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Why Your Enterprise AI Initiative Is Failing (And How to Fix It)

Is your company's AI initiative feeling more like a costly experiment than a game-changing transformation? You're not alone. While 92% of enterprises are investing in AI, a staggering 85% of AI projects fail to deliver on their promises. But why?

The truth is, most organizations are approaching AI implementation backwards. They're buying expensive tools and platforms without first establishing the foundation that makes those investments worthwhile - proper training and education. This gap between AI ambition and AI competency is costing enterprises millions.

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Today, we're joined by someone who's been solving this exact problem. Maryrose Lyons is the founder of the AI Institute and a digital marketing veteran with over two decades of experience. Since 1999, she's witnessed every major digital transformation, and now she's helping enterprises bridge the critical AI knowledge gap. With a Master's in Cyberpsychology and professional certification in AI, Maryrose brings a unique perspective on both the technical and human elements of AI adoption. Through the AI Institute, she's helped countless organizations turn their AI investments from money pits into profit centers, with some clients seeing up to 20% increase in performance after proper training.

The AI Hat Podcast host Mike Allton asked Maryrose Lyons about:

✨ Training Before Tools - Successful AI implementation starts with education, not software purchases

✨ Cultural Integration Matters - Technical knowledge must be paired with organizational change management

✨ Measurable ROI Focus - Proper AI training directly correlates to improved business performance

Learn more about Maryrose Lyons

Resources & Brands mentioned in this episode

Why Your Enterprise AI Initiative Is Failing (And How to Fix It)

Full Transcript

(lightly edited)

Why Your Enterprise AI Initiative Is Failing (And How to Fix It) with Maryrose Lyons

[00:00:00] Maryrose Lyons: And we have about four metrics that we report on to clients. So the first one would be around efficiency gain. So it's literally just by bringing in the AI tools, people are able to, you know, remove a lot of the kind of the mundane admin tasks. Then we look at, uh, improving the quantity and quality of the existing output.

So this is what, you know, executives love because it's actually getting more out of your existing teams. We've seen on average about 28 percent on improvement in output, but it can be sometimes upwards of 30 or 40, 40%, which is whopper. This starts to kind of bring about more innovation in, in the business or in, in enterprise.

All right, firms that we work with and the innovation piece I think is fantastic.

[00:00:47] Mike Allton: Mike's got The hat, the AI kind unlocking power in business you'll find. Cut through the hype, explore the tech. Every week. New ideas to check.

Allton leads us on a digital quest, AI secrets he'll always invest. Tune in tight, don't miss a beat. Knowledge groove. Keep you on your feet. Listen up to the AI flow. Business growth, here we go. Cutting edge insights, that's the plan. With The AI hat, you'll understand.

Greetings program. Welcome back to The AI Hat Podcast where I selfishly use this time to pick the brains of experts at keeping up with and integrating or layering artificial intelligence into social media, content, advertising, leadership and other areas of business . You get to learn to subscribe to be shown how to prepare yourself and your brand for this AI revolution and come out ahead. Now as your company's AI initiative feeling more like a costly experiment than a game changing transformation.

You're not alone. While 92 percent of enterprises are investing in AI, a staggering 85 percent of AI projects fail to deliver on their promises. But why? The truth is most organizations are approaching AI implementation backwards. They're buying expensive tools and platforms without first establishing the foundation that makes them investments worthwhile, proper training.

This gap between A. I. Ambition and A. I. Competency is costing enterprises millions. Today, we're joined by someone who's been solving this exact problem. Maryrose Lyons is the founder of the A. I. Institute and a digital marketing veteran with over two decades of experience. Since 1999. She's witnessed every major digital transformation, and now she's helping enterprises bridge the critical AI knowledge gap with a master's in cyber psychology and a professional certification in AI. Maryrose brings a unique perspective on both the technical and human elements of AI adoption through the AI Institute.

She's helped countless organizations turn their AI investments from money pits into profit centers, with some clients seeing up to 20 percent increase in performance after proper training. Hey, Maryrose, welcome to the show.

[00:03:36] Maryrose Lyons: Thank you for selflessly inviting me on the show, Mike. Thank you so much.

[00:03:40] Mike Allton: So glad to have you here.

Now, before we dive into some of the solutions, help us understand the scope of the problem that I was just talking about. What are some of the common ways specifically that you see enterprise AI initiatives falling short?

[00:03:54] Maryrose Lyons: Well, you nailed it in the introduction there. I think a large number of organizations, you know, with enthusiasm have invested, have invested heavily, and then those projects and those programs have fallen flat on their faces.

And what we find is that if you haven't prepared your staff and your teams enough with what to expect, you can meet with a lot of resistance. And that resistance can oftentimes come from fear. So, you know, a lot of people, a lot of humans are very, very worried that AI, or what we like to call it, digital intelligence can take their jobs.

So, you know, really, before you go into any kind of a program, you In using AI, you should really address the concerns of your staff and make sure that they know that you're bringing these programs, not to replace them, but to help them to work in a different way to remove the mundane and free them up to do more exciting work instead.

[00:04:52] Mike Allton: That's such great advice. In fact, I talked in a previous episode how tickled I was, frankly, to see Microsoft call their product copilot, right? Because AI is not the pilot. It's not supposed to be the pilot. At least it's supposed to be the copilot that's helping us implement streamline, improve, delegate other kinds of tasks, all these kinds of wonderful things.

And I know you've been, like I said, in the intro in digital marketing since 1999, and we've both seen some incredible transformations in this digital space. How do you see this current AI transition comparing to some of the previous digital transformations that we've been through?

[00:05:32] Maryrose Lyons: It's just, it's incomparable you know, my experience with the AI Institute is that when, once you uncover the benefits that can be brought and derived from bringing AI into an organization, it is just amazing the enthusiasm that, that can come about.

So, you know, Ireland is very early adopter kind of a country and I come from Ireland. And I am like the original OG of early adopters in Ireland. So I have been doing this since 2001, delivering training. My first training course was actually right for the web, which was kind of teaching people how to write for, for websites.

And then I've been delivering digital marketing training since 2003. So when I made a progression into AI training back in 2022, end of, it was a natural progression. But the way it's received is just so different. You know, it's not something that people have to have, it's just something that they, that, that is easy to, to adopt.

And when you show people what can be done, you know, I liken it too. It's me like showing people around as opposed to actually something you have to learn. Do you know what I mean, Mike? Like it's not something you have to sit down with a handbook. Once people show you where to go on some basics around data, security and prompting, it's very easy for people to see the benefits it brings to their jobs.

[00:06:52] Mike Allton: Oh, I definitely understand what you mean. It's something we talked about a lot on, on this show, how critical it is to show people specific use cases and examples of how to use Chat GPT, how to use Gemini or Claude or some of the other more advanced marketing applications that are baking in AI like Salesforce and HubSpot and so on.

Before we get into, like, use cases, I'd love if you could share an example of, you know, one of your enterprise, you know, clients or an enterprise that you're familiar with that was struggling with AI implementation and how some proper training from you and your organization helped turn things around for them.

[00:07:30] Maryrose Lyons: Absolutely. I'd love to. So we have a number of clients from lots of different areas. You know, we work with clients in law, finance, obviously marketing, media, also interesting sectors like construction. And the one I would kind of look to share with you now is from the world of business. engineering, which is a sector that you may not think is going to be so deeply embedded with AI so quickly.

But one of our clients is an industry leader in the EU. And they came to us with a very specific problem in that their, their leadership on their board level, they were really wanting to embrace AI. So that's a really good thing when the, when the, the top tier are supporting the, the program. So they had that, they'd already set up an AI task force within the business and they had spent a fair bit of money on investing in tools, but just the whole thing didn't fly.

And the task force was made up of kind of people who are being identified as champions, but they just couldn't make the whole program kind of really roll within the business. So they came to us at the AI Institute. We have an approach which really works, which is that we had a look at a cross-functional set of teams and we did some scoping work in terms of what are the tasks that the different individuals are doing within each, within each team.

And then we were able to roll that up into a very bespoke training program that would pick off the kind of the admin or mundane tasks. That were kind of common across teams. And then we were able to do some very bespoke training on very specific use cases per team. So once you start bringing this through and the, the, the teams and the, and the humans start to see for themselves, the benefits that can be derived.

They were coming back and they were looking for more. So in this particular case of this client we were able to bring a 22 percent increase in efficiency gain. So Mike, 22 percent is basically saving one day a week. So one day a week times across the hundreds of staff that they have. So then the issue is, you know, how are they going to spend this new time?

So we were able to kind of help them and coach them to. Either create more quantity or to create better quality. So the situation that we're in at the moment is that we're just completing phase two. And now they have increased quantity and quality. They have the efficiency gains. And what we're seeing now is that there's new innovations coming through because.

The people are freed up to, you know, do what humans do best, you know, have meetings and ideate and brainstorm. And there's some amazing ideas coming through. So this client is actually a leader in the space and engineering across the EU. And they've now really I suppose, confirmed their competitive position.

They're number one, they're staying number one. And the reason why is because they've embraced AI and they've got support from an organization like ours to make the program a success.

[00:10:35] Mike Allton: That is such a fantastic example. And I like it also because it's not unique to enterprise. I was talking on someone else's podcast, Alex Carlson's the other day, and we were talking about how the investment of AI, it's not exclusive to Big companies and enterprise, you know, a solopreneur, a medium sized business could be using AI to also see that same kind of gains in productivity, which to your point, they could turn around and say, okay, well then we can produce more or we can reinvest and produce higher quality.

And I love the fact that you guys have courses that range from basics to advanced workflow. So they applied a lot of different folks, but when it comes to enterprise, how do you determine what enterprises Kind of need to learn versus what's just nice to know.

[00:11:22] Maryrose Lyons: Very good question. I think, I think a problem, a very real problem is that a lot of overwhelm can be created by receiving, let's say, training, which is like, oh, the different toys that are there. So one of the things we really pride ourselves on is, is really identifying what are their challenges and then we can then prescribe what are the different AI tools or the different apps or the Basically different features that they need to be trained on.

So I think it's important not to be throwing every single tool at, at the teams, because then it's just wasting time in a lot of cases. And as I say, it can lead to overwhelm. So for example, you know, you, you would know from, you know, keeping an eye on, on, on the media, like text to video is being talked about a lot.

It's very sexy. It's very exciting. Yeah. But unfortunately, it's not quite there yet as of the end of 2024 starts 2025. So it's just not something that we would include in our in our enterprise training programs. Likewise, as you can tell from my accent, I've got a regional accent. You know, it's not a U. S.

accent. So, for example, the world of, like, synthetic avatars is, is fantastic and exciting to play with. But it's not something that we bring into our programs because it's just not there yet. So, we're very, very kind of clued up about, you know, What's worth delivering and what should be like included in a program or what should be kind of overlooked until they become more involved.

So I think that's quite important is to not throw all the tools at the people and bring them along the specific use cases that will really add value and make them, you know, enjoy the journey and become better at doing doing their roles.

[00:13:10] Mike Allton: That makes a lot of sense. Now, one thing I mentioned, I just kind of slipped this in, in the outset was that you have a background in cyber psychology and I have a certification NLP, so I've always interested in the, the psychological and the approach that people take when it comes to language in particular.

And AI is obviously very language driven. What is that? psychological component, do you think that that AI adoption that can often be overlooked? And how does that background of yours influence your approach to AI training?

[00:13:44] Maryrose Lyons: Well, very good question, actually. And, and I am also really interested. That was my kind of favorite part of Cyber psychology was evolution of language.

I just think it's, it's a fascinating subject. Sherry Turkle was my, my hero at the time when I was doing that course. I think that I think that the large, you know, companies, they're not really doing themselves any favours with the naming of their products, for example. You know, I, I, You know, I think, ChatGPT, 4.

0, 4. 0 Mini, 0. 1, Mini, Mini, Mini. Like, you know, if they had a good brand marketer on the case, you know, they could name them better. From a psychological perspective, I don't really think there's that much difference in that a lot of the stuff around cyber psychology looks at the differences between how we interact with technology as humans when we're kind of behind a screen versus in real life.

So when it comes to AI I don't think it's any different to what we already know from, say, the early days of the interwebs or, you know, social media and the like. So we did a lot of stuff in the, in the masters around things like you know in social media how you can spot whether people are introvert or extrovert through, you know, the amount of times they change their profile picture.

So, so, so you can learn an awful lot from people based on how they're interacting with when they're, when they're on using a digital device, but one of the kind of models or frameworks that we like to use in the AI Institute is really the whole idea of like within any organization, you know, you're going to have usually dreamers, believers, soldiers, and cynics.

Yeah. So how we kind of look at the kind of enterprise clients that we're working with is that we kind of trying to identify very early on who are the dreamers. And then as, as our role in bringing the training is that we want to really inspire the dreamers to be on board with the program that we're bringing, bringing on.

Then we identify the believers and they're the ones that we want to make into our evangelists. And when you have the dreamers and the believers on board, they will then work to bring on the soldiers, which usually makes up about 65 percent of an organization. And then when you've got all of those three covered, they all will pull along the cynics.

Now there's nothing wrong with a good cynic. We love, we love cynics actually. And you always have cynics in organizations. And the great thing about having the cynics is that they will force us to really, really, really, really, Double check everything, rethink everything, and avoid groupthink. So that's really, I suppose, I suppose how I'm bringing my cyber psychology into the work that I'm doing today.

And I just love the work that I'm doing. It's, it's so influenced and heavily influenced at all times by humans. It's not a technical thing. It's very much a human computer interaction thing.

[00:16:32] Mike Allton: Completely agree. And I'm wondering how much traditional thoughts in terms of how to train people has to come into play And I'll give you an example where I'm going with this.

You know, in, in NLP, we talk about the different learning modes that individuals have, and it helps us identify things like it's a good idea to address the why before you start to get into the how and the what, otherwise the people who need that, the kinesthetic learners in the audience, they're completely lost.

Cause if we just jump into creating a custom GPT and they haven't been, Helped to understand what you'd even use it for and why that's important. They're no longer paying attention. Does that, does that apply? Do you think to AI?

[00:17:15] Maryrose Lyons: I think it does. Yeah. But I suppose that I find that like we're constantly changing the way we deliver everything.

So for example, you know, courses that we're running today look very different to ones that we had two months ago. And one of the things that I'm finding is that because all of the different frontier models. now have their own version of GPT's and because GPT's are so powerful and we advocate that everybody should be thinking about every department having at least five GPT's that's going to support them to work more efficiently.

I'm finding that although the why is really important for GPT's. We're kind of bringing GPTs in earlier into the courses because we need to actually also show them, you know, you've got a GPT on chat GPT, and then you've got like spaces over on perplexity, which I'm a huge fan of, you know, you've got something similar over on, on a Claude.

I wish they'd fix their UX on that. So because we don't want to just bring all the GPTs in one go, we have started kind of dripping them in earlier, which is kind of interesting. You're always a little up there when I mentioned perplexity. Are you also a fan?

[00:18:19] Mike Allton: Well, I'm trying to use all the tools as, as much as I can, which is a bit of a challenge because I only have so many minutes in every day and so many tasks that I have to do.

I'm actually taking a deep dive right now into Gemini which has their gems, which is their version of GPTs, right? They've, they've introduced memory and custom instructions. And so that's an area where I'm doing a deep dive. More for just professional knowledge and background. I want to understand Google's Gemini product, right?

For, for clients who are in that Google environment and, and, you know, that's where they've upgraded things. So that's super cool. I want to say folks that. We're talking with Maryrose Lyons about where some organizations are getting it wrong with their AI implementations and how to correct course. And I've got several more questions that you're just not going to want to miss, but first a quick word from our sponsor.

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[00:20:13] Maryrose Lyons: I love your background music. I was grooving off down to that. Tell me this, did you make that music using AI?

[00:20:21] Mike Allton: No, I didn't. I get I get, that's, that's a good idea. I should start doing that. I'm, I get most of my music from a Facebook sound library. That way I know it's you know, it's royalty free. It's, it's free to use.

I just, I had to, I put a credit in the, in the show notes that music is by Tukai. I think there's a link down there.

[00:20:40] Maryrose Lyons: Well, have I got a tip for you, Mike? You should definitely check out audio. Well, I call it audio. It's actually UDO. So it's audio without an A. And we set up our own, we have a fledgling podcast called Chatting GPD and we wanted to have our own theme tune.

And we went on to UDIO and we created our own chatting GPT podcast intro music. Now, when I tell you it's an earworm, like I'm already homing it in my mind, in my mind at the moment. It's just fantastic. It took us about 25 minutes to do. It was free. So now we do we do all sorts of like intro music for like in our, in our courses.

Like we always have a section at the end called tool time. So we have a. Two time theme tune. And so I actually asked that question because your background music was so good. I actually thought maybe it was an AI driven thing.

[00:21:32] Mike Allton: Yeah. Well, I love that your point about how it only took you 20, 25 minutes.

Cause that's probably how long it took me to search through all the different tracks that are available on Facebook sound library and you know, sort and filter by genre or time. And probably could have just told you to go, Hey, I need a piece of music that's 60 seconds long. That's upbeat or whatever.

[00:21:52] Maryrose Lyons: And that's the name of your show. So ours is like called chatting GPT. And the, the soundtrack, the theme track is, it sounds like the kind of really upbeat Taylor Swift song. And it's like, it's, it's basically using the name of our show. I'm not going to start singing it now in front of your audience, because that would just be

[00:22:09] Mike Allton: fine.

[00:22:10] Maryrose Lyons: But actually check out, check out chatting GPT and you will see what an earworm it is. And it's got the name of the show in the, in the music. Something that would have been far, you know, beyond the realms of a business like ours and our, our brand new podcast. And now everything is possible.

[00:22:27] Mike Allton: Love it.

That's a fantastic tip. And folks, we'll have a link to Maryrose's podcast in the show notes. So you can go check that out, which we would have recommended anyways, but now you've got two reasons to go. You can learn more from arrows and you can hear what she's using for some music. But when it comes to enterprises, cause I want to get back to them for a second, Maryrose, what do you think some of the biggest misconceptions are.

at specifically at the enterprise level about about implementing AI successfully.

[00:22:54] Maryrose Lyons: Aha. Well, I think number one would be that this needs to come from IT, that it's a technical project that needs to be led by IT. And absolutely not. It is from, it applies across all the departments, all the divisions. We find that it works because it works, it solves problems around things like reporting, creating an admin, which land into every single department in every single role.

So that'd be the first thing. It doesn't have to be led by it. The second thing is that it's going to be a very big, hard, complex problem to, to solve and to implement. And it couldn't be further from the truth because, you know, as I said, at the top of the show, it's like, it's about, it's about showing people around.

It's still back to the fact that like, as the human, you are the one that's really good at doing your job. And that's why you're employed in those enterprises. So it's actually about showing you the tools to use. And how to use them. And then it's over to you to get the tool to do the amazing thing. And then it's back to you again at the end to review it, to check it, make your, make sure for accuracy.

Finesse it with a little bit of something and then use it. So it's actually about redeploying how we spend our time. So that'd be the second thing that it's, it's about. technical. And then the third point, I suppose, is that, you know, using AI in your own, it's not something you have to learn how to do.

It's actually something you have to practice. So I suppose it's akin to yoga or meditation. You know, nobody gives you a manual. On how to meditate and then you have to do it. It's actually about the more the more I kind of practice if I make the time in my day to get into doing some meditation, it will become easier after a few tries.

And that's exactly how it is with bringing AI into your daily workflow. One last point I would make on this is that another point is that it's not about age. And a mistake and a misconception is that, oh, we've a very young team. They're all going to be natural adopters of AI. What I have found to be true is it's actually about attitude.

So in the first, maybe six to 12 months of delivering AI training courses, I was actually surprised that the average age that was coming to learn from us was actually Upwards of about 38 and 40 years of age. These were early adopters who saw what was coming, didn't want to get left behind, didn't want to, you know, get overlooked and were embracing AI.

Now, what we found in the last 12 months is that, you know, that kind of age range is kind of spreading now. There were kind of delivering training to anyone up to the oldest gentleman who took one of our public courses was 69. And you know what, Mike, he was he gave me one of the kind of best bits of feedback ever was he told me that he has been retired for the last five years and up to this, he has really enjoyed his retirement, but not now.

He wishes he was back in work because of the pandemic. AI. So I gave him, I gave him an offer. I said, why don't you come and become an intern for us and use all your AI skills and come back into the workplace. So it's not really an age thing. It's an attitude thing. It isn't hard. You don't have to be some tech genius and you might find you might even enjoy it.

[00:26:23] Mike Allton: First of all, that's a, that's a wonderful story. And that actually reflects a conversation I have with Corbett Drummey of Lightricks. They had done a survey with the American Marketing Association and what they found was 77 percent of executives were using AI weekly and only 53 percent of staff were using AI weekly.

Very much to your point, opposite of what traditional adoption of technology might have looked like, like, say, social media, right? Where it was always the younger, you know, individuals, staff, workers, those kinds of things, not the executives. They weren't using it. So that's, that's fascinating. And I love your, you know, Analogy to meditating and yoga.

That's terrific. I'd never thought about it that way before, but you're absolutely right. It is something that you just simply need to do over and over and over again and incorporate it into your daily life. And that's where you'll start to open up all kinds of ideas and potentialities. And that's when you'll start to see a return on the investment.

So let's talk about ROI. What kinds of results are you seeing from organizations that you're working with after they've gone through this kind of proper training with AI?

[00:27:30] Maryrose Lyons: Well, this, this is really where it all comes together and for me, this is like the single biggest difference between the world I came from, which was digital marketing and the world I exist in today.

It just makes me get out of bed every morning because what we're doing is so quantifiable and it's so visible. So, like, you know, Mike, with, with social media, for example, it's about measuring engagement. It's about reach, impressions, and, you know, 3 percent can be quite good. And, you know, when you're trying to sell that into some, you know, hard nosed executive, they're going 3 percent is good.

And, you know, a click through rate on an email of like 27 percent is good. I want more than that. Whereas when you're working with results from AI, it's really quantifiable. It's really quick. The change can happen quite quickly. And we have about four metrics that we report on to clients. So the first one would be around efficiency gain.

So it's literally just by bringing in the AI tools, people are able to, you know, remove a lot of the, kind of the mundane admin tasks. You know, one of the things I always tell people is that, If you're doing a task in work and you sort of have a little voice in your head saying do I really need a master's degree or a degree to do this?

That's something you need to be wrapping AI around because it's something that you probably don't need to be doing yourself anymore. So firstly we kind of look at removing the kind of the mundane boring tasks. Then we look at improving the quantity and quality of the existing output. So this is what, you know, executives love because it's actually getting more from the, it's, it's more for less in the sense of you're getting more out of your existing teams.

So if you want to create more output, you Don't necessarily have to go off and increase your, your head count because you've saved this kind of time and you're, you've skilled up your existing staff to be able to create better results. So depending on the client and the enterprise we've worked with, like we've seen on average about 28 percent on improvement in output, but it can be sometimes upwards of 30 or 40%, which is whopper.

The last piece is that when you have your efficiency, your quantity, and your quality all working together, this starts to kind of bring about more innovation in, in the business or in, in enterprise firms that we work with. So what this means is that, you know, teams are finally getting everything done.

They're finally enjoying the kind of benefits of, you know, doing everything right. And then that frees them up as the humans to get together and to ideate about what they can do that's new. And the innovation piece I think is fantastic. And it also goes back to that original thing we talked about, which is that You know, there is no need for fear about your job being taken away because, you know, the thing that humans do best is coming up with new ideas.

And although the LLNs and the, you know, the models that we're using are fantastic, they're never going to create, well I shouldn't say never, but at the moment, they're not going to create brand new ideas. They can only give you back answers based on the training material that they've been trained on. So, you know, if you're human, they're And you're sitting down with your team to ideate and create something new.

You can keep on doing that, at least for the foreseeable future.

[00:30:52] Mike Allton: I like how you added that caveat, because every time I catch myself saying, Oh, AI will never do this. 24 hours later, it's doing that. AI could never conduct a podcast episode. And then we have That's right. And I know it's early days for that technology, but it's just an easy example of how overall this AI technology is exploding and improving at such an incredible rate.

It really is challenging to say that it won't ever do.

[00:31:23] Maryrose Lyons: Yeah, you're right.

Well, I was just going to say, so that actually kind of brings me into, you know, the way sometimes when you're speaking to people and you find yourself repeating the same message again and again, and as we're stepping into 2025, I think the message I have for anyone listening is that if you think of it, like, you know, What's going on in the way we work the world of work.

It's like a really fast moving river. So I'm kind of thinking of the mighty Zambezi in South Africa or in Zimbabwe. It's a really fast flowing river. And there's a lot of people who are kind of standing on the edges and they're kind of going, I don't know if I'm going to do this or not. And, you know, I had one conversation with some of the other day and they were saying, Oh, I think we're going to prioritize.

upgrading our CRM in 2025. And I was going, no, if you stand on the sideline, you're going to die. And I hate to use that kind of language, but it's, you know, it's very true. So it's like, you have to take that big jump into the free flowing river. You've got the likes of yourself and the AI Institute. We can be there to be your life raft.

It's going to be a big, Fast moving, exciting journey. It's changing all the time, but I think 2025 is the year you actually have to do this if you haven't done it already. Would you agree?

[00:32:38] Mike Allton: Oh, I could not agree more. Absolutely. And, and I'm sure there's some business leaders listening who, Also agree with you.

They're ready to take that leap. They're ready to start implementing AI. What's your advice for them though, where they want to create this culture of AI adoption and innovation within the organizations, the kinds of things we've been talking about today, but they don't want to overwhelm their teams.

[00:33:02] Maryrose Lyons: Yes. Well, I suppose what I would say is that number one, it's about education. It's about ensuring that you're bringing your teams along with you on the journey and that you're, again, back to this thing about overcoming the fear. So you're communicating that, you know, we are going to embrace AI and the reasons we're doing it are because, and you need to be, make sure that everyone is really clear about why you're doing it.

It's not to replace your staff, it's to actually help you, you know, do more with less or do, you know, do more faster. So that'd be the first thing. The second thing I would say is that, you know, it's, it's not about spending huge amounts of money on, you know, you got to invest in RAG technology, or you got to build your own, you know, model.

For example, I think unless you're in a very heavily regulated industry like pharma, for example, you know, it's not necessary to do that. You can literally take some baby steps. There are organizations like my own, the AI Institute, like your own, Mike as well, who are very much at the forefront of evolving the way we bring AI into organizations.

So, you know, go and talk to people like us who have, you know, already evolved and brought results into other businesses and try and understand what approach they have and see if that will work for you. It's about starting off with kind of identifying use cases and, and teams that are going to be open to it.

Thank you. Bringing a lot of education and then only when you've got like everybody in the organization is very comfortable using the publicly available tools that are out there. That's when I'd be going, okay, you know, maybe look at investing in, you know, phase two automation, AI powered automation, which is going to kind of prepare you well for the agentic sequence of events, which I think might be coming our way towards the end of 2025 as well.

[00:34:58] Mike Allton: Absolutely. I've talked before on this show about how at Agorapulse we implemented our own RAG, we use a tool called 1Up and we fed it all kinds of information from our knowledge base, our customer support center, our public blog and created for internal use. A repository where our sales reps and our customer facing teams could simply go into a slack channel or message the bot directly through slack and ask it anything about our tool.

What case studies do we have? How do we go about building an RFP for a government entity in France? Anything we've ever documented is there at their fingertips and they don't have to search a slight. our repository for just documentation or our blog or the, I mean, there's just so much convenience that can happen with that kind of thing.

And it's just yet to your point, another use case. An example that folks like you and I can share with the businesses. Hey, this is an area where you're struggling, where we can improve. Here's a great use of A. I. Now, let's go. You had your example of a river, which is, which is a great metaphor, but like all metaphors, it breaks at a certain point.

So if we're looking ahead, what should enterprise be focusing on now to ensure they're not playing catch up? And that's where the metaphor breaks, right? If they're jumping in, how far do they jump in? I don't know how we could take that metaphor, but how do they make sure what, what should they focus on so that they're not playing catch up over the next couple of years?

[00:36:20] Maryrose Lyons: Well, I suppose coming from an EU perspective, which is where we're based the, and this is going to be applicable to, you know, everyone. If you're selling into any EU country, you have to appear to the EU AI Act, which actually comes into force in February, 2025. So you've no choice. You have to actually embrace that.

So with the EU AI Act for anyone who isn't really familiar with it yet. It breaks down AI into four different types of risk, four different types of risk category. The, there are certain behaviors that are completely banned EU, such as, for example, governments monitoring their citizens, which I'm really grateful for.

We, we don't have that coming in the EU. And there are other kind of, you know, bad practices that are just outright banned. You've got the high risk category, which is around like, we're kind of calling it your money or your life. So anything around finance and health decisions may be made by recruiters.

That's in the high risk category. So that's got a certain set of rules. But the main one, which is the low risk category. Which would be like, maybe a large majority of your listeners would fall into, is literally about transparency and education. So, it's about transparency, about that you use AI tools, and then what that means is that you need to communicate To your both your internal and your external audiences.

So we advocate doing this through your AI policy document. What? You haven't got an AI policy document? Well, we have a really good template on our site. We advocate putting your AI policy document as a footer link on your website. And then all of your external audiences know that we are using AI tools.

These are the tools we are using. We have selected them because the following security and safety protocols. And then it carries over into the education piece again, which is that we have trained our staff and how to use these tools. And that's where, again, it comes back to the act itself. It says that, you know, if you're going to be using AI in your business, You need to train your staff.

So it comes back to what we're about at the AI Institute. Just to carry that through the last final section the final risk AI Act is minimal risk. And that's just like, you know, using filters and Snapchat. So it's, you know, it's nothing to worry about. So if you are selling into the EU, you need to be aware that you need to have an AI policy from the EU.

February 2025. If you need a little bit of help with this, there's a lot of resources online. We've got a great template on our website and, you know, never before has the word policy been as sexy and as exciting as it is in the EU in the end of 2024

[00:39:05] Mike Allton: That's absolutely right. But one of the things we talked about earlier was that disparity between how executives and individual staff are using A. I. And that same disparity sadly also applies to training. Individuals just aren't getting enough training. And I know that's something that you guys do exceedingly well, but we haven't talked a lot about your specific courses.

I'd love if you could share just a little bit more about what you've Your courses, how they're delivered, how those run, give folks a sense of what they could potentially expect if they started to work with the AI Institute.

[00:39:36] Maryrose Lyons: Sure. I'd love to. And we'd, we'd, we'd love for any of your listeners, Mike, to come and join us.

In fact, actually, I would actually go so far as saying I would put up a gorgeous coupon code called Mike 2025. And it will give a 15 percent discount on any of our courses for any of your listeners. So, our courses are all cohort based learning. Okay, so what this means is that when you've taken the core course, which is around, you know, what are the models?

What about prompt engineering, safety security protocols, then you can learn with your tribe. So what, what this means is that you can take our marketing track where you will learn with a very experienced expert course creator from the world of marketing, and you will learn with other marketers in live online sessions.

Okay. So don't do prerecord because a, it'd be extremely difficult to keep up because we update everything literally by the week, but be. There's a lot of magic when you're learning with other people. And there's that kind of wow factor and sharing of experience and sharing of use cases. So that's why we keep a small group sizes and live online learning.

So we have a marketing track. We have a track for salespeople and business developers, so they can all learn how to take the kind of the. Standard, you know, approach to sales and augment it with AI tools and amazing prompts and GPTs that you can build out. Anyone who's taken that course has said that they've actually identified brand new prospects to target that they never have come up with themselves without a little bit of help from AI.

We've got a finance track. We've got a data track, a HR track, but many, many different tracks. They're all on our website. And when you're learning with us, you get your live online learning, you get workbooks. And then we also do these drop in sessions, which. We kind of started them out, Mike, which was for, you know, if people were finding some of the concepts a little hard to grasp, they could get on for a kind of a one, one to one with their course creator and to get their questions solved.

What we find is that these are more just enthusing sessions for people just love to come on and just. speak in a really positive way about what they're learning. So the course creators then use this to kind of like add in a little bit more detail and kind of push the person to kind of go a little bit deeper with it.

So really what we're about is augmenting the way people work. The beginner's course or the core course, it's delivered over three weeks. So it's a couple of hours over three weeks. You get homework, you do your homework and you will change the way you work.

[00:42:15] Mike Allton: That's fantastic. And I love that you've got these different tracks for, for different areas of the business.

That's, that's so clever because you're right. There's, there's so many different uses of AI depending on your job role and being able to really lean into how HR can benefit from AI as an example. Is terrific. And Maryrose, you've been absolutely fantastic. I know this has been really interesting and informative folks are listening.

And by the way, if you're listening, we will have the link to the courses and in the academy and the coupon code that Maryrose so generously offered. We'll have all that in the show notes below, but for those who want to reach out and learn more from you specifically, where should they go?

[00:42:54] Maryrose Lyons: Well, I'm all over LinkedIn.

That's what I'm told. So Maryrose Lyons on LinkedIn, you will find me there. And if you are coming through on LinkedIn, just say that you are, if you're sending a connection request, say you heard here and then I'll be sure to respond extremely quickly. And I genuinely love having conversations with people on LinkedIn.

I spend a lot of time in my DMs and any questions, anything I can help with, anything from our experience that can help you on your journey, I'd be absolutely delighted to help.

[00:43:25] Mike Allton: Brilliant. Thank you, Maryrose. And thank you all of you for listening. That's all the time we've got for today, friends.

But remember if this is your first foray into the world of AI marketing, I've got the perfect resource for you in the show notes below that AI marking primer that I created to quickly get you up to speed on the terms, the tactics, underlying technology, all of it. That's powering this AI revolution.

Check it out and I'll see you in our next episode. Welcome. to the grid.

That's a wrap on another episode of the AI hat podcast. I hope you gained some valuable insights and actionable strategies to boost your business with AI. If you enjoyed the show, please subscribe, leave a review and share it with your network. And don't forget to visit theaihat. com for more resources and to connect with me.

Until next time, keep innovating and remember the future of business is human and it's powered by AI.

In this episode of The AI Hat Podcast, learn how medium - enterprise level businesses are approaching AI training & integration successfully.