• PWC
  • Aug 11, 2017

Monetizing the Industrial Internet of Things


C3.ai contributed to a report by PwC and MAPI that presents a blueprint for moving past the hype and monetizing the Industrial Internet of Things.

Download the full paper

A recent, in-depth report is rich with survey statistics, key findings, and recommendations on how and why manufacturers are pursuing IoT initiatives. C3.ai President and CTO, Ed Abbo, contributed to the effort via an interview with PwC and MAPI. Three sections worthy of highlighting include:
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“Collecting data from platforms such as manufacturing, inventory systems, logistics, machinery or connected products is one thing. Most manufacturers have no shortage of data,” said Ed Abbo, President and Chief Technology Officer of C3.ai, an AI and IoT application development platform provider, in an interview with PwC and MAPI. “But committing to getting the most out of that data is another matter. It is CEO-led mandates for digital transformation initiatives that are driving the large and growing market opportunity for a new generation of enterprise software that leverages big data, AI, and IoT at industrial scale. C3.ai serves this growing demand by enabling large industrial and commercial organizations to rapidly build and deploy next-generation software applications that leverage AI and IoT across all parts of the enterprise to address productivity improvements, new business creation, and competitive differentiation. If companies can’t get this right, they will be at a competitive disadvantage,” Abbo added.
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“We’re definitely seeing companies starting to think about how they can move from simply selling a product to selling a service. For example, in the mining industry, instead of selling earth moving equipment, the model could be selling tons of earth moved. That way, there’s a lot less that a customer has to worry about, such as high capital expenditures and maintenance, for example. Some customers might really favor that model,” Abbo added.
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“Manufacturers that have product data can also better predict that an issue with one customer could trigger a red flag or potential claim by another customer with similar circumstances,” said Ed Abbo, President and CTO of C3.ai. For example, C3.ai worked with one manufacturer that was gathering data on 2,300 of its products in the field, with each product emitting data from hundreds of sensors. Once their data was properly integrated and correlated with other data sets (such as data on inventory, sales, repair service, etc.), the company was able to anticipate, for example, whether proactive repairs were required to avoid potential warranty claims, and whether design or manufacturing changes were needed based on the experiences of other customers.