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Industry Insights: CIO vision 2025 — bridging the gap between BI and AI


Accelerating the smart and sustainable future of manufacturing

AI pushing known frontiers in manufacturing

With rising costs, volatile demand and increasing focus on net-zero operations, the importance of data, analytics and AI has never been greater in manufacturing. As this new survey report by MIT Technology Review Insights points out, the data shows that AI leaders are most numerous in manufacturing and that the industry is growing data and AI investment at above-average rates, indicating the industry’s ambition to become more AI-driven and make Industry 4.0 a reality. At the same time, the industry suffers from what many refer to as “proof of concept (PoC) purgatory” — only about half of data and AI projects make it to production. 82% of manufacturing executives cited data-related problems as a critical factor that could jeopardize their company’s future AI success.

Taking a fragmented approach to data management and AI is one of the leading root causes of this trend. Over three-quarters (78%) of the executives surveyed and almost all (96%) of the leader group say that scaling AI and machine learning use cases to create business value is their top priority for enterprise data strategy over the next three years. Most of the survey respondents (72%) — and almost all leaders (92%) — appreciate the flexibility that a multicloud approach provides for AI development. 

With a more comprehensive approach toward data, analytics and AI, manufacturers can overcome these challenges and transform the way they operate with more sustainable operations, predictive supply chains, insight into their end consumers, and more productive employees.

Case Study


By gaining insight into every aspect of engine performance across its global operational fleet, Cummins was able to engineer optimal maintenance schedules that reduced warranty costs and maximized customer uptime and fuel efficiency, becoming a critical differentiator for its solutions in the marketplace.

Cummins started using AI five years ago to provide value-added services to its customers, such as advice to users of its engines on how to improve fuel economy or steps to take to address a parts failure. The company changed the focus of its AI efforts to prognostics — predicting when certain engine parts will fail. This allows it to suggest replacing those parts during scheduled maintenance — thus avoiding more costly warranty replacement work later.


“This shifted our thinking on what we were doing with AI and the data generated from our engines. Achieving a reduction of just 1% can be worth millions of dollars to the company.”

—Sherry Aaholm, Chief Digital Officer, Cummins

Case Study

CNH Industrial

Precision farming, optimizing water and chemical use, locating carbon sinks, enabling urban farming to reduce deforestation — these are just a few of the existing use cases for AI in sustainable agriculture. CNH Industrial plans to make another: a sustainable tractor.

CNH industrial

“We need to understand the environmental footprint of each component we put into the tractor. That requires obtaining data from each manufacturer on, for example, its wastewater production, its energy consumption and its hazardous material treatment. If a part contains hazardous materials, are they resolvable over time? Does the part need to be treated in a special way at the end of the tractor’s life to avoid damaging the environment? Is it recyclable? Getting this wrong has enormous implications for sustainable farming.”

—Marc Kermisch, Global Chief Information Officer, CNH Industrial

How to future-proof

The research points to these key attributes to instill in your data and technology foundations: openness, multicloud, democratization. An open and unified platform like the Databricks Lakehouse Platform makes it possible to scale AI efficiently — and ultimately, create business value.