Remove Business Objectives Remove Experimentation Remove Uncertainty
article thumbnail

Business Strategies for Deploying Disruptive Tech: Generative AI and ChatGPT

Rocket-Powered Data Science

First, don’t do something just because everyone else is doing it – there needs to be a valid business reason for your organization to be doing it, at the very least because you will need to explain it objectively to your stakeholders (employees, investors, clients). Test early and often. Expect continuous improvement.

Strategy 290
article thumbnail

13 IT resolutions for 2024

CIO Business Intelligence

CIOs are readying for another demanding year, anticipating that artificial intelligence, economic uncertainty, business demands, and expectations for ever-increasing levels of speed will all be in play for 2024. They’re articulating ambitions and formulating objectives, turning those would-be challenges into opportunities.

IT 144
Insiders

Sign Up for our Newsletter

This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.

article thumbnail

Belcorp reimagines R&D with AI

CIO Business Intelligence

These circumstances have induced uncertainty across our entire business value chain,” says Venkat Gopalan, chief digital, data and technology officer, Belcorp. “As To address the challenges, the company has leveraged a combination of computer vision, neural networks, NLP, and fuzzy logic.

article thumbnail

Havmor’s VP IT Dhaval Mankad on ‘melting’ hurdles with a scoop of digital innovation

CIO Business Intelligence

Since we already have the cloud native data lake, we are generating actionable business insights using that data, and plan to leverage them with AI and other new-age tools to uplevel in business. We need to define our business objective before adopting those new tools, because AI is simply algorithm.

IT 98
article thumbnail

Variance and significance in large-scale online services

The Unofficial Google Data Science Blog

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.