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IDC chief research officer: GenAI, from experimentation to adoption

CIO Business Intelligence

Its been a year of intense experimentation. Now, the big question is: What will it take to move from experimentation to adoption? The key areas we see are having an enterprise AI strategy, a unified governance model and managing the technology costs associated with genAI to present a compelling business case to the executive team.

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Experimentation and Testing: A Primer

Occam's Razor

This post is a primer on the delightful world of testing and experimentation (A/B, Multivariate, and a new term from me: Experience Testing). Experimentation and testing help us figure out we are wrong, quickly and repeatedly and if you think about it that is a great thing for our customers, and for our employers. Counter claims?

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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
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How to Set AI Goals

O'Reilly on Data

AI Benefits and Stakeholders. AI is a field where value, in the form of outcomes and their resulting benefits, is created by machines exhibiting the ability to learn and “understand,” and to use the knowledge learned to carry out tasks or achieve goals. AI-generated benefits can be realized by defining and achieving appropriate goals.

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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 290
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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. By understanding their options and leveraging GPU-as-a-service, CIOs can optimize genAI hardware costs and maintain processing power for innovation.”

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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