The Smartest Way to Build Your AI Strategy? Roles

In this presentation, Renee Lorden, Director of Products at ERP Suites, discusses the importance of defining high-impact AI use cases tailored to specific roles within an organization. The session highlights how aligning AI solutions with business goals can ensure a high return on investment and overall success. This session introduces a practical approach by focusing on the role-based framework, which helps identify the most effective use cases at various organizational levels: executive leadership, frontline management, and individual contributors. The presentation emphasizes starting small with quick wins to build trust and understanding within the company while gradually scaling AI solutions. It also touches on the importance of accurate data and appropriate tools for AI implementation. 

Table of Contents    

1. Introduction and Agenda Overview 

2. Getting Started with AI Use Cases 
3. Using Roles to Define AI Use Cases

4. C-Level Executive Roles and Use Cases

5. Frontline Management Roles and Use Cases
   
6. Individual Contributor Roles and Use Cases
7. Conclusion

Transcript

Introduction and Agenda Overview 

Hello everybody. My name is Renee Lorden and I am the Director of Products at ERP Suites. I'm going to be talking about using user roles to define high impact use cases today. And I want to thank you all for attending my presentation. And I hope that you've enjoyed what you've seen so far and are looking forward to the next couple of days of a lot of good information.  OK, So what we're going to talk about today is we're going to start by going over use case considerations for AI.
Then we'll dive into the meat of the presentation, which is using roles to define the AI use cases.
And then we'll wrap up with the a brief summary.

Getting Started with AI Use Cases

First off, before we covered this slide, I want to just point out how important AI use cases are. Defining AI use cases is critical for a successful AI solution. It will help you align with your company's business goals. It'll make sure that what you're doing is a good solution for your company that's going to give you good return on investment. And it's just overall a really good use of your time to upfront to find really good use cases.

So where to start, how to get started? I have attended several presentations and talks on AI and probably the most common thing I hear is we don't know how to get started. We don't know what to start with. We don't know how to define our use case. And most companies are really wanting to invest in AI. They know it offers efficiencies, but they just aren't sure how to get started. And so these are just some things to consider when you're defining your AI use cases. But first is obviously what is the problem that you're wanting to solve and what is the outcome that you're looking to achieve? And what data do you need for solving the problem? And this question could lead to other questions like is the data that I need clean? Is it reliable? Do we need to beef it up? Those types of things. And then another thing to consider is how are you going to measure the return on investment? How are you going to make sure that the time that you invested and the money you invested in it is actually benefiting your company? And then finally, which tool should you use for your AI solution? And that one is interesting because there are a lot of tools out there and they are all evolving so quickly. It's, it's advancing very, very fast and, and it's great, but it, it is something that you will need to investigate. And one of the things I want to point out is for this presentation, we're going to be covering how roles can help you with your use cases. We're not diving into how to create a use case, but we do. I put a tip at the bottom of the slide where we do have a presentation for tomorrow at 1:00 that is building strong AI use cases, and that's where I believe it's Drew. We'll be doing a deep dive into building out use cases.

Using Roles to Define AI Use Cases

How to use roles to help define AI use cases. The phrase crawl, walk, run is used quite a bit in the AI world. I think a lot of people who've implemented solutions have come to the realization that it, it's beneficial to start with the use case where you get a quick gain. It's something where you can implement it fairly easily without a lot of investment, get some return on investment. But what that does is it helps the individuals in your company learn more about AI, how it can benefit them in their job and their roles. And from there, you can build up and scale out your AI solutions so you can advance them to be a little more complicated and and build them out to solve more difficult problems. One of the things with using roles is it does help you identify these quick wins and be able to solve some of the problems with minimal, minimal effort. And it also builds out trust in your organization. Right now it seems like there's varying levels of trust for AI and when users see what it how it can help them and how it can help them do their job and how it can get answers to their questions quickly, they start trusting it and want to use it for more things. And then finally, the concept of using roles to help define AI use cases. It applies across all the divisions in your company. And so it's not specific to like manufacturing. You can leverage it for other areas in your company like legal or finance or IT or HR. It can be leveraged across all the areas in the company.

C-Level Executive Roles and Use Cases

So what we're going to talk about specifically the roles that we're going to be talking about today is executive leadership, the C level roles of frontline management and then individual contributors. So just to talk a little bit about the characteristics for the C level role. People who are in C level roles are typically very strategic. They are looking at how to solve problems across the whole company and how they need to even manage outside the company. And so they drive the company's strategic direction. The decisions that they have to make often impact the revenue and profitability. They frequently are evaluating partnerships, how they can leverage partnerships to increase revenue and then also how they can invest in an acquisition where if there's a void in the company where they can can acquire another company that will fill that void. And then really important are the last two bullets where the C level is focused on ensuring employees are engaged so that they retain their employees and reduce their turnover rate where possible. And then obviously customer loyalty and satisfaction is critical for pretty much any company.

So looking at the use cases for the executive or the C level, they are broad, typically questions if they want answered. And so they may have a question like we formed a partnership with the company 6 months ago. How has that impacted ourselves since we formed that partnership or over the past five years, what has been the biggest impact? What have we done that's been the biggest impact on our Net Promoter score? And the Net Promoter score is, if you're not familiar with that, that is a metric that measures whether your customers are likely to recommend your company as somebody to do business with, to like their friends or colleagues. And another use case would be how much money will we save if we switch to using a different vendor? And so when you think about the use cases at the executive leadership level, they're pretty complex. The complexity is, is like high to extreme. And the reason for that is that a lot like I said before, they are looking at things across the company and even outside the company. And as a result of that, to answer their questions, you need to pull from data sources from all those different areas as well. And so that in itself is complex. And then if you think about trying to answer some of their questions with just generic reports and stuff, it's hard for people to pull all that information together or do all that analysis. It, it'll be very time consuming. Whereas it it is something that AI can do really well, but it's also complex. And so it would likely involve generative AI, which is generating new content based on patterns and it would most likely involve predictive AI as well, which that's analyzing historical data using machine learning and doing like predictions or identifying potential problems or issues.
And then the solution for like C level use case would also probably involve multiple large language models as well. So they're they're pretty complex. And if you are just starting out on your AI journey, solving AC level problem might not be the first thing you want to do. You might want to start smaller than that.

Frontline Management Roles and Use Cases

OK, moving on to frontline management. Some of the characteristics for frontline manager are they focus on day-to-day operations and they manage teams of individual contributors. Their their goals are usually to improve efficiency and reduce costs, increase productivity, reduce errors, reduce risk, improve quality, things like that. And so if we look at use cases for frontline management, some examples are which suppliers have late deliveries frequently or which suppliers have poor quality, which inventory items are high risk for miscounts during cycle counts and what products have the highest scrap costs. Usually when you ask these type of questions, they may lead you to want to ask other types of questions about the products that have the highest scrap cost. Well, what, what are we scrapping? Things like that. And so the complexity for frontline management use cases is medium to high. They're likely to use the generative AI, which is anomaly detection. They may need to use predictive AI, which has been a little more complicated, but they, they also may, you may be able to use like document understanding or something to get more quick wins for frontline management as well. And so that's why it's rated as medium to high. It does require the data in your system to be accurate. And so your, your AI responses are only going to be as good as your data. And so you may have to do some, some data cleanup or or evaluate your data for the frontline management use cases.

Individual Contributor Roles and Use Cases

So individual contributors, some of the characteristics for individual contributors are they have a higher turnover rate. And so you don't typically have a lot of managers with a high turnover rate.
Executives is like really low turnover rate. But individual contributors, that's where in companies that the highest turnover rate is. And so as a result of that, that's where you're doing more new hires and onboarding new employees. And so that kind of leads to the characteristic of learning when you're bringing people on board, they're learning about their job, you know, whatever job it is, they're taking on your company's processes and guidelines and things like that. And they are often the ones who need the most guidance or have the most questions that they need answered just because they're new. And so some example use cases for individual contributors are where is item A located in the warehouse or what are the safety rules for operating certain equipment or handling certain materials or, or things like that. And then what benefits did I enroll in or what company holidays do we have or things like that. So the level of complexity for individual contributor use cases is pretty low. It it can go up to medium, but you can solve the use cases for an individual contributor fairly quickly in some cases. And so like for these examples, you could have a digital assistant that could be available either on a kiosk or on a mobile app where the individual contributor can ask their questions and, and get answers really quickly. And where this helps out is it keeps the individual contributor more productive, but it also helps the person that they're trying to track down to ask the question as well. And so it, it just saves time on both those ends for having a digital assistant for common questions that an individual contributor might have. It does require that your documentation that you're using to train the model is up to date on, you know, your processes in your own boarding tasks and things like that. But it is a fairly quick win for implementing a solution for an individual contributor.

Conclusion

And so in summary, deciding how to get started with AI can be challenging, but if you start with something small that delivers a, a quick win, that's a really good thing. And it gives you experience with AI. It gives you hands on with it, helps you identify how you need to manipulate your data to work with it. And it, I think considering roles when determining which use cases to implement can help. It can help you narrow down the complexity of what you're trying to do right out of the gate. So if you need help defining use cases, ERP Suites offers an AI journey that will, where we can help you identify use cases that align with your business goals. And we can help clarify the problem or the opportunity that you're trying to address. And then also like look and evaluate the feasibility of the use case. And that's really important to make sure that the use case is really going to solve the problem that that you know you're going to is worth the investment. Just to show you, we have a, a couple of options for our AI journey. We have a quick win, one that has three phases and then we have a full ERP suites journey that focuses on building out that longer term progressive road map for use cases. So that's all I have on this last slide. I did include my contact information if you need to send me an e-mail and I included our our sales contact information if you're interested in learning more about the AI journey. Does anyone have any questions? Let me get the platform up. No, no questions. All right, well, thank you all so much. We do have some sessions that we're going to be attending. So if you happen to be at any of these, feel free to look us up. We would love to talk more with you. Thank you everybody.