Why AI Isn't Gaining Traction in Your Enterprise

For all the buzz about artificial intelligence and machine learning, there has been surprisingly little real progress with adoption in the enterprise.

Andrew Froehlich, President & Lead Network Architect, West Gate Networks

April 17, 2019

4 Min Read
Image: Shutterstock

A recent Gartner survey shows a 270% growth in artificial intelligence (AI) adoption within the enterprise over the previous four years. While that's impressive growth, it could be much higher. In fact, Gartner also states in this same survey that only 37% of organizations "have implemented AI in some form." Considering how much hype AI is getting these days, you'd honestly think this number would be far higher. What's more, I’d bet the type of AI being implemented in many of these organizations is largely superficial. I'd also venture to bet that if you're reading this article -- and take an honest look at your enterprise technology -- you can't point to any meaningful AI, either. Why is that?

Most CIO's will opine that it's not the fault of the IT department, or the business in general. Instead, the fault lies in the fact that there is a serious talent shortage in the areas of artificial intelligence, digital transformation and data sciences. While this is indeed a part of the problem, it doesn't get to the heart of the issue. Instead, AI is failing to gain traction because businesses don't even understand what can be accomplished with it in the first place. Thus, you could have all the talent you'd ever need to build the most complex AI services around. Yet, if you don't know how it can be effectively applied, you're still left with nothing.

Some of the cause for the issue rests at the feet of the AI platform vendors themselves. They’ve done a poor job at marketing, not outlining what problems their software can solve. In their defense, however, it’s not totally their fault. Each AI implementation must be built to meet specific and unique requirements. Everything from what is demanded from machine learning algorithms, the data sources involved, and how analyzed information will be used, will be different from project to project. Thus, it’s difficult to create detailed content that explains all this without running the risk of alienating prospective customers.

Because of this, AI platform vendors have largely abandoned highly detailed use-case scenario content in their marketing material. Instead, they use wide generalizations on what can be done with their product. While this may cast a wider net, it doesn't give your technical architects enough information to gain a deep understanding of how the platform can be used.

Another issue within the business is the fact that companies are putting the wrong people in charge of their AI and digital transformation roadmaps. Often, business leaders will place technologists in charge of finding ways to replace manual or partially automated processes with AI. While technologists should indeed be part of the equation, it should be business analysts who are steering the ship. Only those with a clear understanding of the business will understand where the problems reside. Only then should technologists be allowed to step in to architect a solution to the identified problem.

A final part of the problem can be found with the external opinions and advice the business is receiving about AI. It's clear that most IT departments do not yet have in-house staff that are well versed in AI. As such, businesses do what they traditionally do when they have a skills gap -- reach out to consultants. While that's a logical and valid step, don't assume your trusted consultants know much more than you about AI. In fact, I'm finding that most consultants are as woefully uninformed as the client. Few have the knowledge or expertise with multiple AI platforms to really understand when "AI platform A" might be better than "AI platform B" in specific situations. Part of this resides in the fact that the economy is good, and consultants are focused on projects and technologies they better understand. The other is the fact that consultants haven't yet bothered to build strong relationships with multiple AI vendors, nor to properly train their developers and engineers on several different platforms.

If AI isn’t gaining momentum in your business, perhaps it’s time to stop blaming talent shortages. More likely than not, it’s not the root of your problem. Instead, seek to find the right people in your business to drive AI toward problematic areas. Once those areas are discovered, seek multiple opinions on how AI can be used to fix them. If you take these two steps, you may soon find AI flourishing in your once intelligence-less enterprise.

To learn more about trends in AI, check out these recent articles:

Boost Your Analytics, Machine Learning with Alternative Data

When Automation Brings Efficiency to CRM

How to Operationalize Your Machine Learning Projects

AI, Machine Learning, Data Science: What Enterprises Are Doing

The Time is Now: AI for Everyone

About the Author(s)

Andrew Froehlich

President & Lead Network Architect, West Gate Networks

Andrew has well over a decade of enterprise networking under his belt through his consulting practice, which specializes in enterprise network architectures and datacenter build-outs and prior experience at organizations such as State Farm Insurance, United Airlines and the University of Chicago Medical Center. Having lived and worked in South East Asia for nearly three years, Andrew possesses a unique international business and technology perspective. When he's not consulting, Andrew enjoys writing technical blogs and is the author of two Cisco certification study guides published by Sybex.

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