Rackspace Technology, a leading end-to-end, multicloud technology solutions company, has announced a new research report that finds that while Artificial Intellegence and Machine Learning (AI/ML) are on nearly every organization’s radar much work remains to be done to tap their full potential. Rackspace Technology polled 1,870 global IT leaders including India, across industries, including manufacturing, financial services, retail, government, and healthcare to understand the dynamics of AI/ML uptake. 205 Indian correspondents participated in the survey.
The India data reveals that while 68% of respondents said that AI/ML is a high priority for their organization, and 71% of all respondents reported positive impacts of on brand awareness and 68% on reputation, as well as revenue generation and 62% on expense reduction, 43% agreed that measuring and proving the technologies’ business value remains a challenge.
“As AI/ML budgets continue to increase, we are seeing projects proliferate across more areas of the organization, and it’s clear that the AI/ML is advancing in its importance and visibility,” said Jeff DeVerter, Chief Technology Evangelist, Rackspace Technology. “At the same time, the research makes clear that many organizations still struggle with getting stakeholder buy-in, addressing issues of data quality, and finding the skills, resources and talent to take advantage of the AI/ML’s full potential.”
According to the report – AI/ML is a Top Priority for Businesses, but are They Realizing Its Value? – AI/ML ranks among the top two most important strategic technologies for organizations, alongside cybersecurity. 60% of respondents say they are employing AI/ML as part of their business strategy, 70% IT strategy, while 63% of respondents are allocating between 6% and 10% of their budget to AI/ML projects. This compares to a reported spend (as a percentage of overall budget) of between 1% and 10% in last year’s survey.
AI/ML Projects are Accelerating
AI/ML are being used by organizations in an increasingly wide variety of contexts, including improving the speed and efficiency of processes (50%), personalizing content and understanding customers (49%), increasing revenue 49%, gaining competitive edge 51% and predicting performance (51%), and understanding marketing effectiveness (48%).
In an indication of the increasing maturity of the technologies, 37% of respondents said their AI/ML projects have gone past the experimentation stage and 28% are now either in the “optimizing/innovating” or “formalizing” states of implementation. Most organizations are also citing a wider range of use cases, including computer vision applications, automated content moderation, customer relationship management, and biomedical applications.
Progress, and Challenges
With regard to AI/ML adoption, 45% of respondents cite difficulties aligning AI/ML strategies to the business. In addition, the cost of implementation rose to 38%, while 40% of respondents of nascent AI/ML technologies as a barrier.
“The fact that many organizations are having trouble aligning AI/ML strategies to the business and navigating the plethora of new tools available indicates that projects are often falling victim to poor strategy,” added DeVerter. “Garnering support from the right stakeholders, coming to consensus on deliverables, understanding the resources necessary to get there, and setting clear milestones are critical components to keeping projects on track and seeing the desired return on investment.”
Organizational Understanding
From a talent perspective, more than half of respondents said they have necessary AI/ML skills within their organization. At the same time, more than half of all respondents say that bolstering internal skills/hired talent and improving both internal and external training are on their agenda.
Comparing departments, 67% of respondents say IT staff grasp AI/ML benefits while 45% say that operations, 46% R&D, 48% customer service, 49% senior management and boards understand the technologies. Sales, HR and marketing departments are considered by respondents to be the least AI/ML-savvy.
The survey was conducted by Coleman Parkes Research in September 2021. Findings are based on the responses of 1,870 IT decision-makers across manufacturing/logistics, retail, hospitality/travel, energy, healthcare/pharma/biomedical, government, media/entertainment and financial service sectors in the Americas, Europe, Asia and the Middle East. Most of the companies/organizations polled were founded before the year 2000, have from 101 to 999 employees, and an annual revenue between $50m and $1b. They also have anywhere from two to 15 employees dedicated to cybersecurity and they spend 5% to 15% of their IT budget on cybersecurity.