Ep 292: Is AI Transformative or Have We Not Fully Grasped it Yet?

Grasping Generative AI

In a fast-evolving technological landscape, Generative Artificial Intelligence (AI) emerges as a transformative force. However, despite its acknowledged potential, the actual implementation remains limited. Its impact is as prominent and diverse as the electric revolution, not yet ubiquitous and varies as the AI technology evolves at different rates.

Global Implications: Potential US AI Restrictions & Investments in AI

Current events spotlight potential US AI restrictions on China's access to advanced chip technology central to AI. Alongside, the global AI tech space sees active investment, with prominent startups raising significant funding to compete on a global scale, undeterred by any legislative confrontations or deviations from their original mission.


The Democratization of AI: Amplified Accessibility

Generative AI's ubiquity and understandability are transforming the accessibility of AI. Despite being part of a hype cycle, where excitement often leads to disillusionment, AI, like any evolving technology, makes incremental advancements that support widespread adoption.


Workforce Upskilling: A Key to AI Implementation

The implementation of generative AI, however, presents formidable challenges. These include a shortage of critical engineering resources and the need to upskill the existing workforce, not just in resources, but also in enhancing knowledge workers' abilities.


The Gap Between Priority and Implementation

Unfortunately, a disconnect exists between generative AI's acknowledged importance and its adoption. Differing levels of customer maturity and in-house skill sets contribute to this gap. The way forward hinges on recognizing and addressing these challenges.


Driving AI Adoption: A Strategic Approach

Successfully driving AI adoption within organizations necessitates an initial core group of committed individuals. Consistent top-down support, celebrating small wins, and employing the "square root principle" in AI roll-out can effectively accelerate the transformation.


Addressing Communication and Knowledge-Learning Strategy

Potential hurdles to AI adoption include the lack of an effective communication strategy during the transitional stage and the absence of a robust knowledge-learning strategy.


AI Pervasiveness in Consumer-Oriented Platforms

AI's transformative effect is most visible in consumer-oriented platforms such as Netflix and Amazon. Its influence spills over into different industries and will continue to do so over the next 5 to 10 years.


Building Trust in AI: An Imperative

Trust in AI is crucial, infused with transparency, explainability, fairness, reliability, safety, and data privacy concerns. Awareness about legislative issues and evolving case law can aid in navigating these challenges.


Stricter Buyer’s Remorse and Mindset Shift

Despite setbacks, companies continue to grapple with AI implementation, dealing with the complexities of buyer's remorse and the need for a cultural and mindset shift. The importance of limited initial support and the necessity of small wins cannot be overstated in driving AI transformation within organizations.

The transformation to AI is a complex, yet worthwhile task. As with the transition to electricity, the journey may be fraught with challenges, but the full impact of AI adoption is set to unfold in the coming years. Hence, it is time to fully embrace AI's potential and shape a future where everyday AI isn't just possible - it's instrumental.

Topics Covered in This Episode

1. AI and its impact on different industries
2. Significance and implementation of generative AI
3. Strategies for successful AI adoption within organizations
4. Challenges of AI implementation in organizations


Podcast Transcript

Jordan Wilson [00:00:16]:
When we talk about artificial intelligence, it's always people talking about like, oh, it's it's real. It's fake. It's it's hype. Right? But is it actually real? Like, have we realized what generative AI can do, or have we not even begun to understand what it's capable of? So this is something I always think about a lot and, you know, I'm excited for today's conversation because that's what we're gonna be tackling, whether AI is actually a transformative technology or if we haven't even really grasped it yet. So before we get started, just as a reminder, if you're listening on the podcast, thank you as always. If you're on the livestream, get your questions in. You you know, we'd love to, engage with you all. And make sure if you are listening on the podcast, check out your show notes and go to your everydayai.com.

Jordan Wilson [00:01:08]:
Sign up for our free daily newsletter where we will be recapping today's show and sharing a whole lot more. Alright. So before we get into today's conversation, let's first start as we always do by going over what's happening in the world of AI news. So the US is considering some AI restrictions of sorts on China. So the US is considering further restrictions on China's access to advanced chip technology used in AI according to reports. So the potential restrictions would target a new transistor architecture known as gate all around, which can improve chip performance and lower power consumption. There were previous export controls on AI chips to China that had been tightened, and the potential GAA restrictions are still being determined. So it is something to keep an eye on, you know, AI and just the the GPU chips that kind of power AI globally are being treated as a resource.

Jordan Wilson [00:02:03]:
Right? And, you know, the US government is really cracking down on, exports there. Alright. Our next piece of AI news, OpenAI has dodged a lawsuit, but one that wasn't very serious if we're being honest. So, Elon Musk has dropped his lawsuit against OpenAI, the artificial intelligence startup he cofounded in 2015. So the lawsuit alleged the company had abandoned its original nonprofit mission and reserved some of its advanced AI technology for private clients. So Musk's decision to drop the lawsuit comes after a series of critical posts he made on social media against OpenAI after Apple announced their partnership. Musk's lawsuit against OpenAI has been dismissed ending that months long legal battle between the two parties. So month Musk has had accused OpenAI of pursuing profit instead of its original nonprofit mission, but the company claimed, but the company has denied those claims, obviously.

Jordan Wilson [00:03:01]:
And we talked about on the show a couple months ago. We went over it point by point and how that this lawsuit wasn't very serious and there wasn't merit in it. Last but not least, large language model maker Mistral just made a big funding splash. So the French AI startup Mistral AI has raised a funding round of €600,000,000 valuing the company at 5,800,000,000. They are competing with other large AI companies such as OpenAI, to become Europe's AI champion. So Mistral has raised that significant amount of funding in a short period of time, and it's not only, you know, obviously competing with OpenAI, but, you know, Google, Gemini, and ProfitClaud, etcetera, competing on a global scale here. So the French startup is building a large language model and has attracted the attention of major investors, including general cal General Catalyst and Microsoft. So Mistral's cofounder and CEO, Arthur Mintz, has become a notable figure in the European technology scene.

Jordan Wilson [00:03:59]:
Alright. So there's a lot more happening in the world of AI, if you didn't know. So make sure, if you haven't already, go to your everydayai.com and sign up for the free daily newsletter for more on that. But we are here to talk about AI. Is it real? Is it hype? Have we even begun to realize what it's capable of? So So it's not just me talking today. Let's go ahead and bring on our guests. There we go. I'm extremely excited to welcome to the show Jamal Khan, the head of Helix Center For Applied AI and the chief growth and innovation officer at Connection Incorporated.

Jordan Wilson [00:04:32]:
Jamal, thank you so much for joining the show.

Jamal Khan [00:04:34]:
Good morning, Joe. It's good to be on the show.

Jordan Wilson [00:04:36]:
Hey. It's great to have you, especially, man. Technical difficulties today, but, regardless, Jamal, tell us a little bit about your role at Connexion and what Connexion does for those that maybe aren't aware.

Jamal Khan [00:04:49]:
Sure. Connexion is a fortune 1,000 public company that's essentially a global solution provider. We have north of, 36,000 customers, primarily based out of, the US, but, you know, some of those customers have a global footprint as well.

Jamal Khan [00:05:06]:
Connection essentially provides a whole broad set of services, you know, solutions to our clients, and and that's essentially what we do. In terms of my role, as the head of the Helix Center for Applied AI, you know, we've been, as a company, going down this journey now for almost 5 years. A journey that in large measure began, almost as a data transformation journey, which was our own internal efforts on how we become more data oriented. And out of that process, we built a capacity that we're now scaling, to sort of help address the, you know, the challenge that our customers are bringing forth for us, which is how do they navigate their own AI and data journey? And that's the Helix Center for Applied AI. And there are a whole broad set of other areas within connection, you know, the global business is still part of my mandate as as well as the marketing organization. But the one part that really takes most of my time these days is this whole thing called artificial intelligence that that's really pulling me in for a whole broad set of recents.

Jordan Wilson [00:06:10]:
Yeah. Yeah. And and, Jamal, you you know, you just dipped, you know, just dipped your toe on, you know, what connection is and, you you know, what you all do. But as a public company that's been working in this space for a very long time and, you know, as we think about artificial intelligence and, you know, the hype cycle, is it real? Is it transformative? Maybe can you take us back a little bit and even just in your own personal experience and in your career. Right? Because AI is not new. Right? Technically, AI has been around for, like, 50 plus years. But as this generative AI kind of wave started to form, you know, 3 or 4 years ago, How do you think, kind of the the the climate changed in terms of people now who maybe didn't have a a a use case for, you know, neural networks and and deep learning, you know, traditional AI. You know, how has that just changed over the last couple of years with this, resurgence or resurgence of generative AI?

Jamal Khan [00:07:09]:
I guess it's become tangible. Right? So it's in some ways democratized access to this notion or this vague notion that was very ethereal or very academic historically to think about artificial intelligence. It's sort of democratized access to everybody through a very conversational way of interacting with, you know, large language models, LLM. I I remember, you know, I you know, Jensen, the CEO of NVIDIA used to have, or perhaps still does, these fireside chats where he would bring a small select you know, small group of execs and and sort of talk about what's happening in the ecosystem. And I used to consistently go go to those, fireside chats, and about 3 years ago or two and a half, 3 years ago, I used to say, guys, you've gotta really focus on this thing called GenAI. And we're like, okay, but what is Jensen saying? And obviously, we didn't know Jensen was providing the first, you know, you know, DGX, 100 to OpenAI, and he knew all, obviously, the insight. And he would say, guys, you've gotta focus on this GenAI thing that's coming right around the corner, and he was spot on. And so I think the reason why we've seen this amplitude, and I don't wanna use the term hype, is in large measure because it's democratized.

Jamal Khan [00:08:20]:
Become much easier. Now it's no longer the domain of, you know, mathematicians, you know, toiling on deep neural networks and underlying algorithms. It's now you can, in a very simple way, through a very simple, prompt, just ask, LLMs. And it sort of tends to give you a cognitive response, and that's made it very easy for people to understand.

Jordan Wilson [00:08:42]:
So let's let's just go ahead and and skip to the end here, Jamal, and let's just answer the question. So when we're talking about AI, and, you you know, for those that aren't, you know, familiar with, you know, technology hype cycles. Right? But it essentially says that, you know, all technology can essentially be plotted, you you know, from when it comes out and people get super excited and, oh, you you know, we've kind of reached the apex and now we're gonna, you know, go into this, you know, I guess, disillusionment or whatever it's called. You you know, kind of on the downswing.

Jamal Khan [00:09:12]:
The disillusionment. Yeah.

Jordan Wilson [00:09:13]:
Yeah. But, you know, Jamal, like, is is AI, is it actually transformative technology that's gonna, you know, continue to be more and more impactful as we go on, or can we just say, hey. It's just another dot on the hype cycle.

Jamal Khan [00:09:26]:
So it's a really, really good question, Jordan. I think it's it's a good question because it impacts and is likely to impact a whole broad set of considerations from policy to investments to company strategies. Well, for someone who's worked within this space now for 20 years, I I will not argue that it is not transformative because if you've been at something for 20 years, obviously, there's an implicit bias, and I've always believed in artificial intelligence. But I think it's it's a very difficult question to answer because artificial intelligence is not necessarily a ubiquitous technology. Right? It is very diverse. It encompasses, elements such as computer vision. It encompasses elements like large language models. There are different approaches.

Jamal Khan [00:10:12]:
There are different applications and use cases in use studies. So I think in in sort of the arc of AI's diverse ecosystem, there's certain parts of artificial intelligence that are far further along and and more tangible, and perhaps moved beyond that, trough of disillusionment into sort of providing some level of productivity as opposed to others. And so I think you're gonna see this variance in how AI as a collective field of study evolves over a period of time. So I I absolutely believe. I think it's transformative. I think there's certain parts of AI that perhaps are still in the early stages of that hype cycle, but are likely to go through, their process of evolution. But there's a a fundamental reason why we find that this AI boom is not a bust, where historically we've had these multiple booms and bust cycles in AI, and that's something to your point started almost 50 plus years ago. But when you look at processing capability, you're looking at this explosion of data, you're looking at a democratization of tool sets and algorithms, You're looking at this explosion of, you know, an underlying need to become more efficient and bring in automation.

Jamal Khan [00:11:26]:
All of those things drive in large measure, how, you know, artificial intelligence are becoming more real and practical. So I think there's a reason behind why I I believe this time around is not necessarily a bus cycle. But, again, the key point here is it's a diverse ecosystem. We're not gonna see everything evolve at the same time. You're gonna see certain parts of AI evolve much faster than others.

Jordan Wilson [00:11:48]:
Yeah. And, Jamal, you bring up, a great point there. You you know, and you're even talking about your background, you know, been in this space for, you know, 20 plus years. And, you know, I love going back and, you know, hearing this story of, you know, Jensen kind of telling everyone, you know, hey. Pay attention to this generative AI thing. You you know, my my thought is because, you know, all the studies, all the research says that, you know you know, especially public companies, Fortune 500, etcetera, they're all saying that generative AI is one of their highest priorities yet. I I think some of the most recent studies say that only 4% have actually implemented it company wide. Why do you think there is this disconnect between everyone saying, yes.

Jordan Wilson [00:12:31]:
We know it is a top priority versus hardly no one has implemented it from top to bottom. Why do you think that is?

Jamal Khan [00:12:40]:
I think there are multitude of reasons. There is no one single reason. You you and we see we see that cycle of customer maturity. Right? So you've got some customers that are on the upper end of maturity. They've been working in artificial intelligence now for 10 plus years. They made those investments very early on, and it was in some measure either driven through some level of computer vision for quality control functions or data analytics and driving more insights through data. But but those organizations are far more mature in terms of where they are. And then on the other end of the spectrum, you have customers who are still scratching their head.

Jamal Khan [00:13:15]:
They're they're they're asking us that basic question to the question that we're trying to address here as well. Is there is this real? Is this hype? And then within my specific vertical, you know, what are the use cases that can truly provide the ROI? So you've got this broad spectrum of users and different levels of maturity, and then there's another interesting, factor that we're seeing. We're seeing almost this, let's develop our our our skill sets, let's develop our knowledge base, let's develop this understanding of how this can truly impact and apply us, and then let's get our budgets ready. We we we're seeing a lot of companies and and and general trend that we're seeing where there's almost this oxygen that's being grabbed out of other projects and everything else is being slowed down because everyone's preparing for this massive spend that they expect in building out this infrastructures ecosystem and acquiring the skill sets. There's this massive skill set challenge that exists as well in terms of resources that can help address that. So again, I think that the short answer is it's not a ubiquitous pathway, to deployment. You've got different levels of maturity, and and then you've got customers on both spectrums of that maturity, you know, litigating the issue.

Jordan Wilson [00:14:30]:
The the phrase there, get your budgets ready, I think, is is an overlooked portion like like, piece of this. Right? Because I think the the narrative, and and maybe it's just because things like this are easy to sensationalize. But seemingly, this this narrative is, oh, generative AI, it's gonna save you, you know, so much time, so much money. And, you know, you see these studies from, you know, McKinsey saying, oh, up to, you know, 80% of, you know, knowledge workers' tasks could be automated by generative AI. So people, I think, maybe just look at generative AI as as a cost savings, as a, you know, shortcut. But you said something very important there. You you talked about budgets and skill set challenges. So, Jamal, I'm curious, you know, as you've, I'm I'm sure, talked to many people, about generative AI implementation.

Jordan Wilson [00:15:21]:
But, you know, is that just a common misconception or are people just not wanting to fully invest into to reskill and to upskill and to kind of relearn how they work with this skill set and budget, kind of challenge to implementation?

Jamal Khan [00:15:36]:
No. I I think the the intent is there. We we see that there's absolutely the intent to either upscale or sort of acquire those skills. But, Jordan, to be honest with you, it's not an easy transition, even within within connection as we're going to the process of of building a more robust organization. By the way, the skill set upskilling challenge is not just, in in acquiring resources that can deliver artificial intelligence solutions. It's also sort of upscaling even the users of AI. How how do you actually upscale your knowledge workers to be become more effective users on on some of these systems? So I think that's gonna take its natural cycle of time in developing that, and that is a multiyear journey of upskilling. So the intent is there, the the desire is there, and the effort is ongoing.

Jamal Khan [00:16:27]:
The the real skill shortage lies in those those real critical resources. These are your engineering resources. These are your resources that can help you, you know, build your RAG frameworks, that can assist in your GenAI systems. These are resources that can help you arbitrate the different models you could leverage, theoretically, and fine tune. That that skill set is few and far between, and and and that's I think where you have a skill set shortage. So hopefully, that gives you an answer. I know it's it's a it's a little bit of a vague proposition because I've often find that there's no one single way to explain many of these these, issues that we talk about. There's a whole broad spectrum of different varying situations that we need to contend with.

Jordan Wilson [00:17:12]:
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Everyone's prompting wrong, and the PPP course fixes that. If you want access, go to podpp.com. Again, that's podpp.com. Sign up for the free course and start putting ChatGPT to work for you. And, you know, hey. As a reminder to our live audience joining us, thank you as always. But if you do have a question, for Jamal, you know, specifically on gen GenAI implementation and your thoughts on if it's transformative, or not, please get them in now. Jamal, one thing, you know, I I always think about is this issue of trust.

Jordan Wilson [00:18:29]:
Right? Both, you know, trusting the models. Right? There's this this black box of ambiguity of what what are large language models. What is generative AI? How should companies address that trust issue and maybe just trust in the process from a change management perspective? How can companies face that?

Jamal Khan [00:18:51]:
So I I think that is the seminal challenge with AI, and and I I like to sort of unpack that just a little bit. You know, I'm gonna age myself. I I remember, you know, in in the early days of the web, you know, mid nineties, late nineties, You know, I I was working for a start up in those days called VeriSign. And for those of you who don't remember, VeriSign essentially built root certificate authorities that enabled browsers to, you know, essentially undertake SSL transactions and also provide some level of bidirectional authentication. You could have an end user certificate, and then you could have a server certificate. And then based upon that, you could establish trust in an HTTP transaction, which is otherwise a stateless, sessionless protocol. I remember our CEO, Strath and Sklavos, in those days used to say, unless we solve the underlying trust issue that exists within the web, we will never get meaningful applications at scale on the web. And, you know, as a as a 20 something year old person, I remember going into Fortune 50 companies in their boardrooms and try and teach them on public key infrastructure and why trust is a cornerstone of them moving some of their applications, whether these were trading applications, financial applications, banking applications.

Jamal Khan [00:20:07]:
In a in a very analogous way, when I talk to leadership, in in companies around their desire to build out artificial intelligence, we are encountering exactly the same challenge, which is what is that trust framework upon which they can truly build these AI systems because AI has a unique set of challenges as it relates to trust. There's a transparency challenge. There's an explainability challenge. There's an accountability or a fairness or bias mitigation challenge. You don't wanna be in the business of a bank that's built loan, accreditation or loan issuance systems based upon AI algorithms that may have implicit bias around a protected category of people because you're opening yourself to downstream litigation at some point. You've got reliability and safety around these AI systems as they become more kinetic. You know, you've got data and privacy concerns. You've got intellectual.

Jamal Khan [00:21:04]:
So there's a whole broad set of these underlying issues that need to be litigated, that need to be addressed. You've got, you know, legislation, that needs to catch up. You've got case law that needs to catch up. So I think there's this natural organic arc of time that with, you you know, with some of these areas becoming more mature, you're gonna see AI solve that or the industry solve that underlying trust challenge. But I think that is a precursor. If there's anything that's pulling us back or slowing this process because we're seeing buyer's remorse. We're seeing companies out there that have deployed, you know, personal assistance, day 1. And by day 3, they're switching them off because all of a sudden, they're not sure who's got access to what data, what's happening, is data egressing, what what what's the data that I'm bringing in, should my my, graphics team have access to finance data.

Jamal Khan [00:21:56]:
So we're seeing all of that complexity bear itself out as as companies truly try and implement practical projects around AI.

Jordan Wilson [00:22:03]:
It's it's fascinating. Right? Normally, when you think of buyer's remorse in a technology project, you know, you're you're thinking a little further down the line. Right? Maybe maybe weeks or quarters after, you you know, you've implemented something. So, Jamal, one thing, you know, I'm even personally curious about. Right? So, we we we talked about a couple of minutes ago how this process, it's it's a multi year. Right? Upskilling the investment, etcetera. And you even mentioned even for connection. Right? This is it's a transition.

Jordan Wilson [00:22:37]:
So, you know, I'm curious even internally there at connection, you know, kind of when you look at this this hype versus transformative technology. How did you all personally, face this transition? And maybe what are some of the key takeaways or findings, that that you all kind of went through that you think may be helpful to share with others?

Jamal Khan [00:23:00]:
Sure. I I remember vividly the conversation I had with my boss, almost 5 years ago, where, you know, I remember expressing to myself, Tim, if if we can be a data and AI company that is looked upon by our customers as the company to help them in their trends in their sort of journey for AI and data transformation, we will be in a good place. And that was a conversation we had 5 years ago. And I think that sort of spun off in his mind, in my mind, and then, you know, collectively, the board support as well, which was you've gotta start looking in house first. You know, how do you become, the consumers of a democratized data plane? How do you enable your internal resources to drive greater insights? And I know that sounds cliche, but it's a pretty significant challenge. And that required a a complete retrofitting and a and by the way, it's an ongoing journey. It never stops. So there was almost this this underlying cultural shift.

Jamal Khan [00:23:57]:
And if you ask me, one of the biggest challenges that one can have with respect to artificial intelligence is what I would call change management. It's a mindset shift within organizations that, one, it's okay. It's okay to democratize data. It's okay to break those silos and have access and give access to everyone in some measure. And by the way, in that process, there may be some scar tissue as well, and there's nothing wrong with that. So I think that was a a cultural mindset shift that needed to be constantly, constantly driven. And by the way, Jordan, that was not an easy undertaking. These are well entrenched silos.

Jamal Khan [00:24:36]:
These are well entrenched cultural ethos that serves the company well, but you've gotta try and go and and shake that a little bit. And then I think there's a consistent commitment from, upper management, to sort of support that, and, again, all the waste straddles up to the board. And then the last thing is just the avoidance of trying to boil the ocean. Right? You you don't wanna sort of go into these projects saying, you know, AI or data transformation is suddenly going to radically shift the business. You've gotta really identify small wins, and you've gotta almost it's a flywheel effect where these small wins give you that amplitude and acceleration that you need for eventual, adherence to these these transformative shifts. And then the last thing, which probably will irk a lot of people, I am fundamentally of the view that a square root applies here. That initial group of individuals that you want to really try and support your efforts in these journeys is usually a square root of the total that exists. So if you've got a company of 900 people, at the end of the day, there are only gonna be 30 people in that company that can truly initially go on this journey with you.

Jamal Khan [00:25:45]:
And with them, they're willing to sort of, you know, swing with the punches and and and try and really push the envelope and and then be the best evangelist. But if you don't have that that initial 30 core group, in a company of 90, let's say, again, applying that square root rule, you're gonna have, you know, folks who are not really committed to this process. So I think those were the principles that we adopted, consistent support from top down all the way up to the board, small wins, a square root principle, and then just the ability to say, you know, it's fine to fail. It's fine to sort of get a little bit of scar tissue, and there's nothing wrong with that. I think that were some of the elements that we drove internally that I think gets us to where we are today.

Jordan Wilson [00:26:26]:
You know, speaking of the the this square root principle and getting some of those early adopters that can champion, generative AI throughout the the the organization, You know, a lot of it, what we said, it goes back to trust. Right? So, Cecilia's question here, would love to get your your thoughts on this, Jamal. So, she's asking, does the natural arc of time for the trust challenge include the complexity of communication paired with lack of time? Have you seen companies create good models for communication of the AI transition that are inclusive of all stakeholders and issues? Nothing like just getting a fastball, you know, super specific question, you know, so early in the morning. But what are your thoughts on that, Jamal?

Jamal Khan [00:27:07]:
Yeah. I I think that's a really good question, and I think Cecilia is identifying again a fundamental challenge as well, which is how do you have an effective communication strategy internally, you know, given the compression of time that exist? Cecilia, I think I'll I think the point I'll make is that, you know, there is no such thing as a compression of time per se because I think there is a natural natural arc of of learning that happens over the organic arc of time. So I think let's I'll give you certain examples of what we've done. You know, we started very early on these these training curriculum, which we made available to our resources. We're and this was through LinkedIn Learning and other systems. We were very open in in, you know, having our resources take courses. We we continue down the path where what we call fireside chats. These were consistent fireside chats that we ran every every month.

Jamal Khan [00:28:02]:
And what all of these environments gave all of our employees the ability was in a very disarmed way, in a very sort of environment where everyone can be vulnerable, where we said you you can ask the basic question. You know, we want you to come along this ride with us. And I think that that sort of communication strategy, knowledge learning strategy, and and again, that ability for folks to say, hey. You know, we're willing to sort of slow down so that you can come along. Gave us all this culture and environment within connection. Connection, by the way, is a very familial sort of, culture to begin with. We're we're we're very much as a company more driven by how we deal with our employees and treat our employees, and I think that was an important aspect on on our journey. And I think that that is an important aspect to sort of address this this complexity of our technology and its inherent communication with the the lack of time.

Jamal Khan [00:28:57]:
But I think from a timing perspective, Cecilia, I I think it's just an organic arc. It just takes the time it's gonna take, and there's not much we can do about that.

Jordan Wilson [00:29:05]:
Love that. I think we have one more here from the audience I'd like to get to. So, yeah, Jamal, when we it it seems like for the last year, we've been asking this question, is AI transformative or do we not even fully, you know, understand it? So to Monica's question here, you know, asking how much longer do you think, at least here in the US, we will be talking about AI implementation versus flow, full blown adoption. So what's what's your thoughts on that?

Jamal Khan [00:29:32]:
So so, Mike, I think you you'll be surprised to know that there that AI adoption is already well underway and and quite often in a way where, you know, it's behind the scenes. You know, every time you go to Netflix, every time you go to Amazon, you know, from a very consumer oriented perspective, it's using predictive models. The likelihood is that we're in a Walmart or in a Target. You've got, you know, computer vision based models that's looking at challenges such as shrinkage. So I I think you'll be surprised how pervasive artificial intelligence already is in its adoption and, you know, utilization within sort of the enterprise corporate structure. You straddle into the other end of the spectrum. Let's say, you move into vertical like the DOD vertical. You know, there's they're already considerable amount of work around what's called the c 6 standard.

Jamal Khan [00:30:19]:
And the c 6 standard is about bringing artificial intelligence to the edge combat systems, where combat systems have the ability in a very automated way to assess what's the best way to actually conduct a kinetic action. So you'll be surprised how pervasive AI already is. So I I think, the the I think the the the almost the Hollywood esque approach to AI is something, that in in some ways, I I think is a departure from reality that, you know, we're not likely to see the bicentennial man or we're not likely to see sort of the Terminator walking around for a while. But I I think my argument would be that AI is quite pervasive. It's it's subtle. It's sometimes, you know, behind the scenes, and we're not even aware of it.

Jordan Wilson [00:31:08]:
So, Jamal, kind of as we as we wrap up here because we've, we've gone through a lot, you know, a lot we've hit on a lot of important points from, you know, the trust and explainability of generative AI, you know, needing budgetary supports around initiatives and kind of this, square root principle that you talked about of identifying, you know, internal champions and stakeholders that can push AI forward. But what's your kind of your takeaway message for business leaders out there that are still, you know, fully asking this question, is AI transformative or do we not even understand it? What's that takeaway?

Jamal Khan [00:31:43]:
I I think we don't under AI absolutely is transformative. What we don't understand is is how it's gonna impact all of us. And I think it's almost, you know, Andrew Ng uses the terminology AI is like electricity. He always uses that analogy. And and then when you unpack what he means by that is if you think about when electricity became mass scale and was distributed at mass scale, it had a massive impact on everything. It had an impact on certain industries that just disappeared overnight. For example, like, you know, the candle lighting industries, the, you know, the street lighting industries, the the wailing industries, you know, ice manufacturing industries. They literally, you know, overnight in a very short compressed amount of time just disappeared.

Jamal Khan [00:32:27]:
But AI but electricity, when it first came out, gave rise to a whole broad set of new industries. You had entire towns shift. You know, you had in historically, when you're looking at the industrial base, in factories and industries were built near towns. I'm I'm from the northeast. You you go into any sort of one of these northeast towns, you you you'll see, unfortunately, the decay over over decades of how those mill towns that were built around rivers and streams just didn't need to because they were no longer using water, as a means of driving, their their machines. They could sort of move into more urban centers and decide consolidating around industrial layers, around cities. So I think that's a very similar situation we're likely to find in AI. It's likely to fundamentally impact a lot of businesses, in in sometimes, unfortunately, negative ways, but it's likely to open up a whole broad set of new categories of businesses that we haven't even conceived of.

Jamal Khan [00:33:24]:
So, again, I believe that AI absolutely is transformative, and it's happening due to those convergence that I initially talked about. But what its impact's gonna be is something that we will unpack over the course of the next 5 to 10 years, if not longer.

Jordan Wilson [00:33:40]:
Just a lot to think about, you know, to start your morning here. So so much good information and takeaways. I can't wait to go, relisten to this conversation now and write our newsletter for it. So, Jamal, thank you so much for joining the Everyday AI Show. We really appreciate it.

Jamal Khan [00:34:00]:
Sure. Absolutely, John. Great to be on.

Jordan Wilson [00:34:01]:
And, hey, as a reminder, everyone, yeah, Jamal just dropped a ton of knowledge on our heads. Don't worry. We're gonna help you, make make sense of it and give you some practical takeaways, from a super informative conversation. So if you haven't already, make sure to go to your everydayai.com. It's gonna be in the show notes. Sign up for our free daily newsletter. And if this was helpful, please tell someone about it. You you know, repost this, if if you're listening on social media.

Jordan Wilson [00:34:29]:
If because if your company still hasn't been able to make this transition, I think today's show is really gonna help with that. So thank you for tuning in today, and we hope to see you back tomorrow and every day for more everyday AI. Thanks, y'all.

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