Then (Mobile apps) and Now (Conversational AI)

Then (Mobile apps) and Now (Conversational AI)

Then: The year 2008 when Apple launched the App Store, that brought revolution in digital experience. Everyone wanted a mobile app for their business and soon we had thousands of mobile app and hundreds of mobile app development providers. I have seen many mobile apps built by businesses but quickly taken out because they were not really serving the purpose and customers were not finding any value in having them. Why? Because mobile apps needed the enterprise data embedded in multiple systems to be made available in the form of lightweight APIs (– something that did not exist), and that too in a certain structure. This, in turn, meant that enterprises adopting mobile apps had to then invest significant time and money to prepare the data, build the APIs and then expose it. A costly mistake!

Now: Fast forward a decade and we are seeing the same pattern emerge for new age digital experience – Conversational AI, targeted towards always-on digital native generation, who loves more conversational experience than the old-world menu driven websites & mobile apps. Most businesses see this as an opportunity (and rightfully so) and want to capitalize. Wouldn’t it be great to serve your customers in an easy flexible conversational medium that can understand the intention of the customer and respond with contextually relevant answers? But before you dive in and start building, if we have learned from the “then” and avoid our past mistakes, it is important to pause and ask a few fundamental questions around your organization’s data readiness.

You could start by asking a few fundamental questions:

  1. What data will your conversational AI app need and Do you have this data? Driven by the use-case and customer journey you implement, this might vary from customer service data, orders data, product data, inventory data and the list goes on.
  2. In what format should you provide it to your conversational app? Do you need to build APIs in a certain structure that will expose this data or Can you expose your data as raw extracts and the conversational app can intelligently make sense out of it?
  3. Conversational AI app will be one among multiple digital touchpoints with your customers and therefore how to make sure that you are able to extract knowledge and insights from your Conversational AI app and take these learnings to the other touchpoints?
  4. How to break the I-C-E and how do you keep it up-to-date? ICE– Intent, Context and Entities are the defining building blocks of your conversational AI app and here is what each of them means for your business
    • Intents – It is about deriving the “intention” of your customer from the message she sent to your conversational app. Deeper the intents understanding, richer will be the conversational experience for the end user. Shallow conversational apps will decipher 3-5 top-level intents while Vertical AI apps will interpret 50-100 intents that are specific to your domain.
    • Context – This refers to the context in which your customer has sent the message. Is she trying to discover products when looking at a category or is she asking a question on the material when looking at a specific product? Finer the context, more engaging the conversational experience.
    • Entities – these are domain specific significant terms such as denier, pregnancy suitable, veins and scars, weight, plus size, etc which has a meaning in relation to your business and products. Wider the entities more relevant will be the conversational app response capabilities.

Success or Failure of your Conversational AI experience is fundamentally driven by the data preparedness of your organization and answering the above questions upfront will help you to avoid the same mistakes we witnessed during the Mobile App era.

I look forward to your feedback, thoughts, brickbats – just reach me at and let’s start a conversation.