Wipro Logo
Services Industries Cloud Cybersecurity Digital EngineeringNXT HOLMES Geographies

Service Offerings

Data, Analytics & AI
Applications
Digital Operations and Platform
Consulting
Infrastructure Services

Service Offerings

Data, Analytics & AI
Applications
Digital Operations and Platform
Consulting
Infrastructure Services

Client Themes

As a service
Big Data
Blockchain
Cyber Security & Enterprise Risk
DevOps
Enterprise Ops Transformation
Industry 4.0
Open Source
Product Lifecycle Management
Software Defined Everything

Client Themes

As a service
Big Data
Blockchain
Cyber Security & Enterprise Risk
DevOps
Enterprise Ops Transformation
Industry 4.0
Open Source
Product Lifecycle Management
Software Defined Everything
Aerospace & Defense
Automotive
Banking
Capital Markets
Communications
Consumer Electronics
Consumer Packaged Goods
Education
Engineering, Construction & Operations
Healthcare
Insurance
Medical Devices
Natural Resources
New Age Markets
New Age & Media
Network & Edge Providers
Oil & Gas
Pharmaceutical & Life Sciences
Platforms & Software Products
Industrial & Process Manufacturing
Professional Services
Public sector
Retail
Semiconductors
Travel & Transportation
Utilities
Aerospace & Defense
Automotive
Banking
Capital Markets
Communications
Consumer Electronics
Consumer Packaged Goods
Education
Engineering, Construction & Operations
Healthcare
Insurance
Medical Devices
Natural Resources
New Age Markets
New Age & Media
Network & Edge Providers
Oil & Gas
Pharmaceutical & Life Sciences
Platforms & Software Products
Industrial & Process Manufacturing
Professional Services
Public sector
Retail
Semiconductors
Travel & Transportation
Utilities
America
country usa
United States
country canada
Canada
country brazil
Brazil ( English  |   Portuguese )
country maxico
Mexico ( English  |  Spanish )
country latam
Latam
Continental Europe
country benelux
Benelux
country nordic
Nordic
country france
France
country dach
Dach ( English  |  Deutsch )
Africa
United Kingdom & Ireland
Asia - Pacific
country
Asean
country
Korea
country
China
country
Australia
country
Japan ( English  |  Japanese )
country
Taiwan
India & Middle East
country India
India
country
Middle East
Global Site
America
country usa
United States
country canada
Canada
country brazil
Brazil ( English  |   Portuguese )
country maxico
Mexico ( English  |  Spanish )
country latam
Latam
Continental Europe
country benelux
Benelux
country nordic
Nordic
country france
France
country dach
Dach ( English  |  Deutsch )
Africa
United Kingdom & Ireland
Asia - Pacific
country
Asean
country
Korea
country
China
country
Australia
country
Japan ( English  |  Japanese )
country
Taiwan
India & Middle East
country India
India
country
Middle East
Global Site
<

AI and Automation – Challenges in Adoption

fav-article share icon
Services
Industries
Cloud
Cybersecurity
Digital
EngineeringNXT
HOLMES
Geographies
Contact Us
About Wipro
Careers
Locations
Leadership
Investors
Innovation
News and Events
Insights
Analyst speak
Products & Platforms
Partner Ecosystem
Sustainability

AI and Automation – Challenges in

Adoption

While the pace at which AI solutions are getting built has improved significantly over the last few years, the success of adoption of AI solutions is still low, for example, the self-service automation adoption in many mature client instances is still less than 15-20%, based on our experiences and also market observations. 

The critical question therefore is: How do we ensure that we are applying AI at the RIGHT place in the RIGHT process at the RIGHT time?

The challenge starts with finding the right business scenario where AI capability can be applied, which can also yield measurable outcomes. Once you cross this first big challenge, then it is about getting the buy-in from the business owners and building their trust in AI to let it take decisions. It’s important to be grounded on what AI can do, as there is a lot of hype around it and to set expectations with business leaders on timeline it takes for AI solutions to mature and start giving return on investment. There are unique challenges in AI, like getting “clean data” to train machine learning models. These are typically not given due importance in the plan, as project managers are more used to planning RPA solutions. With the AI skillsets in high demand, lack of right talent can bring down the shutters on the project. Equally important are the domain or subject matter experts. Many AI projects have failed as the domain of the end users has not been understood and contextualized well. In this document, we will look at the various challenges faced in AI Automation projects and discuss ideas on how we can prevent some of these proactively, to help enterprises adopt AI in a much bigger way.

Read more here:

© 2021 Wipro Limited
Privacy Statement Disclaimer RSS Feed
© 2021 Wipro Limited

Service Offerings

Data, Analytics & AI

Applications

Digital Operations and Platform

Consulting

Infrastructure Services

Industries

Aerospace & Defense

Automotive

Banking

Capital Markets

Communications

Consumer Electronics

Consumer Packaged Goods

Education

Engineering, Construction & Operations

Healthcare

Insurance

Medical Devices

Natural Resources

New Age Markets

New Age & Media

Network & Edge Providers

Oil & Gas

Pharmaceutical & Life Sciences

Platforms & Software Products

Industrial & Process Manufacturing

Professional Services

Public Sector

Retail

Semiconductors

Travel & Transportation

Utilities

Cloud

Cybersecurity

Digital

EngineeringNXT

HOLMES

Contact Us

About Wipro

Careers

Location

Leadership

Investors

Innovation

News and Events

Blogs

Analyst speak

Products & Platforms

Partner Ecosystem

Sustainability

X

Contact Wipro

Please fill the required details to access the content

loading.gif