TGI Friday’s may have a reputation as a casual restaurant and watering hole, but its messaging to customers was hardly conversational. The well-known chain sent out regular blasts through traditional broad-reach media and, more recently, social media, yet it increasingly wanted to re-create the banter that happens organically when regulars belly up to the bar.
In lieu of hiring a battalion of customer service “bar keeps,” TGI Fridays recruited an enterprise conversation platform infused with a shot of machine learning and artificial intelligence (AI) to personalize its messaging and overall customer experience. Now, patrons can chat up the AI for happy hour suggestions and appetizer specials, engage in small talk using emojis, make reservations, and order takeout via social media channels and through Amazon Alexa.
“We thought about how technology could help us create that one-on-one personalized messaging outside of the bar without having to hire 1,000 people to respond to individual guests,” says Sherif Mityas, vice president of strategy and brand initiatives, as well as acting CIO, at TGI Fridays. “We wanted to be part of the conversation when someone was thinking about where to go for happy hour or get recommendations on the most popular drink. That’s where the initial power of chatbot technology comes into play.”
The restaurant chain’s chatbot, created with Conversable, is just the appetizer in what is expected to be a full course meal as AI and machine learning capabilities take root in other enterprise systems, from security platforms to sales systems. While hardly newcomers to the technology scene, AI and machine learning have burst into the mainstream in recent months. Stories about robots, autonomous vehicles and smarter consumer products are grabbing headlines, and voice-powered digital assistants like Alexa and the recommendation engines of companies like Netflix and Amazon have become familiar parts of our everyday lives.
At the same time, technologies such as Google Deep Mind and IBM Watson, once ivory tower research projects, are also gaining notice as the engines that power a variety of applications in sectors like healthcare and finance (H&R Block’s tax preparation service is one example).
Early days still
Despite the hype, it’s still early days for AI, especially in the enterprise. The technologies are still evolving, although much more rapidly today, thanks to nearly unlimited computational power, the collection of vast amounts of data and advances in neural network capabilities. While the terms AI, machine learning and deep learning are used somewhat interchangeably, there are differences among them, and failure to grasp those differences can lead to confusion.
AI constitutes the broader concept of employing machines or systems to carry out tasks intelligently. Machine learning is an application of AI whereby a system learns how to act on its own based on the data being collected. Deep learning, a subset of machine learning, applies many layers of neural network models and algorithms to solve highly complex and data-intensive problems.
In a recent Forrester Research survey, just 17 percent of the respondents said that they will be implementing or expanding their use of AI systems over the next year. However, 55 percent said they intend to invest in the technology over the same time frame. Nearly half of those polled said they hadn’t yet seen any results from their AI initiatives, and the lion’s share have invested or plan to invest less than $1 million in such efforts through 2018.
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