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Do you have an Enterprise Chatbot Strategy?

As Chatbots gain more traction in the enterprise, a lot of our customers are wondering on how to get started with the implementation cycle. Based on our experience with rolling out multiple chatbots across industries we have divided Enterprise Chatbots in three broad categories. Salient features of each category have been listed. In our experience, this is one of the best strategies to implement Chatbot, and transition through the phase where Chatbot starts handling your communication and business processes gradually. Most customer journeys for Enterprise Chatbot rollout will be from left to right.


Creating a Chatbot Strategy for Enterprise: Chatbot Journey: Stage I II & III


Basic Transactional Advance
  • This is basic chatbot that answers FAQs.
  • Most of the early adopters have rolled out the FAQ chatbot
  • Mostly content driven with basic information provided by the Bot
  • Investment is low
  • ROI is not compelling as there is minimal traction and adoption with these roll-outs
  • No Back-end Integration
  • Primary usage of AI is NLP engines
  • Early adopters are now progressing to this stage
  • These are back-end integrated bots that fetch data from system of records and can trigger workflows and such
  • Can handle end to end transactions
  • Investment is medium range
  • ROI is compelling as companies can increase revenue by acquiring new business and can reduce customer service costs as Bots take over the system interaction
  • Adoption is good as most enterprise transactions can be managed by these Bots
  • This is the next level to be reached after transactional Bots
  • In this stage Bots are not only wired with backend systems but also programmed to conduct data analysis (on system of records) at runtime
  • Advance AI algorithms are applied that go beyond just NLP
  • Investment required is very high as a combination of domain experts and specialized AI engineers are needed at this stage to make advance Bots that provide insightful interactions
  • ROI can vary based on execution and adoption
Examples (In Financial Services)

  • What are the Branch hours of operation?
  • What is customer service phone number?
  • How do I open a new account?
  • How do I place an order for a check book?

Basic Chatbots are mostly informational with static or at the most CMS based content

Examples (In Financial Services such as Banking)

  • What is my current account balance?
  • I need to pay my Bill for xxx amount to xxx utility?
  • Place an order for a new check book
  • My spouse and I need to open a new joint savings account

Transactional Bot can complete all transactions listed above as they can interact with system of records and business processes including workflow triggers

Examples (In Financial Services)

  • How do I balance my Financial Portfolio to improve my returns?
  • How much line of Credit can I get with my current balance and history?
  • Does it make sense for me to open a savings account?
  • Do I have a refinance option to reduce my current mortgage rate?


Advance Bots will answer questions above by applying AI algorithms on a combination of transactional data and warehouse data



Chatbot: A journey… not a destination


Change should be gradual and planned, not sporadic or sudden. Customers who want to implement chatbots, should also tread gradually, in a piecemeal fashion. It is the best to start by introducing a Chatbot that answers basic FAQs. Ensure that you have the necessary infrastructure and set-up in place including analytics that will allow you to incrementally measure and drive adoption. Once you get accustomed to a basic chatbot – it is time to evolve. Move right, towards transactional chatbots that use database or records to trigger work flows and then transition to advanced bots based on AI algorithms applied on a combination of transactional and warehouse data.



Irrespective of which phase you might be in, we at Streebo believe that Chatbot will become a powerful channel for your customers, vendors and employees to interact with your organization. Retailers like Rare Carat® introduced a Chatbot to handle queries around cut, color and clarity of diamonds, and amassed 1,234,770 strong user base and 2,296,117 searches on its platform in just 338 days, while travel Chatbot Mezi® acquired by American Express, handles nearly 60% of booking requests. A medium sized co-operative credit society also transformed their customer service by building a Chatbot on Facebook Messenger. These success stories are proof, that early adopters are getting good traction with the technology and have an early mover advantage over competition.




About Streebo and its Chatbot Builder


Streebo, headquartered in Houston, Texas is a Digital Transformation player with a portfolio of Digital software tools such as Mobile Forms, Digital Experience Accelerator and Chatbot Builder. These tools allow enterprises to rapidly assemble web, mobile and chatbot solutions across a plethora of industries including Banking, Insurance, Retail & Government among others – Streebo works closely with a range of Enterprise Software providers such as IBM, Samsung and Redhat and leverages various platforms from these companies to build secure and scalable solutions that are currently live and deployed at some of the largest companies around the world.


Streebo’s Chatbot builder is a low-code no-code browser-based tooling that allows both line of business and IT with minimal or no AI skills to rapidly assemble chatbot solutions wired with backend system of records such as Content Management systems, ERPs, Relational Databases and such with a point and click interface. Streebo’s Chatbot solution is powered by industry leading IBM Digital Experience platform and supports a variety of NLP engines including IBM Watson Assistant. Solutions assembled with the Chatbot builder can be deployed both on premise or on cloud (IBM Softlayer, Amazon EC or Microsoft Azure)


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Want a personalized demo? Or a trial? Drop us a note and our lab research engineers will help you get started.

11 October, 2018

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