CRM Evolution, Day 2: Artificial Intelligence Will Drive Customer Engagement

WASHINGTON -- Artificial intelligence assumed the spotlight on day two of this year's CRM Evolution conference, as speakers agreed that companies must begin to make use of process automation to drive customer engagements and, ultimately, improve the experiences they're left with.  

During a panel moderated by Brent Leary, co-founder of CRM Essentials, executives from SAP, Oracle, Lithium Technologies, Microsoft, and Saleforce.com explained why AI is destined to play an important role in shaping the industry.

"My thesis is that AI is definitely going to change CRM as we know it and how we engage with customers forever," said Volker Hildebrand, global vice president at SAP Hybris. "My prediction is we're all going to be witnessing the rise of the machines."

Of a similar opinion was Rajan Krishnan, group vice president of applications product development at Oracle, who said that thoiugh AI has been in "hibernation" for roughly 20 years, now is a "great time to be in AI across the board--not just from a CRM standpoint, but from a broader enterprise standpoint." Increasingly, these systems are moving from "hindsight to insight to...foresight," he said.

Michael Wu, chief scientist at Lithium Technologies, said that one promise of AI technology is to provide customers with consistent automated, data-driven experiences without requiring businesses to sacrifice speed and scale. While humans tend to a good job at solving their customers' problems, they are often affected by their moods, differences in their levels of subject-matter expertise, and other variables. Intelligent systems, however, can ensure that all customers are afforded the same levels of attention and service. At its best, AI improves customer experience, Wu said, and creates a "win-win" scenario for both businesses and buyers.

Thanks to advances in computing and the growth of data sources, AI has the potential to work much more efficiently than it has in the past, said Marco Casalaina, vice president of products at Salesforce.com's Einstein division. But companies have to understand that for it to work to its fullest capacity, it must digest data that will allow it to learn over time. In this era, he said, "training is the new coding." Chatbots, for instance, can't work "out of the box." To make them work, "you basically need to have successful conversations, talking about the same stuff that your agents would be talking about with your customers. And those are hard to come by: A lot of the times they're not recorded, they're not in text format"--and there is a chance that the human agents made mistakes in the process. "Labeling and training data sets is now one of the most difficult parts of doing AI," he said.

This means that humans will also have to oversee the machines and guide them toward success, rather than letting them "run amok" without "adult supervision," added Krishnan. Companies also must not use AI simply because they understand it to be a popular area of investment; they should have a good idea about what business challenges they wish to solve with the technology.

But like any human, AI is still prone to errors. Hildenbrand pointed out that while a 90 percent confidence rate might be good for one specific business case, it might not be good enough for another. A 90 percent chance of a sales lead converting to a purchase is fairly good, but 90 percent accuracy in providing correct answers to customer queries is "pretty bad," Hildebrand said; it means that one out of every 10 customers was given erroneous information. "It's important to understand what the use case is, what [you're] trying to achieve, and how much [you] want to automate."

Kishan Chetan, principal PM manager of Microsoft CRM Dynamics 365, noted that some industries have advantages. Communications and financial services have experience with data mining, and tend to get "up and running much quicker." Chetan also stressed the importance of having competent people within an organization who are able to implement the AI. And, of course, managing change is vital, to ensure that professionals embrace technology rather than worry that their jobs will be eliminated.

In an afternoon session, titled "Analyltics, Artificial Intelligence, & the New Reality for Automated Interactions," Esteban Kolsky, founder of ThinkJar, shared a framework for enterprises that want to get started: First, find out if there is a demand for automation, then determine what approach to use (functional or outcome-based), and then test it out.

"Where do I start? Well, the answer is very simple," Kolsky said. "Start with the simple stuff that you can do. Pick a simple process, figure out how to automate it, automate it, document the results, and see what happens. If it doesn't work, do it again. If it doesn't work twice, it's not for you. It's really that simple."


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