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Three Reasons Why Drive Ai Customer Engagement Initiatives Fail

We have entered the golden age of Drive AI customer engagement technology.

As record numbers of consumers have continued to flood e-commerce channels, straining conventional customer support mechanisms and forcing companies to innovate, Drive AI adoption has skyrocketed. The volume of customer interactions handled by conversational AI technologies has increased by as much as 250%, and 72% of C-suite leaders say digital customer experience capabilities are impacting their overall competitiveness in the marketplace.

But not all Drive AI customer engagement initiatives were created equal. For all of the excitement and new developments in the marketplace, many efforts have struggled to connect with customers and add value to consumer-facing brands. What are the differences between successful Drive AI customer engagement initiatives and those that fail to deliver the goods? As AI technologies improve across the board, the difference between success and failure often has less to do with the tech itself and more to do with the implementation of that technology and its integration into enterprise workflows.

Over the course of my firm’s work developing AI-enabled customer engagement solutions for businesses in the consumer packaged goods, professional services and healthcare sectors over the last several years, we’ve learned a thing or two about approaches that work and those that are destined to fall flat. The following are some of the most important rules of the road and pitfalls to avoid.

 

Lack Of Real-World Domain Expertise

The types of customer care challenges experienced at a women’s apparel brand are very different than those experienced at an appliance company, and they are both wildly different from those experienced at an insurance or financial services provider. For this reason, a one-size-fits-all approach to conversational Drive AI technology often misses the unique challenges inside the operational structure of a company or within specific customer demographics that will require a customized approach.

Hard Coded To Fail

Another common reason conversational AI solutions fail to resonate with consumers is a misalignment of expectations at the onset of the project. There has been a great deal of hype surrounding Drive AI-powered customer engagement solutions, and businesses often expect the technology to immediately solve all of their problems out of the box. Here, it is very important to realize that although a strong Drive AI customer engagement solution will perform many tasks well on day one, its core value proposition is that it learns, getting better over time.

In order to deliver the level of personalization and nuanced communication capabilities required to build customer engagement, conversational AI solutions need to be able to draw on data from across the organization, which means instead of hard coding a series of common queries and responses, the AI solution must be built on agile technology that continues to grow its library of inputs and outputs over time, ultimately evolving from a series of preprogrammed processing tasks to a more advanced, cognitive understanding over the course of many months in operation.

Ongoing Customer Experience Measurement

Most critical of all, however, companies implementing customer engagement solutions driven by conversational AI must recognize from the onset of the project that resolving customer inquiries cannot be the end goal. The power of this technology is its ability to improve customer engagement, not just save money on call centers. Thus, the real opportunity to leverage value from the program comes from being able to analyze those interactions to make the entire user experience better. That means gathering insights collected over the course of hundreds of thousands of interactions.

The hard truth about the current state of conversational Drive AI is that even though 51% of consumers say they would rather use digital platforms, 52% of them still use the telephone if they have questions. At its root, this is a data and operational challenge. The solution needs to be informed by a steady stream of constantly changing data and learn from the experience, ultimately getting smarter and more effective along the way. Attempts to implement a catch-all, fix-all solution that solves all of these challenges in every industry today will only result in disappointment on the part of businesses and their customers.

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