Qualifications, data quality and lack of organizational acceptance are cited as obstacles; In addition to cyber security, AI/ML is a top IT priority for companies
Rackspace Technology, a leader in end-to-end multi-cloud technology solutions, released a new report accompanying the The result is that while artificial intelligence and machine learning (AI/ML) are on the agenda of almost every company, there is still a long way to go to fully realize their potential. Rackspace Technology surveyed 1,870 global IT leaders across industries including manufacturing, financial services, retail, government and healthcare to understand the dynamics of AI/ML adoption.
While 62% of respondents said AI/ML is a high priority for their business, and 70% of all respondents reported positive impacts on brand awareness and reputation, as well as revenue generation and cost reduction, 36% agreed that the measurement and proving the business value of the technologies remains a challenge.
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“As budgets for AI/ML continue to grow, we are seeing projects spread into more areas of the business and it is clear that AI and ML are gaining in importance and visibility,” said Jeff DeVerter, Chief Technology Evangelist at Rackspace Technology. “At the same time, the report makes clear that many companies are still struggling to get stakeholder buy-in, resolve data quality issues, and find the skills, resources, and talent to leverage the full potential of AI/ML. ”
According to the report, “ AI/ML is a Top Priority for Businesses, but are They Realizing Its Value ? “ AI/ML is one of the two most important strategic technologies for companies, alongside cyber security. 72% of respondents say they are using AI/ML as part of their business strategy, IT strategy, or both, while 69% of respondents allocate between 6% and 10% of their budget to AI/ML projects. In comparison, reported spend (as a percentage of total budget) ranged from 1% to 10% in last year’s survey.
There are more and more AI/ML projects
AI/ML are being used by companies in an increasingly diverse range of contexts, e.g. B. Improving the speed and efficiency of processes (52%), personalizing content and better understanding customers (44%), increasing sales, gaining competitive advantage and forecasting performance (42%), and understanding marketing effectiveness ( 36%).
Indicative of the increasing maturity of the technologies, 66% of respondents indicated that their AI/ML projects have moved past the experimentation phase and are now in either the “optimization/innovation” or the “formalization” stage of implementation condition. Most companies also report a broader range of use cases, including computer vision applications, automated content moderation, customer relationship management, and biomedical applications.
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advances and challenges
Regarding AI/ML adoption, 33% of respondents say it’s difficult to align AI/ML strategies with business – up 10% year over year. Additionally, implementation costs increased from 26% to 33%, while 31% of respondents see emerging AI/ML technologies as a barrier, up from 13%.
“The fact that many companies struggle to align AI/ML strategies with the business and navigate the plethora of new tools available shows that projects often fall victim to poor strategy,” added DeVerter. “Having the support of the right stakeholders, consensus on deliverables, knowledge of the resources needed to get there, and setting clear milestones are critical components to keeping projects on track and achieving the desired ROI.”
organizational understanding
When it comes to talent, more than half of the respondents said they have the required AI/ML skills in their company. At the same time, more than half of all respondents say that strengthening internal skills/hired talent and improving internal and external training are on their agenda.
Comparing departments, 69% of respondents said IT staff understand the benefits of AI/ML, while 43% said operations, R&D, customer service, C-suite, and board of directors understand the technologies. Sales, HR, and Marketing departments are considered the least experienced with AI/ML by respondents.
Survey methodology
The survey was conducted by Coleman Parkes Research in September 2021. The results are based on responses from 1,870 IT decision makers in Manufacturing/Logistics, Retail, Hospitality/Travel, Energy, Healthcare/Pharma/Biomedical, Government, Media/Entertainment and Financial Services across the Americas, Europe, Asia and the Americas Near East. Most of the companies/organizations surveyed were founded before the year 2000, have between 101 and 999 employees and annual sales between USD 50 million and USD 1 billion. They also have between 2 and 15 employees dedicated to cybersecurity and spend 5% to 15% of their IT budget on cybersecurity.
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