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The AI Revolution: What’s Real and What’s Not

The AI Revolution: What’s Real and What’s Not 

It’s hard to find a product today that doesn’t claim to use machine learning to provide artificial intelligence (AI). The funny thing is that the marketing has changed while most of the products have not. It’s amazing how so many products have supposedly morphed into AI-based solutions overnight, despite little evidence of any product development effort. This, of course, means that the vast majority of these solutions do not offer AI, and what we’re reading in their marketing materials and websites is merely aspirational messaging.

To be fair, it’s going to be really hard for a vendor to attract attention today if they don’t claim to use AI to improve the performance and output of their solution. Given the choice between purchasing a product that continues to do what it always did or one that uses AI (which typically means machine learning) to enhance its capabilities, most people are going to go for what they believe to be the newest and greatest cutting-edge offerings.

The problem is that most of what people are buying is hype, and this is going to result in disappointment as enterprises realize that AI is really in the earliest stages of commercialization. The potential is great, but the current generation of technology and the applications are far from fully AI-enabled. As I look through the market, the vendors who are closest to delivering AI-enabled applications are selling a great deal of professional services with each solution in order to build out their products.

The driver behind the AI revolution is the need for productivity and quality improvements, which are important for all enterprise applications and essential for people-intensive front- and back-office service organizations. Imagine a voice self-service solution (also known as an interactive voice response system, IVR) that self-corrects when it realizes that customers are dropping out at a certain point in the script (application). If machine learning were applied, the solution would identify the issue by itself and then make a change to the appropriate components of the script without human intervention. Another great use of AI would be to embed it into an automatic call distributor (ACD) to continuously improve and optimize routing. Imagine an ACD that continuously enhances its routing algorithms, ensuring the right transactions are delivered to the best-suited agents/associates. These examples sound great, but are not fully-baked today. Most of what is currently referred to as AI are business rules created and modified by humans. These approaches are not new, although there are changes in how they are being applied and rolled out as vendors strive to make their solutions more intelligent and AI-ready.

The first AI application I was introduced to over 30 years ago was knowledge management (KM), and it’s a perfect example of how AI is not yet ready for prime time commercialization and adoption in enterprise applications. KM is still an ideal application for machine learning-enabled AI, as the number-one challenge with these applications is keeping them up-to-date. If AI worked, this would have been addressed years ago, but unfortunately it’s still a problem, and more development effort will be necessary before AI can truly drive the processes in enterprise solutions.

DMG is bullish on the current AI revolution. What’s different this time is that companies in many IT sectors are making investments to try to embed AI-like capabilities in their solutions. And it’s more than just messaging. The benefits for the contact center technology market are going to be tremendous, even though what is being added is not true machine learning-enabled AI.

Many vendors who have jumped on the AI bandwagon are making investments in their solutions’ architecture, providing value-added functionality and enhancing their user interfaces and experience, to make their solutions smarter and easier to use.

Final Thoughts

AI is driving a much-needed round of investment in many systems and applications for many IT sectors, including contact center. AI is not yet ready for broad commercial adoption, but the push to include it in many solutions is driving vendors to rethink their application logic and deliver a new generation of technology that is easier to implement and use, as it is designed to be smarter and faster than anything that came before. Many of these solutions, including intelligent virtual agents (IVAs) and robotic process automation (RPA) applications, are capable of delivering significant productivity and quality improvements, even though the underlying technology is not true AI but is typically a basic form of machine learning. This generation of systems is not yet fully AI-enabled, but is on the right path, and many of these solutions are generations ahead of the 20- to 30-year-old systems that many companies are using. DMG encourages you to take a look at our new industry reports on IVA and RPA for more information on exciting developments in these emerging technologies.

DMG Consulting LLC is a leading independent research, advisory and consulting firm specializing in unified communications, contact centers, back-office and real-time analytics. Learn more at

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