Remove Experimentation Remove Interactive Remove Measurement
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Escaping POC Purgatory: Evaluation-Driven Development for AI Systems

O'Reilly on Data

ML apps needed to be developed through cycles of experimentation (as were no longer able to reason about how theyll behave based on software specs). The skillset and the background of people building the applications were realigned: People who were at home with data and experimentation got involved! How will you measure success?

Testing 174
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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 364
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How to Set AI Goals

O'Reilly on Data

This in turn would increase the platform’s value for users and thus increase engagement, which would result in more eyes to see and interact with ads, which would mean better ROI on ad spend for customers, which would then achieve the goal of increased revenue and customer retention (for business stakeholders).

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Where CIOs should place their 2025 AI bets

CIO Business Intelligence

Deloittes State of Generative AI in the Enterprise reports nearly 70% have moved 30% or fewer of their gen AI experiments into production, and 41% of organizations have struggled to define and measure the impacts of their gen AI efforts. One opportunity is for CIOs to help their marketing departments improve brand loyalty.

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3 musts when recruiting vendors for AI

CIO Business Intelligence

Two years of experimentation may have given rise to several valuable use cases for gen AI , but during the same period, IT leaders have also learned that the new, fast-evolving technology isnt something to jump into blindly. The next thing is to make sure they have an objective way of testing the outcome and measuring success.

Testing 99
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Interview with: Sankar Narayanan, Chief Practice Officer at Fractal Analytics

Corinium

A properly set framework will ensure quality, timeliness, scalability, consistency, and industrialization in measuring and driving the return on investment. It is also important to have a strong test and learn culture to encourage rapid experimentation. What is the most common mistake people make around data?

Insurance 250
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Glossary of Digital Terminology for Career Relevance

Rocket-Powered Data Science

Chatbots cannot hold long, continuing human interaction. Traditionally they are text-based but audio and pictures can also be used for interaction. They provide more like an FAQ (Frequently Asked Questions) type of an interaction. Consequently, they can have extended adaptable human interaction. Industry 4.0