Put AI to Work
April 15-18, 2019
New York, NY

Simple, scalable, and sustainable: A methodical approach to AI adoption (sponsored by Accenture)

rajendra prasad (Accenture)
11:05am11:45am Thursday, April 18, 2019
Location: Petit Trianon
Average rating: ****.
(4.00, 1 rating)

What you'll learn

  • Learn how leaders and change makers in large enterprises can make AI adoption successful


After crossing the first AI implementation milestone, CIOs and CTOs often ask, “What’s next?” AI engineering best practices don’t exist yet, making scaling and sustaining these solutions a challenge.

Accenture has developed an easy-to-use methodology for achieving predictable high-speed production releases and sustaining reliable AI solutions, based on experience implementing AI-led automation for more than 100 clients. Accenture’s approach helps align AI and business needs and benefits and establish a well-defined methodology for the entire AI engineering lifecycle, from data engineering (ingesting, cleansing, normalizing, and optimizing) to selecting the right AI models. It also helps identify key change management actions for people, processes, and technology.

Rajendra Prasad (RP) explains how leaders and change makers in large enterprises can make AI adoption successful.

Photo of rajendra prasad

rajendra prasad


Rajendra Prasad (RP) leads automation and artificial intelligence for Accenture Technology Services. In this role, he focuses on driving efficiency into the delivery of Accenture services across the application lifecycle and leads a global team of highly qualified professionals who help IT organizations achieve success in their automation and Agile transformations. RP also leads the team that created and deploys Accenture myWizard, an intelligent automation platform with artificial intelligence at its core. He has 23 years of experience, more than 20 patents and patents pending, and 30 papers published in international journals and conferences.

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Robert Cohen | SENIOR FELLOW
03/21/2019 5:43pm EDT

How similar is your approach to the AutoML offerings from Salesforce and others?