Artificial intelligence and natural language processing are getting a lot of attention lately for their ability to deliver results and quickly analyze lots of information.
NLP-based solutions like Amazon Alexa and Apple Siri allow us to call on our home hubs and smartphones to buy products, get directions, and much more. And apps like Starbuck’s My Barista lets users order coffee and predicts what else they might like.
These solutions do that by employing AI to interpret human language. They can understand accents, language, and how punctuation can effect meaning. And, as with the case of My Barista, they can even predict what is likely next.
Karthikeyan Sankaran, director of data science and machine learning at LatentView Analytics recently noted that in addition to these applications, NLP can be used for brand sentiment analysis and call center operations. It can also be used to make sense of information related to financial markets, media and publishing, and recruitment.
“Understanding the emotional tone of consumer social posts in [the] public domain, knowing the trending opinion and having a real-time view of the customer’s pulse is a critical element of brand marketing. NLP helps to derive these insights from textual data,” Sankaran wrote. “High volumes of consumer interaction [in call center environments] creates the need for a critical capability to prioritize which tasks to act upon first. Using voice to text, NLP and machine learning can more quickly deliver insights to the most important customer inquiries.”
For example, Telefonica is expanding its AI efforts by using Microsoft Cognitive Services and Azure Bot Service to build a digital assistant for customer service. It will help customers pay their bills, schedule TV recordings, and more.
In a blog published earlier this year, Rob Thomas, general manager of IBM Analytics, made the distinction between consumer and enterprise AI applications.
“AI is about mimicking and improving the human function; said another way, bringing human features to technology,” Thomas wrote. “In the consumer world, that is mimicking speech, vision, and daily interactions. In the enterprise, it is mimicking and improving enterprise functions, such as Logistics, Marketing, Finance, Operations, and HR. While it is similar in concept, the difference is as stark as the Cugnot Steam Trolley and a Tesla.
“Enterprise AI is about solving sophisticated business problems in highly dynamic environments,” he explained. “This requires an understanding of well-defined use cases and starting points, and an acknowledgement that, per Erik Brynjolfsson, ‘the bottleneck now is in management, implementation, and business imagination.’
“Of course, the entry points for AI vary from organization to organization. In some cases, companies jump directly to the top of the ladder and adopt established AI technologies for specific use cases (e.g., H&R Block’s use of IBM Watson for personalized tax planning),” Thomas continued. “But in many others, organizations begin to build out their enterprise AI environment by getting their IA in order.” (IA stands for information architecture.)
[To learn more about AI and NLP, TMC invites you to attend The Future of Work Expo. The event will take place Jan. 30 through Feb. 1 in Fort Lauderdale, Fla. For more details, visit: http://www.futureofworkexpo.com/]