The Coherent Business Podcast
The Coherent Business Podcast is for leaders, thinkers, and builders who believe business can be more than just efficient—it can be whole, human, and meaningful. Hosted by Aram DiGennaro, each episode invites reflective practitioners into open-ended conversations at the intersection of virtue and utility, structure and soul, theory and practice. Together, we wrestle with the fragmented conceptualizations of modern enterprise and explore how to design organizations that are not only effective, but coherent—places where purpose, people, and performance align. If you're searching for post-reductionist answers to real-world business problems, you're in the right place.
The Coherent Business Podcast
Adeel Mohammed on Governance, Bias, and Keeping Humans in the Loop
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In this episode of the Coherent Business Podcast, host Aram DiGennaro talks with Adeel Mohammed, a cybersecurity and AI governance consultant working across multiple countries to help organizations navigate the growing role of AI in decision-making. Opening with a discussion of Neal Stephenson's Snow Crash and its eerily prescient warnings about humans unknowingly absorbing the "unquestioned logic of machines," Adeel and Aram dig into a core distinction: data hygiene versus decision hygiene. While most organizations have spent decades ensuring clean, well-governed data, few have asked how their decisions actually form, what assumptions shape them, and whether humans remain conscious participants in the process.
The conversation covers how AI systems can quietly inherit and reproduce historical biases (in hiring, sales optimization, executive communication) not through malice but through pattern-reinforcement, and why "human in the loop" has to mean more than a rubber stamp. Adeel argues for organizational coherence and governance as guardrails, not to slow AI down, but to preserve intentionality while moving fast. He shares practical steps for building a shared language around decision-making, and closes with a challenge to leaders: the organizations that win won't be the ones using the most AI, but the ones that stay coherent while using it.
Key Topics: AI governance, decision hygiene vs. data hygiene, cognitive bias at organizational scale, human-in-the-loop decision-making, coherence and governance as guardrails, balancing technical practice with leadership and sales.
Resources:
Adeel's Links:
Website: https://www.adeel.solutions/
Company Linkedin: https://www.linkedin.com/company/adeel-solutions/
Aram's Links:
Linkedin: https://www.linkedin.com/in/aram-digennaro/
Coherent Business Project Website: https://coherentbusinessproject.com/ — For leaders, thinkers, and builders who believe business can be more than just efficient — it can be whole, human, and meaningful.
Organizations that succeed don't necessarily be the ones with the most AI. They will be the one who remains coherent while using it.
SPEAKER_02Good morning, everybody. Today's guest is Adil Mohamed. Adil is a cybersecurity and AI governance consultant who is really doing some innovative things in multiple countries around the world on AI governance. And we're here today to extend some of the conversations that we had at Virginia Council of CEOs a couple weeks ago, where we're talking about the new leadership challenges as AI becomes a part not only of our technology stack, but actually of our decision stack. So Adil, welcome to the show. Thank you, Adam. Thank you for having me.
SPEAKER_00It's a pleasure to be here.
SPEAKER_02Yeah. So I want to start out by talking about Snow Crash by Neil Stevenson, 1992 book, but he talks about virtual reality. He talks about avatars and uses that word. He describes QR codes and he weaves in everything from ancient Babylonian mysticism to speaking in tongues into quite a novel. I think you've read it as well. What did he understand 30 years ago that is even more relevant today, literally today in 2026?
SPEAKER_01Yeah, thank you for bringing the snow crash reference. What I feel like is that this data is being used as a very brilliant shorthand for the dangers of human unknowingly absorbing and acting on the unquestioned logics of machines, because we don't know what's behind it, as we mentioned about virtual reality. And again, this is unanswered, unknowingly, you know, as a logic of machines. So that's an amazing fiction novel. Yes, you were right. This I think 1992, but still it's uh it's an evergreen one.
SPEAKER_02Yeah, no, that's a great way to put it because he talks about how these data can get corrupted, but really it's not just data, it's data processing can get corrupted and create these scripts that hijack people's ability to think for themselves and their ability to make decisions. And those scripts can be religious in scope or religious in appearance. That's kind of the foil that he used. What he's gesturing at is really much deeper that anytime we have something that's dealing with how we process data, it's changing the way that we function.
SPEAKER_01True, true. 100% right. That's the beauty of that novel that highlights very early on about these concerns and objectives which we are discussing today. So amazing novel, I must say.
SPEAKER_02So for you, what's the through line, or what are some through lines to your career and your work? You've worked with networks and security, AI governance, you've done consulting, you do speaking. What holds this all together?
SPEAKER_01Yeah, so you know, before recording this button, we were discussing about that, you know, for a layman term. Basically, I am the one who is spending years studying how systems behave under pressure. I started from networking that taught me about connectivity, then the cybersecurity taught me about the fragility, and then the AI governance taught me the cognition part, right? The organizations are increasingly operate through this invisible systems now, which is now in effect, like especially after AI, the prioritization, the communication, the judgment part of it, and what what this all means. So I'm in this space of we are trying to secure organizations and individuals.
SPEAKER_02Okay, and you're saying that the invisible systems have a role, and the visible systems are the things that we normally think of technology have a role, and you're trying to weave those together.
SPEAKER_01Exactly, exactly. So the idea is that in this ever increasingly AI role, where we you have these areas which I mentioned about prioritization, communication, judgment, culture, and especially the meaning, what we are seeing is that firms have increasingly operating through these invisible systems, which they don't even know. And if they don't know, how they can secure it, how they can restrict it, how they can change the biasness of that system, because again, they are invisible.
SPEAKER_02So that sounds like a good idea, and it sounds increasingly important. And actually, I I want to talk just a little bit about why that becomes important. You know, we talked about the difference between data hygiene and decision hygiene for decades. We've put a lot of energy into making sure that data gets stored, error corrected, protected, garbage in, garbage out, right? It's one of the oldest technology mantras. So we've talked a lot about data hygiene, and now the question is becoming decision hygiene or meaning hygiene or context hygiene. How do we pull those things together? What do you see as some of the cutting edges of the way people are talking about that and the way people are addressing it in real world contexts?
SPEAKER_00Lovely, Adam.
SPEAKER_01I must need to repeat this sentence so that the audience can digest this. That you know, most organizations are obvious with this data hygiene part, as we are saying, data is the new oil, right? So very few think about this decision hygiene, which you are amazingly right timing to highlight this point. So, near to me, I must say that data hygiene was you know about clean data, quality imports, again governance and structure, right? Whereas the decision hygiene, near to me is the understanding how this decision emerged, what shaped them, and what assumptions exist before coming to this decision, and whether humans still understand the logic or not. So organizations audit data pipelines, but mostly nobody audits the formation of that judgment. Sure, sure. So there are some practical examples also, like we are seeing everywhere from our hiring recommendations to AI assistant strategies to AI generated board summaries, right? So executive communications and like these are they are they are everywhere. I'm not saying that you know AI is not correct. The issue isn't whether AI is correct or not, the issue is whether humans remain conscious participants in this decision making or not.
SPEAKER_02Okay, I I think that's really key. Do humans remain conscious decision-making participants in the decision or not? One of the things that I'm reminded of is Steve Jobs when he famously disliked presentations. So people would come in with something shiny to show to them, or they'd have a slide deck and he'd just tell them, get rid of that, tell me what you think, tell me what the decision is. Because if you can't explain it to me, if there isn't a human link with the data or with the drafts or whatever that you're showing, then I'm not interested, or I don't trust it in the same way. I feel like we're we're just getting further and further away from that, where everybody comes with a with an output from Claude or Copilot, and man, all the information is in there, all the analysis in there. And so you have these really useful tools, but we have less and less human connection and trust with what those are. But I'm not sure what happens next because we're not going to stop using those tools. Like I promise you, because we have access to this, we're going to keep using it. So, how can we use it just a little better, just a little more wisely?
SPEAKER_01Yeah, so there's a very diluted QC gen ownership in this era. You perfectly highlighted that if you want to reduce the noise, sometimes, not always, but sometimes these presentations are also a part of noise, right? That's why you famously quoted Steve Jobs here that he's only interested in, you know, what you can bring to the table, right? If you can just answer it. There's so much noise that I am also starting to see these type of documents. Like I give you an example that I was sitting with a client and similar, you know, fancy powerpoints and all that stuff. And in the middle of the discussion, only the party A, who was a decision maker, asks the contractor, the supplier, can you just answer what you are bringing to the table? And that was an amazing moment because we just paused that discussion for 20 minutes, and the guy came back after 20 minutes. Just one pager PDF with six questions, how he's answering. And what is the question that, you know, what I can bring to the table, how I can generate money, actual business use cases. So that was an amazing eye-opener for me. That yes, you know, people are frustrated by these all AI-based LinkedIn posts and R2 replies and those dashes. And the dashes, that's right. We are seeing them everywhere, you know. Just to include this main point of this diluted decision making, that earlier it used to be one person owned decisions, right? Now we have so much influences. We have these dashboards, these summaries, these co-pirals, automations, AI-generated insights, right? You call it as a marketing term, any any words, but workflows and so many tools all are shaping our outcomes. And decision making is becoming more distributed across these systems and very fastly. Plus, the accountability structures are also evolving.
SPEAKER_02Tell me how accountability structures are evolving and tell me if you see any difference in the size of the organization. I mean, one of the things that I've always noticed is the bigger the organization, the more formal processes you have in place, the more formal accountability there is. But at the same time, responsibility and decision making and accountability become so complex that the actual control that you have gets smaller and smaller. So, how does this play out across different sizes of organizations?
SPEAKER_01Yeah, so basically, now we need to go back to drawing boards and we need to ask some hard questions. And like I always say about the use cases. So, in terms of the decision makings, the questions can be that, you know, which combination of systems and assumptions and humans shape this decision. Or number two, who made this decision? So AI is not removing humans from decision making, it's basically diluting where authorship actually lies or or begins. So the decisions are again not made by one person. And the accountability model of organizations is still human-centric, but the decision-making process has all already become, I must say, system-centric.
SPEAKER_02So what I hear you saying is that even before AI, decision-making structures can tend to be driven by the way that the system functions, which is complicated. Sometimes it's actually emergent or complex. And so then what how does AI interact with that? Like if we recognize that as well, that companies do what they do partly by design and partly by magic or happenstance or personality or weather or whatever, we recognize that that's the case. When AI now becomes a part of that decision-making process, how does that change things?
SPEAKER_01So there is a very famous code that you are the average of five people you spend the most time with. So I'm seeing that the same thing is happening in terms of communication, in terms of, frankly speaking, from presentations to email writing and everything. So it's tough to handle that. But I need to highlight one thing here for all our audiences that try to get into more training and awareness part, which we are not highlighting usually training and awareness. Exactly. So see, you hired an employer. You will not ask him to do exactly what is in your mind from the day first. You you will give him some 30 days, 20 days, whatever, as per organizations as a learning curve and adjust him to that thing. So now, even if you are taking help of those AI systems, we are not training them. Okay, hang on.
SPEAKER_02Let me see if I'm getting where you're going. Like, so you're saying when I hire an employee, I don't expect them to go straight to work. I give them training. It's specific to me, their boss, specific to the company, specific to their role. So I don't just assume, hey, you know a bunch of stuff, get to work. You need to be doing the same thing with any kind of exposure that we have to AI systems. Is that what you're where you're going? 100%, 100%.
SPEAKER_01So right now, how we do that. Exactly. So the practical use case is that you should have a foundational, scripted, properly defined PDF, which you are using as a master prompt with every other channel which you are using for your LLMs or whatever chatbots. Agreed. Yeah, totally agreed. Right? So so he should know that your preference is to write especially from S, not from E. Yeah, yeah. He should know that you are not using E. So, like even if you upload some like 15, 20, or you know, hundred emails of how you write, if he knows your pattern, if he knows your choices, it will take good decisions. AI is is all about learning from the past. Like, might be a human employer can miss minor details, but AI will not.
SPEAKER_02Yeah, no, I I mean I did this recently. I started writing a book and I wanted to use AI to like fill in, like, I'm gonna do the writing, but it's gonna help me rewrite paragraphs, help me write, you know, transition sentences, all that sort of thing. So the first thing I did is I wrote a full brief, had it browse who I am and the work that I've done, uploaded hundreds of pages of transcripts and writing that I've done, had it analyze that and create a voice guide. Very detailed. So now when it writes a sentence, it knows how I use metaphors, how long my sentences are, where I put comments, what words I don't use, except ironically, very detailed. It seems like a lot of work, but if you're going to have an agent that helps make decisions in your company, at least do that much work, right? At least give them half a day of training so they know how not to screw up your company.
SPEAKER_01By the way, uh Adam, just need to highlight that I'm a big fan of this AI governance advocation. So humans should always be in the loop. We are talking about more passively, not actively, like AI is not directly taking decisions, but passively, why? Because again, due to those dashboards and workflows and you know writing styles where all the companies are trying to look the same after this chat GPT era. They'll they're losing some of them, their their character. But but we are talking about passively. So 100% right that if you have that master prompt for for your AI, and people are switching AI. Like at least you know, stick to like again, if we can depict it to the employer that you don't change employer every 15 days and you don't let him go, let her go without training. Right, right.
SPEAKER_02Okay, yeah, so if it's not doing what you want, don't fire it immediately. First train it and see if its performance improves.
SPEAKER_00100%, right?
SPEAKER_02Yeah, that's a very good practical step that I think companies can take to help keep their decisions on the rails. What are other practical steps that you'd like people to understand?
SPEAKER_01I will highlight here about the AI and lease system and plus the sovereignty part, where again, going back to the same snow crash connection also comes in. That is the psychological effect, but very grounded. So AI systems are increasingly shaping what feels reasonable, valuable, or true inside the organizations. Again, not forcefully but subtly. This explains the summarization, influencing attention or recommendation. By scale, I must say that this becomes a very invisible belief system which is happening right now. And from the sovereignty angle, organizations are increasingly depend on whether a company can still think independently or not, what we are seeing right now. That are these decisions internally formed or they are coherent from any generalized machine logic. That's such an interesting problem.
SPEAKER_02I mean, because on the one hand, when everyone else has access to more information and more information processing, we have to also have access to that or we will fall behind. At the same time, if we don't maintain our distinctives, then what is keeping us ahead will also fall apart. And I don't think there's an obvious solution to that. I think it's just one of the realities we have to learn to live with and keep in mind as we're leading in 2026 and following. I mean, it's funny a deal. A year ago, were these things happening? Or what is the ramp up period that you see of how these things are coming to the fore as real live today's leadership concerns? How was it a year ago and how is it going to be a year from now?
SPEAKER_01Before AI companies were moving very slowly, usually, you know, falling behind because the information was moving slowly. But now in this era, it's like 2x and 5x. So now I must say that AI is reshapes this information before even humans have encountered it. And organizations are used to compete on operational efficiency. Now it's all about that a company is using AI versus a company is not using AI versus a company which is burning more tokens. That's what the comparison is nowadays, and and that's that's increasing every single day because now they are competing on coherence, judgment, alignment, and the ability to think independently, which is like we often talk about keeping up with AI, right? But the organizations are really asked that can our culture evolve at the same speed as our technology or not?
SPEAKER_02Yeah. Well, I I think one of the competitive pressures that's playing out is can you use AI as effectively as your competitors? But as we all approach the frontier where AI is shaping our decisions, then what determines who wins is more and more a human question, just like you're saying. It's your culture, it's your coherence, it's your judgment, it's your ability to think differently. So you see where I'm saying it's almost ironic that as technology improves, technology becomes less and less of a factor in some of these head-to-head races.
SPEAKER_01Again, the problem is that it's a rush, right? And it's coming from the board side that you have to be with AI. We need that AI wrapper around our company, AI driven, AI first. But try to understand that AI is not like all other softwares. Correct. We are discussing this in a practical and memorable way, that most companies are deploying this like a software. Please don't treat like that because it's behaving like an employer, right? That's the mind shift I must say. That if if people understand it in a in that way, we will not treat AI as another software and not a permanent like requirement that we have to put it from the board side and original practical use cases. And that's where the this coherence problem also comes, which you were mentioning.
SPEAKER_02Yeah, no, I'm glad you said that, Adil, because I think it is important to point out the differences between how AI functions differently from other technologies. So, you know, first we have books and data, and then we get ways to access that data faster. Then we have software, we get ways to process that information faster. And then we get AI, and the function of that is completely different.
SPEAKER_01To add on this coherence problem, I need to add that there are like the main idea is that there are different teams, different prompts, different tools, different assumptions and organization goals. Organizations have become operationally effective while this cognitively fragmented world. And then, you know, we have marketing, for example, uses one logic and then other department finance is using other AI is scaling, whatever coherence already exists, but whatever fragmentation already exists, that's the question.
SPEAKER_02Yeah, yeah, yeah. Well, I mean, it's like one of the old management principles is if you add people to an inefficient process, it gets more inefficient. If you add people to an efficient process, you can scale. And so if you add automation to a process that is incoherent, that no one understands, that doesn't use judgment at the right places, then you're just making bad decisions faster. So you mentioned how AI is actually changing how people write and communicate. Is this sort of a trivial problem? Is it just a new meme, or do you think it's an early sign of a snow crash problem?
SPEAKER_01Yeah, so you know, we are already seeing this that as I mentioned earlier, that organizations are beginning to sound like the same from LinkedIn posts to Intel messages, corporate tools. Frankly speaking, you know, sometimes I I'm trying to write that, you know, very influential person from a head of the company. I don't directly attacking on that person, but might be he's using some PR company who is who is writing their LinkedIn post and replies. Sometimes I I really try to explain in the comments, but I don't want to get into the fight. But you know, everything is like becoming so polished and optimized and emotionally flattened, right? Yeah, yeah. So this is AI is increasing this fluency, but fluency is not the same as originality.
SPEAKER_02Right, right. Yeah, I think we're gonna have some interesting realignments of how our attention works in the next year because we want fluency, we want efficiency, we want kind of something that'll quickly take us to the frontier of what is benchmark or best practice or widely available. And then as that becomes the norm, not the exception, but the norm, we'll start wanting the opposite, and we'll start wanting, I don't know, people who misspell their words and people who get angry and barroom fights and that kind of human edge is going to start to occupy an interesting space. I don't know what it is yet, but I can see it coming.
SPEAKER_01Yes, I remember we are entering to a singularity. So I'm not anti-AI, but this is scary. You know, it's just like sales that everybody wants to buy, but if you bombard me with salesy messages, I might not reply you.
SPEAKER_02Balance, you know. Yes, yes. This is interesting what you said about the singularity. You know, years ago I asked a friend of mine, what'll happen when we reach the singularity? And he said, absolutely nothing, because information fundamentally doesn't change who people are or where they're going. Purpose changes who people are and where they're going. And I feel like we're seeing a little glimpse of that now, that we're quickly approaching singularity-like symptoms, where all of this information analysis and creation of software and processing can happen almost instantaneously by almost anyone, but it fundamentally leaves us in the mess in the middle, and we have to go back to the original question: who are you? Where are you going?
SPEAKER_01Why? To to what can humans do, what remains unique, what is meaningful for another human to become. Who got that? I'm sure you know this guy, this was the CEO of Google Ads who created some of the models. So he said that in 2030, his prediction is like, you know, very much near, but he said that in 2030, everybody will be at the beach. Either AI has taken all the jobs or AI has finished everything.
SPEAKER_02In either case, we are at the beach. One of the things we talked about is treating AI like Employees that you can train. How far can we take that analogy? Or how far can we take that technique? And where might it break down?
SPEAKER_01Yeah, I discussed this part that again, most companies are treating this like a software, whereas operationally it's it's more behaving like an employee. So please go for all the all the labels of onboarding and exiting an employee similar to AI. It needs onboarding, it needs context, it needs cultural understanding that depends on company to company. It needs boundaries where we as governance guys come into place and it needs supervision also. And boundaries are very much. We will discuss about AI governance, but that is really required. I give you an example, there was uh like AI scientist who was using open claw in in the basic instructions, she she said that don't do anything without my permission. And you know, like once you are chatting a lot, AI tried to summarize everything into for his own knowledge, right? So basically, during summarization, it just it just did not add that line. And once it skipped, it was taking decisions like anything. And when she was arguing with AI that why are you taking decisions, the AI said that you never say that I need to stop before taking the decision. So it's like a generic AI system that reflects uh generic internet cognition. And if you want very coherent outputs related to your organizations, you have to train it to your organization's worldview.
SPEAKER_02Well, and that's how we treat employees as well, right? We give them training, but then we also involve them in very high context situations, rituals, stories, and we keep reminding them over and over. You know, you're here to serve the customer. Our core value here at this establishment is cleanliness, because if the restaurant isn't clean, people don't want to eat here. You know, we remind people of this over and over. And so it's not surprising that AI also needs that. And we're getting used to how to manage it.
SPEAKER_01Yeah, you're 100% right. You are giving the non-sale, the political thing, the just thing, the history. And you know, these are some unwritten rules, and these are similarly applicable for AI.
SPEAKER_02Yeah. And we actually, humans also we have a different mechanism for it, but we also compress the chat from time to time. We scrap the stuff that doesn't seem relevant, that isn't salient, that we haven't heard for a while. The boss said it when he hired me, but I haven't seen anybody sweep the floor since he must not care. Right. So we do the same thing. We adjust it according to our own biases. And this is another thing I'm wondering if you can weigh in on, because to me, the more an AI makes decisions for me or is involved in my decision, the more I'm exposed to a set of intentions that isn't mine. I didn't come up with it. Who came up with it? It was a combination of how math and data works, engineers, and a really, really big tech company that came up with the pattern of patterns that's now driving my decisions. Is there any danger of a disconnect, or is really this flattening danger, this averaging danger, sort of the main thing that you see surfacing?
SPEAKER_01Yeah, so I wrote the book on this topic, AI and Us, the ethical choices. So biases is is is one of the biggest things because in the AI era, it's no longer just about race, gender, or fairness, right? The deeper issue is that again, going back to this cognitive thing, that bias at organizational scale. AI system don't only inherit these human biases, but they inherit the historical assumptions, the optimization prioritizations, and institutional worldview, which you mentioned about these, you know, tech giants, and then the dominant pattern embedded in their data. So it's not always malicious, but often it is invisible normalization, which we need to keep in effect. And if organizations are slowly adopting themselves around these biases systems, it's a red sag, you know, because they are not noticing about this.
SPEAKER_02So, what else would you name as practical things that companies can do? We've talked about training AI like employees, we talked about watching and being aware of how decisions are made. What else do you recommend when you're working with companies?
SPEAKER_01So I mentioned about this number one is the coherence part that is really much required, and secondly, the governance. So, governance for your audience is it's like guardrails for your AI, which is really, really required. And we are not talking about slowing AI down, but it's it's about preserving the intentionality while moving fast. Okay, so every playground has a referee, but if there is zero referee, that's a problem. If there are 15 referees, that's also a problem. So it needs to be like in a in a balance. So practical steps, I must say that we should define organizations' principles, we should define human override points. It should not or it should override humans. Um, then we should define what is acceptable AI rules and then align them to the teams on this usage policies and philosophy. And lastly, we need to create this shared language around this decision making.
SPEAKER_02I think the idea of shared language around decision making is really interesting. Can you give an example of what that might look like? How we would talk about some of these issues?
SPEAKER_01Yeah, so imagine that you hired a guy which is which has turned on some historical high performances in the company. Now, if if if the historical leadership roles were dominated by one personality type or one background or one communication style, the AI will quietly start reproducing the same profile again and again, right? Sure, sure. Not because it hates others, but because it makes that historical pattern for that optimal truth. Similarly, in the sense of if there's a sales AI optimized conversion rate or something, which can slowly push teams towards manipulative decisions or communication styles, because again, the system is rewarding that engagement. It's the optimization biasness, which we have already. Yeah, yeah, yeah.
SPEAKER_02So what you're recommending is to try to make these things more visible so people are seeing them, they're talking about them, they know how decisions are made, and then they can be tweaked as biases emerge. True, true. So it seems like the danger also on the opposite end. Do you run into people who are just anti-AI? And is that a danger as well?
SPEAKER_01So, you know, some sometimes people are literally anti-AI, but frankly speaking, I'm extremely pro-AI. I think AI is one of the most powerful tools humans have ever created. Question is not in which direction you are using the AI, the question is that we should remain conscious of its use, right? We need to be why I'm saying again and again, human in the loop, human in the loop, human in the loop. That is really required. Again, from the governance point of view, also, that you know, we we struggle, it develops judgment, and then the reflection develops wisdom, and then the synthesis basically develops these leaderships, right? So that is really required. I'm seeing NDAI people, but I feel that things will be good if we are trying to, you know, control it in a pro-governance way.
SPEAKER_02Yeah, to me that's really key. And one of the things that I see rolling out over the next couple of years is that the surface area of technology becomes vastly larger. And like you say, the potential for that to be a good thing is enormous. At the same time, in order for it to be a good thing, the surface area of our human abilities and engagement and wisdom and consciousness also has to get a lot more surface area. Otherwise, we end up on the beach in the wrong sense.
SPEAKER_00And we are not here to, you know, eliminate every human development process in the pursuit of optimization. Right.
SPEAKER_02So you're working on a doctorate while you're also running a company, doing some speaking, taking time to be on this podcast. What are you trying to accomplish by holding together that academic and the technical practice?
SPEAKER_01Adam, I was more towards the sales cycle, as you know, that once you are in a in a leadership roles or you are handling your own firm, you are consulting for your own business. You are more into the sales side of it, less than consulting, less than technical. So that's why for my own good, I started doing research, I started my tock tit, I started writing books, and I started doing these podcasts and keynote statements and all. So that I'm I'm in in that circle, I'm not forgetting technical stuff, and I'm I'm practicing those also by publishing articles, by publishing newsletters, or by publishing journals. So that's why I'm trying to handle this very carefully. And you know, if I will go all in on sales side, then this technical stuff will be rusted.
SPEAKER_02It seems like you're trying to hold together some complexities that a lot of people maybe aren't even seeing yet. So I hope that together we're able to get the word out and hopefully help some people avoid some of the worst pitfalls. How would you want people to find out about you in some of your work? What would you point them to?
SPEAKER_01Thank you, Aram. I remember to adil.solutions, just a-del-e-l.solutions, S O L U T I O N S. And from there they can find me on LinkedIn and other ways to communicate.
SPEAKER_02Okay. Any last message you want to get out to our audience?
SPEAKER_01Yeah, so lastly I I must say that the organizations that succeed don't necessarily be the ones with the most AI. They will be the one who remains coherent while using it.
SPEAKER_02We've been speaking with Adil Mohammed about AI governance, decision hygiene, and what's coming next. Thank you, Adil. So kind of you. Thank you, Adam, for having me.