AI for Automotive & Mobility

Our point of view

Engineering copilots and warranty intelligence across the programme lifecycle.

We start where the operating constraint is, connect the systems that hold the truth, and only then choose the model. Governance is designed with the workflow, not bolted on.

Priority AI opportunities

Engineering knowledge copilots
Demand and inventory forecasting
Digital twins and scenario simulation

Use cases

Automotive & Mobility use cases in production scope.

Business function

AI technology

Business outcome

Implementation horizon

4 use cases

Computer visionOperations3-6 months

AI-powered quality inspection

Manual inspection is inconsistent and cannot keep line speed.

AI approach: Line-side vision models with active learning on operator corrections.
Required data: Line cameras, defect libraries, inspection outcomes.
Systems involved: MES, QMS
Expected impact: Quality — Lower escape rate and reduced rework.
Time to value: 3-6 months

Impact ranges are illustrative until validated with your data.

Discuss this use case

Predictive MLSupply chain3-6 months

Demand and inventory forecasting

Forecasts miss under promotion and disruption volatility.

AI approach: Hierarchical forecasting with causal features and scenario overlays.
Required data: POS, orders, promotions, external signals.
Systems involved: ERP, planning, WMS
Expected impact: Cost — Improved forecast accuracy and reduced stockouts.
Time to value: 3-6 months

Impact ranges are illustrative until validated with your data.

Discuss this use case

Generative AIEngineering & R&D0-3 months

Engineering knowledge copilots

Engineers rediscover solutions already documented elsewhere.

AI approach: Grounded retrieval over specifications, CAD metadata and issue history.
Required data: PLM records, drawings, test reports.
Systems involved: PLM, ALM, document stores
Expected impact: Speed — Shorter root-cause analysis and higher design reuse.
Time to value: 0-3 months

Impact ranges are illustrative until validated with your data.

Discuss this use case

OptimisationEngineering & R&D6-12 months

Digital twins and scenario simulation

Changes are tested in production because there is no safe sandbox.

AI approach: Twin runtime fed by live telemetry, used for what-if simulation.
Required data: Asset models, telemetry, process parameters.
Systems involved: MES, historian, PLM
Expected impact: Quality — Fewer disruptive trials and better change decisions.
Time to value: 6-12 months

Impact ranges are illustrative until validated with your data.

Discuss this use case

Reference architecture

How the intelligence connects.

PLM→Telematics→Warranty data→Knowledge graph→Engineering copilot

Business impact indicators

Root-cause time-40% Illustrative

Warranty leakage-9% Illustrative

Reuse of prior designs+25% Illustrative

Accelerators

Relevant accelerators.

Accelerator

Enterprise Agent Studio

Design, evaluate and operate governed agents against real enterprise tools.

Solves: Agent prototypes stall because permissions, testing and observability are missing.
Architecture: Tool registry, policy engine, evaluation harness, trace store.
Integrations: ERP, CRM, ITSM, Identity provider
Implementation: 6–10 weeks to first supervised agent in production.
Governance controls: Scoped permissions · Approval thresholds · Full traces

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Accelerator

AI Knowledge Hub

Entitlement-aware retrieval across every enterprise knowledge source.

Solves: Copilots answer without provenance or respect for access rights.
Architecture: Connectors, hybrid index, re-ranking, citation service.
Integrations: SharePoint, Confluence, Object storage, Ticketing
Implementation: 4–8 weeks to a governed knowledge surface.
Governance controls: Entitlement propagation · Citation enforcement · PII redaction

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Accelerator

Document Intelligence Engine

Turn high-volume documents into validated structured data.

Solves: Manual keying and review dominate document-heavy processes.
Architecture: Layout parsing, extraction models, confidence routing, review UI.
Integrations: ECM, Workflow, ERP
Implementation: 4–6 weeks per document family.
Governance controls: Confidence thresholds · Human review queues · Accuracy monitoring

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Accelerator