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.
Rajendra Prasad (RP) is the Global Automation and Artificial Intelligence Lead for Accenture Technology Services. In this leadership role, RP focuses on driving efficiency across the IT application lifecycle for Fortune 500 companies. With his more than 25 years of industry experience, he partners with clients on their journeys to stay competitive and achieve business strategy in our post-digital world. RP holds 13 patents, including in AI, and over 120 patents-pending, and has had 30 papers published in international journals. He leads the team that created and currently deploys Accenture myWizard, an end-to-end intelligent automation and delivery excellence platform with Artificial Intelligence at its core.
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