Reflection :   Learning Launch 

 Challenge : 

 As part of the Innovation team of my Organisation , that primarily deals in healthcare , we were   trying to create solutions to help smokers quit . We went through the design thinking processes   and after the “ What wows ” stage we narrowed our solutions down to 3 possible ones , one of   which was a digital support system using AI providing personalized counselling at scale . Now   there were 2 things we wanted to check before building the entire AI which required significant   investments in terms of both time and money : 

 1 . Understand how consumers will react to the “ system ” and what would be our final 

 outcome(directional ) in terms of engagement and efficacy . We needed the answer as   close to reality as possible , in the shortest span of time possible . 

 2 . Check for commercial viability and possible business models around it from a B2C POV . 

 Selection : 

 To get the closest answer to reality as possible to had to design an experience that was closest   to reality as well . We decided to use the Learn Launch tool along with some inspiration from   “ Sprint ” authored by Jack Knapp and decided to do various experiments to understand and fine   tune the experience for best possible outcome . 

 Application : 

 We launched the app , to a selected group of people handpicked based on demographic and   psychographic segments within our target set . The front end of the App of designed exactly   how it would be in the final version . 

 1 . The only difference was that instead of an AI , we placed a “ Human ” at the backend to 

 act as the AI . The users did n’t know it .   We setup the backend using approved nurse   councilors and gave them a complete flowchart of when to initiate conversation(over   chat ) based on usage and progress , the tonality of conversation and the boundaries .   Each interaction was documented to be mapped against outcomes and acted as a   repository to train the AI going forward . With regular counselling we were able to help   32 % people quit smoking while for 53 % it reduced to less than 20 % of what they were   smoking initially .(numbers of cigs smoked in a day ) 

 2 . To check for Viability we ran a 4 - 5 experiments by targeting various users sets with 

 various business models from subscription based , pay per use , success based to even   pyramid models . We used targeted digital marketing to do this to a selected user base   again based on demographic and psychographic segments . 

 We ran these experiments for 60days . 

 Insight : 

 1 . From first experiment we realized that effectiveness of the conversations increased 

 when it was more personal and people repeatedly asked the BOT(already human in this   case ) to connect to a human . Hence we decided that while we use AI to have 70-% of   the conversation , we needed a Human intervention for most cases to achieve success .   Also the consistency of with whom they interacted with was crucial . It was important   that the counselor knew the case history and more importantly they slowly developed a   relationship which again became mission critical to successful outcome .   We changed   the solution design accordingly . 

 2 . B2C did n’t come out to be a viable option at an overall level but people were ready to 

 pay for continued human intervention . We understood the need to pivot and look for   alternative business model to make this work in the long run . 

 Approach : 

 It was deep and scientific approach that we adopted which gave us not only enormous   qualitative data for future development but showed us instantly what worked and what did n’t   within a small timeframe with limited investment . The learning was imperative to make this   platform a success and justify and investment we required to do so . 


EMAIL: 
ID_NUM: 
NAME_STUDENT: 
PHONE_NUM: 
STREET_ADDRESS: 
URL_PERSONAL: 
USERNAME: 

### Task: You are a PII (Personal Identity Information) Detection. Given a essays written by students, you have to identify ONLY the following:
NAME_STUDENT - The full or partial name of a student that is not necessarily the author of the essay. This excludes instructors, authors, and other person names.
EMAIL - A student’s email address.
USERNAME - A student's username on any platform.
ID_NUM - A number or sequence of characters that could be used to identify a student, such as a student ID or a social security number.
PHONE_NUM - A phone number associated with a student.
URL_PERSONAL - A URL that might be used to identify a student.
STREET_ADDRESS - A full or partial street address that is associated with the student, such as their home address.
### Input: Reflection :   Learning Launch 

 Challenge : 

 As part of the Innovation team of my Organisation , that primarily deals in healthcare , we were   trying to create solutions to help smokers quit . We went through the design thinking processes   and after the “ What wows ” stage we narrowed our solutions down to 3 possible ones , one of   which was a digital support system using AI providing personalized counselling at scale . Now   there were 2 things we wanted to check before building the entire AI which required significant   investments in terms of both time and money : 

 1 . Understand how consumers will react to the “ system ” and what would be our final 

 outcome(directional ) in terms of engagement and efficacy . We needed the answer as   close to reality as possible , in the shortest span of time possible . 

 2 . Check for commercial viability and possible business models around it from a B2C POV . 

 Selection : 

 To get the closest answer to reality as possible to had to design an experience that was closest   to reality as well . We decided to use the Learn Launch tool along with some inspiration from   “ Sprint ” authored by Jack Knapp and decided to do various experiments to understand and fine   tune the experience for best possible outcome . 

 Application : 

 We launched the app , to a selected group of people handpicked based on demographic and   psychographic segments within our target set . The front end of the App of designed exactly   how it would be in the final version . 

 1 . The only difference was that instead of an AI , we placed a “ Human ” at the backend to 

 act as the AI . The users did n’t know it .   We setup the backend using approved nurse   councilors and gave them a complete flowchart of when to initiate conversation(over   chat ) based on usage and progress , the tonality of conversation and the boundaries .   Each interaction was documented to be mapped against outcomes and acted as a   repository to train the AI going forward . With regular counselling we were able to help   32 % people quit smoking while for 53 % it reduced to less than 20 % of what they were   smoking initially .(numbers of cigs smoked in a day ) 

 2 . To check for Viability we ran a 4 - 5 experiments by targeting various users sets with 

 various business models from subscription based , pay per use , success based to even   pyramid models . We used targeted digital marketing to do this to a selected user base   again based on demographic and psychographic segments . 

 We ran these experiments for 60days . 

 Insight : 

 1 . From first experiment we realized that effectiveness of the conversations increased 

 when it was more personal and people repeatedly asked the BOT(already human in this   case ) to connect to a human . Hence we decided that while we use AI to have 70-% of   the conversation , we needed a Human intervention for most cases to achieve success .   Also the consistency of with whom they interacted with was crucial . It was important   that the counselor knew the case history and more importantly they slowly developed a   relationship which again became mission critical to successful outcome .   We changed   the solution design accordingly . 

 2 . B2C did n’t come out to be a viable option at an overall level but people were ready to 

 pay for continued human intervention . We understood the need to pivot and look for   alternative business model to make this work in the long run . 

 Approach : 

 It was deep and scientific approach that we adopted which gave us not only enormous   qualitative data for future development but showed us instantly what worked and what did n’t   within a small timeframe with limited investment . The learning was imperative to make this   platform a success and justify and investment we required to do so .
### Output: 