3 points YoY, but variance across stores is huge. Field a quarterly AI-moderated VoC across your dealer footprint and segment by store, region, and customer type to identify outlier behaviors worth replicating.

6. Vehicle concept and feature validation

Replace the $80K clinic with a Koji study: 200+ buyers screened through your CRM, asked to react to renderings, feature lists, and pricing in a 15-minute conversation. Use Koji's structured questions (ranking, MaxDiff-style ranking, scale ratings) alongside open-ended why probes.

7. Mobility and subscription-service discovery

Car-share, fleet-subscription, and EV-charging-network startups need to interview niche audiences (rideshare drivers, fleet managers, urban renters) constantly. Async AI interviews fit those participant pools far better than scheduled Zoom calls do.

How Koji works for automotive teams

While traditional tools like Qualtrics and SurveyMonkey force you to choose between structured surveys and expensive moderated interviews, Koji gives automotive insights teams an AI-native middle path:

• AI-moderated voice and text interviews that adaptively probe each respondent. A buyer who says "the screen is confusing" gets followed up with "which part — the home screen, the climate controls, the EV menu?" — not a generic next question.
• 6 structured question types (open_ended, scale, single_choice, multiple_choice, ranking, yes_no) so a single Koji study can mix MaxDiff-style ranking, Likert scales, and adaptive qualitative probing — see the structured questions guide.
• Automatic thematic analysis that codes 100+ interviews in minutes — what manual researchers used to spend a week on.
• Custom AI consultants trained on your brand, vehicle lineup, and competitive set so the moderator asks the right follow-ups.
• Multilingual — interview LATAM, European, and APAC markets in their native language without commissioning separate field firms.
• Real-time insights dashboard with quality scoring, theme detection, and quote extraction as interviews complete.

Teams using AI-assisted research report a 60% faster time-to-insight than traditional methods, with cost-per-interview at roughly 5–10% of a full-service IDI.

Sample automotive interview questions

A discovery interview for an EV consideration study might include:

1. Walk me through the last time you actively shopped for a new vehicle. What kicked it off? (open_ended — narrative)
2. On a scale of 1–10, how seriously did you consider an EV? (scale)
3. Which of these factors would have to change for you to seriously consider a BEV next time? (multiple_choice — charging access, range in cold weather, total cost of ownership, tax credit, model availability, dealer experience)
4. Rank these EV brands by the order you'd consider them. (ranking)
5. What's the one thing about your current vehicle you would NEVER give up? (open_ended)

Koji's AI moderator then probes wherever the answer is interesting — a respondent who says "tax credit" gets asked which credit, when they learned about it, and whether the changing political landscape affects their timing. That kind of follow-up is the difference between a stat ("buyers care about tax credits") and an insight ("buyers under 35 in tier-2 cities discovered the IRA credit through TikTok and don't trust it will survive policy changes").

Implementation checklist for automotive teams

• ☐ Identify the highest-leverage research question for this quarter (EV consideration, service VoC, lost-shopper, concept test)
• ☐ Source a participant pool from your CRM, DMS, panel partner, or website intercept
• ☐ Draft a 6–10 question discussion guide using Koji's structured + open-ended mix
• ☐ Set quality thresholds (Koji scores each interview 1–5) and exclude low-quality responses
• ☐ Run a 20-respondent pilot, then scale to 100–500 for the full study
• ☐ Review the auto-generated themes and quotes; export to your insights repo
• ☐ Brief stakeholders within 5 business days of close — instead of the 8–12 weeks a traditional firm takes

Who on the automotive team should use Koji

• OEM product/UX teams designing the next infotainment, ADAS UX, or in-cabin assistant — run prototype reactions at scale.
• OEM brand and marketing teams validating campaign concepts, messaging, and positioning across markets.
• Dealer group ops benchmarking VoC, lost-deal interviews, and service satisfaction across stores.
• Captive finance arms understanding lease, finance, and trade-in decision drivers.
• Aftermarket and parts brands running concept testing and brand tracking studies with installers and end-customers.
• Mobility startups (rideshare, fleet, charging, parking) running continuous discovery with niche participant pools.

Each role uses the same core Koji workflow — publish, recruit, synthesize — but tunes the AI consultant for their vertical and question set.

Related Resources

• The complete guide to AI qualitative research
• How AI interviewers work
• AI interviews vs surveys: when to use which
• Structured questions guide — 6 types every AI study should use
• AI research for ecommerce — vertical playbook
• Customer discovery interviews at scale
• Win-loss analysis for product and sales teams

Sources: J.D. Power 2026 EV Consideration Study (April 2026); McKinsey EV buyer journey research; Cox Automotive 2025 Car Buyer Journey Study (released Jan 2026); Foureyes 7th Annual Automotive Dealer Industry Benchmarks Report; J.D. Power 2026 U.S. Customer Service Index (CSI) Study; Maze 2026 Future of User Research Report; industry IDI pricing data.