In March 2022, I wrote about the steady emergence of artificial intelligence (AI), and its growing potential to foster unprecedented efficiencies, anticipate customer actions, influence purchasing decisions and deliver better experiences. I also pointed to critical requirements in at-last realizing the promise of AI and machine learning, specifically a heightened focus on data centralization and data quality.
Since then, we’ve seen abundant anecdotal evidence of a widening embrace of AI and machine learning by organizations across industries. Even in the face of geopolitical uncertainty and macroeconomic disruptions over the last 18 months, the fact that the AI and machine learning use case has strengthened is a testament to its essential potency. (At the time I’m writing this, OpenAI has just rolled out an updated version of its viral chatbot, ChatGPT.)
It's against this backdrop that Rackspace Technology® recently conducted its third annual AI and Machine Learning Research Report. The results confirm that AI and machine learning is expanding as a key driver of competitive advantages. Even in the face of global economic uncertainty, organizations are turning to AI and machine learning technologies to solve critical business challenges.
AI and machine learning survey results
The new survey polled 1,420 global IT professionals across industries, including financial services, manufacturing, retail, hospitality, government and healthcare to understand the dynamics of AI and machine learning uptake. It reveals that a growing number of organizations — 69% — rank AI and machine learning as a high priority for their organizations — an increase of 15 percentage points as compared to 2021.
It's notable that almost a third of respondents said they have only started launching AI and machine learning projects within the past year. That compares to 17% who began implementing AI and machine learning two years ago, and 11% three years ago.
There’s no question that we are still in the early days of adoption. Though most respondents said AI and machine learning is a high priority, only 41% say they have realized substantial benefits. In comparison, 33% have seen modest benefits, while just over a quarter (26%) say it’s too early to tell.
Despite the progress being made with the new technologies, many more organizations still don’t grasp the advantages of AI and machine learning. Often, they are dealing with internal resistance to its adoption or use. Some 62% of respondents say there has been internal pushback on adoption within their organizations.
That said, more organizations are leveraging AI and machine learning to improve the speed and efficiency of existing processes, with 67% of organizations classifying it as an area of focus, a 15% increase over 2022.
AI and machine learning remains slow
Even if uptake of AI and machine learning remains below the speed limit, the survey points to an array of business functions that only now are coming into focus as candidates for its application.
We are seeing the use cases increase in multiple areas, including:
- Predicting business performance and industry trends
- Boosting innovation and productivity
- Risk management
- Hiring and recruiting
- Cultivating a better understanding of the business and its customers
- Improving worker safety and security
Collectively, it testifies to the burgeoning utility of AI and machine learning. And therein lies the key to unleashing its full promise.
The pan-industry cloud computing connection
The trajectory for exciting new AI and machine learning deployments is inextricably linked to the pan-industry migration to cloud computing. The centralization of vast data stores created by cloud computing is the jet fuel enabling AI and machine learning to flourish.
We are well into a new generation of cloud technology, as evidenced by the advent of specialized cloud designs that offer specific solutions for particular types of data based on what businesses’ specific needs.
AI and machine learning implementations rely on centralized, consistent, normalized data, and data integrity remains the weakest link in the chain right now. It’s only through high data integrity that AI and machine learning can perform the complex, unprecedented functions that will provide companies with a sustainable competitive advantage.
While that’s improving, that is another barrier to AI and machine learning adoption — the need for the capabilities and talent required to manage data effectively. In addressing these issues, more than eight out of 10 survey respondents said they have made an effort to recruit employees with AI and machine learning skills in the past 12 months, while a similar proportion have actually grown their own AI and machine learning workforce in the last 12 months.
How to take AI and machine learning to the next level
What happens during this next phase of AI and machine learning adoption will be crucial to maintaining momentum. Businesses that are beginning to use the technologies, or are struggling to move deeper into their AI and machine learning initiatives, should focus on three key areas:
- Establishing an overall strategy and creating buy-in across leadership
- Strengthening their data quality and accuracy
- Training and upskilling their talent
For their part, stakeholders will need to be mindful of several key ingredients as they set out to create an effective AI and machine learning strategy, including:
- Establishing a business case and expectations for deliverables, milestones and timelines
- Creating a data governance process that accounts for the roles, responsibilities and processes for proper data management
- Determining standards and metrics that can be used ensure data is accurate, complete, consistent and reliable
- Monitoring and auditing data regularly to identify and address data quality problems such as incorrect values, duplicates or outliers
- Addressing data quality and accuracy issues by cleaning up processes, including setting definitions and eliminating data silos
Over time, organizations are demonstrating a growing willingness to take on the challenges of implementing AI and machine learning in one or more parts of the business. That’s because they see the benefits today — and the potential benefits of tomorrow. With a steady focus on achieving data integrity, we will have cleared the last major hurdle to making AI and machine learning a more integral part of our daily lives.
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About the Authors
Chief Technical Evangelist
Jeff has 25 years of experience in IT and technology, and has worked at Rackspace Technology for over 10 years. Jeff is a proven strategic leader who has helped companies like American Express, Ralph Lauren, and Thompson Reuters create and execute against multi-year digital transformation strategies. During his time at Rackspace Technology, Jeff has launched and managed many of the products and services that Rackspace Technology offers, as well as supporting merger and acquisition activities to enhance those offerings. Jeff is the father of two young men and husband to his wife Michelle of 27 years. When not at Rackspace Technology or around San Antonio, you can find Jeff doing land restoration on his ranch in the Texas hill country.Read more about Jeff DeVerter