By Donna Fluss
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 while the marketing has changed, 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 most 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 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 latest 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 still in the early stages of commercialization. The potential is great, but the current generation of technology and 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 to build out their products.
A Push for Improvements
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 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 improve and optimize routing. Imagine an ACD that continuously enhances its routing algorithms, ensuring that the right transactions are delivered to the best-suited agents or associates. These examples sound great, but they 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 Past and the Future
The first AI application I was introduced to over thirty 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; more development work is still needed 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. The benefits for the contact center technology market are going to be tremendous, even though it will take a few more years before AI is ready for prime time.
AI is driving a much-needed round of investment in many systems and applications for various IT sectors, including contact centers. 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, can deliver significant productivity and quality improvements, even though the underlying technology is not true AI but typically a basic form of machine learning. This generation of systems is not yet fully AI-enabled, but it is on the right path, and many of these solutions are generations ahead of the twenty to thirty-year-old systems that many companies are using.
Donna Fluss is president of DMG Consulting LLC. For more than two decades she has helped emerging and established companies develop and deliver outstanding customer experiences. A recognized visionary, author, and speaker, Donna drives strategic transformation and innovation throughout the services industry. She provides strategic and practical counsel for enterprises, solution providers, and the investment community.