At our next Digital Innovation Insight Summit, we’ll be unlocking insights and exploring innovations for future-ready enterprises. First onto the debating table, as you might expect: artificial intelligence (AI).

AI is expected to contribute $15.7 trillion to the global economy by 2030. But it won’t if it can’t add value effectively. This effectiveness is critical. AI is a fascinating topic to discuss and explore; it feels innovative and creative, but where are the hard numbers regarding its value to businesses?

In the automotive industry, McKinsey predicts a 50% increase in productivity with the use of AI. A topic we recently explored in two roundtables with twelve business leaders from multinational automakers and power technology companies.

What did they have to say about obstacles on the road to AI productivity – and how to navigate them successfully?

Data Quality and Integration

This emerged as the first of three major talking points and a fundamental barrier to the successful implementation of AI. The director of data platforms for one multinational automaker spoke for all of them, saying, “Data quality has ruined many a pilot project. That’s just a real challenge for manufacturing data in particular.”

Another participant, a product manager at a multinational automaker, mentioned the challenges of ensuring data quality when transitioning from legacy systems and integrating with them. They also noted that the time required to train models is substantial, and accuracy may not exceed 90%, underscoring the reliance on high-quality data.

The Takeaway

Poor data ruins projects. Integrating AI with legacy systems requires robust data governance to unlock meaningful insights. Treating data as a critical asset is fundamental.

A Value-Driven Approach to AI Adoption

All our roundtable attendees agreed: embracing AI is one thing; achieving lasting adoption requires careful consideration of its value to specific organizational functions and roles.

As the vice president of a global power technology company put it: “It’s about time we start looking at value-centric solutions in comparison to, hey, you’ve got to be adopting new tech. Because you’re creating a lot of tech debt for the company.”

Another executive explained their strategy of starting with customer engagement use cases, where the risk is lower, before moving to more critical areas like manufacturing and product development.

Next Steps

To overcome skepticism, focus on solutions that offer clear and measurable ROI. Strategically prioritize use cases, starting with lower-risk areas like customer engagement, to demonstrate tangible benefits.

Driving Productivity with AI

All our roundtable attendees expressed significant interest in leveraging AI to enhance productivity within the automotive industry. A director from one of the top 10 automakers worldwide said his company is aiming for an “AI-first mindset” to encourage employees to seek AI-driven ideas and wants to “increase developer productivity”. Another of the top 10 said they see AI as a tool for faster task completion.

However, our discussions also addressed the challenges in realizing these gains. Adoption is a key hurdle, with uneven usage potentially hampering enterprise-wide productivity. Selecting the right use cases that deliver tangible value is critical, as simply deploying AI (like copilots) without a clear purpose may not yield results.

Set Yourself Up for Success

Everybody wants AI, and most people are trying it somewhere, from call centers to production lines to sales. The keys are an AI-first mindset, data quality, and the need for a structured, value-driven approach to AI implementation.

To continue exploring how your peers navigate these challenges and what’s next in digital innovation, join us at one of our upcoming Digital Innovation Insight Summits.

To see all our forthcoming summits, visit our events page.