Enterprise architecture for artificial intelligence

Enterprise architecture for artificial intelligence

The road to enterprise intelligence starts with the humans behind the curtain. This presentation explains how to reduce the friction of AI adoption in the enterprise using systems thinking and people-centered workflows.

The future of software is being driven by intelligent applications. According to Gartner, by the year 2020, more than 85% of customer-to-business interactions will be carried out without humans. Currently, 81% of IT leaders are investing or plan to invest in artificial technology solutions. Given these trends, more companies are pivoting from preprogrammed software applications to intelligent applications. But while AI is a powerful tool for businesses, it can also lead to unintended and dangerous consequences, which are directly linked to the people and data that train the machines to learn

[box type=”shadow” align=”alignleft” class=”” width=”450px”] Download Enterprise architecture for artificial intelligence[/box]
Total
0
Shares
1 comment
  1. The Article Explains all the things related to AI in Enterprise architecture. Artificial intelligence (AI) is probably the most important new technology today. It has clear use cases, and the value that it’s produced so far is indisputable – just think of the digital assistant on your phone, driverless cars, even Gmail uses it.

    InsideAIml is World’s Best AI Learning Platform with Profoundly Demanding Certification Programs, Designed only for AI learners.
    Visit: https://insideaiml.com/courses

Comments are closed.

Related Posts
Read More

Simulation & Modeling Resources

Simulation and computer modeling tools allow engineers to model and evaluate real world events in a computer generated environment. Here are a few simulation tools and projects to get started:
Read More

Characteristics of Machine Learning solutions

It is estimated that 87% of data science projects never reach production. One of the pitfalls to developing a production-ready machine learning solution is the ability to define if it's an appropriate tool for solving the problem. Not every problem should be solved with machine learning.
Read More

Systems Thinking Resources

Systems thinking is a discipline used to understand systems to provide a desired effect. It provides methods for "seeing wholes and a framework for seeing interrelationships rather than things, for seeing patterns of change rather than static snapshots." The intent is to increase understanding and determine the point of “highest leverage”, the places in the system where a small changes can make a big impact.