Timemachine scheduler8/18/2023 ![]() applications and models throughout the company should be inventoried, mapped, and continuously monitored. Now, data governance must become data and A.I. Most companies have established data governance to ensure compliance with data privacy regulations, such as the EU’s GDPR. applications, and you can’t enable integration and scaling. Without enterprise consistency, you won’t have a clear line of sight into your A.I. workloads to the ways you ensure compliance with both regulation and your company’s core values. It’s about developing company-wide approaches and standards for critical enablers, from the technology architecture needed to support and scale A.I. But this is not about choosing either centralization or decentralization. ![]() Governance may sound to some like heavy-handed, top-down oversight. ![]() We believe they are well-equipped to apply three basic lessons: Data governance must become data and A.I. But most of today’s CEOs have learned valuable lessons from prior technology inflection points. is evolving so rapidly that there is no definitive playbook. ![]() On the other hand, allowing the new technology to proliferate within their companies in uncoordinated, even haphazard, ways can lead not only to duplication and fragmentation, but to something much more serious: irresponsible uses of A.I., including the perpetuation of biases, amplification of misinformation, and inadvertent release of proprietary data. use, they risk missing a historic opportunity to supercharge their products, services, and operations. If they take a wait-and-see approach or simply clamp down on A.I. With each development, business has been confronted with a new technology that holds both great promise and considerable uncertainty, adopted seemingly overnight by consumers, students, professionals, and businesses.ĬEOs recognize the challenge. is now demonstrating value in creative and imagination-driven tasks. It’s a safe bet that various forms of artificial intelligence, from algorithmic decision-support systems to machine learning applications, have already made their way into the front and back offices of most companies. ![]()
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