How can artificial intelligence help organizations solve digital transformation challenges? Consider these examples - from customer service to resource optimization.
The state of AI in the enterprise: 10 telling stats
How many of your peers already use AI? What are they spending? How’s the talent market? Let’s explore the data
Plenty of technologies go through a hype cycle. With artificial intelligence, it’s plural: Hype cycles is more appropriate, and they’ve been recurring for quite some time.
As the London-based venture capital firm MMC Ventures noted in its report, “The State of AI 2019: Divergence,” people have been heralding the AI age for decades. This time, however, there’s growing evidence that AI is getting ready for prime time, especially in business contexts.
“After seven false dawns since the 1950s, AI technology has come of age,” writes the report’s author, David Kelnar, head of research and a partner at MMC Ventures.
[ How does RPA fit in with AI? Read also: How to explain Robotic Process Automation (RPA) in plain English. ]
A wealth of data – not just in MMC’s report but from a wealth of other sources – suggests that the hype has merit this time around, though of course not every data point is positive. Let’s dig into ten numbers – and then some – that speak to the state of AI in the enterprise today.
37 percent of organizations have implemented AI in some form
That’s according to Gartner’s 2019 CIO Survey – a 270 percent hop from four years ago, when just 10 percent of respondents in an earlier version of the same survey said they’d deployed AI or would do so shortly. This also signals a potential divide between IT departments that are actively working on AI projects and those that are not.
“If you are a CIO and your organization doesn’t use AI, chances are high that your competitors do, and this should be a concern,” Chris Howard, distinguished research vice president at Gartner, said in a release.
[ Can AI solve that problem? Read How to identify an AI opportunity: 5 questions to ask. ]
3x AI adoption in the past 12 months
MMC Ventures’ data says AI adoption has tripled in the past year. It also says that one in seven large companies have AI in production. This is just scratching the surface, though: MMC expects that “in 2019, AI ‘crosses the chasm’ from early adopters to the early majority.”
[ Why is this a big year for AI? Read AI in 2019: 8 trends to watch ]
2 out of 3 large orgs go live with AI in the next 24 months
MMC doubles down on that majority projection and predicts that over the next 24 months, two-thirds of large companies will have gone live with AI initiatives.
$77.6 billion global spending on AI by 2022: IDC
The growth-oriented numbers above fit with IDC’s projected global spending on AI and cognitive systems: It will hit $77.6 billion in 2022, according to the research firm. That spending is growing because it appears to be producing results.
“IDC is already seeing that organizations using these technologies to drive innovation are benefitting in terms of revenue, profit, and overall leadership in their respective industries and segments," David Schubmehl, research director for cognitive and AI systems at IDC, said in a release.
If IDC’s projection holds fairly true, that would reflect a 37.3 percent compound annual growth rate in worldwide spending on AI and cognitive technologies from IDC’s $24 billion forecast in 2018.
50 percent plan to deploy machine learning apps
One factor to consider in the feverish discussion about AI is that the term means different things to different people. At a minimum, AI encompasses a variety of specific disciplines, such as natural language processing and machine learning, that are (unfortunately) sometimes referred to interchangeably. So it’s helpful to dig into specific technologies: A 451 Research survey conducted in 2018 found that nearly half of respondents had already or planned to deploy machine learning applications in their organizations during the next 12 months. Moreover, the survey found most adopters were motivated by perceived benefits other than reducing headcount.
[ Read also: AI vs. machine learning: What’s the difference? ]