GenAI- Identifying revolutionary use cases to create Gen AI based bots / virtual assistants

 

  1. Identifying revolutionary use cases to create Gen AI based bots / virtual assistants:



With the bloom of artificial intelligence, innovations are disruptive in every sector, which are marking a fresh era in the digital age. Players like OpenGPT, Gemini, Llama, Claude are coming up with a new bunch of personalized virtual assistants everyday, solving a variety of purposes for everyone - Students, techies, elders and so on. The most common use cases include, but not limited to,


  • Activities like Copywriting

  • RAG based (Answer questions by browsing)

  • Diffusion models (Generate creative content like images, videos)

  • Learning assistants

  • Coding assistant

  • Low code no code models

  • Games and puzzles


And so on. 


So, what's the driving force behind these inventions ? Is the market getting congested with tools with similar use cases ?


Are human jobs at risk of being replaced by bots ?


How to identify potential use cases for head-turning inventions ? 


Let’s deep dive into the series.


Identifying use cases for building tech involves deep empathy with the right set of target audience, with multiple iterations at every stage. Here are few tips to finish this task, craft your solution successfully.


  1. Choose your audience


Follow STP (Segmentation, Target, Purpose) to identify your audience(s). Gather stats like market size, demographics, age group which are crucial for the next round of scoping your project size.




  1. Identify problem statements


Deep dive into the problem statements - Sometimes it may be visible on the surface level, but search for answers if you feel it is an area to work on. 


For instance, marketing teams used to have sheets which are filled with timesheets and planners called their prospects, manually entering the details about the interaction. This was widely accepted as one the tasks of a marketing role as well.


Meeting assistants came into the picture, automating every single task almost - Starting from creating schedules, organizing meets, researching on the speaker, taking notes throughout and follow up, empowering agents to focus on just the conversation, and nothing else !!


           Inventions which can uncover hidden layers of betterments would definitely steal the show


  1. Scoping the product

With an estimate of all the data above, build a strategic plan on the different phases of your product - Basic build to highly personalized levels. 


Build a vision long enough to include iterations based on user feedback(s), and action plan on how the team is going to make use of the data in the best way possible.


  1. Signature features which speak your brand


Distinguish your product and stand out from the crowd even if you’re working on the same idea your competitors are probably working on. Carefully scrutinize the features and empower your assistant with capacities which will help them build a personal connect with your users, and solve real problem statements.


I recently came across a case where a user commented on a social media post. It read, “I’d honestly appreciate it if we build AI agents to detect cancer before it turns malignant, than to automate my emails.”


Another user mentioned, “I want to do the reading, writing by myself and would prefer automation in mundane tasks like household chores and planning grocery lists”


Priorities keep changing with time, inventions should evolve as well !!


  1. Compare, compete 


Keep a tab on peers who provide identical services, their style of personalizing and the user experience. While inputs from users and internal teams can be much useful, competitor research helps you make informed decisions in this fast paced environment.


Having seen how to pick the right use, how to measure the effectiveness of a virtual assistant ? 


Do we need to follow the same metrics throughout the product life cycle to measure the efficiency ?


Stay tuned for the next update !!

Comments

Post a Comment

Popular posts from this blog

GenAI - 2) Identifying problem statements in using virtual assistants / support chatbots:

  2) Identifying problem statements in using virtual assistants / support chatbots: In the last edition, we saw an example where User X orders food from a reputed platform, and he tries to reach out to support for help. With the options the bot offers, there is no solution and the loop continues. (Bot asks to call the delivery agent where the user reached out as he is unable to).. Giants like Amazon invest significant efforts to continuously audit and improve their customer service everyday - to be identified as earth’s number one customer centric company. But why is customer service considered so important in building a brand / company’s identity ? Whether it’s a manufacturing / service sector, it’s very possible that there are going to be errors - in the process or quality of offerings to their customers. Collecting feedback from users is crucial to introspect, iterate and improve the process and stay ahead of competitors and retain, expand the customer base. Inbound feedback is ...
Read more

GenAI - 3) Gen AI chatbot case studies - Different levels of efficiency

  3) Gen AI chatbot case studies - Different levels of efficiency  With continuation to our last blog on pain points and tips to use chatbots in support teams, we continue here with classic examples which you’d have come across when chatting with one.  To introduce the baseline, support teams work closely with the following teams. There might be more, but we focus here on the core roles. 1) Workflow management and operations teams to balance contact volume and manpower requirements 2) Content Management Team (CMS) for regularly updating content as per new updates and feedback from quality assurance teams 3) AI engineers who work on the bot’s functionalities and improvisations. Here, we have 4 case studies by deep diving on training models from few reputed brands. This article analyses how the bot handles the contact and the resulting user experience. Case 1 - User X contacts the support team to know updates about a specific policy. To his dismay, the bot typed the enti...
Read more

Gen AI - 1) How cool are Gen AI based customer service chatbots ?

  How cool are Gen AI based customer service chatbots ? With the bloom of Gen AI based automated solutions in almost every sector, the transition from live support to automated chatbots in customer service / technical support areas is phenomenal over the past few years. Players with automated chatbot services like Freshdesk, Zendesk etc have a solid role to play in reshaping the user experience for new age users, who value time and effort more than money. But, are we able to provide the best to them, let’s check it out.          Before we deep dive into the subject, here are some interesting findings about chatbots in customer support. The global chatbot market size was estimated at USD 5,132.8 million in 2022 and is expected to expand at a compound annual growth rate (CAGR) of 23.3% from 2023 to 2030 59% of customers expect a chatbot to respond within 5 seconds  62% of consumers would rather talk to a chatbot than wait 15 minute...
Read more