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To counter such statistics, CIOs say they and their C-suite colleagues are devising more thoughtful strategies. How does our AI strategy support our businessobjectives, and how do we measure its value? The time for experimentation and seeing what it can do was in 2023 and early 2024.
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?
A 1958 Harvard Business Review article coined the term information technology, focusing their definition on rapidly processing large amounts of information, using statistical and mathematical methods in decision-making, and simulating higher order thinking through applications.
He plans to scale his company’s experimental generative AI initiatives “and evolve into an AI-native enterprise” in 2024. This vision represents a fundamental shift, positioning AI as an integral part of our business fabric rather than just an add-on. “AI But at the end of the day, it boils down to statistics.
As Belcorp considered the difficulties it faced, the R&D division noted it could significantly expedite time-to-market and increase productivity in its product development process if it could shorten the timeframes of the experimental and testing phases in the R&D labs.
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Without clarity in metrics, it’s impossible to do meaningful experimentation. Experiments allow AI PMs not only to test assumptions about the relevance and functionality of AI Products, but also to understand the effect (if any) of AI products on the business. Don’t expect agreement to come simply.
Unlike experimentation in some other areas, LSOS experiments present a surprising challenge to statisticians — even though we operate in the realm of “big data”, the statistical uncertainty in our experiments can be substantial. We must therefore maintain statistical rigor in quantifying experimental uncertainty.
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