EP 356: Why Having an AI Strategy Might Be a Strategic Mistake

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How many hours has your company spent coming up with an AI strategy? It's what all of the C Suite executives are talking about. Board members, everyone, toiling, spending hours upon hours reading research papers, trying to copy their competitors, all for that perfect AI strategy. Well, what if having an AI strategy is actually a strategic mistake? We're going to be talking about that today and more on Everyday AI. What's going on y'? All? My name is Jordan Wilson and I'm the host of Everyday AI and this thing is for you. It is your daily livestream, podcast and free daily newsletter helping us all learn and leverage generative AI to grow our companies and our careers. If that sounds like you, I hope it is. If you're listening on the podcast, make sure to subscribe, but also check out your show notes for more, including a link to our website at your everyday AI.com because every single day we keep you up with not just the AI news, but we also recap our conversation with for each day. So make sure if you haven't already, go do that. And while you're there on our website, we extended this a little bit. We're going to be wrapping up our Thanks a Million giveaway to celebrate a million downloads at Everyday AI. So make sure you go sign up for that. All right, before we get into today's topic on why having an AI strategy might actually be a strategic mistake, let's go over what's happening in the world of AI news. So Adobe is set to launch a generative AI powered video creation tool later this year. Yeah, this is late breaking news, not even an hour old. So Adobe is set to introduce a new generative AI powered video creation and editing tool, marking a significant step in the company's expansion into AI based video generation. This development is noteworthy as it positions Adobe to compete in a rapidly growing market already occupied by notable players like OpenAI, Stability, AI Runway and others. So the tool will be named Adobe Firefly Video Model and it will be released in beta later this spring, according to reports. Joining Adobe's existing Firefly suite of image generating applications, it can create 5 second video clips from a single prompt, interpreting both text and image inputs, which could enhance the creative process for users. All right, next piece of AI news. Sam Altman is set to meet with White House officials tomorrow to discuss AI's energy demands. So the CEO of OpenAI is set to meet with top US officials at the White House to address the significant energy challenges posed by the rapid development of artificial intelligence. So Altman will be joined by other other prominent tech leaders from Google, Anthropic, CEO, etc, meeting with White House senior officials. So the International Energy Agency reports that a single request on chat GPT as an example, can consume about 10 times more electricity than a regular Google search, with projections indicating that AI energy usage could surge tenfold by 2026. So this meeting among top tech CEOs and brass might follow earlier initiatives by the Biden administration encouraging AI companies to have new systems tested externally and to clearly label AI generated content. Speaking of Sam Altman, he'll be one of the people joining Oprah Winfrey tonight as Oprah hosts a special on AI's impact on daily life. So Oprah Winfrey is set to host a prime time special that will dive into the significant effects of artificial intelligence on everyday life, airing tonight on abc. So the special is titled AI in the Future of Us, An Oprah Winfrey Special. And it will feature a panel of AI experts discussing various aspects of artificial intelligence, including its influence on the job market and the potential risks associated with its rapid development. So notable interviewees include Sam Altman, The CEO of OpenAI, Bill Gates, the co founder of Microsoft, and Christopher Wray, the FBI director who will share insights on AI's capabilities and the need for accountability among tech executives. All right, so that one should be an interesting one. And you know what? They'll probably be talking about AI strategy, which is what we are going to be talking about today. I'm very excited for today's conversation. If you're joining live, you can see my smile. I'm a little giddy because I love this topic. I love talking about strategy. And when it comes to AI, I think so many companies are spending countless hours trying to build an AI strategy, but that might not be the right course of action to take. All right, so it's not just me blabbing on today. I've got a great guest. So please help me. Welcome to the show. Let's see, there we go. There. We got her. So we have Hanan Alhadi, who is a guest lecturer and AI strategist at Oxford University. Hanan, thank you so much for joining the Everyday AI Show.

Yeah, thank you for having me, Jordan.

All right, I'm excited for this one. Could you give everyone a little bit of your background? So what do you do as a guest lecturer at Oxford?

Yeah, so what we do is we try to educate students in the education executive education program on strategy and AI. And the idea is to, you know, as part of these lectures and creating these case studies, the idea is to give the CEOs and the C suite executives a very well rounded, holistic understanding of AI so that when they go back to their jobs, they actually know how to make decisions around AI. So that's kind of part of the lecturing that I do. And then obviously as an academic practitioner, I lead projects that are AI powered. So I try to bridge the gap, gap between Siri and practice. And being a practice academic is really the best position to have.

So Hanad, you really have a front row seat in terms of how businesses are trying to drive their company forward using AI. Right. So you're lecturing and teaching CEOs on this exact topic. So you know, I'm curious, from your viewpoint, maybe what AI moves are companies getting correct from your viewpoint and maybe what are some of those common mistakes or pitfalls that you're seeing C suite executives fall into?

Yeah, so one of the things that we're seeing that companies across all industries doing correctly is identifying first of all, they are acknowledging that AI has, it comes with its own power and the potential to realize benefits and value from AI is literally uncapped. So that's great that they acknowledge this. Another thing that they do correctly is they try to identify opportunities and use cases to leverage AI, which is great. So there is that energy in these companies and we're focusing on mature organizations, those are the organizations with the deep pockets, with the largest workforces, where we see that they are actually falling behind or actually making slight mistakes is how, how they approach AI. And we'll pick on the automotive industry, for example. One of the pitfalls, one of the mistakes, it's a, is that they perceive AI as its own standalone strategy. And so commonly, very recently, you're seeing AI strategy. I'm developing an AI strategy and this is what my AI strategy is going to enable me to do. And we argue that this is the wrong way to approach AI. AI in of itself is a tool just like any other tool, whether you leverage it to automate manual processes and get operational efficiencies. At the end of the day, it's a tool and its function is really to enable your strategic plans. It's supposed to enable your enterprise strategy, not be perceived as a strategy in of itself. And there's a whole lot of education that we give around what strategy is at a corporate level, at a business level, at a functional level. And when you think of AI, whether you're talking about traditional ML or generative AI, it really fits as one of the building blocks for the enabling a functional level strategy. So those are some of the things that we're seeing across all industries.

I love it. We're already getting into the crux of it. I love starting at the end and giving the answers right away and unpacking it. So, and hey, as, as a reminder to our livestream audience, thank you for joining us, great guests for today. So Denny and Gene and Cecilia, Fred, Michael, everyone else, get your questions in if you have them right because we are literally getting an education from, from one of the top people out there doing it. So Hanan, I'm, I'm guessing right, if, if this can be a strategic mistake, if creating an AI strategy can actually be a bad thing, why are so many of these, you know, C Suite, you know, and big companies, Fortune 500 companies, why are they spending so much time, energy, resources and money into their long term AI strategy?

Probably a couple of reasons. Number one, the promise of what AI can deliver is actually fairly communicated across all industries. Technically, you have to be living under a rock not to realize that AI has its own power. There's that energy, there's that momentum. The other reason is the fact that AI does actually have proven to deliver a lot of value to generate new products, products to enhance features. And for most of the industries, that's really what they're, what they're in for. It's to really develop a product, deliver it in the best way possible to differentiate the customer experience. So when you see the AI, AI powered solutions or products can help you enable all of these things. It becomes a hype, it almost becomes the flavor of the month for a lot of the companies. In a lot of my speaking engagements, I like to draw a distinction between technology developing companies and technology consuming companies. When you talk about Nvidia, Google, these are the technology developing companies. And for those companies you can actually have your own AI strategy because that's really your bread and butter for the vast majority of companies. You really are a technology consumer. You're supposed to take the technologies that exist, customize them, but the idea is to leverage these technologies to enable what's in your portfolio of products and services. And so between, between having a lot of hype around AI and the fact that AI does actually deliver a lot of value, some of the CEOs, some of the senior executives can confuse this with I can actually do an AI plugin and it will produce what I want and, and that's, and that's really, that's really not how you're supposed to approach it.

So how should companies be approaching it then? Right, Because I'd say the writing on the wall and I say this, you know, all the time. The future of work is generative, right? You're, we're seeing generative AI and large language models come to where we work, right? We don't really have an option, right? If you're using Microsoft now, you have copilot, right? If you're using Apple or Macs Mac products, you're going to be seeing Apple intelligence now following you around everywhere. So how can companies do the right thing of using all of this kind of new technology and tools they have available to them? But you know, it seems like when companies are using the latest technology, they always want to have a plan, they always want to have a strategic vision. So how do you kind of balance the two and not fall into that trap that we're kind of talking about?

A couple of things. One, approach it holistically and then also approach it strategically. When you think of AI and all of the benefits and the value that AI powered solutions promise, for the most part they revolve around three things. Number one is enabling data driven decisions because of the amount of data analysis and that you can actually and the insights that you can get from good data analysis. So data enabling data driven decisions, differentiating customer experiences and also optimizing your operational efficiency between these three pillars. This is really what AI powered solutions for. Right now we're in 2024. Those are the three pillars in which AI is really promising a lot of value. So when companies understand these three pillars, then we have the second thing which is approach it strategically. The idea is if you put park AI for a second, park it aside for a second, you need to go back to the North Star for your organization. What's the strategy? What is it that you're actually hoping to be for your customers in the short, mid and long term? You should have your corporate level strategy. When you really are focused at that level on growth and business model that's going to enable that growth. And then the idea is when you use the strategy cascade, you go down to a business level strategy, you decide on how you want to differentiate or be a cost leader and then you come to the functional level strategy. And that's when you actually say, okay, in order for me to enable the growth, in order for me to be the next Rolex or the next Walmart, what are the building blocks for These competitive advantages, Technically you have one of four efficiency, innovation, customer responsiveness, and quality. What CEOs and VPs need to understand is where AI fits. AI can enable one of these building blocks to approach AI strategically. The idea is to understand exactly what you need to get out of this technology as a company, regardless of the technology, what is it that you want to deliver? That's number one. And then number two, understand where AI can help you. And then three, integrate AI into the strategic plan. And in that, that's when you actually have a should have a specific objective for AI and then you would actually manage it just like you manage any other project. If you say I will develop an AI strategy and I will identify use cases to leverage AI, you're really approaching it from the opposite end. And what's going to happen is if we pick on the automotive industry, they end up falling in the same trap they fell into when mobility, for instance, was the thing to go after five or seven years ago.

Are you still running in circles trying to figure out how to actually grow your business with AI? Maybe your company has been tinkering with large language models for a year or more, but can't really get traction to find ROI on Genai. Hey, this is Jordan Wilson, host of this very podcast. Companies like Adobe, Microsoft and Nvidia have partnered with us because they trust our expertise in educating the masses around generative AI to get ahead. And some of the most innovative companies in the country hire us to help with their AI strategy and to train hundreds of their employees on how to use Gen AI. So whether you're looking for ChatGPT training for thousands or just need help building your front end AI strategy, you can partner with us too. Just like some of the biggest companies in the world do. Go to your everydayai.com partner to get in contact with our team or you can just click on the partner section of our website. We'll help you stop running in those AI circles and help get your team ahead and built a straight path to ROI on GEni. You know a lot, a lot of good takeaways there. This is one of those y' all like you have to go read today's newsletter because Hanan is dropping like books of knowledge on top of our head. So we're going to be distilling it all. Don't worry. My, my like my notes are, are going crazy over here. So. One thing though I'm curious about is I think companies, especially when we talk about, you know, C suite decision makers, they've been rewarded over the past couple of decades for taking a very methodical approach to technology, right? Companies first had an Internet strategy, right? And then they, you know, probably had a mobile strategy, a cloud strategy. And they had time, right? They had sometimes many years or a decade or more, right, to fully develop and implement a strategy. So is maybe the, the danger or the pitfall? You know, I know we talked about, hey, AI, you know, you can't think of it as just a tool, but how much of it is also the, the pace or the speed of development that could make, you know, taking a slower approach actually maybe a disastrous strategy.

I'll share with you my viewpoint that really don't represent any entity. These are just my viewpoints as an academic practitioner. The pace at which AI space is evolving is at an exponential level. If any company think that it would actually keep up, probably they will not be able to keep up. So the idea is really to be focused and, and targeted. Exactly what is it that you need to get out of, out of AI? So we also a lot of times teach the signals that companies actually receive from their macro environment. Right now, relative to the AI space. There are a lot of signals. If any company actually chases all of the signals, it will really land on nothing. The idea is to be really focused, focus on the specific AI related signals that you think will help you enable your vision as a company. And therefore taking a slower approach, more focused approach, more calculated approach towards AI would actually serve a lot of the companies in the long run as opposed to chasing the next shiny thing. There's also, and it probably might sound a little bit too bold for most, when you think of the composition of most boards, especially for mature organizations, very respectfully, you are talking about people in their mid-50s, if not 60s, naturally by default. This isn't really the crowd that you would associate to be super up to speed with the latest technologies out there or what we can get from them. So we're not really calling for dismantling or board compositions, but what we're saying is there really, there is a legitimate need for education, right? So you need to educate, you need to coach, especially those people in senior executive positions on the benefits of really approaching AI strategically, taking a slower pace at AI as opposed to shining every, chasing every single Chinese signal around AI. Because that's really going to leave them with, with nothing likely.

Yeah, that's, that's a great point that you bring up there that a lot of times, you know, whoever may be steering the ship might not really care. They might be on their way out or they might just have an old archaic way of thinking which I think is an important call out to make. One thing that I always see, right, we do a lot of consulting for small, medium large enterprises and a lot of times decision makers will just kind of want to sprinkle some, some AI on something, right? They see a huge problem within their organization, customer experience, sales, marketing, whatever, and they just kind of want to sprinkle some, some magic AI dust on this. Is that something that, that you see are companies just looking at AI as this, just this magical solution for, for everything?

Oh yeah. So it's what I call AI washing, very similar to what we call. And unfortunately, so what companies still go through when it comes to like greenwashing AI washing is when you, when you send your paper to the board and it actually has an AI strategy or an AI, you know, projects or deliverables. And truthfully though, there isn't really a lot of substance. So that's really what AI washing is when you apply the term AI onto projects, onto deliverables, so that you say, yes, I'm using AI, yes, AI is going to help me achieve A, B and C. One thing that companies are actually doing correctly with AI is, is really around the operational efficiency, right? So yes, they are leveraging in isolated pockets. They are leveraging AI to automate some manual processes and therefore they are gaining some value from optimizing some labor hours. But these are still an isolated pockets and they're not really strategically well threaded with the rest of the organization. So that's kind of the AI washing that we're seeing and very similar to a lot of washing that the companies have done. Whether it's the green washing or the mobility washing. It's, it's what you call like this, you know, adding AI sprinkles to the, to the, to the business plan, essentially.

You know, one, one other aspect I see a lot, and you know, I'm curious if you see this as well, is, you know, big organizations, right, Like I've, I've worked with and talked with outside of this show, you know, some of the largest companies in the world. And sometimes I'm shocked because they'll be devoting a lot of resources to, you know, maybe fine tuning their own large language model, you know, rag, bringing in their, you know, all of their data in house, all of these great things. But then they also don't even understand maybe how a large language model works. And they don't even necessarily try to understand or investigate really the science behind these kind of magical models. Right. Is maybe part of this not understanding what's going on under the hood that can actually be a trap or a strategic disadvantage?

Absolutely and rightfully so. AI is a fairly complex field. If you try to even explain how deep learning works and if you try to use an analogy of how neural networks work and how that thought process that's really the cornerstone of deep learning, that concept in of itself is not very, it's not an easily digestible concept. And so AI as a field, as a science field, is a complex field. So when you add the complexity of it, the pace at which it's evolving, all of these things make it not very easily digestible, not very easily understood. And therefore it does require a lot of effort, a lot of serious effort to actually understand what it is and what is it about AI that can actually help your organization achieve. It's not a straightforward science and therefore, yes, it makes it a lot easier for, for companies to actually make the common mistakes that we see them make.

A great, a great question here from our audience, Cecilia asking so as, as you teach CEOs you teach create their strategies, do you have them focus on mission first and include stakeholder management of all the types of stakeholders, internal and external, and then how?

Oh yeah, of course. So you start with your vision. Many, many years ago we, we taught the, the shareholder view of the enterprise. We walked away from this and now it's the stakeholder view of the enterprise, which is a holistic understanding of, of all of your various stakeholders, holders look and care about when it comes to your organization. So that's a no regret move to start with your vision and mission. That's also a no regret move independent of the technology. So the idea is you need to understand what your company is wanting to achieve and then two, what something like AI as a field can help you achieve and then plug it exactly where it needs to be plugged in in your organization. And that's one thing that we try to educate the CEOs and the VP and you know, the people in that C suite. The other things that we try to, to educate them on is really a good understanding of the real value, tangible and intangible value of AI. But you're spot on. You start with your vision and mission, a stakeholder view of your enterprise, and then understand how something like AI can actually help you even get further. You should always strive to achieve your North Star. Your North Star should not change because of AI. You need AI to help you to get to your North Star faster maybe, but it shouldn't be the, the other way around.

Hanan, earlier in the conversation you kind of talked about this, you know, approaching it holistically and strategically and then you kind of broke it down into these three main pillars, right? Data driven decisions, differentiating customer experiences and optimize, optimizing organizational efficiency. So on that third piece that seems what so much of AI, quote unquote strategy focuses around, right? Like how can we maybe do more with less? It is that it seems like that ultimately goes to the conversation of AI in jobs, right? Like it's just a natural kind of cause, cause effect. Is that a problem? Are maybe decision makers spending too much time focusing on efficiency or about, oh, maybe we don't have to, you know, hire as many people now is, is that maybe a problem and is it creating maybe too much fear around AI implementation or strategy?

It is, it is absolutely. First of all, it's a low hanging fruit. It's the easiest use case to leverage AI. It's not a bad use case. But when all of the research is essentially telling us right now, when you think of the automation and the value that you get from automating manual processes, it's about 30%. 30% is not high enough to actually replace workforce. Knowing that no one employee does just a single task. It's a long hanging fruit, it's a good use case and it's probably the most common use cases that we see large organizations go after when they talk about AI or any AI powered solution. The idea though is to, when you approach it holistically and you say, okay, I've, I've just enabled my team to leverage this AI powered solution and they've saved 30, 25, 30% of their time to be a responsible C suite. The idea is what can you do with the employment efficiency that you just gained with transparency and clear communication for your workforce, educating them and that the fact that AI is not there to replace their job because you really don't want to create that reluctance for your workforce to actually leverage it. You want them to perceive AI as a thought partner, as really a partner for future development. And so great, they've automated their manual processes. They've actually gained few hours from their day to approach this responsibly. The idea is to understand how can you now utilize the skin and the efficiency? What can you, what, what can you do with the, with the, with the employment efficiency that you just gained? Are you going to upskill them? Are you going to, you know, allocate, carve out some time to actually have them go for even more advanced features. But to actually stop at AI is going to save 30% of your time. That's just half of the story. But like you said, it's the, it's a low hanging fruit. It's a very common use case. But C suite professionals and executive seniors and leadership positions, they really need to think beyond the 30% gain in operational efficiency.

About more than efficiency. Right? That's, that's what we always focus on. So Hanan, we've covered so much in today's episode. This should be required listening for everyone, I believe. But you know, as we wrap up what is maybe the one important takeaway or maybe one next step, you know, for decision makers out there who have maybe been spending so much time and potentially wasting time thinking of a long term AI strategy, what is your one piece of advice for them to still leverage AI without falling into those big mistakes and traps that we talked about earlier?

Yeah, a couple of things. Number one, reconsider your lens through this AI strategy. It's not the right lens. And then number two, when you do this, try to integrate it into the strategic planning process for your organization so that you understand exactly where it fits as opposed to chasing it as a standalone strategy.

Love to hear it. That is great advice from someone that is already teaching the business leaders of today how to prepare for tomorrow. So Hanan, thank you so much for joining the Everyday AI show. We really appreciate your time.

Thank you for having me.

And hey, as a reminder everyone, yeah, that was a lot. So much knowledge that just got dropped on our heads. It's like we need a hard hat. So make sure if you haven't already, go to your everyday AI.com Sign up for that free daily newsletter. We'll be recapping today's show, show and a whole lot more. Thank you for joining us. Please join us tomorrow. Join us. I'll see you tomorrow and every day for more Everyday AI. Thanks y'.

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