AI Agent Use-Case Selection Framework 🎯

Key Takeaways

Hey, how it's going guys? In this video, I'm going to uh show you a framework that I have been working on. Uh it's for selecting the right use cases. You know, when you want to work with AI agents, uh we have been living in the hype era. Uh when it comes to AI and for whatever problem statements do we have, we want to solve using AI agents. you want to create agents for everything and that's not how it works uh to be honest mainly in enterprises this video is not for beginners I will say okay this video is mainly for decision makers uh who wants to you know invest a lot of resources infrastructure and money indirectly uh in your AI initiatives this video is for you okay this video is not for beginners uh beginners you should enjoy learning agents uh every use case people trying to build agents because that's what the hype demands, right? That's what the investors are asking in the boardroom. But this video is for you. I was learning a very good article, a framework by data IQ or data highQ whatever you pronounce, however you pronunciate that and I took that as an inspiration and created a very comprehensive framework a scientific framework that how should you select a use case you know uh for an AI for AI agents or the agentic AI era that we are heading towards. Let's jump in and see how we can do it. So if you look at here on my screen I have a tool that I built on top of it. like it's called AI agent use case selector. Identify high impact AI agent opportunities and avoid the hype. That's what it is. And you can see I have two things over here. One it's called browse presets and the other is try yourself. Browse preset means preset in programming or in development terminology means that somebody has already set of things done for you or set of task executed or set of resources available. That's called presets. Pre and sets. So I have few examples. Let's say customer support agent, data analysis assistant agent, meeting scheduleuler, sales prospecting agent, document processing agent, simple calculator. For let's say we have been working on these kind of task for what kind of use cases we really need AI agent or there are few task which can be just done through basic automation. So we have seen RPA based automations. We have seen now we have been saying agent based automation workflows using nan.com D5 gumloop flow wise whatever doesn't matter but this is not how it works in enterprises right so what I was doing and I will give this link in description I was reading this data IQ how to select high impact now focus on this word high impact see if you are learning agent that will cost you pay no money it will be1 to $2 like let's say we're using a lot of AI tools to build agents Okay. Uh if just through APIs and whatn not, right? The API cost. But if you are somebody who is working an enterprise $5 million project, it's beyond that. Okay. There's a lot of things that you have to talk about the ops platform, the data ops, the agent ops, the governance layer, the risk management strategies behind it. It's not easy to build AI systems for enterprises. A lot of goes behind it, right? You can see it says the cost of getting AI agent use cases wrong. There's a lot of failures. You know, people and that's what kills the PC. people just do P and then end a project because they don't have a right strategy behind it. Right? And that's what that's what it's important. Uh do you need an AI agent the three question test? So data IQ came up with this three question test. Complexity level, data source diversity and process types. Process is the most important thing when it comes to bringing up these technology to an enterprise. You have to first understand the human processes to kind of automate a workflow or automate a process. It's not that easy. right now. They have this five-step framework for choosing AI agent and I yeah I will give this link. I took that and created this comprehensive use case selection kit. AI agent use case selection kit that you see here. Uh it's let me just give you a walkrough and then I'll show you the tool that I built on top of it and both are available if you want to uh we I have more detailed breakdown. If you want just let me know. I'll I'll give you give this to you. You can see it says I'm creating a selection kit, a bundle kit for anyone who wants to go deep into it, not only scratching the surface. You can see it says uh we have a few stuff over here. Overview audience as I said it's not for beginners. It's for innovation heads mainly for the innovation head people, the CIOS and the CTIOS, right? Architects and people who can make decisions when to use outcome. This this kit that I have created the user selection kit. This shall be used or this should be used for uh discovery session just before you start building a proof of concept. This is for your discovery phase where you do ideation brainstorming and so on and so forth. Building any before building any AI agent PC. Uh outcome is a short list of agent use cases categorized by physibility and value. Okay. So this is a I talked about the tool that I'll show you. Let's say I have ticket support agent. So the value type, description, complexity, data, process and verdict I have created in these categories. In the value type, we have to understand what kind of value type it is, what kind of value it creates. You can see it's a process automation task. We have processes and then it automates the process. If you create an agent that looks at your ticket and try to you know solve it, resolve it or whatever. Description is automates it ticket try using emails and knowledge base. high complexity data is moderate and process is semiflexible then vertx is pilot now you can pilot with this ticket support agent let's say you have a maintenance scheduleuler worker support schedule maintenance using sensors and ERP look at my YouTube channel I created a video of sort YouTube sort on building I I built an AI agent to monitor EV charging station electric vehicles so electric vehicles charging stations they have different sensors uh if sensors fail it the agent automatically informs the operator So if such kind of use cases if you have pilot now okay clinical trial assistant is a very transformative thingy it's not there are a lot of things goes behind it you can see clinical trial mainly in the healthcare right research assistant for clinical documentation or uh eligibility of patient for clinical trials you can see automate trial site selection via EHR data history and criteria complexity is really high data is really highly diverse and process is adaptive so this is a transformative use case you will not get immediate success it might fail right because there are a lot of things in uh you know involved you know in this particular use case so I have created this you can see tryyou yourself scoring seat I'll just go up verdict guide uh you can see swift sweet spot pilot now high value second wave quick win so this is a template that I created governance layer risk assessment mitigations cognitive load autonomy greed use cases to model fit map ROI metrics if you are interested let me know I'll share this with you. Uh you know it will be available look at the Gumroad link uh in this there will be a link in the description to kind of get this template this uh template for use cases selection. Now you see this AI agent use case selection right if you talked about customer support agent this is a recommended use case for built you know to build AI agent for this particular use cases data is makes processes adaptive complexity is high recommended but don't do it for meeting scheduleuler people are not building agents for scheduling meeting which is probably not required it's overeng engineering thing right if you are building AI agents a multi- aent system for scheduling meeting it's it's really a uh overengineered processes Okay, you probably don't have to do it. A a barebone automation workflow will also do micular. Let's say simple calculator for anything like phops or you know mathematical calculation based on user inputs. You don't need an agent to build it. I see people are creating edit an agent for such things which is not required right? Why would you make it such complicated uh overengineered processes now how do we try yourself thing? In the try yourself we have score your use cases based on these parameters. Now if you look at these parameters we have inference complexity that talks about how many variables influence the decision. If there's a lot of variables which involves the decision let me say I make this 10 you know inference complexity. Then I have data diversity. It says structured unstructured multissource gender based because we also have to look at the biases the distribution of data or diversity in the data. So let's say the data diversity is also very high and process flexibility are workflows adaptive or resid. Let's say a workflow is really reset. The process is really rigid. Then you might have to give a second thought. If it's adaptive that's really fine. So I'm going to let's say make it process flexibility high ROI potential return of investment. I should I make this I capital. It says time savings cost reduction or revenue lift. Measuring success of EI project is also very tricky because it's not generic. Let's say if you're working on you know a ticketing thing then it might be for time saving right it saves a lot of time and it has indirect or direct relationships with cost and you know revenue left or more manpower and so on and so forth. So ROI potential is also important. So let's say I just make a bit high. Ease of implementation how much you are data ready. If you are not data ready forget about it. Data is the real mode right? That's what we have been seeing. You have to be data ready. You have to have a data platform. Okay. Uh to be honest before you start an AI journey. So data readiness, integrations and take risk. Let me let's let me tell you that I am not data ready. Okay. User adoption is adoption likely or internal push back. Sometimes what happens that board wants to push an AI solutions to the users or the employees or the people who are working for them. Any stakeholders like let's say in general it's not a good thing. Don't push back. Maybe then first create an awareness session with them right create a awareness or a upskilling session and help them understand most of the HRs are like thinking that their job will be wiped out because you know agents are creating job descriptions end to end right screening and policy chat bots and so on and so forth. So for that we need to create awareness that it's a complimentary thing it's not going to take your job or whatever let's say user adoption is also less. Now you can see it gives you an evaluation result. It's a past eligibility test for AI agent use case. High complexity, low value use case. This is a high complexity and low value use cases. You have to proceed with caution. Now this is coming up based on this framework that I have created over here that you see this framework AI and selection use case based based on top of data IQ. That was the starting point. Credits to them. All the credit goes to data IQ. I'm not stealing anybody ideas here. So high complexity, low value. Proceed with caution. Let's say I increase the ad option you can see it it it goes really high. Uh you know if I increase the ease of implementation everything get passed. But if I minimize the ROI potential and process flexibility you can see low process flexibility. Standard automation is better. Take a route of the old school RPA or just building automation workflows. Those kind of things fill up. You don't have to bring AI agents into the equation if you know ROI potential is not there and process flexibility is also list and so keep this in mind these are really important uh this tool uh if you want to try it out you can see browse presets and so on and so forth fantastic right so I'm going to keep on adding more stuff in this just building some frameworks for people to kind of navigate the challenges when they're working in the enterprise settings it's not that easy as I said you have to look at the ops start the platforms the governance the risk management and whatn not right so if you have any such questions if you are looking for something which will help you scale uh let me know happy to help you with this frameworks uh there are more frameworks how do we select models how do we select stacks how do we design prompts how do we uh select use cases how do we measure success behind how do we measure a success how do we look at quantifiable measures right for a given AI project so it all comes into some kind of scientific methodology and a framework. It's not that straightforward. So, uh if any such things, happy to help. If you like the video, please hit the like icon. If you haven't subscribed the channel yet, you know what to do. Please subscribe the channel. That helps me to create more such videos in near future. I hope you like this video. Please share the video and channel with your friends and to peer. That's all for this video guys. Thank you so much for watching. See you in the next one.