Remove Machine Learning Remove Metrics Remove ROI
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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). Make ‘soft metrics’ matter Imagine an experienced manager with an “open door policy.”

ROI 72
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What you need to know about product management for AI

O'Reilly on Data

If you’re already a software product manager (PM), you have a head start on becoming a PM for artificial intelligence (AI) or machine learning (ML). AI products are automated systems that collect and learn from data to make user-facing decisions. We won’t go into the mathematics or engineering of modern machine learning here.

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Where’s the ROI for AI? CIOs struggle to find it

CIO Business Intelligence

I have found very few companies who have found ROI with AI at all thus far,” he adds. The concern about calculating the ROI also rings true to Stuart King, CTO of cybersecurity consulting firm AnzenSage and developer of an AI-powered risk assessment tool for industrial facilities.

ROI 143
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Analytics By Design, For The Analytics Win

Rocket-Powered Data Science

Analytics are the products, the outcomes, and the ROI of our Big Data , Data Science, AI, and Machine Learning investments! Analytics are the outcomes of data activities (data science, machine learning, AI) within the organization. AI strategies and data strategies should therefore focus on outcomes first.

Analytics 193
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A Simplified Approach to Generating ROI from AI Apps

CIO Business Intelligence

Nowadays, management wants return on investment (ROI) calculations as part of any AI proposal. But how do you calculate ROI on something completely new and different—or on something as complex as AI, which brings with it lots of issues such as data privacy concerns, regulatory compliance complications, and all-new security risks?

ROI 105
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Interview with: Sankar Narayanan, Chief Practice Officer at Fractal Analytics

Corinium

What do you recommend to organizations to harness this but also show a solid ROI? A playbook for this is to run multiple experiments in parallel and create ‘MVPs’ (fail/learn fast), as well as incorporate feedback mechanisms to enable an improvement loop, and scaling the ones that show the fastest path to ROI.

Insurance 250
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Escaping POC Purgatory: Evaluation-Driven Development for AI Systems

O'Reilly on Data

People have been building data products and machine learning products for the past couple of decades. Business value : Once we have a rubric for evaluating our systems, how do we tie our macro-level business value metrics to our micro-level LLM evaluations? This isnt anything new. How do we do so? Evaluation : Same as above.

Testing 174