Remove Cost-Benefit Remove Experimentation Remove Modeling
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Top 8 failings in delivering value with generative AI and how to overcome them

CIO Business Intelligence

This stark contrast between experimentation and execution underscores the difficulties in harnessing AI’s transformative power. Data privacy and compliance issues Failing: Mismanagement of internal data with external models can lead to privacy breaches and non-compliance with regulations. Of those, just three are considered successful.

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GenAI sticker shock sends CIOs in search of solutions

CIO Business Intelligence

The early bills for generative AI experimentation are coming in, and many CIOs are finding them more hefty than they’d like — some with only themselves to blame. According to IDC’s “ Generative AI Pricing Models: A Strategic Buying Guide ,” the pricing landscape for generative AI is complicated by “interdependencies across the tech stack.”

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Achieving cloud excellence and efficiency with cloud maturity models

IBM Big Data Hub

Cloud maturity models are a useful tool for addressing these concerns, grounding organizational cloud strategy and proceeding confidently in cloud adoption with a plan. Cloud maturity models (or CMMs) are frameworks for evaluating an organization’s cloud adoption readiness on both a macro and individual service level.

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When is the right time to dump an AI project?

CIO Business Intelligence

If they dump a pilot that’s not meeting expectations too soon, they may miss out on huge benefits down the line, but if they hang on too long, they can waste huge amounts of time, money, and resources. On the one side, Forrester recently warned organizations not to look for AI ROI too soon, because they could miss out on AI’s benefits.

ROI 135
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CIOs to spend ambitiously on AI in 2025 — and beyond

CIO Business Intelligence

Nate Melby, CIO of Dairyland Power Cooperative, says the Midwestern utility has been churning out large language models (LLMs) that not only automate document summarization but also help manage power grids during storms, for example. Only 13% plan to build a model from scratch.

ROI 136
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The bigger the better? Approaching Generative AI by size

CIO Business Intelligence

From budget allocations to model preferences and testing methodologies, the survey unearths the areas that matter most to large, medium, and small companies, respectively. Medium companies Medium-sized companies—501 to 5,000 employees—were characterized by agility and a strong focus on GenAI experimentation.

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Why enterprise CIOs need to plan for Microsoft gen AI

CIO Business Intelligence

It’s embedded in the applications we use every day and the security model overall is pretty airtight. The cost of OpenAI is the same whether you buy it directly or through Azure. Its model catalog has over 1,600 options, some of which are also available through GitHub Models. That’s risky.”