Sunday, September 30, 2018

Designing Enterprise Chatbots - Dear Enterprise chatbot: what is our CEO’s annual salary?

This morning, my brokerage sent me a survey asking me about their new Chatbot Ted (or was it Suzy?). The survey was simple enough. The 5-6 questions asked me if I used other chatbots and virtual assistants like Siri, Cortana, Google Assistant, etc. It also touched on security concerns I had about sharing personal details with a the brokerage's Bot.

The past year has seen tremendous advances in chatbots integrated with machine learning, cognitive computing and Artificial Intelligence (AI) techniques. The bots are moving from quirky, esoteric tech tools that amuse us to serious business productivity tools that enhance the UI/UX design and usability of systems.

Organizations are evaluating bots as the front-line of customer service to address routine queries before (expensive) humans are engaged. Most major banks, brokerage houses, Airlines and eCommerce companies have rolled out cutely named Bots and virtual assistants that pop up when we try to engage with their portals. Many of these bots are being designed to take on basic human interactions like the recent demo of ‘AI Assistant’ by Google's Sundar Pichai that went viral.



In the demo, Google showed off its Assistant having a human-like conversation with folks at a hair salon and a small restaurant. The rather simple use-case also highlights the complexity of human communications, even for seemingly mundane tasks like making reservations that we take for granted while dealing with other humans, even those who have different sounding accents. Enterprise Chatbots that blur the line between human and system interactions, are also starting to appear in the corporate world.

Motivated by such viral videos, some CxOs, are asking their internal Business-technology teams to evaluate Bots for their internal users. Typical corporate Use-Cases include service help-desk functions for IT, HR, payroll and other shared services. Incidentally, these are also areas where organizations have been attempting to minimize manual interactions by enabling employee self-service techniques including searchable databases, FAQs, personalized portals and internal social-networking.

Technologies to power chatbots include commercial and open source tools that enable Machine Learning (ML) algorithms and natural language understanding to learn appropriate answers to user queries over time. While enabling and integrating chatbot tools in a corporate landscape can take some time and effort, training the Bot to learn the corporate context may require a lot more effort that shouldn’t be underestimated.

Hi HR-Bot, what is our CEO’s annual salary?


Design and training an enterprise chatbot requires functional context and access to enterprise data; data that may exist in silos. An FAQ of typical queries to the sales help-desk may include queries about monthly targets, details of product catalog, positioning and even sales data for the prior months. Such data may be business sensitive, and restricted even within the teams. For example, the sales team in the mid-west may not want their sales numbers exposed to other account teams, leave alone published to rest of the organization without aggregation or masking.

The design for an enterprise chatbot may also have to restrict information based on roles of the person querying. It should recognize that the senior executive asking if “margins of ACME account have improved since last month?” is authorized to review such information.

An account management team I once worked with was paranoid about the queries in a FAQs that could expose 'salary ranges' for some roles. Teams like that will certainly not appreciate queries to a Bot that ask "what’s our CEO’s salary?" Never mind the fact that most of us can just google the information from public sources; and a well designed bot enabled by machine-learning will eventually learn to search for that information on the internet.

The design of a machine-learning enterprise Chatbot also needs to guide (read: control) it to stay in the context of its enterprise domain. Many of us also continue to learn from experiments in the social media; like Microsoft’s ‘innocent’ chatbot, Tay that was ‘corrupted’ by Twitter and digirati in less than 24-hours.

Advances in AI, ML and NLP are pushing the envelope, and promising productivity gains by enabling self-service. A well-designed Chatbot, enabled in a specific functional context – like an IT, Claims or Benefits service desk – can aid productivity and also employee engagement while minimizing manual effort in responding to queries. However, given the current challenges in human interactions with systems, chatbots from Banks, brokerages and eCommerce companies are not being designed to be truly Machine Learning tools. At least not yet. While they respond cutely to routine questions, they don’t really ‘learn’ from queries posted by random users on the web.

Bottomline:  While there is a lot of promise that cool enterprise Chatbots hold, wider adoption in large organizations will also have to go hand-in-hand with organizational design.




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