Industry · Dealers, after-sales and aftermarket

Automotive & dealerships

Lead follow-up, after-sales and parts — no lead left behind.

Book an industry diagnostic →See the sample case

Pain points

What keeps owners up at night

Too many leads, slow follow-up, many go cold

Heavy workload of bookings, reminders and follow-ups

Inaccurate parts stock ties up cash

Slow roll-up of multi-store numbers

Roles

Which roles AI transforms, and which it augments

“Transform”: AI executes, people spot-check. “Augment”: AI drafts and prompts, people decide.

Role	Mode	What AI does / what people keep
Lead follow-up	Augment	AI does first-round follow-up and scoring
Service advisor assistant	Augment	AI handles bookings, maintenance reminders and follow-ups
Parts manager	Augment	AI suggests parts orders and transfers
Customer service	Transform	Standard questions answered by AI
Reporting clerk	Transform	Store reports generated automatically

Department agents

AI employees we typically deploy

Lead Follow-up AgentAfter-sales AgentParts AgentCustomer Q&A AgentCockpit Agent

Super AI Workbench

Dealer group board: the workbench for this industry

Demo UI with placeholder numbers.

BOOK.AI · Dealer group boardDemo UI · placeholder numbers

Cockpit
AI employees
Workflows
Knowledge
Audit log

Revenue¥ ——

Labor cost¥ ——

Output / person——

Open exceptions——

AI task completion by department (illustrative)

Sales

Ops

Finance

Supply

AI assistant · Monday briefing

Lead funnel and response time
After-sales revenue, returns and satisfaction
Parts turnover and shortage alerts
Store-by-store comparison

Sample case

How we would approach this industry

Sample scenario · not a real client

An auto dealer group (sample)

This is a sample scenario that illustrates our approach in this industry — not a real client. All results are modeled per company during the diagnostic.

01 Discover (2–3 weeks): map the organization and break down roles such as “Lead follow-up” and “Service advisor assistant” into tasks and time share.
02 Build (90-day pilot): launch Lead Follow-up Agent and After-sales Agent in parallel with the existing process.
03 Adopt & improve (months 4–12): extend to Parts Agent and Customer Q&A Agent; adjust headcount only after each pool meets its guardrails.

Change in execution labor cost
Change in core process hours
Modeled payback

These metrics are modeled with your company’s real data during the diagnostic.

FDE in practice

Public FDE practice in this industry

Third-party cases from public reports — unrelated to the BOOK.AI sample above — to show how this industry is approaching AI.

Case information is compiled from public reports; figures are as reported by the original sources, and all rights belong to their owners. BOOK.AI was not involved in any of these projects.

Image from the source page: FAW Group — © Alibaba Cloud; rights reserved by the owner · Image source

Public case · not a BOOK.AI clientAutomotive & dealerships · China

FAW Group × Alibaba Cloud

FDE intensity: MediumCo-build

Service staff needs reduced by 50% (as reported)

As reported by Alibaba Cloud · 2025 · provider-published

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Background

A large workforce generated heavy office and process enquiries; business analysis was slow.

How the FDE work was done

Co-built the “Hongqi Yunmei” assistant, continuously expanding its knowledge base and iterating models, with an industry model planned next.

Results (as reported by Alibaba Cloud)

Service-staff headcount need reduced 50%; BI analysis from 7 days to 5 seconds.

Takeaways

A group-wide super assistant can absorb office and process enquiries first, then expand into analytics.

Provider: Alibaba Cloud
Delivery mode: Co-build
Region: China

Screenshot of the source page (captured 2026-10-19): FAW Group — © Alibaba Cloud (page screenshot; content © source) · Image source

Read the original ↗Original title (Chinese): 中国一汽携手阿里云建设企业智能体-红旗云妹

English summary by BOOK.AI; figures as reported by Alibaba Cloud. Quotation marks indicate wording from the source.

Image from the source page: FAW Toyota Motor Sales — © Tencent Cloud; rights reserved by the owner · Image source

Public case · not a BOOK.AI clientAutomotive & dealerships · China (JV)

FAW Toyota Motor Sales × Tencent Cloud

FDE intensity: MediumCo-build (strategic partnership)

Bot self-resolution rate from 37% to 84%

As reported by Tencent Cloud · 2025 · provider-published

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Background

High owner enquiry volume; the old bot resolved little and escalated a lot.

How the FDE work was done

A strategic partnership since May 2024 across service, marketing and office, with the focus on upgrading the service model.

Results (as reported by Tencent Cloud)

Bot independent resolution from 37% to 84%.

Takeaways

After-sales service is high-ROI for auto: high volume, repeat questions, easy to measure.

Provider: Tencent Cloud
Delivery mode: Co-build (strategic partnership)
Region: China (JV)

Screenshot of the source page (captured 2026-10-19): FAW Toyota Motor Sales — © Tencent Cloud (page screenshot; content © source) · Image source

Read the original ↗Original title (Chinese): 腾讯云X一汽丰田：智能客服AI大模型独立解决率提升至84%-腾讯云开发者社区-腾讯云

English summary by BOOK.AI; figures as reported by Tencent Cloud. Quotation marks indicate wording from the source.

Image from the source page: Lear Corporation — © Lear; rights reserved by the owner · Image source

Public case · not a BOOK.AI clientAutomotive & dealerships · US

Lear Corporation × Palantir

FDE intensity: MediumFDE / long-term co-build (5-year expansion)

“more than $30 million in savings during the first half of 2025”; 11,000+ employees on the platform

As reported by Lear · 2025-09-04 · company statement

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Background

An auto supplier facing tariff swings, line balancing and heavy admin.

How the FDE work was done

Palantir connects quality, supply chain, purchasing, manufacturing, finance and design for Lear’s IDEA programme.

Results (as reported by Lear)

11,000+ employees use the technology; IDEA saved $30M+ in H1 2025.

Takeaways

Once functions share data, savings tend to show up in purchasing and supply chain.

Provider: Palantir
Delivery mode: FDE / long-term co-build (5-year expansion)
Region: US

Screenshot of the source page (captured 2026-10-19): Lear Corporation — © Lear (page screenshot; content © source) · Image source

Read the original ↗Palantir and Lear Announce Five-Year Partnership Expansion to Accelerate Automotive Technology Transformation | Lear Corporation

English summary by BOOK.AI; figures as reported by Lear. Quotation marks indicate wording from the source.

Public case · not a BOOK.AI clientAutomotive & dealerships · China

Changan Automobile × Deepzero (Shenyan Intelligence)

FDE intensity: MediumCo-build (scripts, knowledge base, SOP, dashboards + ongoing ops)

High-intent lead visit and test-drive rates up 20%+ (as reported)

As reported by ifenxi (via Sina Finance) · 2025-12-02 · media report

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Background

Lots of low-quality leads; manual calling and nurturing were inefficient.

How the FDE work was done

The provider delivered scripts, a knowledge base, SOPs and dashboards, then ran operations to rebuild the lead flow end to end.

Results (as reported by ifenxi (via Sina Finance))

Productivity doubled; high-intent visit and test-drive rates up 20%+.

Takeaways

Delivery plus ongoing operations beats one-off projects for lead agents.

Provider: Deepzero (Shenyan Intelligence)
Delivery mode: Co-build (scripts, knowledge base, SOP, dashboards + ongoing ops)
Region: China

Read the original ↗Original title (Chinese): 破解存量市场运营难题，长安汽车用Agent重构线索价值链

English summary by BOOK.AI; figures as reported by ifenxi (via Sina Finance). Quotation marks indicate wording from the source.

See public FDE practice across all 14 industries →

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