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Vaibhav Bansal co-wrote this post with Satish Jha 

A witty, ever-reliable butler is a must-have for crime-fighting billionaires — just ask Bruce Wayne. The closest thing that Tony Stark has to a butler is his AI assistant Jarvis, with its alert, intelligent and unaffected responses to Ironman’s perpetual snark.

While we are still years away from creating a chat bot as sophisticated as Jarvis, the recent advancements in NLP, (natural language processing, the technology that powers chat bots,) have been very encouraging. Someday, we believe these AI agents will become so advanced that they allay the common belief that AI can’t be as perceptive or intuitive as humans. Though such a future is a few decades away, conversational AI is already solving countless problems and driving better engagement and experience.

In the consumer space, Google, Alexa, and Cortana are leading the NLP-driven customer experience revolution. There are, however, no corresponding NLP leaders on the enterprise end. There are numerous solutions (such as Bot Engine, ArtiBot, Botsify and SnatchBot), domain specific chat bots (such as Einstein, Zoho SalesIQ and Hubspot) and a variety of “me-too” products. Though enterprises across the board agree that conversational AI is the way forward when it comes to driving effectiveness and customer experience, most haven’t succeeded in laying out a strategic plan for adoption.

From our experience and work with conversational AI over several years, we have identified six key factors for successfully driving conversational AI programs across the enterprise:

  1. Act now. Conversational AI is at an inflection point. According to Research and Markets, the global conversational AI market is set to grow from $4.2 billion in 2019 to $15.7 billion by 2024, at a compound annual growth of 30.2%. Increased integration with advanced AI capabilities and robotic process automation (RPA), availability of many conversational AI platforms for chat bot development and significant reduction in experimentation/POC cost are some of the key drivers for the proliferation of chat bots. With the growth in conversational AI firmly underway, it’s important that your organization not be left behind.

  2. Start with a strategy and a conversational AI road map. Today, within pharma, we see chat bots used for patient engagement, adverse event reporting, drug information, enterprise Q&A, IT help desk, sales force assistants and more. We recommend starting with a strategy around conversational AI that articulates a clear “why.” The strategy needs to be followed up with an implementation road map ideally starting with straightforward, frequently-asked questions to complex, action-based use cases such as data retrieval, request creation and knowledge management.

  3. Use leading NLP platforms. Reinventing the wheel is not the way to go with chat bots. Google Dialogflow, Amazon Lex, Microsoft LUIS and IBM Watson are four key leading platforms that let you develop chat bots. These standard platforms can be customized to deliver advanced dashboards, reporting and analytics to suit specific needs. In some use cases, custom-built platforms might make more sense. For example, specific, domain-based text analytics might require a more custom approach.

  4. Choose the channel wisely. A chat bot or conversational agent should ideally be accessible through a single channel such as Skype, Sametime, Slack, Jabber, Teams or a single mobile app. Avoid app-fatigue. Use a channel or app that’s already popular among your target users to ensure easy adoption and reduce your change management efforts. At times, a new app or channel might be needed to replace multiple, existing channels. 

  5. Give your chat bot a name and personality. A chat bot persona can help establish a greater connection with its’ consumers in a much more personalized way. A persona for a chat bot also helps improve user recall and drives higher engagement.

  6. Promote change management and continuous improvement. Enterprises need to work with early adopters, communicate success stories and drive key impact metrics such as number of chat bot users, customer satisfaction and self-service completion rate. They also need to continuously improve the conversational AI solution by integrating conversational AI with RPA and other automation solutions to drive new use cases. For example: Chat bots can be connected to an enterprise IT service management platform (such as ServiceNow or Remedy) to support incident creation and status requests.


Conversational AI is here to stay. Soon it will be the key customer experience driver for your internal and external stakeholders. As digital and end-to-end customer experience is now table stakes, enterprises need to re-imagine customer experience today to drive competitive advantage in the future.


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Topics: Pharma, AI, pharma business model, natural language processing, nlp, conversational AI