From Data To Model Risk: The Enterprise AI/DI Risk Management Challenge (Part 1)
by Kamesh Raghavendra, Chief Product Officer, The Hive
In recent years, artificial intelligence (AI) has moved from being fodder for post-apocalyptic fiction to occasionally dystopian reality. The technology has also enabled several valuable advances in consumer services.
To date, AI’s risks and benefits have remained largely limited to consumer-facing applications. And while the security and other unintended and unforeseen risks have affected many individuals and seem challenging to remediate, the causes — like cyberattacks, fake news, and privacy invasion — are increasingly well-understood.
Over the last several years, however, AI and related technologies like decision intelligence (DI) have moved beyond Silicon Valley and Seattle to penetrate a new type of company: the large business enterprise. Companies like MasterCard, Grainger, Ford Motor Company, and others are looking to AI/DI for a competitive advantage. Reflecting this maturation in the market, SAP, for example, has made its launch of SAP Leonardo — specifically targeted towards machine learning in the enterprise — a strategic priority. Built on large data stores and using the latest in fast, high-quality AI methods, these enterprise initiatives by SAP and others are accelerating even more rapidly than the first wave of consumer-focused AI developments.