Why smart enterprises are thinking AI Posted on : Apr 13 - 2017

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.

Mastering AI takes time

One factor holding up the spread of AI in the enterprise is the learning curve, because most IT leaders and executives still don't fully comprehend the nuances of the AI stack, much less understand how to apply the technologies to solve real business problems, experts say. On top of that, organizations are grappling with the usual budgetary, business case and talent gap concerns that remain barriers to implementing many cutting-edge IT projects.

"Last year, everyone got so focused on chatbots, machine learning and AI that they started to use [the terms] all magically and interchangeably, but it created massive market confusion," says Ben Lamm, CEO of Conversable. "Every major company now gets the point that AI can have massive implications to the business -- they just don't know how to get there."

The first wave of interest seems to be around leveraging AI technologies to improve customer experience and support. Fifty-seven percent of respondents to the Forrester survey cited improving the customer experience as a reason for using AI, with 37 percent reporting that they're implementing or planning to implement intelligent assistants, and 35 percent saying they're working to develop cognitive products for customers.

One area where machine learning and smart algorithms are starting to have a significant impact is in detecting known and unknown attacks, thereby allowing IT security professionals to take a more proactive security posture. Sales and customer service are other areas where AI technologies are starting to deliver results: In a survey conducted by the Accenture Institute for High Performance, 40 percent of companies said they're using machine learning to improve sales and marketing performance.

"The interest in the enterprise in doing something with AI is really high," says Joshua Feast, CEO and co-founder of Cogito, which markets a platform that leverages real-time intelligence and machine learning to help call center workers better engage with customers. "The problem CIOs are finding is that a lot of the things they want to do are on the margin, like implementing a different type of UI on a website. The only way to move core metrics is to impact major operations . . . and CRM, sales, security and call centers are the best way to do that." View More