How to Become the AI Person Everyone Trusts

0:00 This is Beyond Confidence with your host, Divya Parikh. Do you want to live a more fulfilling life? Do you want to live your legacy and achieve your personal, professional, and financial goals? Well, coming up on Divya Parikh's Beyond Confidence, you will hear real stories of leaders, entrepreneurs, and achievers who have stepped into discomfort, shattered their status quo, and are living the life they want. You will learn how relationships are the key to achieving your aspirations and financial goals. Moving your career or business forward does not have to happen at the expense of your personal or family life, or vice versa. Learn more at www.diviapark.com, and you can connect with Divya at contact at divyapark.com. This is Beyond Confidence, and now here's your host, Divya Park.

0:46 Good morning, listeners. It's Tuesday, and... What I want to share today is that there are so many conflicting stories that are out there. There's so many news about artificial intelligence everywhere. People are getting laid off, companies are spending money, and then people are finding out that there is so much AI slop. And you may be thinking, okay, what is AI slop? AI slop is... then you have to do the rework. Then people are leaning too much on AI.

1:27 There's so many warning signals and we'll be covering some of them today. So be sure to stay till in the end because I have a special gift for you. And think about it. If AI is not doing the work it's supposed to do, then what good is it? Also think about it. In the companies, what is happening is people are using AI everywhere. Some companies have AI governance where they are thinking through how is their organization fitting in the market? How does it fit in the industry?

2:10 What are the industry trends? What are the processes? What are their systems? And how they are going to roll out AI? And I call it responsible use of AI. So how do you bring in AI responsibly? Most companies may be doing AI governance, but most of the time what is happening is because I coach so many of my clients how to bring in AI. And as part of that, one of the first things we do is AI governance. How do we bring in AI? How do we create the responsible use of AI? How do we humanize AI? So one of the key things to know is that the most common What I'm seeing in different industries, different sectors, at different levels, people have started using AI at different levels. And as a result, what is happening is everyone is using AI on their own. And someone in the company is already becoming the expert. Someone is becoming the person that people turn to for asking questions.

3:31 And What happens is that when we do that, people are learning at different paces. Now, it could be you. You may be going and asking someone else about AI. So here is the gap here. Here in the gap lies the opportunity where you could become the person that people turn to for asking AI for implementing AI. So how can you help people going beyond a question about writing a prompt? Writing a prompt, doing the chat, it's old.

4:16 We are way beyond that. So how do we go about where we use AI responsibly to improve our work, to automate our work, to create time and space so that you can do strategic thinking, that creative thinking. So as you know, I'm Divya Parikh. I'm the host of the Beyond Confidence. And today we are going to unpack how can you become someone that people trust with that question. And you might be running a business. You may be an entrepreneur, a founder. You may lead a team. You may be a CEO. Or you may be just kind of figuring out where does AI fit in my role?

5:06 So the key is that I'm going to invite you to start AI with one part of your work that you really understand well. You know what makes it difficult. You know what a good result looks like because that's what gives you a useful place to start. So What we'll do today is that we will look at what are the three stages of AI adoption and what are some of the systems that can help you move beyond a chatbot for one thing at a time. Because just like maybe improve my email, summarize this, give me data from Excel spreadsheet, analyze this, give me the brief points, give me the executive summary. So Along the way, also what I'm going to do is I'm going to share some leadership principles. Because what I want you to do, where a lot of people are failing, is that they just start using AI anywhere, everywhere. For meaningful use of AI, where you are training AI, where you're building your metacognitive skills, which of course I've gone through in details in my book, The AI Agency, And you can definitely get a copy of yours and do a deeper dive over there. Today, we will be talking about how can you start responsibly

6:43 and bring in meaningful adoption. So remember, the first thing to do is, what is one of the things, one of the tasks, one of your responsibilities that recurs every single week? So now what's going to happen is if you are doing that thing over and over again, and if you make that improvement, it's going to save you time. It's going to bring in efficiency. It's going to increase productivity. Okay, so let's talk about three stages of AI. So what happened is in the pre-AI era, let's kind of look at one task that you have to provide A weekly client's update. And of course, this is just an example.

7:32 And you can take this example and adapt it to your work. So as a result of like, I'll just kind of say like, you know, I'll talk about my client, John. So this is one of his things. So what John had to do, John is a product manager. And they are working on a new feature on a product that they have already built. But he is also interacting with different clients. So every week, what he does is he collects information not only from different clients, but also from his engineers, from quality and from different people. And then what he has to do after collecting the information, what has changed? why the new feature will help, and how they can build that new feature. So he has to collect the information, write the update, and follow up on missing details. And then, of course, after a certain time period as they're building, they test it out, send it out.

8:40 So there are different stages of collecting the information, but you're still collecting the information. And John... What happens is that John needs to send this update out multiple times. So this is the task. So now let's take a look at how the three different stages would happen. So let's say if he were doing it before the AI era, he would have to do it manually. And if he had to share that work with one of the other colleagues, he would Also update his colleague and that would take time. So of course, you know, there were a lot of improvement methods like Lean Six Sigma or just sitting down and evaluating where it could be enhanced, where could things be cut, where could things be streamlined. But what does that do? It requires person time and context as well, whether you're doing it or whether you're handing it to your colleague. So now in the second stage, where most people are, which is AI arbitrage. Here, what we are doing is we are working with AI. So you may have an existing process. You may be writing an email.

10:01 You may be sending out a report. And what you do is that, you know, I can deliver this completely. So same way, John could deliver it. So what John started doing was John was feeding the context and information to Claude, and he also has access to ChatGPT. And what it does is he gave the large language models the information and asked it to draft the update. And of course, then he would review the draft, correct it, and send it. So of course, this is an improvement because what would have taken him, let's say, six to eight hours now went down to two hours. But what is still happening? There's still a lot of coordination that a human being has to do. You've got to find the information. You've got to explain the large language model what matters. You need to train it. You got to start the next step. So basically, let's say if the large language model, and this is I'm talking about chatting and prompting with the model. So you are still spending the time in assessment and in providing information. So yes, there's definitely benefit, but there is room for improvement. And

11:34 now let's take a look at what is the third stage of AI adoption. The third stage is becoming AI first. And when I talk about becoming AI first, what that means is you evaluate what you're going to improve. So as we talked earlier, so what did we say? Like, you know, John said in one of our coaching sessions, This takes up so much of my time. So we sat down, we evaluated the process, and he definitely thought that improving this and making it AI-first process would be really helpful to everyone. So we sat down, we looked at where the gaps were, where could we bring in efficiency, we used some Lean Six Sigma, and now we designed the process as to where people would contribute and where AI could contribute. So now, as you can see, it's very specific. We are being intentional over here. We are designing the process. It's not just kind of sitting and doing random chat. So for example, what happened is that as we designed, we created some agents and this configured system could gather approved information, not just random information, but the agents were defined. Agents were told that you're going

13:04 to go reach out to these people, whether it was engineers or clients, like, you know, sending emails on the behalves, but you're only going to ask them this information. And once that information is received, you are going to put it through a checklist. Then you're going to draft using agreed standards. and flag gaps for a person to resolve. Okay, and John would review it before that information is sent out to the required people. So now you may say, hmm, AI can hallucinate, AI can go rogue, AI can make mistakes.

13:56 But the power of AI first design system is that you can actually track what AI has done. And you just don't hand it over to AI in the first step. Think about when you onboard an employee, you give them a three months of runway or a month's runway or a 15 days runway before you hand them the whole project themselves. So this is no different. It is always human in the loop. When I talked about the agreed upon criteria, human has designed those criteria. John designed those criteria.

14:41 John gave it the golden example of what does that draft need to look like. And also, the key is, the beauty of this process is that before we had done the AI-first design system, Everything was sitting in John's head. And let's say if John were to leave the company, nobody else would know that how the process decisions were being made was happening. So now what happens is the AI-first system is designed to coexist where AI and humans coexist together, where humans can see what is AI doing every step of the way And we are not handing off AI the full functionality. It is doing most of the work. And if it makes mistakes, it's an iterative process.

15:42 And if you are thinking, let me create an agent and be done with it, that's not what I'm talking about. I'm talking about having a human in the loop at specific junctures, so that you are sure and confident that AI is doing its work. And even once you know that, yes, it has become good, it is like an onboarded employee, the idea is not to hand it off completely. Remember, AI may do the work, but the decision belongs to a human. And when I say human it can be you as a founder it can be you as a CEO leader manager wherever you are at because remember when there's a sensitive issue it calls for a conversation you're not going to send a cold email and the beauty of this is that this process makes those decisions visible instead of leaving it as tribal knowledge. But now, as you move towards this stage, it requires a lot of care.

17:06 It requires setup. So the key is that does this redesign process improve the result that people can rely on? So the key is Where is that recurring responsibility in your work? And what is still waiting for you every single time? So that's important to know before we get started. And now, okay, let's say you're ready for the AI first. And now you decide, oh yeah, I saved seven, eight hours.

17:49 Where does that time go? Think about it. I'm going to go back to John's example. So John saved a lot of that time. And if you know about the Eisenhower priority matrix, it says that it's important to work on something that's important and something that is going to have benefit long term. So think about there are so many projects that have been postponed. Think about those initiatives that could help your colleagues. Think about the initiatives that could take your organization further in the industry. It could help you create ideas. It could help you create products that could serve your clients better. So it is important that we have taken a look at, right? You are not working because AI native, AI first, you're working on something that's urgent and important because working on that thing, working on that initiative, working on that process improvement, working on efficiency is going to benefit you, your boss, your team,

19:19 you as a founder, your whole business, your clients. your whole organization. And what it does is, and we're not going to cover all the quadrants over here, given the time we have. So now that saved time allows you to be creative. That saved time allows you to become the glue that helps teams, that helps people. You become that AI person that people trust because you're not just bringing in AI willingly. You're bringing in with a system.

20:01 You are bringing in with a design process. And as a result, what you're doing is you are future-proofing your career. You are the one person that never gets laid off. You are the one person who will have the opportunity for the next company. So the other organizations will want you. So this is going to take time and it's pulling your critical thinking skills and it's building your metacognitive skills. All right, so we have talked a little bit about that. And John has done that. So he went from product manager to, to becoming a director. I mean, we have been coaching together for three years and now he became the VP of the whole product development group. And how was that possible? Because we also included three systems that support the work. So what we are going to do is like, think about, we are going to go back to John's task, what he was doing. There are three parts to consider in any organization. So the first part, if you kind of take a look at it, is it's a shared body of knowledge. You can call it tribal knowledge that's sitting in people's heads, that could be in standard operating procedures, that could be existing in systems.

21:25 So you can call it as the company brain. Now, the second is a set of rules. It's not like you can just take a SOP, run with it, write a protocol, create a report. There are certain guidelines. There are certain criteria. And the second set of criteria, the guidance, like, okay, if this happens, if the client is unhappy, here's how we deal with it. If this is wrong with the product, quality is going to step in. Engineering department is going to take a look at it. So there is a whole system around it. Let's call it the governance. And then we can call it AI governance. And then the third is the processes. It's the employees, it's the teams that carry out the work. Now when we transfer it to AI, it is the AI workflows, AI agents that carry out the work. So, okay, so we talked about the three systems, right? Company brain, AI governance, and the AI execution. So now let's kind of talk about like, okay, how do we begin? Let's say we are going to go begin with the tribal knowledge. So now you have a colleague who has joined the company just recently. Now

22:56 they would need to know who the client is, what the project is. what has been promised, where the current project information lives. They would also need to know where the project is currently. So these are all the information that is sitting there that they're not aware because they're new. So similarly, AI needs the same context as well, which most people forget to provide. So think about it. Now, if you put AI in that place of that new colleague, AI also needs that shared knowledge source. Now, it may be approved project notes. It could be current scope of work, the format, and making sure that all the information is accurate enough and also the access is appropriate. Here's where human in the loop is essential. So you're not giving AI access to things that you don't want to give.

24:07 And what could you do over here? You can create a folder. You can create a drive, which is just for AI. which does not contain the information that you don't want to share with it. So you see what we are doing over here. We are bringing in responsible use of AI. Okay, so now what you have done is you have given all the information to AI's company brain. And we can call it like, you know, AI intelligence, right? So now it knows exactly how the business is operating.

24:48 It knows exactly, depending on the level you're doing. If you're doing it a small scale, it knows how the project is operating. It knows how that particular task is doing. So this can be applied at different levels. Now, the second piece is the AI governance. All right, now let's go back to your new colleague. When you've given that information, they need to know what to do with it. What is, I have all this information, what is the outcome that I need to provide? And where are the decision points? And if I'm stuck, where do I go for help?

25:30 What is the delivery time? If there is missing information, what will happen? Do I need to flag the information? Do I need to Review the message. So all of these things that you would sit down and explain to a colleague, it's no different with AI. You are going to be creating very specific rules, the guardrails, as to like one of the things I always do is that as you are working through the process, whether it's my agent or operating systems, But it shows me exactly what it did and how it checked itself so there can be different checkpoints. It can stop that, you know, if you're stuck, if you're missing some information, you will not proceed.

26:22 So what happens is, and also you can create checkpoints where you can tell those AI agents that, Do not move forward till you have had a yes from me, till I have reviewed the decision. So again, it's very important to have human in the loop and keeping human at critical junctures, ensuring that there is visibility because it's an iterative process. You'll have to improve just like we as human beings are learning. Artificial intelligence is no different. So that is the AI governance because You don't want to leave things open for interpretation. You want to keep it in a way that is able to apply good judgment along with your judgment, and we are working hand in hand. Now, the third part is we have created the AI governance. The third part is the AI execution.

27:26 So in cloud, there is skills. In artificial chat, GPT and Gemini, they have other things. So what is an agent? What do I mean here by agent? Agent is something where you design an agent to be a system that is configured to carry out steps towards a defined goal. Using all the information that you have provided, Using the tools it has permission to access because now an agent could be accessing another tool. An agent could be accessing the folders. So that becomes the AI execution. AI knows what is the scope of work. And this is we have talked about before. What is the outcome we are looking for? There you are going to be involved. So you...

28:26 are responsible for the outcome, AI is responsible for the execution. So now these three things come together. And what happens is we will know where the problem began. Was the source of information outdated? Were the instructions unclear if there's something wrong happens? If the output is not what we are looking for, did we miss a step where we did not provide AI enough information? So basically what happens is that as you're working with AI, initially it may take longer time. And I'm talking about long-term benefit, not looking at the short-term use of the time because now you're using AI judiciously. So the key thing is, This is just the beginning where what you want to do is that you are deciding how to use AI. We have looked at three different stages and where it's going to make the most benefit because recently Guardian has shown that people are just using AI willingly. And I was talking about some of the warning signals that leaders need to pay attention. One of the warning signals is that you just give AI the full use. You're not checking its work. And people are working at different levels. And

29:59 when people are working at different levels, AI is producing work, which is slop. We're seeing it on social media and now it's being seen in organization. And what happens is that AI is slop. Work that is below par is passed on to somebody else and now they have to rework on it. Then they have to send it back. So rather than saving time, what it is doing is it is messing up because there are no systems like we talked about the three systems. So think about that and also see how can you bring in the responsible use of A.I.? Also, what are you going to do with the time you recover back? Put that time on your calendar and you will learn something that added time is creating time for creativity, for better use of your time.

30:59 So remember, start out small. And I talk about how to go in details, how to use responsible AI using impact feasibility matrix in my book, The AI Agency. It is much more in details, given the time we have, we'll not have time to go more into it. Start out small enough to understand the process completely and improve. Share what you learn with your colleagues. Include the parts that did not work. So this way, the AI adoption is happening responsibly and we are helping leaders. We are keeping human in the loop. We are training AI. We are building our metacognitive skills as such so that you're future-proofing not only your career, but other people's careers as well.

31:59 And as we have talked, Someone in your company may become the person others turn to for AI. Are you going to be that person? And you can grow into that responsibility by helping people making informed choices about work that they care about. So if you are serious about it, I invite you to reach out to me. and connect with me. And just for today, when you reach out to me, whether it's on LinkedIn, whether it's my email address, as it will be given in the show notes, mention the episode, and I will personally help you design your one task for the improvement as to how you could go about it. Because for me, humans matter. For me, humans come first, and it's all about you. It's all about how can we make you the person that everybody trusts.

33:11 You become the AI person that everybody goes to. So thank you for joining us. Thank you for spending your time with me. And I want to thank each and every one of you who is part of our Beyond Confidence family, because without you, the show would not be possible. And We are here to serve and support you to help you live the best life you can. And thank you, Juan, for making the show technically possible. Be well and take care until next time. Thank you for being part of Beyond Confidence with your host Divya Parikh. We hope you have learned more about how to start living the life you want. Each week on Beyond Confidence, you hear stories of real people who have experienced growth by overcoming their fears and building meaningful relationships.

33:57 During Beyond Confidence, Divya Park shares what happened to her when she stepped out of her comfort zone to work directly with people across the globe. She not only coaches people how to form heart connections, but also transform relationships to mutually beneficial partnerships as they strive to live the life they want. If you are ready to live the life you want and leverage your strengths, learn more at www.divyapark.com. And you can connect with Divya at contact at divyapark.com. We look forward to you joining us next week.