Remove Risk Remove ROI Remove Uncertainty
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3 ways to avoid the generative AI ROI doom loop

CIO Business Intelligence

By Bryan Kirschner, Vice President, Strategy at DataStax From the Wall Street Journal to the World Economic Forum , it seems like everyone is talking about the urgency of demonstrating ROI from generative AI (genAI). GenAI itself can report week-on-week progress, putting it to work across your organization–including the ROI.

ROI 72
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Build a strong data foundation for AI-driven business growth

CIO Business Intelligence

Data silos, lack of standardization, and uncertainty over compliance with privacy regulations can limit accessibility and compromise data quality, but modern data management can overcome those challenges. Achieving ROI from AI requires both high-performance data management technology and a focused business strategy.

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12 Cloud Computing Risks & Challenges Businesses Are Facing In These Days

datapine

This increases the risks that can arise during the implementation or management process. The risks of cloud computing have become a reality for every organization, be it small or large. The next part of our cloud computing risks list involves costs. In both cases, the return on investment (ROI) is healthy. Compliance.

Risk 237
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How to Set AI Goals

O'Reilly on Data

Customer stakeholders are the people and companies that advertise on the platform, and are most concerned with ROI on their ad spend. Technical competence results in reduced risk and uncertainty. AI initiatives may also require significant considerations for governance, compliance, ethics, cost, and risk.

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Sustainability: Real progress but also thorny challenges ahead

CIO Business Intelligence

Dealing with uncertain economic environments, which can distract from sustainability issues: Energy prices, price inflation, and geopolitical tensions continue to fluctuate, and that uncertainty can impact focus on environmental sustainability. So far, however, companies seem to be staying the course.

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Escaping POC Purgatory: Evaluation-Driven Development for AI Systems

O'Reilly on Data

The coordination tax: LLM outputs are often evaluated by nontechnical stakeholders (legal, brand, support) not just for functionality, but for tone, appropriateness, and risk. Hallucination risk : Add stronger grounding in retrieval or prompt modifications. LLM-powered software amplifies this uncertainty further.

Testing 168
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What you need to know about product management for AI

O'Reilly on Data

Machine learning adds uncertainty. Underneath this uncertainty lies further uncertainty in the development process itself. There are strategies for dealing with all of this uncertainty–starting with the proverb from the early days of Agile: “ do the simplest thing that could possibly work.”