Follow The Brand Podcast with Host Grant McGaugh

The Evolution and Impact of Agentic AI: A Journey with Mandeep Maini

• Grant McGaugh CEO 5 STAR BDM • Season 5 • Episode 36

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Join us on a fascinating exploration with Mandeep Maini, a pioneer in artificial intelligence with over two decades of expertise. Discover how her journey from studying television's educational impact led her to become an AI aficionado, transforming the way industries like manufacturing and healthcare operate. Mandeep shares insider insights into the early days of AI, when the field was dominated by academics and niche players, and discusses how game-changers like ChatGPT have democratized AI, making it accessible to all. We shine a spotlight on the evolution of large language models and their potential to redefine business productivity, underscoring the power of real-world use cases.

Uncover the complexities of integrating agentic AI into enterprises, where human oversight plays a crucial role in balancing innovation with risk management. From overcoming trust issues and bias to ensuring comprehensive data access, Mandip draws parallels to the internet era's gradual acceptance, hinting at a similar trajectory for agentic AI. Our conversation takes you into the realm of healthcare, where AI promises to revolutionize access and efficiency amid clinician shortages and long wait times. With a hopeful nod to the future, we encourage organizations to embrace these technological advancements, poised to redefine not just industries, but our everyday lives. This episode is a thought-provoking blend of history, innovation, and the boundless potential of AI.

Thanks for tuning in to this episode of Follow The Brand! We hope you enjoyed learning about the latest marketing trends and strategies in Personal Branding, Business and Career Development, Financial Empowerment, Technology Innovation, and Executive Presence. To keep up with the latest insights and updates from us, be sure to follow us at 5starbdm.com. See you next time on Follow The Brand!

Speaker 1:

Hello everyone and welcome to the Follow Brand Podcast. This is your host, grant McGaugh, coming all the way from Miami, florida, about to speak to one of my good friends that I have met through the HIMSS Network that's the Health Informatics Systems Society a couple years back, and she always intrigued me because she was telling me that she got into artificial intelligence about two decades ago I'm like two decades ago. You know, we always think about AI as something like brand new out of the box. You know, it's the big buzz right, the hype that's always going around. He said no, this has been around for a little bit, so we're going to unpack some of that, talk about some of the things that she says is important that we need to know about, and we're going to have a great conversation. So, mandip Manny, would you like to introduce yourself?

Speaker 2:

Thank you so much, grant. I would love to speak to you this morning. You started with my experience in AI from two decades ago. Let me start there.

Speaker 2:

So I moved to this country to go to graduate school. I was at the Harvard Graduate School of Education. I had come with the intent of pursuing the role of television in education, but when I got there I was intrigued by the role of AI and how it could augment the process of learning. So I dropped the TV idea and I went full force into AI and how it could be leveraged for, you know, augmenting human processes, including learning, including learning. My first two jobs were in AI applied expert systems in manufacturing, customer support of AI, languages and services and so forth. After that it kind of went quiet. The industry went quiet. You didn't hear many jobs advertised, or should I say I didn't hear about many jobs advertised in AI and so forth. So I got into healthcare at that time and pursued my you know learning there, although at the back of my mind I always knew that there was opportunity for using AI in healthcare, you know, whether in training or learning or in actual clinical processes, and we'll talk about how we got there.

Speaker 1:

It's so important to understand what I call the like, the beginning. You know what got you. I call those pivotal moments. You started in one direction but then you end up in a completely different direction. But it is your kind of like, becomes your life's mission of what you know, your passion for living and your purpose right of what you're doing. And so you've kind of answered some questions we already wanted to know, like have you always worked in AI? Tell us how you kind of move that around. I want to know in AI, and you tell us how you kind of move that around. I want to know we're going to just jump right into it. What has changed in the AI spectrum now when it comes to like chat, gpt, like launching in 2022? When that kind of like hit, you've been in the industry for a long time and then you said, wow, large language models. Did we see that actually coming and becoming the thing that it's become now? What are your thoughts around that?

Speaker 2:

So 20 years ago or in the passage of these 20 years ago, ai was being employed in healthcare as well as other industries, but it was the accommodations, the academics, the researchers who were using it and applying it, pursuing it. When ChatGPT came out, it was for the average person. Anybody could use AI To do what? Primarily to generate content, you know, whether it is text or images and so forth, to answer questions, so anyone could use it. Therefore the buzz that it created, you know. So traditional AI was kind of used by niche players, should I say the academics, the researchers, healthcare folks as well were using it for radiology and scheduling and so forth. But chat GPT changed the bus, because now it was in everybody's hand to be able to use it at a very low cost, and that's why it became so prevalent and everyone started talking about it, and that's what has brought us to where we are today.

Speaker 1:

I think that's interesting history, that it has all it has been around. It's been in a niche world, maybe not as elaborate or maybe not. The database wasn't as large, but you were doing prompting, getting information back that you can then utilize. I know I utilize it a lot now, especially when it first came out doing branding, marketing and just getting information. I said, wow, I can get information quicker because I was used to. You know you go to Google and you kind of Google everything and try to discern and get articles and try to pull it together. This was like immediately and it's very specific to what you wanted as far as detail and in a human-like interaction, right of conversation, whether it's text, audio or video, as you just alluded to. I mean, this thing is just amazing.

Speaker 2:

Absolutely, and it prompts you on right. So Google in the past would stop at giving you an answer, whereas now it'll ask whether it's a Google LLM you're using, or chat, gpt or anthropic. Any one of those will lead you on to the next thing have you thought about you know, what do you think about this? What follow-up questions, would you like to know more, and so forth. So it leads you on like a human being would when you're having a conversation with them.

Speaker 1:

Yeah, the reasoning aspects. I've seen that now with Deep Seek. You go into it and it actually shows you how it's thinking through the prompt that you've asked it and it's very interesting. So there's different levels of these LLMs that I'm noticing now as well, and it really depends, I guess, on use case. Use case is so important. You had talked to me about, you know, putting AI into business and enterprise applications. Some people have rushed right away early adopters into this field. Some have gotten great gain out of it productivity gains Others have not. Some see it more of a soft ROI that comes back. Some people see it as a hard ROI. You had talked to me about something called agentic AI A-G-E-N-T-I-C agentic AI. Help us understand, talk to us as a layperson what is agentic AI and how is it different from traditional AI as we know it?

Speaker 2:

That's a great question, grant, because everyone is talking about agentic AI piece of software that is relying on large knowledge base underneath or a large language model to do things or do functions autonomously. So you don't need to provide input to an agent. It already knows and has a goal and can do a multi-step process autonomously. It can tell you the reasoning. As you just mentioned, deepseek is doing that. Others will do that very soon, if not already, reasoning on how it arrived at a particular recommendation or situation and decided to move forward with it. So an agent is interactive, so it can tap multiple pieces of data.

Speaker 2:

So let's say we're talking in the healthcare space. An agent could be asked to monitor your health. So let's say you're talking about diabetes and it is monitoring and it is tapping into the variable device. It's tapping into your electronic medical record, it's tapping into your lifestyle, behavior and so forth and if it finds an anomaly, it's not coming back to you and saying, hey, here's an alert. It is automatically going to the next step of informing your physician to do something about it or informing you to do something about it, rather than waiting passively. So they're interactive, they're autonomous, they can reason and they can carry on multi-step functions like a human being would.

Speaker 1:

That is a game changer, is a game changer.

Speaker 1:

That is definitely a game changer. It's actually like thinking through, reasoning through, without that human in the loop intervention or oversight to a certain degree, and I guess it really has that capability. You know, when I think of agent AI, I think all right, you have customer service, right. And then there's always a challenge right now in turnover, high turnover in the customer service realm. You know, we get that tier one, tier two, tier three, just to get support for whatever it is product or service that we're looking for. How do you see the use case of a robot, a bot, an agentic AI bot, in that scenario? Is that a good or bad thing?

Speaker 2:

I think it is going to be a very valuable thing, not just from an experience customer experience point of view, but also in terms of ROI. So imagine now you go to, you know any site, any kind of industry for help and most of them have these chatbots that are pre-programmed with questions and answers and you ask a question, it answers. However, if the question you are asking varies a little bit from where how the bot has been programmed, it keeps repeating the same answer and it's a very frustrating customer experience. If you're using an agent who, I just said before, can learn on the fly and can take action to the next step, you don't get stuck. If the agent is not able to answer a question, it might ask you would you like to be connected to a human being? You know a customer representative to ask the next question, or it is understanding why you're asking a particular question and because it has looked at your data that it can do autonomously and interactively and say are you asking this question because of A, b and C? And so it's a much more interactive conversation that actually leads to value provided to the customer and you know if customers feel they're getting value, they will pay for that brand.

Speaker 2:

You know I've heard of a lot of customers and I will include myself in that the level of frustration, if you're not getting it. I talked a little bit about ROI, human beings and we're talking about the customer service use case which can be across industries. Human beings cost and even as they are outsourced to cheaper labor countries, they can cost anywhere. You know, $30 an hour, $25 an hour and so forth. Agents will be in the cent or low, $1, $2 an hour. So the amount of savings is going to be tremendous, along with the customer experience that is going to be way better than it is with the current technology that we have.

Speaker 1:

Now that is interesting because you can't avoid that. You know being in business. You're saying, hey, I'm already paying $25 to $30 an hour for a particular human customer service experience experience. I can get a better experience through a AI agentic, ai, avatar, bot, whatever you want to call it for $1 to $2 an hour after deployment. Remember, it's got to be deployed, the implementation of it and have that wraparound experience.

Speaker 1:

Here's my question on this, or my concern. So we've had these problems and I talked to you a little bit about this a couple of days ago and I'll give you a real scenario Is that I had a friend of mine that had rented a rental car. They landed, took the airplane boom, got a rental car, but they had left their device in that car. There was something wrong with that particular vehicle, so they had, before they even left the airport, they had to get a different car. He said, oh, you know, let's put you in this car instead of that one. So they did that, but he left the device in that particular car. So he left, he went to his destination. Then he realized he didn't have that device. Now there, he called the airport. They gave him a 800 number. The 800 number is not local, it was down like in Texas or some other location. He said, hey, I just need someone to go and get the device out of that car and then put it into lost and found and I'll pick it up.

Speaker 1:

Not so simple, not simple at all. He could never get directly back to the rental car service that he was at in the city he was at. The direct number was not available. It became a nightmarish scenario and by the time they actually figured it out, of course the device is no longer in the vehicle. Nobody could even locate it and the story went from there. But I've heard of other people saying hey, it's just, sometimes I need a human in the loop, but I need very ease ease of use with technology. Sometimes technology can get in the way. Sometimes, even though you have a human in the loop, the human can't get out of the way of the technology to get a simple hey, if you just call them and ask them get out there, it could have been so simple. How can they? Will this exasperate the situation or do you think this could be a solution?

Speaker 2:

So I just want to address two things you've mentioned before. I answer that question. Absolutely, there will be some upfront investment before we go to agents and saving the money that we talked about. So people should be aware of that. You've mentioned human in the loop a few times and when we first started and I think it was due to caution and fear of reluctance to adopt people you said no, no, there will be a human in the loop and fear of reluctance to adopt people. You said no, no, there will be a human in the loop. It's not the machine going wild and doing anything Now and coming back to the example of your friend, it's a human collaborating with the agent.

Speaker 2:

So in that particular example, I think I don't know exactly what happened, but there's obviously fragmented data.

Speaker 2:

So the person sitting in the customer service center in Texas or wherever he or she was, didn't have access to data at the local airport where the car had been rented and didn't know which car it was that had been swapped for a new car and so forth. But in the new agentic AI model, the prerequisite is that they will have to have comprehensive data, but they will have the ability and access to the different fragments of what are currently siloed data and should be able to come up with an answer when this person is engaging with the agent. And, if he or she cannot, should be able to say did I answer your question, would you like to speak to a human representative, or can I call you back in 15 minutes after I have, you know, identified where your iPad is, and so forth. So it'll be a collaboration. It won't stop at a recommendation saying you need to call back this number and so forth, which is what leads to frustration. It'll be closing the loop on a action, a goal that was identified for it.

Speaker 1:

So let's go talk about risk and challenges, right, with the agentic AI and something you just brought up human adoption. Will we adopt, will we get used to it, will we reject it or will we embrace it? Talk to us a little bit about these risks and challenges of using AI in enterprise applications.

Speaker 2:

So whether human beings adopt it is up to us, human beings, who are bringing it forward or helping them adopt it. Trust, as you know, a big factor in the equation, so we have to go through, one by one, to address the issues that might prevent people from adopting it. One of it is do you have comprehensive data? Do you indeed have all the data that would be needed to make a decision to move to the next step? Two, is your data unbiased? So, have you in fact, included data from all demographics? So, in the car rent example you were talking about, have you included data from all different sites? Have you included data from everywhere? It needs to be included and it's not just using a small segment of data to make a determination on how to move to the next step. So, in case of human beings, have you used let me talk about the healthcare field, because that's where my focus is on. If I'm relying on an agent to make a decision to move forward to the next step, is that agent equipped with data from people from India, for instance? Is there no bias in the data? Are they only relying on data from different ethnicities or demographics or age groups or diseases, or what have you. So make sure that that is complete and people can see that through trial and error questions.

Speaker 2:

Is there oversight by regulation? You know we are. We live in a world where we have laws that protect us, you know, for data privacy and so forth. Is that going to be applicable even in this situation, before I go on and start using it? Who's going to be accountable? So let's say, the agent makes a wrong decision, who will be accountable? Will it be the maker of the agent? Will it be the model provider, the LLM? Will it be the sticking to a healthcare example? Will it be the healthcare organization and so forth? So we have to cautiously move through and address all those issues before there will be large-scale adoption. But it can be done and is being done and will continue to be done, and I think there will be large-scale adoption.

Speaker 1:

I'm going to agree with you, but over time, just like on anything new that comes out, the internet's now been around, let's just say, 25 years and in major use, right, but it didn't happen overnight. Just like trusting putting your credit card on the Internet didn't happen overnight. You know, over time you got the trust and that's because certain entities as you were saying, government entities throughout the world got together and said okay, now this is a trusted experience and we'll back you up, just in case, hey, if somebody gets your numbers or anything of that nature, there are avenues in place that will help you. So we got to see where that goes. There's a lot of things in the air right now in the AI legislation world. What is this going to really look like from a governance standpoint? We don't really know, but I'm glad you brought up that point.

Speaker 1:

I always look at almost like an automobile. If, say, you did run into someone or something, who's at fault? That's what I heard you say who's at fault? So there's a process you have to go through to determine what that fault is and maybe we get AI insurance. I don't know, we'll see what that looks like, but we've been man and machine have been interacting for a long time in the industrial age, for less than 130 plus odd years. It is a tool, it is a machine, but it is an intelligence that we have now released into the wild, let's just say, of our human kingdom, that can think or outthink potentially the average human being. That's not something we're used to dealing with, but I look at it almost like having I date myself the West Webster's Dictionary on steroids. I get my answers immediately because it has all this knowledge and it has it real time, and that's what I like about it.

Speaker 1:

Now, I know we talked a little bit about the. You know, if agentic AI will be adopted in enterprise architecture, what will be the impact on the workforce? Because you just said if I'm paying the customer service agent, I mean just to support that whole process is $25, $30 an hour and I'm going to replace it with a dollar or two. But what happens to that human in the loop? What happens to the workforce? Will they be laid off? I mean there's a lot of concern, even in Silicon Valley, certain people I know there will be disruption, but can you give us an idea of what you see on the horizon?

Speaker 2:

That's a really important question and consideration. When it comes to adoption, how would we go about it? A very important aspect of AI implementation and that's where I focus my work on is AI adoption in healthcare, specifically, is preparing the workforce, helping them understand how their role will change. As I mentioned before, it's not so much humans being replaced as much as humans working alongside AI agents to do the mission of the workforce, which will change not necessarily in numbers, but in skill set. So we no longer need human beings to do actions that can be automated easily. We've been seeing that even with, you know, the internet and so forth, we will be needing human beings to provide skills that an agent cannot provide. So in the healthcare space, for instance, no matter how good an agent may be in analyzing whether a person has a specific kind of cancer or not, the agent can never show empathy, either to the patient or to the patient's family, so on and so forth. So there are skills that, in spite of AI, only human beings can provide.

Speaker 2:

The strategy that a particular organization needs to do, to follow, to embrace, are, again, things that only human beings can do. Can they do it better with the help of AI? Absolutely, so it'll be a collaboration between human and AI and the human beings who are embracing it and willing to and receptive to changing their business models. Their own skills will probably continue to thrive, and it may be a little bit difficult for some other folks, but that's where change management comes in, and the organizations will have to be very mindful and skillful on how they prepare their workforce. It will not happen overnight, clearly. It will take time and you know we can have test cases where we employ agents and help part of the workforce embrace the technology and so forth, and then they will become the evangelists if they see the value in it. So we will have to work very carefully to make that determination which will help adoption of the technology.

Speaker 1:

I like that, the change. I remember working and there was not a computer in front of me. Then I watched over time it was a green screen. I remember that I was working at a reservation center years and years ago. It was a green and it wasn't intelligent at all, it was just input and output. I mean, that was basically it. And then I watched that change into. You know, you get your own PC and you had a blue screen. Right, you're using Microsoft type products, and it had a lot of different. It had more capabilities. Right, you're using Microsoft type products, and it had a lot of different. It had more capabilities. Right, it wasn't just input, output, you had applications that you can utilize for all kinds of different workloads and things of that nature. Then we got into mobile compute and then social.

Speaker 1:

We're at the very beginning stages of what AI can really do, and you brought up something when we spoke last I thought was interesting. It's that timing-wise. So let's say you're interacting with the AI, bot, agentic AI and it's gone through a scenario, but now the human has to get involved. I want to know what that interface will look like, because you now have to come up. If you're the agent, the human agent. How do you know the history of what's taking place? So when you come online with that human-to-human interaction, you're in step, you're in time. You don't have to be like you know what happens, like in the hospital, right? You give all your information and then you go through all this thing and then an hour or two later you go to another department and they start asking you the same question over and over again. So you don't want that. How is that going to be worked out, do you think?

Speaker 2:

Absolutely, and I think you've hit upon one of the biggest challenges in implementing agents. It's what I call orchestration. So, from a customer point of view, like I said, in any business the most important person is the customer and you need to keep the customer satisfied, engaged and so forth. So, from a customer experience point of view, this interplay between agent and human being has to be seamless. So the patient again in a healthcare example where I am focused on should not be concerned about am I talking to an agent? Am I talking to a human being? What is going on here? Oh, I'm going back to it.

Speaker 2:

That needs to be absolutely seamless and that's why the term orchestration is being used.

Speaker 2:

And there are companies who will enable that.

Speaker 2:

Clearly, there will be software companies that will enable that, but, as adopters of this technology, organizations have to be careful of that as well.

Speaker 2:

So you may employ agents, let's say, in the customer service space, but when there is a handoff to a human being, because the agent was not able to satisfy the customer or the customer wanted to speak to a human being, there has to be a handoff and it has to be seamless. And that's where the you know the quick turnaround of information between agent and human being. Human being will be able to see the history of what has gone on, not in a time, long time way, but very quickly, and get to the issue to to resolve it, and so forth. So that orchestration, I think, is going to be one of the biggest challenges in employing virtual labor, so that it is so business models know, so organizations know within their business model where are they going to deploy agents and where are they going to use human beings and how is the interplay between the two of them going to be orchestrated so that the collaboration is valuable, productive and seamless from the point of view of the customer.

Speaker 1:

This is important and I want to ask you this I wonder, when you have it, like right now, you get a disclaimer, this call is being recorded, that kind of thing. Well, they say like you are talking to, because it's so the fidelity of some of these attention to AI engines. You cannot tell if you're talking to a robot, a program or a human being. I wonder, when you get a disclaimer hey, I'm Grant, I am an AI, your AI avatar, let's say and just put it out there so you know who you are talking to for the most part. And then when they do a handoff, I'm going to get Mandeep on the line with us. She's my human counterpart, let's say, and she can assist us further with this particular challenge. Do you see that happening?

Speaker 2:

I think that would be nice if that happened, because you don't want to be having a long conversation with an agent, although you know agents are being used in romantic situations and companionship and so on and so forth, in romantic situations and companionship and so on and so forth. But you know, in a business setting, you probably want to know who you're talking to and I think that'll depend on the organizations that are employing it. Talked about this orchestration issue that companies will need to solve. How do they want to do it? Will they create their own agents? Will they? You know there are companies who are developing agents. Will they create their own agents? Will they? You know there are companies who are developing agents. Will they have their brand listed, you know, and so forth. So it'll depend upon the partnership between the companies that are deploying agents and their vendors or partners who are developing the agents and so forth, how they want to do it.

Speaker 2:

But I believe there should be a distinction when you know. So again, we talked about adoption a little while ago. The adoption may be slower or faster depending on your age group, for instance, or your industry, for instance, or the use case, for instance. So you have to solve for cases where the person at the other end may not be so technology savvy or may not be so trusting of the person that they are seeing on the computer screen. So I think it could be be, particularly in the early stages, absolutely okay to say you're talking to an agent now, but I can connect you to a human being if you so want.

Speaker 1:

I think what you brought up, and even earlier about the difference around where the human in the loop, let's just say, is so important Feel feelings, you have feelings, you have empathy. I know a bot could possibly simulate feelings and empathy, even though we know it doesn't have that because it's not a living thing. But there could be error or confusion around that. I always remember that movie, her, h-e-r Her. I don't know if you're familiar with it or not, but the gentleman is talking to a bot and he's falling in love with the bot and the bot is just so, you know, convincing and then able to order up like physical things to come to the house, maybe, whether it where it may be, simulate dinners, I mean, for a person who's isolated, it's not a bad idea. But you're not talking to an actual human being. But you're simulating these feelings and you're simulating emotion. What do you think that's gonna go? As that, if that's our you know, our imaginative layer and our ability to have strategy and feeling in situations can be simulated, what do you think the impact will?

Speaker 2:

be. Gosh, that's a tough one. I don't know they have been. You know, from a companionship perspective, they've been talked about and used for people who need that companionship. Whether it is, you know, could be an older person, a younger person, maybe a person with some emotional needs, and so forth could be used. I have to admit I don't know much about how that will be used, other than to say that, since companies are creating those use cases, there is most likely a place for such use cases in the evolution of this technology. I don't know much about it, but you know a nurse to hold your hand or to you can talk to if you are in extreme pain and it's the middle of the night, and so forth. Who will show that empathy to you? Are there agents that will be pre-programmed to be more helpful, a little more empathetic, even if not exactly like a human being, and so forth? So I think that'll continue to evolve and it'll be interesting to see how we get there.

Speaker 1:

I think you brought up a great point. What you just said the human touch, the nurse touching your hand that's something. That feeling, that transference of energy there cannot be simulated in a virtual setting or even a genetic AI or physical robot. Setting at least at this point, it would be interesting. Setting at least at this point, it would be interesting. If you have any final thoughts as you think through what we've talked about over the last 30 or 40 minutes or so and I want you to be able to showcase your expertise, because you are certainly an expert in this world I was always intrigued with you just knowing that you've been in this industry for a long time and seeing these different transformations that now have taken shape and what will potentially be our future. What thoughts would you like to leave us with?

Speaker 2:

So you know we started with talking about. I've been working in AI for the last 20 years, not actively, but nonetheless I feel really excited about this point in time, and particularly for the healthcare industry. Healthcare industry has always been sidelined as, oh, they're a laggard, they never use today's technology, and so forth. But if you look at the issues in the healthcare industry today the long wait times, the fragmented data, the medical jargon and so forth it's very difficult for people to access healthcare and lately, since the pandemic, there has been fewer numbers of clinicians and so forth. A lot of nurses left the profession, and so forth.

Speaker 2:

So I think in healthcare in particular again, I cannot speak to other industries per se this agentic AI technology can be a game changer to create more access, to create more transparency, to create more transparency, to just leverage data that currently is siloed and human beings have difficulty doing it. Like you mentioned, Grant, you answered the same question three times during one medical visit and so forth. So I feel very excited about what this technology can do and I think I welcome organizations who are innovative and thinking forward to adopt this technology. People like me are around to help them think through and prepare through all the stages of education and preparation and implementation, integration, so on and so forth.

Speaker 1:

But I think there is a lot of value in what is to come so, true, I'm going to thank you again for being with us, because you're sharing knowledge that we're all we're going to this unknown world and we need people like yourself that truly understand this technology and these capabilities and what can be a better experience of what we're looking for? Right, and this is this is. This is wonderful, and so I would encourage you to first let us know on contact, because I know a lot of people like this. You've got to start with the idea and take a look at the current state of the business and what what those two gaps are, and take a look at the current state of the business and what those true gaps are, and then understand what you're getting yourself into.

Speaker 1:

You know, not just deploying the technology, but the retraining of the workforce to understand their place, because right now people are like, oh, I don't have a place. I'm the one taking triage in the college and I know, especially in the technical world, you have tier one, you have tier two, tier three, tier four. Maybe that kind of style like tier one may be more automated. You know, depending on what it is, it's a simple fix, but as the complexity and the situation is more, and now I'm going to say, like this human, like meaning, you need more human interaction. It realizes this and then it can make that hand out, so that orchestration piece you talked about, so, so important. So how do we get in contact with you?

Speaker 2:

So you can reach me on LinkedIn. I have my website Ingenuity Advisory Partners. You can reach me on LinkedIn. I have my website, ingenuity Advisory Partners. You can reach out to me through there. Grant, if someone contacts you, feel free to get them my phone number or email. I'll let you use your discretion on doing that. But, yes, I would love to continue the discussion and it doesn't have to be a consulting engagement. You know, I do a lot of public speaking and just educating people, as you mentioned, and that is the purpose of this particular session as well just educating people on what is possible, what they can do to prepare for it and why they needn't fear, or how we can help them overcome the fear. That would lead to more trust and more adoption and so forth. So I welcome anyone who would like to be in touch with me through LinkedIn and my website.

Speaker 1:

All right, and I have one more question for you, and this is a personal question, but I always I've been asking this a lot for my my guests. I've been asking this a lot for my guests and this is how I get real live feedback. Mandy, how did you enjoy your podcast interview on the?

Speaker 2:

Follow Brand. I loved it. It was informal. It's like you were in the drawing room and we were just having a conversation over a cup of coffee. It was a pleasure. I really thoroughly enjoyed it. Thank you so much.

Speaker 1:

I enjoyed talking to you. You're such a wealth of knowledge and I do feel more comfortable about agentic AI before we had this conversation and where we're at now, like, oh, I see I had some aha moments and I'm hoping our audience has some aha moments as well and I encourage everyone. You can see all the episodes of Follow Brand at 5 Star BDM. That is the number five. That is star S-T-A-R BDM. B for brand, d for development and for masterscom. I want to thank you again for being on the Follow Brand Show. Thank you so much.

Speaker 2:

Thank you, Grant.