2018 marked the peak of enterprise AI hype with nearly every brand on the planet deploying an AI strategy. Companies spent money—a lot of money—on the promise of using machines to deliver better experiences to their customers. And a very important lesson was learned: AI doesn’t add enough value on its own—it’s the solution across it all that allows machines to engage with humans in a natural and contextual way that makes AI valuable.
2018 marked the peak of enterprise AI hype with nearly every brand on the planet deploying an AI strategy. Companies spent money—a lot of money—on the promise of using machines to deliver better experiences to their customers. They invested in the bright, shiny object—a bot that can be created in minutes, a bot that can book your next salon visit or order an Uber for you, an application that can offer product recommendations—and they learned a lot in the process. That AI isn’t a strategy, it’s a tool. And perhaps most importantly, that AI doesn’t add enough value on its own—it’s the solution across it all that allows machines to engage with humans in a natural and contextual way that makes AI valuable.
In 2019 enterprise bots will grow up to be the intelligent virtual assistants we need them to be.
Right now, there is no common ground with most of the AI technology consumers engage with. Instead of knowing who you are by simply the sound of your voice, most people are identified as a fresh user every time. Logging you in, authenticating you and knowing your history will be critical—and it all must be done seamlessly. Pins, passwords, security questions won’t cut it.
Further, there is a difference between a bot that can schedule a haircut and a virtual assistant that can address a complex question or problem that can only be solved with two-way dialog—for example “what is the best investment for my parents?” We’ll see a paradigm shift from bots with limited and downright frustrating conversational abilities, to industry-specific, virtual assistants that tap enterprise-grade AI to support meaningful and contextual conversations.
And context and anticipation is key. Truly intelligent systems will know why you’re calling. For example, a retailer will know—before you ask—that you have an order in-process and will provide with accuracy your shipping status and delivery timing. A travel and hospitality company will use AI and machine learning models to identify keywords within the context of a conversation to offer suggestions based on their profiles and previous interactions.
Businesses will invest in AI solutions that can generate ROI.
Companies will start demanding better knowledge of the ROI of their AI investments. They will be looking for a more accurate way to measure AI’s impact on the bottom line and customer experience. This starts by considering your unique data acquisition situation. What data do you have and what data you can get? What data do you not have, that you need to solve for? And they will look for new ways to leverage their existing AI investment by blending AI and human touch—identifying where AI can support humans in their daily roles, and when to transition from a machine-driven interaction to live person for a better outcome.
Bottom line, if you’re asking how to deploy more AI, you’re asking the wrong question.