This site uses cookies to improve your experience. To help us insure we adhere to various privacy regulations, please select your country/region of residence. If you do not select a country, we will assume you are from the United States. Select your Cookie Settings or view our Privacy Policy and Terms of Use.
Cookie Settings
Cookies and similar technologies are used on this website for proper function of the website, for tracking performance analytics and for marketing purposes. We and some of our third-party providers may use cookie data for various purposes. Please review the cookie settings below and choose your preference.
Used for the proper function of the website
Used for monitoring website traffic and interactions
Cookie Settings
Cookies and similar technologies are used on this website for proper function of the website, for tracking performance analytics and for marketing purposes. We and some of our third-party providers may use cookie data for various purposes. Please review the cookie settings below and choose your preference.
Strictly Necessary: Used for the proper function of the website
Performance/Analytics: Used for monitoring website traffic and interactions
What if the principles that transformed softwaredevelopment over the last decade could be the key to successfully implementing AI in your organization? Patrick Debois is credited with coining the term “DevOps” and has been instrumental in shaping how organizations approach softwaredevelopment and operations.
Fast-forward to today, when softwaredevelopment is part of every companys portfolio. More companies not just tech are adopting a set of structures and practices best suited to delivering value in this way. These structures and practices are called the product operating model. Adopting a product mindset makes sense.
Any group suffering from the growing pains of scaling software delivery can attest to this value. But while the increasing number of companies adopting VSM has changed how teams build from project to product, a new innovative approach hits the spotlight: generativeAI (genAI). Before we go deeper, let’s establish what genAI is.
Often misrepresented as anti-technology, their cause was instead deeply rooted in the human need for fair wages and sustainable work conditions. History recounts that these artisans spared manufacturers who embraced the latest technologies but ensured their workforce earned a living wage and were treated respectfully.
In today’s fast-paced world of business and technology, efficiency in software delivery is not just important; it’s crucial. The idea of cutting waste, a principle from manufacturing, is relevant more than ever in softwaredevelopment and knowledge work as a means to increase efficiency.
Recent advancements in GenerativeAI and machine learning (ML) have enthralled enterprises and consumers alike, as we recently saw with the launch of GPT-4. Its primary purpose is to help users with diverse technical backgrounds and skills create their own applications, automate workflows, and analyze data with ease.
We organize all of the trending information in your field so you don't have to. Join 29,000+ users and stay up to date on the latest articles your peers are reading.
You know about us, now we want to get to know you!
Let's personalize your content
Let's get even more personalized
We recognize your account from another site in our network, please click 'Send Email' below to continue with verifying your account and setting a password.
Let's personalize your content