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4 hidden risks of your enterprise cloud strategy

CIO Business Intelligence

As enterprise CIOs seek to find the ideal balance between the cloud and on-prem for their IT workloads, they may find themselves dealing with surprises they did not anticipate — ones where the promise of the cloud, and cloud vendors, fall short versus the realities of enterprise IT. That’s where the contract comes into play.

Risk 113
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Gen AI without the risks

CIO Business Intelligence

Gen AI will become a fundamental part of how enterprises manage and deliver IT services and how business users get their work done. Developing and deploying successful AI can be an expensive process with a high risk of failure. For the average enterprise, it’s prohibitively expensive. Not at all.

Risk 121
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How to become an AI+ enterprise

IBM Big Data Hub

While many organizations have implemented AI, the need to keep a competitive edge and foster business growth demands new approaches: simultaneously evolving AI strategies, showcasing their value, enhancing risk postures and adopting new engineering capabilities. This requires a holistic enterprise transformation. times higher ROI.

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Microsoft and Cognizant team up to boost enterprise Copilot adoption

CIO Business Intelligence

Microsoft is opening another route for extending the reach of its Copilot offerings in the enterprise through an expanded partnership with global professional services company Cognizant. Ensuring human oversight and rigorous quality checks can mitigate the risks associated with AI errors.” Generative AI, IT Consulting Services

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Successful Change Management with Enterprise Risk Management

Speaker: William Hord, Vice President of ERM Services

A well-defined change management process is critical to minimizing the impact that change has on your organization. Leveraging the data that your ERM program already contains is an effective way to help create and manage the overall change management process within your organization.

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CIOs weigh where to place AI bets — and how to de-risk them

CIO Business Intelligence

There are a lot of risks and a lot of land mines to navigate,” says the analyst. Coming to grips with risk The first step in making any bet — or investment — is to understand your ability to withstand risk. Covanta is also using AI to perform legal review on contracts, detecting and highlighting points of possible risk.

Risk 125
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Building Tax Planning into Enterprise Risk Management Strategies

Jet Global

Tax planning is playing an increasingly important part in corporates’ enterprise resource management (ERM) strategies, driven by the many uncertainties created by political, economic, and pandemic-related trends. Take Responsibility for Risk Oversight. Take Responsibility for Risk Oversight. Foster an Appropriate Risk Mindset.

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ERM Program Fundamentals for Success in the Banking Industry

Speaker: William Hord, Senior VP of Risk & Professional Services

Enterprise Risk Management (ERM) is critical for industry growth in today’s fast-paced and ever-changing risk landscape. Do we understand and articulate our bank’s risk appetite and how that impacts our business units? How are we measuring and rating our risk impact, likelihood, and controls to mitigate our risk?

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How to Solve 4 Common Challenges of Legacy Information Management

Speaker: Chris McLaughlin, Chief Marketing Officer and Chief Product Officer, Nuxeo

After 20 years of Enterprise Content Management (ECM), businesses still face many of the same challenges with finding and managing information. Strategies to avoid the risks of modernization by future-proofing your organizational infrastructure. You'll come away from the webinar understanding: Why ECM still poses business challenges.

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LLMOps for Your Data: Best Practices to Ensure Safety, Quality, and Cost

Speaker: Shreya Rajpal, Co-Founder and CEO at Guardrails AI & Travis Addair, Co-Founder and CTO at Predibase

Large Language Models (LLMs) such as ChatGPT offer unprecedented potential for complex enterprise applications. However, productionizing LLMs comes with a unique set of challenges such as model brittleness, total cost of ownership, data governance and privacy, and the need for consistent, accurate outputs.