When AI Stops Taking Orders and Starts Making Decisions: The Leadership Revolution Nobody Saw Coming with Dr Jamila Amimer

The ground is shifting under our feet as AI moves from answering questions to taking action. We dig into what that transformation really means for leaders: how operating models evolve, where risk compounds, and what it takes to capture speed without inviting chaos. With Dr. Jamila Amimer, CEO of MindSenses Global and a recognized AI strategist, we unpack practical steps to go from pilots to production and build systems that are fast, reliable, and governed.

We start by separating AI families—predictive, generative, and agentic—and why each demands a different approach to design, safety, and measurement. Dr. Amimer explains why spatial AI and the convergence with robotics will redefine context and capability, and how to prepare now without tossing out today’s LLM investments. From domain expertise and humans in the loop to controlled knowledge bases and action approvals, we lay out the essential guardrails to minimize hallucinations, manage model drift, and avoid compounding errors at scale.

Then we turn to the human layer. HR data becomes a strategic asset, revealing task flows and handoffs that inform agent orchestration. We talk through preserving meaning and motivation as agents absorb routine work, and how equitable upskilling—analytical thinking, data literacy, exception handling—keeps teams engaged and effective. Accountability and auditability aren’t abstract; they’re the difference between a clever demo and a trustworthy system your board will support.

If you’re ready to move beyond hype and design AI that plans, decides, and acts with confidence, this conversation gives you the operating principles to start today and scale tomorrow. If this resonated, follow the show, share it with a colleague who cares about AI governance, and leave a review so we can reach more leaders building responsibly.

Thanks for tuning in to this episode of Follow The Brand! We hope you enjoyed learning about the latest 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, visit 5starbdm.com
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And don’t miss Grant McGaugh’s new book, First Light — a powerful guide to igniting your purpose and building a BRAVE brand that stands out in a changing world. - https://5starbdm.com/brave-masterclass/

See you next time on Follow The Brand!

Transcript

Cutting Through AI Hype

SPEAKER_00 0:02

Welcome back to the Follow Brand Podcast. This is your host, Grant McGall, and this is the show where we cut through the noise and get to the strategy behind what's next. And today we're diving into a genetic AI, the shift from AI, the answers to AI that can plan, decide, use tools, and take action. And once AI moves from assistant to actor, everything changes. Everything changes. Your operating model, your risk exposure, your governance, and your competitive events. So how do leaders capture the upside, the speed, the smarter decisions, the real efficiency without inviting chaos, also compliance issues of automation that breaks the moment that really hits? And that's why I am joined by Dr. Jamila Amimar, CEO and founder of MindSens Global, a consultancy helping organizations apply AI to improve performance and decision making across high-stakes industries. And she's recognized as a top global thought leader in AI and known for bringing executive level clarity to complex systems. So, Dr. Jamila, welcome to the Follow the Brand Show.

SPEAKER_01 1:22

Hi, Grand. Thanks for the invite. I'm very pleased to have this discussion with you and looking forward to talk about uh AI trends and in particular agent think uh AI.

SPEAKER_00 1:34

This is where we want to get started because all of these things, and we just before we jump down, we're talking about man, think about how AI just kind of took over the the the the stage of a lot of things that we're doing, not only two or three years ago, and now we're hearing about this whole new application of AI, agentic AI, that can do some very, very uh, very, I would say human-like functions. So I think it's very, very important. So I want to hear from you, kind of what do you feel we're at right now, and how are you guiding executives, especially with the definitions around agentic AI, and what is the strategic significance of moving from assistant to actor?

AI Trends Beyond Text Models

SPEAKER_01 2:21

That's a great uh question, Grant. So um we will be talking about AI, and just for the audience, so they know. So AI is not uh something recent. You know, we we we had AI in the 1940s, the 1950s, the 1960s, and up to today. Uh today everyone knows about uh generative AI, which is Chad GPT and you know Gemini and all the models. And now people are talking about agentic AI, but AI is a lot more. So there is a whole umbrella of AI techniques that are grouped together. So I want to talk first about the trends of AI in general, and then we deep dive into agentic AI. So in 2026, uh we will definitely move from experimentation to integration. You know, the time of you know piloting AI and experimenting with AI and having some specific use uh cases, this is all over. Now we are talking about you know full-scale integration if you want to have that competitive edge. So it's beyond just having a strategy, just beyond just having an understanding, is really you know, implementing AI on a production scale. This is if you want to create that value creation for your enterprise. So that's the first focus, integration. The second one, and uh I'll just talk a little bit about it because it's too technical and don't want to go into technical discussions for the audience. So um one trend we will see going forward is the move from um text-based language model. So the people who have heard of ChatGPT and genitive AI, this is the kind of AI that is based on language or texts. So what the model does is predicting uh text. So you give it the question, it gives you an answer, but each time it's predicting the next word in the sentence. So it's a text phase. But most of the researchers have come to the conclusion that intelligence as a whole is not only based on language, and this is why you see some constraints and limitation of the current AI we have. So it's based on texts and it's based on statistics. So I'll give you an example. Um so statistically, AI can find a correlation between uh malaria and fever, but it wouldn't understand that it is the malaria that is causing the fever. So the whole causal effect reasoning is missing from the current AI. So what researchers are talking about right now is to move from language-based type of AI to what they describe as a word or spatial type of AI. So AI can that can um learn from videos, from images, from the spay, you know, a spatial dimension rather than just words. And we will see a lot of this. So uh, for example, the people who have heard uh, you know, the the chief AI officer of uh Meta who has basically left uh uh Meta or you know the former Facebook to form uh you know his own company, then you know he he and his team will focus on this type of spatial or world type of AI. So we will see a lot of things going forward, at least from an RD perspective in that space. Uh, we will also see uh a lot of initiative in terms of what they uh it has been described as um physical convergence uh convergence. So this is using AI with robotics, because robotics is something you can, you know, you can feel, you can touch, you know, compared to the software AI. So we will see a convergence uh or a combination between uh hardware, you know, in terms of the robotics, and then the software of AI. So we will see a lot of implementation of robotics.

Build On LLMs Or Start Over

SPEAKER_00 6:40

This is important, what you just said. Uh, and the three things you just touched on that one was language, and that the world right now is being you know enamored by the language text models, the large language models. But you're telling me that there is a new wave. Not even talking about agentics, we're just talking about spatial AI and context, context, right? Really understanding what it's seeing, not just statistical and predictability of words language. So we're really humans are providing the imagery uh uh around that. And sometimes it's it's accurate, sometimes it's it's not. But you're saying to me is that these um algorithms will be able to actually see. Maybe that's why some of our uh uh images are getting better that I've seen like in Gemini uh Nano Banana and some other things are getting better in this area. And now you just talked about which I knew was going to be coming, is that you're gonna have a physical um element, let's say, within your home that is a robot or in a manufacturing plant or whatnot, that's not utilizing the AI brain, so to speak, uh, to operate. And you really do need that spatial component because it needs to have some vision about what is happening around there. I just wanted you to stop you there because that's very, very important to where we're going before we leap too far. As you stated, we got to get away from just modeling these things and and strategically looking at them to actually implementing these uh things. And here's the question I would ask you. People like, oh, but we've already started down this road of implementing um uh what you call our out-of-the-box LLMs, and now you're telling me there's a whole new type of model that's coming out. Are we are we gonna build on top of that or are we gonna have to scrap what we're doing and move forward with something different?

Start With Business Problems First

SPEAKER_01 8:44

Yeah, so that's a great question because what we have seen over the last several years is that in in the AI field, things move very quickly. So you you know, you uh you know frequently hear of you know new techniques, new models, and so on. So you have to one, you know, you have to uh be on top of the topic and you know follow the trends and you know, kind of like be active rather than reactive in this space. Uh for the spatial AI, I wouldn't worry right now about it because they're just starting the RD, you know, the research of it. So it will take you know some time, you know, for you know, to get like some breakthrough, back through uh breakthrough, you know, in in that area. But it's it's good, it's coming. So the idea is kind of uh one is you have to uh be on top of uh things, but the other thing, and this is uh a very crucial thing, is that um you shouldn't really be bothered uh about AI in itself. So, you know, as a consultant or an advisor, when I talk to companies, they always say, I know I want to uh implement this model or I want to use Chat GPT or I want to use this and this tool. And then I always stop the discussion, say, you know, let's park AI telling about your problem, let's understand your problem. And then part of the problem statement and problem understanding, let's find which type of techniques or AI, you know, area can fit your problem. So you start, you always start from the business context. Uh, you know, don't start with AI because you will just get lost. I I think the idea is that for leaders, they need to focus on um uh you know kind of uh the rate of return. So you have the dilemma of incremental type of AI versus like moonshot kind of trap type of AI projects. And the focus, you know, you have at least at, you know, if you are a beginner, you have to start with the incremental AI. So you need to find you know which technique, which model you can use that can, you know, in a very short time, uh, you know, save you money or you know, save you like you know, the speed, or you know, you know, something tangible for your business. So you have to start with an incremental AI. Once you get to that stage, then you can then think strategically about like more kind of long-term vision. You know, uh, should I build capacity so then in a few times, uh, you know, in uh you know, three, four years' time, I have the talent to be able to deal with, for example, spatial AI or another type of AI. But right now, you know, um, getting so if there are like, I'm exaggerating, but let's assume there are like a hundred ways you can apply AI for your business. Do you have like a methodology or do you have a framework that you know enable you to select and aggregate and screen and rank you know the top three out of the hundred? So that's kind of as a leader what you need to focus on.

Incremental Wins Before Moonshots

SPEAKER_00 11:59

Because we're seeing a great advancement in the technology, and so I like the adoption rate of artificial intelligence by your everyday users, right? Your everyday user is like a mobile user now. I don't know if people are seeing as a platform. AI is a platform that you can utilize very, very intelligently. But have we really trained the human um workforce to work side by side, if you will, in an assistant type world? Like, how do I now incorporate AI into my workforce that is actually seen as an assistant? And then, you know, we're not even talking about as an actor actually doing things, but just as an assistant right now for the human workforce. Have you found that that is a either a bottleneck in the development of AI or the use of AI, or to get these those business outcomes that people are looking for in AI? What's your take on that?

Workforce Readiness And Adoption

Integration Failures And Data Governance

SPEAKER_01 13:02

Yeah, that's a great observation, uh Grant, because uh this is one of the um key areas or reasons for why AI projects fail. So they start with the pilot and they apply AI in an isolated way. And then, you know, they have a great model, great accuracy, great outcomes. But then once then they integrated within the business, this is when things starting for falling out. And because they haven't thought about the things that need to be thinking at the early stage of the design. So, for example, uh, data governance is missing. So there isn't a clear idea on uh the workflow. So, from you know, what is the workflow, what is the right process design, from where do you get the data, who is accountable for it, even when once you get the decision, you know, the uh the outcome from the AI model, who is accountable for that result, who is going to sign that decision, you know, all that uh is missing. So you have a nice shiny tool, but then you don't, you know, when you plug it, it goes it goes all over because all the necessary steps, you know, the real kind of integration embedding in the business steps have been missed. And you know, all these you should really start from the the the beginning. There is another uh aspect that uh as all teams tend to forget about it, is the the training of the model. So obviously, there's a lot of uh emphasis on training uh the model uh at the the the design stage. You know, everyone is exciting and they're experimenting with those different techniques and trying to collect the data, then they're they they run the model and then they're they tweak the accuracy and that they're happy with the results. But then they, okay, let's assume then they go over the implementation, they succeed, but then they forget about it. They leave the model running for two, three, maybe you know, a couple of years without fine-tuning it because most of the AI models they have a tendency to drift. So even if you have a fantastic model that you have implemented and it's working fine on production, it doesn't mean it's the end of the story. You have always to keep an eye on your model. Is it drifting? You have to fine-tune it, you may even have to, you know, uh retrain, you know, some parts of it. You know, it's an active, you have to have an active monitoring, otherwise things will uh will uh you know, you will get like very nasty surprises. There is an example of uh, I forget the name of the startup, and maybe it's better not to mention it, but uh there was an AI startup in the real estate uh you know uh area, and they used AI to try to predict you know uh price houses, and then based on those predictions, they were in the business of selling and buying houses for for you know householders. And because they haven't uh checked whether the model has drifted by time, they ended up you know losing millions of dollars because kind of uh the their algorithm, their model was suggested to buy houses at uh a cheaper price than the actual market price. And this is because once they produce and implemented the model, they haven't monitored for uh you know changes in market prices. So they haven't fine-tuned the thing. And if you don't do that, if you don't monitor the drifting of your model performance, you may end up with uh big consequences uh in in the future. So this is also one of uh the thing. Uh so there are a lot of um you know kind of challenges that the business needs to uh take uh into account. You mentioned uh agenting AI, so that's also going to be one of the themes for uh 2026. So the theme um for agenting AI would be like two things. One is around uh orchestration, so moving from an individual type of uh you are dealing only with one agent to dealing with a whole set of agents, maybe tens of agents, hundreds of the agents. I'm exaggerating, but you know, you could be dealing with thousands of agents if you are a very, very big corporate corporate.

SPEAKER_00 17:42

Yeah, enterprise-wise, yeah.

SPEAKER_01 17:44

Yeah, so that's kind of one of the theme. Um, so that is uh uh before you got there.

Model Drift And Active Monitoring

SPEAKER_00 17:52

Before you go to agentica, which is another it's a it's a big pool, you brought up drift, and I want the honors to really lean in on what you're talking about because I think this is one of the things we need to get around this. And I've talked to some others about it because a lot of times we're we're used to things being what I call deterministic, meaning one plus one equals two. Well, in a in a problemistic world, which is where AI sits, one plus one doesn't always equal two. Sometimes it equals three, just like me, I'm one, you, you're one, but we're in a group that equals three, meaning we don't know exactly what Grant is going to say or how you're going to answer. We make we can make assumptions around that, uh, but it's not a clear uh mathematical equation, let's put it like that. So when you have drift, and I've seen this happen before, this is why you get different answers to the same question, no matter what model you're using, whether it's chat or it's claw or perplexity or other ones, you're like, wow, it's slightly going to be always different. So, but in certain models in production, let's say you're making a widget, and that there's a there's a there's a process that has to be followed step by step, like a recipe. You can't have drift in that because it has to be exact in what it's producing to get the desired result. Just like when you go into a McDonald's for the most part, you get the same experience, whether you're in America, you're in France, you're in Europe, or you're somewhere else. It's pretty much going to be the same type of experience, very little variation. This is important to understand in the current models as you now start to talk about identific AI and start to understand that. Can you afford drift in in the use case that you're using it for or not? And then you start getting into what you you related to earlier on spatial AI that might correct some of that. I'm not sure. What what do you what would you think about that?

Predictive vs Generative vs Agents

Hallucinations And Compounding Risk

SPEAKER_01 19:57

Yeah, that's a great question. So Going back to the family of AI. So people need to realize that not all of the AI techniques are equal. They don't have equal opportunities and they don't have equal risks. So generative AI and agenting AI, which is a subset of generative AI, is the riskiest of it. Because then you have another family of what we call predictive AI based on machine learning and other techniques. For example, just to give an example, what do we mean by predictive AI versus generative AI? So generative AI, you are using AI to generate a paragraph, an essay, a picture, a video. You are generating an input. Predictive AI, you are not generating any uh you know this type of things. You are using it to try to predict something. So an example will be, and this is mostly the earlier version of machine learning. So that's you know the earliest version of AI, uh, you know, before 2022 or before uh you know Chat GPT uh became uh famous. So that predictive uh type of AI will be used. So if you are a factory, maybe uh a car um manufacturer and you would like to predict when your uh factory may break down, you will use AI to try to predict when uh a certain type of machinery may break down. So then you try to uh have a maintenance scheduled, you know, uh done to avoid that breakdown. That's very different from when we talk about generative AI, because generative AI you are not trying to predict uh a cost or try to predict a risk or try to predict an event happening. You are generating uh things, you are generating uh like uh um a blog, your generic video, uh, and so on. So agent AI is a combination, you have two things. You have agency, so you have autonomous uh you know, kind of digital agent that acts, but they're mainly or most of them, they're using uh generative AI. So they're using like an uh an agent to uh generate uh something. Again, you are still in the generative aspect, so using AI to generate to generate a report for you, or to generate, I don't know, a purchase, or you know, something something like that. So with so we have the drift, but like something that is also uh very relevant when we talk about generative and agent AI, because it's based uh from uh you know uh on generative AI is what we call hallucinations. So you get kind of like uh you know this mix, and uh and because it's um it's based on tokens and trying to predict what is the next word, this is why you never get the same answer, even when you ask the same question. And this is very problematic for uh big businesses. So if you are using one agent, you may get one problem. Uh if you are using thousands of agents, you may get uh you get what you call, you know, uh people in banking and financial services, they always talk to you about compounds, you know, the compound effects of interest rates. You get the same thing for HTAR. If you are using thousands of agents and each agent can make a mistake, imagine you know, you have one mistake multiplied by a second mistake, multiplied by the third mistake, and it goes into the the thousand of mistakes. Imagine what kind of consequences they have.

SPEAKER_00 23:56

Yes.

Guardrails For Trustworthy Agentic AI

SPEAKER_01 23:59

So what you have to be very careful. So obviously, there are like things. So there is a merit into it, but we want to be realistic. So, on one hand, obviously, there are like values to get from agenting AI, but you have to be very careful when you apply it. So, first of all, you have to apply it for uh an area where you are an expert. So, for example, um, let's assume I'm an expert in financial services. So, if I'm using agenting AI in finance or banking, you may assume that if the AI agent gives me a wrong answer, I have the background or the capabilities or the knowledge to know that the answer is incorrect. But if I'm in financial services, but then use agenting AI, maybe let's say for healthcare or something completely different that you know I have no knowledge of, then I cannot judge whether the outcome I'm getting is wrong or right. So you have to use it in where you have a domain expertise. You also have to have humans in the loop to kind of do the testing and judge the quality of those results. Um I see that uh currently, because of the media, there is uh some hype, a lot of hype around especially Agenic AI, but I think over time, maybe not now, in five, six time, you know, you we may start seeing something really valuable in that area rather than just kind of uh experimenting and hyping, uh, you know, uh uh in this space.

SPEAKER_00 25:41

This is important to understand, and and you bring up a great point of when to you know separate the hype from what is real, what you can put into your business and realistically have um the outcome that you're looking for. You you hear about it in customer service, maybe doing some triaging, some very uh simplified appointment scheduling, um, and um you know integration with your CRM? I think these things are very possible from an automation standpoint, but when we start talking about governance as a strategy asset, my question to you would be in that area that we just talked about. What are the non-negotiable principles of a trustworthy identic AI program? We're talking about accountability, that you talked about controls, auditability, and approvals. What do you what is your take on that?

Accountability And Regulation Gaps

Limit Exposure And Keep Humans In Loop

SPEAKER_01 26:41

Yeah, so the governance issue is applicable for the whole of AI. So obviously, you know, we really need to know. So even if you take the um um, I know it's not really agentic, but it it, you know, there are like similarities into it. So if we talk about uh autonomous vehicles or driver's car, so we need to have a clarity, uh not only accountability of the company, but we also need legal frameworks. So there is a whole regulation. Some countries have advanced regulation, some countries are trying to get in there, but it's fuzzy and you know it's not clear what is meant by the regulation. But let's take the example of a driverless car and let's assume that car um is running on AI, on agitat AI. So um let's assume, God forbid, there is an accident and someone dies or someone gets injured. You know, who will be accountable for that incident? Uh so is it um is it the company who produced the AI software? Is it the manufacturer who manufactured that hardware, you know, the car itself? Is it the company who trained the model uh you know on the different traffic lights and the roads and the signalists? You know, there is um there isn't a clarity, you know, in if you look at the whole process, there isn't an uh you know, uh clarity on who is accountable. So you say, you know, I'm accountable for this segment and you are accountable for that segment, and you know, and the rest, especially if you have like uh you know a long uh you know kind of like end-to-end uh you know chain. So the whole accountability, it's not always clear. Uh obviously, this is an extreme example, but you may have like smaller examples, you know, within your enterprise or within your businesses. So you have to uh think about how do you deal with uh you know governance and accountability, who has the final say? I think it's always good to kind of have a combination of AI and humans, you know, have humans in the, you know, so you can but you know, you know, you can have AI uh run the model and maybe suggest to you, or I use the word advise you on the outcome. But in the end of the day, it's the human who should be making that final decision, you know, if you want to be on the safe uh area. Another one is uh if you want really want to be safe, especially if you are experimenting with the newest type of methods of AI. So we're not talking about the you know the traditional type of AI, but the most recent, like agenting AI and so on. So try not to um experiment with AI in a very uh what say exposed area. So let's not have it kind of have this type of model dealing directly with your customer. There have been examples where you know a chatbot has been uh left loose with customers, and that chatbot started criticizing the the company and suggesting them to go to the competitors because the company was so uh uh uh uh you know lazy and you know uh not competitive and poor, you know, uh poor performance uh and so on. Um but sometimes uh the consequences could be even greater than that. So we know, for example, this is going um a step backwards. So we're not talking about agitating, but we are talking about generative AI as a whole. So the the lack of language model, the lack of Chat GPT. So we know uh that there are like current law lawsuits, you know, uh against, for example, those companies like OpenAI and you know the similar companies where uh people may have uh you know unfortunately and sadly you know lost their lives and committed suicide because uh you know kind of the environment was not safeguarded for this type of applications, you know. Um sometimes you may get some suggestions that off you know of the mark, that if you are like in a vulnerable position, you and if you have like some mental issues or you know, uh you know, you could end up in very uh you know kind of sinister type. So going forward, that's all one of the themes I see the sus the societal clash will continue because on one hand we have tech companies pushing for AI and you know sometimes maybe pushing AI um too soon without enough and proper and rigorous uh you know kind of mechanism and testing around it. And you know, we have the society who could be impacted. So that is also a trend to be uh aware of. So so it's about a trade-off, you know, you have to deal with it, you know, don't go blind, you know, side that you have to be aware of the consequences and the the risk uh attached to AI.

Controlled Knowledge Bases Over Open Web

A Boardroom-Ready Principle

SPEAKER_00 32:06

Um, another one is uh before you continue, I just wanted to jump in on that because you are you're right on on some some of the pulse of what we're seeing, especially if we look at what happened even in in social media, uh with you know applications like Facebook, you know, Instagram, others, LinkedIn, things of that nature, and how it um it it works. Obviously, the the platform itself is fine, but the impact on society as a whole, and and you're starting to see like what is the culture of uh of our of our human world like and how would they utilize these tools uh that you may or may not have foreseen if they would you know utilize it this way, uh, but they are. Uh and some of that is unpredictable. I think human behavior could be some of the most unpredictable things that are out there. How do you safeguard that? How do you put disclaimers potentially there? Uh just like you know, it used to have it on a pack of cigarettes, you know, you're like, hey, just so you know, you smoke these things, you're gonna get cancer. I I don't know what that would look like from an AI perspective with some of the platforms uh as they come out because it's so new. We have our laws are just behind the stuff is moving so so rapidly, it just can't keep up uh with with all the things that are happening. Um but I am starting to see some things come out of this, meaning you can't like in an Egyptian model, instead of going out to the internet, so so to speak, for all its answers, it can use a controlled database, knowledge space within your company, let's say, which is like an old world, old world, let's say called intranet. Like, hey, we have a controlled environment, and you give safeguards around your AI model saying, Hey, you only get answers from this tool. Now, I don't know if it'll reinterpret or things like that. I'm not sure how that will work, but we have to get a you know control of our our our our robots, our bots, and things like that. Here's here's my question because we have to wrap up, but here's the question. I I want to make sure that you I ask you this, I think it's important. If you could give one boardroom ready, like operating principle for the authentic era, something that leaders can apply right now, like immediately, what in your opinion, what would that be and why?

HR Data As The New Backbone

Meaning, Motivation, And Upskilling

SPEAKER_01 34:44

That's a great question. So I think for yeah, let's assume we tackle all the issues and you know uh we can mitigate the risk uh you know linked to agenting AI. But one big component as a leader, so if you have if you want to um have like you know AI agents in your uh business, you know, doing uh some of the stuff, uh, you know, uh, you know, kind of admin um you know procedures and so on, uh businesses uh in your enterprise. So uh one crit so one area that like businesses have somehow neglected is HR data, because HR data you only you know deal with it uh you know, I don't know, if you are dealing with uh paying salaries for people or you know, or pension or you know, redundancies and so on. But now the uh HR data is going to be very, very crucial for agenting AI, because if you are going to implement agents to do the work that is being done by some you know some of your staff, you need to know kind of uh the workflow of the the you know the the jobs that are being done by your people. So you say, for example, you know, person A or John do this, and once they done it, they pass it to Susan, and Susan done this. So all this type of data you could find that in the HR departments, which tends to be neglected because you never think of it like that way. You usually think about the money or you know the salaries when you think about uh HR data. So you need kind of like this type of data to make it work you know in the enterprise. If you want, you know, have to have uh AI agents uh, you know, kind of to do some of the work. Another aspect, so let's assume it's successful and you mapped the tasks and then you have successful uh you know AI agents and you're monitoring them and you're keeping an eye on their performance and so on. Um, that is the human element that comes into it. So if as a person, well as a staff, I used to do a certain job, but then then I see that uh maybe let's say 50% or 25% of my job is doing being done by an AI agent, that could create um a lot of meaningful, because then I may feel less meaningful for the job and for the company. So as a leader, you need to find ways to keep yourself motivated overall because usually people invest their effort and time if they feel been uh their work is being meaningful for the company, if their work is being appreciated. So you have to think about that psychology and the human kind of like a AI agent relationship in terms of the performance of the humans.

SPEAKER_00 37:48

Sure, sure, sure. That's a great point, what you just brought up. Didn't even think about that, but you're you're right. Like, how do you fill that vacuum that's there, that sense of meaning? You human beings have a sense of meaning and purpose and things of that nature, and we don't want to just take that away without without understanding. If we take something away, we gotta give something back. And what is that going to be on the other side of the ball? I think this is important knowledge that we need to consider as we look at what can be automated, what can an agent actually do? Um, but at the end of the day, what is the outcome? As you said a little earlier. What is the net net of doing this? And what does that look like? You know, not only financially, you know, technically, um, but from in it operationally, but the human component is is so so important. I always say, well, you're gonna cut off your nose in spite of your face, kind of thing. Like, you know what bots don't buy from bots, and you know, you know, it's kind of like all right, you know, you can't escape the value in that. I think that's very, very important.

Equity, Impact, And Education

SPEAKER_01 38:57

I would if I may add, uh, one point is you really have to think about the upscaling of your stuff. Yeah, so you cannot just let them uh you know like that. And then in that, uh, so I don't know about the statistics of the US because obviously I'm based in the UK. So even in terms of the uh impact on the workforce, there is a disparity between female and uh male. So there was a study about the UK, but in the financial services, and what they found in the financial services, they looked at uh repetitive, mundane type of work. You know, we have to keep repeating and repeating, you know, the same stuff, boring uh, you know, stuff, the kind of stuff that could be done by agentic AI. And what they found is that most of those roles are being done by women, because most of the roles they used to be maybe part time roles, you know, kind of like you know, flexible roles. So even in terms of the impact, there will be a disparity in terms of how we Women and men will be impacted, and you you know, maybe some also some ethnicity into it. So you have you also have to take that into account.

SPEAKER_00 40:09

Yeah.

SPEAKER_01 40:10

It's not equitable uh opportunities and it's not equitable impacts.

Closing And How To Connect

SPEAKER_00 40:15

We have to look at the whole thing because there are definitely a lot of impacts around that as you change roles, job roles, responsibilities, and titles, and the upskilling part of that. Um, I truly believe that that we are we it will cause us to have to come upscale. We've got to be more critical thinkers, analytical thinkers, uh things of that nature in order to bring value to to the business and lifestyle that you have. And that goes all the way from you know K through 12 all the way up. Um that we because this type of, I think, technological shift is is it's a paradigm shift that's affecting everything from the very beginnings of learning all the way throughout the lifecycle of retirement uh for certain people because of how work is getting done. How is it being done? And it you can probably do it quicker, better, faster, but then what is the impact of all of that? Um, so we got to really think through that. I want to thank you for sharing your thoughts and your your insights. And I know the audience is like, I want to talk to her more. Um, how can they get in touch with you? So they first of all tell us a little bit more about the business that you have and how we get in touch with you so we can take advantage of that.

SPEAKER_01 41:36

So, first of all, Grand, thanks for the invite. I really enjoyed uh the discussion and I'm happy to come back again if uh you know you you want me to address maybe some uh other topics. Uh so I currently lead the Mind Centers Global, which is the boutique AI management consultancy. And what I do and my team is basically helping businesses apply AI. So whether it's uh just developing an AI strategy or uh you know developing AI tools for them, but also you know, um advising on AR startups and you know in which which companies to invest uh in. Um so I am on LinkedIn, very happy. You know, if uh you know some of your audience want to connect with me, they can find me in the LinkedIn under the name of Dr. Jimila Mimar. And uh I can if they want to look at my company is us on LinkedIn, and uh my website is uh www.mindsenses.co.uk.

SPEAKER_00 42:34

Excellent, excellent, excellent. I want to thank you and everybody over in the in the UK. You're doing excellent work. This is exactly what we want to hear about. And the fact that you are leading this charge and you are the CEO of this organization, and you're bringing the top of mind information out. I really, really appreciate you being on the show and that your entire audience can see all the episodes of Follow the Brand. They can do so at five star BDM. That is the number five, that is Star S T A R, that's B for brand, D for Development, and for Masters.com. And I want to thank you again for being on the show, and we will definitely have you back.

SPEAKER_01 43:16

Thanks for having me.

SPEAKER_00 43:17

You're welcome.