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.
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.
Real gains from AI require companies to redesign core workflows and operations, says Collet. Using AI just to speed up processes or improve efficiency won’t justify large-scale investment.
Collet was speaking at an online event hosted by CVA and the Qorus Mobility Community. Together with Sam Heymans, CEO and co-founder at car leasing firm Lizy, and Julien Defosse, worldwide FSI market development lead at Amazon Web Services (AWS), Collet discussed how automotive finance providers can move from AI pilots to production.
Executives rank top obstacles to AI adoption
Executives polled at the event acknowledged that business integration is the main obstacle blocking greater adoption of AI. Around 67% identified integration as their biggest barrier. They ranked it ahead of organizational and governance challenges, investment constraints, and lack of internal AI, data, or technology competencies.
Heymans at Lizy warns that integration challenges should not deter companies from deploying AI across their organizations. The risk of falling behind competitors by not adopting AI is bigger than potential business integration problems, he says.
Heymans acknowledges that the rapid development of AI makes it difficult for companies to make long-term plans around the technology.
“Making long-term business plans with AI is a waste of time. You just have to invest in it directionally because it is the future and if you don't do it, your company might die.” [1:01:45]
Lizy, which launched its digital car leasing platform in 2019, is already applying AI across operations such as vehicle registration, credit checks, lead generation, and contract updates. It runs more than 250,000 automated actions a month and has achieved productivity gains equal to 20% of its full-time employee base. Heymans says the company is looking to extend its use of AI to applications such as fleet health monitoring, voice-activated scheduling agents, and dynamic delivery and routing.
“We are very lucky to be in automotive finance. On the one hand, we have a high barrier to entry and on the other, we are a perfect playground for AI and automation because we have a lot of repetitive tasks and a lot of unstructured data.” [27:45]
To gain the most from AI, companies need to look beyond operational tasks, says Defosse at AWS.
Opportunities for automotive finance companies include enhanced risk management, fraud detection, and real-time KYC checks.
To scale AI beyond operational tasks and integrate it across the business, companies need to make sure their existing processes work efficiently.
“If you work with AI on a broken process, the process is still broken. You can augment with AI but you still need to do the hard work behind the scenes.” [49:43]
Some firms are not getting the returns they expected from their investment in AI because they’ve neglected to fix their underlying business processes, says Defosse.
He adds that limited access to good quality data is another stumbling block. “That’s probably the major blocker; access to data, and data quality.” [45:20]
Six steps to AI business integration
CVA’s Collet says it’s essential that automotive finance providers avoid treating AI as a side project. AI needs to be embedded into real workflows, with clear owners, budgets, KPIs, and integration plans. Collet sets out six steps to achieve such integration:
1. Extend AI use across the organization by ensuring that employees use tools such as large language models systematically, collectively, and with a clear purpose rather than as occasional add-ons.
2. Embed AI into business workflows instead of confining it to isolated productivity gains or stand-alone tools.
3. Rethink operations by assessing how better access to data and machine intelligence could change the way the business works and where AI might open disruptive opportunities.
4. Identify where value is created in the business and then select the use cases most likely to improve performance, build knowledge, and extend capability.
5. Choose a select group of use cases, prove their value, and build the platform and partnerships needed to support them at scale.
6. Scale AI across the business by industrializing successful use cases through stronger architecture and governance, expanding the use of data, decision tools, and partner services and gradually moving away from legacy applications.
AI offers automotive finance providers an opportunity to improve performance, lower costs, and serve customers better. To secure such benefits they will need to move beyond pilots, fix their business processes, improve their data, and embed AI across their organizations. The transition will be challenging but a greater risk is moving too slowly and being overtaken by quicker competitors.
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