Finding workers with the right skills and successfully transitioning current employees to new ways of working are big challenges for any business working with AI—much less one operating at the scale and complexity of a major automaker.
By Melissa Leon Pons, Workflow contributor
Isabel Baqué is focused on one of the greatest engineering challenges of our time: creating a mass-market self-driving car.
“Even though autonomous driving is still in the early stages, we think it has huge potential for return on investment,” says Baqué, the chief business digital officer and procurement director of automaker Stellantis.
Netherlands-based Stellantis is the world’s fourth-largest automaker, which was created in 2021 through the merger of Fiat Chrysler Automobiles and the PSA Group, known for brands such as Peugeot, Citroën, Opel, and Vauxhall. Baqué is responsible for building an agile organization within Stellantis that can quickly adapt to and leverage AI tools as they mature.
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The value of AI to the carmaker ranges from increasing operational efficiency to powering next-generation user experiences such as proactive maintenance alerts and advanced driver assistance systems—along with the big prize of enabling fully functional autonomous vehicles.
Managing this work across more than 140 countries with varying compliance and ethical frameworks requires a flexible structure that centralizes some resources while enabling relevant innovations to take place in different locations.
“We call it a ‘glocalized’ approach,” says Baqué. This model recognizes the expertise of local employees while allowing centralized teams to focus on consistency and efficiency. Regional teams get both the power of global scale and the autonomy to create value through their unique understanding of individual markets.
Getting buy-in from top management was a critical step in rolling out the strategy, says Baqué. “We really need the C-suite’s leadership, vision, and support to foster innovation through the organization.” she says. “It’s not a nice-to-have; it’s a must.”
Another part of the strategy was dividing development work between in-house and external teams. The idea is to focus Stellantis employees on core competencies while collaborating with partners on applications such as natural language processing and computer vision that fall outside the automaker’s expertise.
Data sharing within Stellantis and among its partners is thus extremely sensitive and a high priority. This heightens demand for data security, which covers the company’s own intellectual property, partner data, and information it gathers from the owners of its products. “Because our in-house AI teams and external partners need to collaborate closely to ensure a seamless integration, we establish very clear agreements for data sharing, intellectual property rights, and cybersecurity to protect everyone’s interests,” says Baqué.
Finding workers with the right skills and successfully transitioning current employees to new ways of working are big challenges for any business working with AI—much less one operating at the scale and complexity of a major automaker. “When you need to adopt new methodologies, you always find pushback from some employees,” she says, adding that some employees have been hesitant to work with AI. “So, we are continuously communicating clearly the benefits of AI adoption and investing in different comprehensive training programs to equip employees with the skills to work alongside artificial intelligence systems,” she says.
Baqué acknowledges that companywide AI transformation will take time. "We still have a long way to go because there are many initiatives, like autonomous driving, that are not in place,” she says. “But we are working very hard to get there."
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Melissa Leon Pons is a research associate on Oxford Economics’ thought leadership team.