AI and machine learning are the driving forces behind a whole new category of innovation. Most business leaders understand the importance of AI and machine learning to future success in their industries. But for many, AI and machine learning maturity remains elusive.
In January 2021, we conducted a survey of 1800+ IT leaders across the Americas, Europe, Asia and the Middle East. The report divided respondents into six sectors — including government/public sector, digital native, healthcare, financial services, manufacturing and retail — to compare trends and insights into their respective AI and machine learning journeys.
Takeaways from the AI and machine learning adoption report
The survey defines the qualities of AI and machine learning maturity across sectors, reports on the five main categories of adoption challenges and provides guidance on accelerating your AI and machine learning journey. Data points cover insights around scalability, data quality, leadership buy-in, KPI definitions and talent gaps. Highlights of the report include:
- One in eight respondents across industries report being at the beginning of their AI and machine learning journey. Only 17% report being confident, with fully mature AI and machine learning capabilities scaled across their entire organization. Though retail reported the highest level of maturity (22%), retail respondents also led the pack in reporting being in the infancy stage (55%). This speaks to a wide variation in retail — from big box stores to niche retailers.
- Retail, healthcare and manufacturing are the top three respondents in both data quality categories. All three industries report difficulties in deriving actionable insights from data and experiencing failed initiatives due to dirty data.
- As with most emerging technology, talent is a barrier to AI and machine learning adoption across all industries. Though retail led respondents with 31% stating that finding talent is an issue, every industry consistently reported that talent is slowing down their ability to move forward.
- AI and machine learning ROI are notoriously hard to track. But without the numbers, it’s difficult to demonstrate value and maintain stakeholder support. Yet, nearly one out of four respondents in the government/public sector (24%) aren’t measuring the value of their AI and machine learning efforts and the same percentage (24%) report that a defining a clear strategy or success metrics are a challenge.
To learn more about how AI and machine learning adoption differs across industries, download the report, “Overcoming AI and Machine Learning Hurdles Across Industries."Download the full report
Overcoming AI/ML Adoption Hurdles Across Industries
About the Authors
Rackspace Technology Staff - Solve
The Solve team is made up of a curator team, an editorial team and various technology experts as contributors. The curator team: Srini Koushik, CTO, Rackspace Technology Jeff DeVerter, Chief Technology Evangelist, Rackspace Technology The editorial team: Gracie LePere, Program Manager Larry Meyer, Creative Management Royce Stewart, Chief Designer Simon Andolina, Design Tim Mann, Design Abi Watson, Design Debbie Talley, Production Manager Chris Barlow, Editor Tim Hennessey Jr., Writer Stuart Wade, Writer Karen Taylor, Writer Meagan Fleming, Social Media Specialist Daniel Gibson, Project ManagerRead more about Rackspace Technology Staff - Solve