10 Core Principles of Enterprise AI

For businesses, the COVID-19 pandemic has been a test. It has been a pressure test of organizational resilience, agility, and adaptability. For many enterprises, the pandemic and the resulting economic shutdown have become a test of survival. And it has clearly intensified the urgency around digital transformation.

At C3.ai, in our conversations with organizations across industries globally – conversations that, despite the economic shutdown, have increased in both volume and urgency over the last few months – we are seeing a heightened focus on the need to accelerate digital transformation. The gist of those conversations reduces to two basic questions that organizations ask us: How do we scale AI across our enterprise, and how do we do it rapidly?

Over the next several years, virtually all enterprise application software will become AI-enabled. The vast majority of these enterprise AI applications will run on elastic cloud computing infrastructure from leading vendors such as AWS and Microsoft Azure. Building, deploying, and scaling enterprise AI applications on these public clouds will be a top priority.

That said, building this new class of cloud-based enterprise AI applications involves significantly greater complexity than previous generations of enterprise software, including numerous requirements around data integration and normalization, machine learning modeling, and cloud infrastructure orchestration.

Based on C3.ai’s decade of experience helping global organizations apply enterprise AI across multiple industries – including manufacturing, aerospace, oil and gas, defense, healthcare, and utilities – we recently identified and codified 10 core capabilities for a complete enterprise AI platform.

–Thomas M. Siebel, CEO of C3.ai

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