Delivering on the Promise of Automotive AI

Automotive and mobility players stand at a pivotal moment where harnessing AI and Gen AI can pave the way to industry leadership. This opportunity hinges on using advanced tools to empower teams, reconfigure processes, and align technology investments with a clear focus on boosting value and lowering costs in the P&L. Moving Gen AI from a proof of concept to a powerful profit driver is the next logical step for companies facing urgent shifts in technology, trade policies, and consumer demand—particularly as the transition to electric vehicles accelerates and other industries race ahead.

Automotive and mobility executives now see the strategic importance of fully embracing AI, including Gen AI, to drive greater efficiency. After years of incremental progress, these tools are mature enough to deploy at scale. Yet the risk of falling behind remains significant for any company that fails to integrate new technologies into its core business. The good news is that leading organizations already capture value through large-scale, real-world AI applications. Recent successes at GM, BMW, and other firms demonstrate how a bold vision can quickly translate into tangible benefits.

Drawing on insights from projects, Santiago & Company has identified a "Stairway to Gen AI Success" with four stages. By following this progression, organizations can convert the technology's potential into solid economic value. Automotive and mobility firms that have embraced these principles have reduced costs by 8% to 12% from their baseline, often generating a return on investment of up to 15 times within three years. Three elements in this journey stand out: aligning people to new tools, redesigning processes to capitalize on AI's capabilities, and anchoring implementation in measurable P&L impact. These pillars serve as the foundation for unlocking and sustaining transformative benefits.

People's Adoption Sparks Sustainable Change

Companies that concentrate on building widespread AI adoption realize the most significant gains. Instead of focusing on algorithms (10% of the effort) or technology (20%), successful adopters invest 70% of their resources in supporting and training people to work with AI tools. This change management focus can double user adoption rates—from around 30% to about 60%. Although building a smooth, efficient tool is essential, ensuring that employees integrate new technology into daily workflows matters as much.

Recently, BMW introduced an AI-driven, chat-based interface called Knowledge Navigator, which searches internal documents and data sources with remarkable speed and precision. This platform reshaped quality and procurement processes by eliminating the need for prolonged searches or repeated calls to colleagues. It also provides traceability and validation by linking quoted information to original files. Consistency and efficiency have become cornerstones of BMW's approach, and users across the organization increasingly treat AI-driven solutions as vital resources for rapid decision-making.

Reaching this level of adoption requires consistently demonstrating that AI brings tangible benefits to each user. Ensuring that AI tools live up to their potential depends on rethinking existing processes. While widespread use is crucial, it must be coupled with rigorous process redesign to make AI a natural part of critical operations.

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Process Redesign Unlocks Greater Capability

AI and Gen AI can only achieve their full potential if companies adjust internal processes to harness these technologies. Firms that adopt a holistic approach—extending beyond technology teams and into core operations—often see efficiency improvements of up to 50%. The collaboration between General Motors and Microsoft exemplifies this principle. GM's Marketing and Data & Analytics teams developed an AI-driven audience selection engine that analyzes a blend of first- and third-party information to refine campaign design. Early tests showed that this tool outperformed older models while optimizing costs and data security.

GM achieved this by reworking underlying workflows. The company gained more precise control over critical processes by bringing back in-house data ingestion and audience creation. At the same time, business leaders collaborated closely with technology experts to guide campaign design using AI-derived insights about who might be most receptive to specific vehicle brands or features. The result was a seamless transition from raw data to bottom-line impact.

BMW has further advanced its procurement operations by creating AI-powered Tender Assistant support. This solution automatically drafts and modifies RFPs and tender documents, embedding best practices into each step. Through chat-based features and faster collaboration between procurement and business teams, BMW reduced the total time spent on these processes by half. This improvement is only possible when workflows are fundamentally reimagined to put AI at their center. Even then, process redesign alone is not enough to lock in value. Ensuring a direct link to financial performance is the final piece of the puzzle.

Execution to P&L Aligns AI with Business Outcomes

At the outset of any AI initiative, executives must set clear expectations for how these technologies will drive revenue and profitability. They also need a way to track outcomes rigorously. Such efforts can be complex and time-consuming, so many successful companies create a dedicated AI transformation office empowered by senior leaders to steer implementation. Although examples of fully realized, end-to-end AI transformations remain relatively few in automotive and mobility, other industries offer models to tie AI directly to the P&L.

One chemicals firm pursued a comprehensive AI overhaul. The company inventoried its processes to identify the most promising AI opportunities, ensured each new tool would complement the others, and set clear savings targets based on specific timeframes. They then monitored actual results against these targets to manage performance. This systematic approach allowed the firm to maximize returns from each AI tool and keep the entire portfolio of initiatives on track.

Automotive and mobility companies can follow a similar path. Because many firms are still exploring Gen AI, the gap between those who lead and those who lag remains small enough to close within a year or two. A methodical plan for adopting AI and Gen AI can help businesses capture the full range of benefits, from lowered costs to improved operations, and position them to thrive in a rapidly evolving market.

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Building a Lasting Advantage

Extracting meaningful value from AI and Gen AI can be demanding, yet several prominent players in the automotive and mobility sector are already moving beyond potential to genuine payoffs. Organizations reshape the industry's competitive structure as they redefine processes, cultivate widespread adoption of AI tools, and tie outcomes directly to financial metrics. The future will belong to companies that commit to a measured, data-driven approach, focus on behavior and mindset shifts, and set ambitious targets for cost and revenue gains.

Leading these transformations requires decisive leadership, but the results—substantial savings, stronger operational efficiency, and new sources of income—promise lasting competitive advantage. Ultimately, those who embrace these principles will emerge as standard-bearers in a market that shows no signs of slowing down.