Business integration stalls AI adoption in automotive finance

Automotive finance providers could improve their businesses significantly by adopting AI, but integration challenges are slowing their progress

Advances in artificial intelligence (AI) have the potential to substantially improve the performance and efficiency of automotive finance providers. But adoption has been slow.

Despite AI’s ability to automate high-volume repetitive tasks, handle large amounts of unstructured data, and support complex decisions, few automotive finance companies have moved beyond pilot projects.

Markus Collet, partner at Corporate Value Associates (CVA), estimates that AI could more than double the profitability of some auto finance providers, to around 4.3% of assets under management. The consulting firm has identified around 120 potential use cases for AI downstream in the automotive industry. They include marketing support for new car sales, underwriting improvements for insurers, and end-of-contract alerts for leasing companies.

But to fully capitalize on AI’s potential, automotive finance providers must integrate it into their business systems and processes. 

“The main difficulty is not having an idea or setting up pilot projects. It's actually using those pilots as a structural element in the company’s operating model so that AI is a fundamental way of working across the organization,” says Collet, who heads CVA’s automobility platform. [15:08]

Key takeaways

• Automotive finance providers can benefit significantly from AI, but many have yet to move beyond pilot projects.
  
  
• Business integration is the main barrier to wider AI adoption among companies downstream in the automotive industry. 
  
  
• Real gains require firms to embed AI into core workflows and operating models throughout their organizations rather than using it as a stand-alone productivity tool.
  
  
• Firms that fail to adopt AI risk falling behind competitors that are quick to use the technology to improve performance and efficiency. 
  
  
• Poor business processes and weak data quality limit returns on AI investment and slow wider deployment.
  
  
• CVA’s Collet sets out six steps to scale AI across the business that begin with broader employee engagement and lead to industrialized deployment at scale.

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