Instant messaging apps have taken over. WhatsApp, iMessage, WeChat, Signal, Slack, Facebook Messenger, Snapchat — billions of users exchange information in bite-sized chunks on any or all of these platforms on a daily basis. In fact, as of mid-2015, people were spending more time on messaging apps than on social networks, and as messaging apps become increasingly more sophisticated, this trend shows no sign of reversing.
Messaging platforms have expanded far beyond simply enabling users to send and receive text messages, photos, and videos. Many of them allow users to exchange documents and files, voice memos, location information, and sometimes even cash. And intriguingly, they’re creating new opportunities for us to interact not just with each other, but thanks to chatbots, also with machines.
Rise of the chatbots
A chatbot is a service that, in the most basic form in many implementations today, responds through pre-programmed rules to queries it receives through a messaging interface. Despite the name, a chatbot doesn’t necessarily do any chatting. Rather, you tell it what you want, from ordering products to triggering actions, and it responds accordingly.
Chatbots have been around for decades. Eliza, a program that simulates conversation by asking a handful of questions and repeating parts of the
answers, dates back to 1966. Many of today’s chatbots are Eliza’s direct descendants, operating via messaging channels like SMS, Facebook Messenger, or Slack, and most chatbots today respond using predefined message templates.
Chatbots are ideally suited for delivering services from within a messaging app to its users in a frictionless, personal way. Instead of having to install and launch a separate application, the user can text a chatbot just like a human contact via the messaging app to hail a cab, buy a t-shirt, order a pizza, reserve a conference room, approve a workflow, or submit a vacation request. Some experts even believe that chatbots will replace applications to a certain extent, since a chatbot with limited or no graphical components that operates within a messaging platform is cheaper to build and run than a full-featured app.
As an example, SAP itself is piloting a chatbot at HanaHaus, a café and co-working space operated by SAP in downtown Palo Alto, California. Customers who want to make, extend, or cancel a reservation for a workspace or ask related questions can do so by sending a text message to HanaHaus in casual language such as “I need a desk for two people tomorrow afternoon.” The HanaHaus chatbot responds requesting any other necessary information, like start and end time, and confirms the information before processing the user’s credit card and confirming the reservation in a more frictionless way than the traditional web- or app-based approach.
The machines talk back
As machines get more sophisticated at understanding and responding in natural language, we’re also seeing a massive growth in another type of conversational application: digital assistants like Apple’s Siri, Amazon’s Alexa, Google Assistant, and SAP’s upcoming Copilot. These dedicated apps and, in the case of Amazon Echo and Google Home, devices are enabled with natural language processing (NLP) that helps them understand casual speech input by text or voice in more sophisticated ways and in a multitude of functional areas. They can interact with other applications, parse open-ended questions like “how do I get to the nearest subway station?”, “what’s the score for the Giants game?” and “what are the top 3 deals I still need to close this month?”, all through one consistent interface, and they can take action or deliver an answer just as a human assistant would.
These use cases are already materializing right now. Based on the results of a survey of more than 1,000 IT and business professionals, sponsored by SAP and conducted by IDC, 20% of companies are already using virtual digital assistants to interact with employees and/or customers today, and more than two-thirds are actively evaluating or considering it for the next two or three years.
Conversational artificial intelligence
Based on the recent promising advancements in machine learning and AI, we’re going to see an even more dramatic evolution as static, rule-based conversational applications like most of today’s chatbots give way to artificially intelligent solutions. We’ll be able to talk to these applications as if they’re people, and they’ll be able to learn from transactions and user behavior to create and enhance their own understanding to further refine their responses. Using them to access content, request customer service, and make transactions will become seamless. For example, the HanaHaus bot’s logic is already no longer based on pre-defined rules, but rather on its ability to learn from input examples, which enables it to enhance its capabilities the more it is used, just as a human learns a new language.
In other advanced and extended scenarios for conversational AI, a technician might send a photo of a broken part to a parts and maintenance bot, which uses deep learning-based image processing to identify the part, automatically submits a replacement order, and sends the technician the predicted delivery date and installation instructions via the same messaging channel. An employee could also use Slack to submit a leave request to HR’s scheduling bot by sending the message, “I’ll be taking the first week of August off.” Or a customer could use a messaging app to contact a customer service bot with a question about how to use a product and receive a link in seconds to a video of the solution. Systems might also get in touch proactively with users based on certain dynamic criteria or even take action autonomously within given constraints, enabling users to focus on more important tasks.
The conversation continues
At first, chatbots will augment apps. Then they may replace them, until eventually, text and GUI interfaces themselves may well fade away in favor of simply…talking. For a consumer, that might look like telling your phone to make a 7 pm reservation at the nicest restaurant within 10 miles of your home with an available table. In a business context, it might look like asking a tiny black box in your warehouse, “What are the 3 most important orders we need to fulfill this week, and what’s the best way to make them happen?” and getting the optimal response an instant later.
Some other possibilities might include approving multiple workflows from within a messaging app, submitting expense reports by voice interface, reserving conference rooms by SMS, speaking to IoT devices to configure them and retrieve data, and interacting with AR/VR applications without any mouse and keyboard. We’ll say what we need, and the smart systems behind the scenes will apply machine learning to determine what we want, ask questions to clarify and add context, and then deliver on the request, whether that involves running reports, providing customer support, or changing business travel plans on the fly.
Like children, the more we talk to these systems, the smarter they’ll get. Instead of forcing us to learn how they work, they’ll learn how we work and adapt themselves to suit. This isn’t simply the emergence of a new interface. It’s an entirely new paradigm for computing, and in terms of the business world, the end goal is nothing less than enterprise AI.
Download the executive brief Let’s Talk About Conversational Computing
To learn more about how exponential technology will affect business and life, see Digital Futures in the Digitalist Magazine.
For more insight on AI in the enterprise, see Machine Learning: New Companion For Knowledge Workers.Comments