Remove Cost-Benefit Remove Document Remove Experimentation Remove Risk
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7 steps for turning shadow IT into a competitive edge

CIO Business Intelligence

Ask IT leaders about their challenges with shadow IT, and most will cite the kinds of security, operational, and integration risks that give shadow IT its bad rep. That’s not to downplay the inherent risks of shadow IT.

IT 135
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Accelerating Cost Reduction: AI Making an Impact on Financial Services

Cloudera

In the ever-evolving landscape of the financial services Industry, change is a constant and transformation is a requirement — to stay at pace with new regulations, risk mitigation, and the technological developments that support transformation.

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Business Strategies for Deploying Disruptive Tech: Generative AI and ChatGPT

Rocket-Powered Data Science

3) How do we get started, when, who will be involved, and what are the targeted benefits, results, outcomes, and consequences (including risks)? encouraging and rewarding) a culture of experimentation across the organization. Encourage and reward a Culture of Experimentation that learns from failure, “ Test, or get fired!

Strategy 289
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What Is Model Risk Management and How is it Supported by Enterprise MLOps?

Domino Data Lab

Model Risk Management is about reducing bad consequences of decisions caused by trusting incorrect or misused model outputs. Systematically enabling model development and production deployment at scale entails use of an Enterprise MLOps platform, which addresses the full lifecycle including Model Risk Management. What Is Model Risk?

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3 key digital transformation priorities for 2024

CIO Business Intelligence

Meanwhile, CIOs must still reduce technical debt, modernize applications, and get cloud costs under control. Create these six generative AI workstreams CIOs should document their AI strategy for delivering short-term productivity improvements while planning visionary impacts.

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CBRE’s Sandeep Davé on accelerating your AI ambitions

CIO Business Intelligence

Sandeep Davé knows the value of experimentation as well as anyone. Davé and his team’s achievements in AI are due in large part to creating opportunities for experimentation — and ensuring those experiments align with CBRE’s business strategy. And those experiments have paid off. Artificial Intelligence, IT Leadership

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Bringing an AI Product to Market

O'Reilly on Data

Without clarity in metrics, it’s impossible to do meaningful experimentation. AI PMs must ensure that experimentation occurs during three phases of the product lifecycle: Phase 1: Concept During the concept phase, it’s important to determine if it’s even possible for an AI product “ intervention ” to move an upstream business metric.

Marketing 362