February 27, 2021

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New Global Rackspace Technology Study Uncovers Widespread Synthetic Intelligence and Machine …

Push release information from World Newswire. The AP information staff was not associated in its creation.

SAN ANTONIO, Jan. 28, 2021 (World NEWSWIRE) — RackspaceTechnology™  (NASDAQ: RXT), a main end-to-conclusion, multicloud know-how remedies organization currently introduced the final results of a world study that reveals that the the greater part of organizations globally deficiency the inside means to support vital synthetic intelligence (AI) and device mastering (ML) initiatives.

The study, “Are Businesses Succeeding at AI and ML?” was performed in the Americas, APJ and EMEA regions of the earth, and indicates that although quite a few corporations are eager to incorporate AI and ML tactics into functions, they usually deficiency the expertise and existing infrastructure essential to employ mature and effective AI/ML packages.

This review shines a light-weight on the wrestle to balance the opportunity positive aspects of AI and ML towards the ongoing issues of receiving AI/ML initiatives off the floor. When some early adopters are currently looking at the benefits of these systems, other people are nonetheless making an attempt to navigate typical soreness details these as deficiency of interior understanding, outdated technological know-how stacks, bad information high-quality or the incapability to measure ROI.

Added essential results of the report involve the adhering to:

  • Organizations are nonetheless exploring how to put into action experienced AI/ML abilities — A mere 17% of respondents report experienced AI and ML abilities with a product manufacturing unit framework in spot. In addition, the the vast majority of respondents (82%) claimed they are nevertheless exploring how to employ AI or struggling to operationalize AI and ML designs.
  • AI/ML implementation fails normally due to absence of interior methods — Extra than a person-3rd (34%) of respondents report synthetic intelligence R&D initiatives that have been tested and abandoned or failed. The failures underscore the complexities of making and working a effective AI and ML software. The top causes for failure involve deficiency of data excellent (34%), deficiency of expertise in just the business (34%), lack of generation all set facts (31%), and badly conceived approach (31%).
  • Effective AI/ML implementation has clear added benefits for early adopters — As businesses look to the long term, IT and functions are the major spots in which they prepare on incorporating AI and ML capabilities. The info reveals that organizations see AI and ML probable in a assortment of business units, including IT (43%), functions (33%), shopper services (32%), and finance (32%). Additional, organizations that have properly applied AI and ML systems report elevated productiveness (33%) and improved consumer satisfaction (32%) as the major positive aspects.
  • Defining KPIs is critical to measuring AI/ML return on financial commitment — Along with the problems of deploying AI and ML tasks arrives the problems of measurement. The top rated key efficiency indicators utilised to evaluate AI/ML results contain financial gain margins (52%), profits expansion (51%), info assessment (46%), and purchaser pleasure/internet promoter scores (46%).
  • Businesses flip to trustworthy associates — Several organizations are nevertheless deciding whether or not they will create interior AI/ML assist or outsource it to a dependable companion. But specified the large risk of implementation failure, the vast majority of businesses (62%) are, to some diploma, working with an skilled supplier to navigate the complexities of AI and ML improvement.

“In approximately just about every industry, we’re looking at IT decision-makers convert to synthetic intelligence and device mastering to improve efficiency and client fulfillment,” mentioned Tolga Tarhan, Main Technological know-how Officer at Rackspace Engineering. “But before diving headfirst into an AI/ML initiative, we advise prospects to thoroughly clean their knowledge and data procedures — In other terms, get the correct information into the ideal devices in a trustworthy and price tag-efficient method. At Rackspace Know-how, we’re proud to deliver the know-how and strategy needed to be certain AI/ML tasks shift further than the R&D stage and into initiatives with extensive-phrase impacts.”

To obtain the complete report, make sure you go to www.rackspace.com/clear up/succeeding-ai-ml.

Study Methodology

Executed by Coleman Parkes Research in December 2020 and January 2021, the survey is centered on the responses of 1,870 IT final decision-makers throughout manufacturing, digital indigenous, money services, retail, governing administration/public sector, and health care sectors in the Americas, Europe, Asia and the Middle East. The study queries included AI and ML adoption, utilization, added benefits, affect and long run programs.

About Rackspace Technological innovation

Rackspace Technologies is a top end-to-end multicloud technological innovation expert services organization. We can structure, establish and work our customers’ cloud environments throughout all significant technological know-how platforms, irrespective of technological know-how stack or deployment model. We lover with our clients at each phase of their cloud journey, enabling them to modernize purposes, make new products and adopt impressive technologies.

Media Make contact with
Natalie Silva
Rackspace Company Communications
[email protected]