INDUSTRY REPORT • CONSULTING OUTPUT
Automotive & Transportation • Strategy Domain

EV Adoption • MaaS • Supply-Chain Emissions • Dealer Readiness

By OneMind Strata — Industry Research - June 2025. At the Approach stage, our findings align AI Market Signal Mining with EV adoption models, MaaS strategies, supply-chain emission scope, and dealer/OEM cultural readiness.

EV Adoption Models Mobility-as-a-Service Supply-Chain Emissions Dealer Cultural Readiness AI Signal Mining Approach Stage
Automotive & Transportation — Strategy Domain

AI Approach: Build a Roadmap that Wins the Mobility Future

Stage: Approach  |  AI Offering: AI Roadmapping  |  Research: Foundations of AI Models  |  By OneMind Strata — Stratenity Research

Electrification Autonomous & Connected Mobility Services Resilient Supply Chains
Engagement Objective / Problem Statement

The automotive and transportation industry is facing a period of unprecedented disruption. Electrification, autonomous driving, connected mobility services, and shifting consumer expectations are forcing incumbents and new entrants alike to rethink their strategic positioning. Our consulting engagement seeks to define a strategic roadmap for AI integration, with a focus on aligning advanced modeling capabilities to core strategic choices. The central problem is clear: legacy operating models and decision frameworks are insufficient to capitalize on AI-enabled opportunities while managing risks of disruption. The objective is to help the client transition from fragmented AI experiments toward a coherent enterprise roadmap that supports long-term competitiveness.

Industry & Client Context

The global automotive sector is moving rapidly toward software-defined vehicles, with AI powering everything from predictive maintenance and demand forecasting to autonomous fleet optimization. Traditional revenue models based on unit sales are giving way to recurring subscription-based services, requiring new capabilities in data-driven customer engagement. At the same time, supply chain fragility—exposed during recent geopolitical and pandemic shocks—demands resilience powered by predictive AI models. For our client, a mid-sized automotive OEM with a global footprint, the strategic context is particularly acute. They have invested in data infrastructure but lack an integrated view of how AI can create sustainable differentiation. The market trend is shifting toward ecosystem-based competition, where value will accrue to firms that can orchestrate mobility platforms rather than simply sell vehicles.

Consulting Hypotheses
  1. The client’s fragmented AI pilots lack alignment with core strategic objectives, reducing ROI and creating organizational fatigue.
  2. Without an AI-enabled strategy for supply chain resilience, the client will continue to face volatility in production costs and delivery commitments.
  3. A structured AI roadmap that leverages foundations of AI models—from supervised learning for demand forecasting to reinforcement learning for autonomous systems—can significantly improve operational agility and strategic positioning.
  4. Governance gaps, particularly around responsible AI, are constraining scaling efforts and exposing the client to reputational risks.
  5. Strategic value creation in this sector will come from AI-driven ecosystem partnerships rather than in-house siloed innovation.
Engagement Approach / Workplan

Phase 1: Discovery & Alignment — Conduct executive interviews, analyze current AI initiatives, and map them to strategic imperatives such as electrification, digital services, and mobility ecosystems.

Phase 2: Diagnostic Assessment — Benchmark the client’s AI capabilities against peers and leading practices, with a focus on the foundations of AI models (data maturity, model lifecycle governance, algorithmic transparency).

Phase 3: Roadmap Design — Develop a 3–5 year AI roadmap aligned to corporate strategy, specifying priority use cases (predictive maintenance, connected services, autonomous fleet management).

Phase 4: Business Case & Operating Model — Build financial impact models, resource allocation scenarios, and an operating model blueprint for AI governance, talent, and partnerships.

Phase 5: Executive Validation & Commitment — Facilitate workshops with leadership to refine and approve the roadmap, ensuring alignment with strategic direction and shareholder expectations.

Boundaries & Assumptions
In Scope: Strategy-level AI roadmapping Evaluate foundational models for automotive Integrate with corporate strategy Explore tech partnerships
Out of Scope: Detailed technical builds Code-level vendor selection Legal adjudication on AV compliance
  • Client has basic data infrastructure (ERP/CRM/telematics) in place.
  • Leadership is committed to funding AI initiatives if ROI and risk mitigation are compelling.
  • Regulatory frameworks will continue to evolve but not prohibit foundational AI model applications in mobility.
Expected Client Outcomes
  • A clearly defined engagement objective linking AI integration directly to strategic value creation.
  • A structured articulation of the industry context and disruption drivers relevant to their position.
  • Validated consulting hypotheses that focus subsequent work on the most critical levers of value.
  • A transparent, phased engagement plan that outlines discovery, diagnostic, and design activities.
  • Agreed-upon boundaries and assumptions that minimize scope creep and align leadership expectations.
  • Confidence that the engagement is directionally aligned with strategy, financial priorities, and AI foundations, ensuring the roadmap is not an abstract exercise but a concrete, business-driven journey.

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