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Generative AI in the Enterprise

O'Reilly on Data

In enterprises, we’ve seen everything from wholesale adoption to policies that severely restrict or even forbid the use of generative AI. Unexpected outcomes, security, safety, fairness and bias, and privacy are the biggest risks for which adopters are testing. Another piece of the same puzzle is the lack of a policy for AI use.

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Managing machine learning in the enterprise: Lessons from banking and health care

O'Reilly on Data

In earlier posts , we listed things ML engineers and data scientists may have to manage, such as bias, privacy, security (including attacks aimed against models ), explainability, and safety and reliability. Governance, policies, controls. credit scores ). AI projects in financial services and health care. Image by Ben Lorica.

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10 AI strategy questions every CIO must answer

CIO Business Intelligence

The 2024 Enterprise AI Readiness Radar report from Infosys , a digital services and consulting firm, found that only 2% of companies were fully prepared to implement AI at scale and that, despite the hype , AI is three to five years away from becoming a reality for most firms.

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5 key areas for tech leaders to watch in 2020

O'Reilly on Data

One possible trend-in-the-making is that of a slowing Go, which—following several years of rapid growth in usage (including +14% from 2017 to 2018)—cooled down last year, with usage growing by a mere 2%. Drop.NET from the tally on methodological grounds [2] , and Go cracks the top five. to be wary of. Figure 1 (above).

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Navigating the cloud maze: A 5-phase approach to optimizing cloud strategies

CIO Business Intelligence

In some cases, the business domain in which the organization operates (ie, healthcare, finance, insurance) understandably steers the decision toward a single cloud provider to simplify the logistics, data privacy, compliance and operations. Its a good idea to establish a governance policy supporting the framework.

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CISO viewpoint part 1: AI’s impact on people, policies & processes

CIO Business Intelligence

At the same time, they realize that AI has an impact on people, policies, and processes within their organizations. Since ChatGPT, Copilot, Gemini, and other LLMs launched, CISOs have had to introduce (or update) measures regarding employee AI usage and data security and privacy, while enhancing policies and processes for their organizations.

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Data’s dark secret: Why poor quality cripples AI and growth

CIO Business Intelligence

As data-centric AI, automated metadata management and privacy-aware data sharing mature, the opportunity to embed data quality into the enterprises core has never been more significant. Governance connects policies to practice, aligning standards, roles and responsibilities. Level 2: Tactical Basic profiling tools implemented.