A Roadmap for Implementing Artificial Intelligence into Your Organization

Artificial intelligence (AI) is a fast-growing component of the consumer world. Things like Siri on your mobile device and Alexa or Google at home make life more convenient for millions of people around the world.

The satisfying potential of contact center automation that solves your customers’ needs.

Have you noticed that more and more automation is finding its way into our daily lives? Think of all the new (or near-new) conveniences we have available to us. Home appliances, self-checkout, consumer electronics, entertainment, medical devices and smart buildings help us every day. In a recent blog post, I talked about IVAs (Intelligent Virtual Agents), which are deployed widely to enhance the customer experience by streamlining and handling the customer need immediately, with little to no human intervention.

This article originally appeared in VentureBeat.

According to recent Gartner reports, an estimated 85% of artificial intelligence (AI) projects will fail through the year 2022. This estimation is likely to cast some doubt on the realm of artificial intelligence and its ability to deliver value. However, the reason that so many AI projects fail is not due to the AI processes themselves, but rather the lack of strong data governance, collaboration, and problem definition. Organizations that successfully leverage AI typically start their design with the goal of AI in mind. This approach allows organizations to work backwards, guiding them through the foundational components that help contribute to the end goal.

Virtual assistants can offer an incredible amount of help to your customer care organization. Before we jump into that, let’s start by focusing on what a Virtual Assistant is—or better yet, an Intelligent Virtual Assistant.

In my last blog post, I advocated for beginning your customer experience modernization journey by consulting with IT. My next piece of advice centers around what the end goal of your modernization efforts should be: allowing your customers more access to self-service—and I mean good self-service.

When you hear the term “machine learning,” do you think to yourself, “How does machine learning really work?” Well, machine learning uses historical data, and what I mean by this is past data. This data could be from databases, Hadoop systems, CSV format, or streaming data from a social media website.

2020 raised the bar on customer experience across the board. Most people spent more time at home using more devices connected for more time, driving up both costs and awareness. In response to the changed landscape, utilities have pivoted to provide more personalized service, helping customers use energy more efficiently while providing access to more services virtually. From an internal operations perspective, many agents went remote, supported by cloud infrastructure and AI.

Machine learning is changing the way the world thinks and operates. Let’s face it, when the words “Machine Learning Algorithm” present themselves in a room full of peers, everyone takes a deep breath. Some are confused by the thought of math; others are thinking, “Oh no, which algorithm do we use for this problem?”