Assessing the Barriers to AI

Arthur Cole

Artificial intelligence (AI) is the next technology to enter the hype cycle, and while it will most certainly have an effect on IT operations, the details as to how it will be implemented and how to generate a maximum return on investment are still unknown.

At the moment, of course, the key challenge is overcoming the significant barriers to deployment, which include not only disruption to legacy architectures but to long-established business cultures as well.

According to a recent study by Vanson Bourne on behalf of Teradata, a good 80 percent of enterprises are currently investing in AI, with telecommunications firms, professional and customer service providers and financial institutions leading the charge. While most outfits believe that the technology will produce significant ROI over the next decade, only about one-third see the need for additional investment over the next three years in order to remain competitive. At the same time, issues like the lack of advanced IT infrastructure and the need for AI-related skillsets in the workforce are seen as major barriers, as are the ever-present budgetary concerns.

One of the initial use cases for AI is likely to be security. Cyber protection firm Cylance recently survey more than 650 European enterprises and found that 77 percent are already employing AI as a means to prevent breaches and conduct advanced threat detection. What’s more, nearly three-quarters believe that the security challenges of the future cannot be addressed without some form of artificial intelligence.


It’s important to realize, however, that AI deployments so far have been extremely rudimentary and that the true power of the technology probably won’t emerge for a number of years. As SAS showed in its most recent industry survey, an overwhelming number of AI strategies at the moment are in the very earliest test/dev phases or are even still plans on paper. The key takeaway right now is that virtually everybody is at least talking about AI and many organizations are starting to form visions as to how it can benefit IT operations. But as others have found out, key challenges surrounding implementation and its effect on jobs have yet to be fully assessed.

There is also still some confusion as to which particular form of AI is best suited to the enterprise. According to Gartner, most data scientists will gravitate toward deep learning in the coming year, given its ability to discern hidden patterns and potential opportunities in large data sets. Elsewhere, however, organizations are likely to deploy neural networks, speech and image recognition, and various levels of reactive to proactive algorithmic processing. Indeed, one of the key challenges going forward is finding the exact mix of technologies to produce optimal results for any given application or workload.

If AI is like any of the technological revolutions of the past, it will solve a multitude of problems that currently bedevil IT operations. By the same token, however, it will also create a suite of new problems, most of which will likely be addressed through greater application of AI.

Still, one thing is certain: The smarter enterprise systems and processes become, the greater the need for human oversight to keep them all in check.

Arthur Cole writes about infrastructure for IT Business Edge. Cole has been covering the high-tech media and computing industries for more than 20 years, having served as editor of TV Technology, Video Technology News, Internet News and Multimedia Weekly. His contributions have appeared in Communications Today and Enterprise Networking Planet and as web content for numerous high-tech clients like TwinStrata and Carpathia. Follow Art on Twitter @acole602.


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