AI Beyond the Hype: Practical Solutions for Real Businesses

How this transcript was made

Transcribed by machine from the published audio, then corrected by hand. It is not a verbatim record: the changes below were made, and are counted.

This episode

57 speaker turns, 145 changes.

• 30 turns labelled "Guest" resolved to Alastair McDermott

What was changed in these transcripts, and what was not

Generated: 2026-09-20 from 2026-09-20-named/, itself transcribed from the audio that is live on the feed. Every change below is mechanical and counted. No sentence was reworded, no claim altered, nothing removed for being awkward.

The editorial decision

Owner's choice, 2026-09-20: light clean. "Um", "uh" and stammered word repeats ("and, and") are removed, because nobody intends to say them. "You know", "I mean", "kind of" and "sort of" are KEPT - they run to roughly 2,300 instances across the catalogue and stripping them would make the page read as somebody else.

Counts across the whole set

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stammered repeat removed	667
um / uh removed	654
space before punctuation	241
split contraction	132
guest turn named (Joe Casabona)	101
organization -> organisation	71
diarisation fragment returned to the host	62
ai -> AI	57
empty turn dropped (was filler only)	36
synthetic outro restored from script	21
organizations -> organisations	18
hyphen followed by space	14
realize -> realise	14
A.I. -> AI	10
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behavior -> behaviour	8
synthetic intro restored from script	8
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behavioral -> behavioural	7
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personalized -> personalised	5
skeptical -> sceptical	5
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optimization -> optimisation	4
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canceled -> cancelled	4
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What was deliberately left alone

• "Program". A computer program keeps that spelling in UK English; only a broadcast or a scheme is a "programme", and only a reader can tell which was meant.
• "License" and "practice". Both have a noun/verb spelling split in UK English that a rule cannot decide.
• Capitalised words mid-sentence that would otherwise have been respelled: 4 of them, most often part of a name. Left as spoken.
• Retired show and brand names. Spoken by the host and guests. A transcript that removes what was said is not a transcript.
• "Co-pilot". Sometimes Microsoft's product, sometimes the metaphor. A rule would corrupt the honest ones.

The synthetic segments

The opening voiceover on the archive episodes and the closing call to action are read by Max, the show's AI narrator, from a script written in advance. Transcribing them means guessing at words already on file, and the guesses were wrong in places - one opener came back as "an AI that boned him up" where the script says "phoned him up". Those passages carry the authored text instead, and are attributed to Max rather than to the guest: the diariser groups Max with the non-host speaker, which would otherwise put a real person's name on words they never said.

Alastair McDermottI was asked to speak to a group of business leaders recently about AI and the practical implementation and going beyond the hype, which is where I think we're at now.

There is a lot of hype around AI, but I think that a lot of that hype is warranted. This is something that we genuinely need to pay attention to now. AI is no longer a futuristic concept.

It's actually reshaping industries right now and it's incumbent upon us as business leaders to think about how we are going to use AI in our businesses and to start to implement,

because we're going to get left behind by our competitors if we don't think about AI and don't use AI. So this is something I think is very important to look at

Right now.

And I think the timing is important because the pace of acceleration is incredible. So this is my presentation to a local business group here in Ireland and I've got the slides available.

If you visit learn.AI.guide, you can get a free guide to getting started with AI. I'm going to be updating that guide and I'll be sending that if you sign up for the email list there.

I'll send an updated version of that pretty soon in the next week or so. I'll also be sending the slides and a video of this presentation to that list as well.

So if you visit learn.AI.guide, you'll be able to get all of that. And now here is my presentation, AI for business leaders, AI beyond the hype.

So one of the things I've noticed about AI is there's a lot of hype and I think that the hype is damaging because the possibilities are genuine and real and can be implemented even today in

our businesses. So there was a quote that came out from Sam Altman, who is the CEO of OpenAI. They're the ones who make ChatGPT.

And this has been now referred to as the quote by a lot of people because this is talking about what AI is capable of doing in positive and negative sense.

So he said it will mean that 95% of what marketers use agencies, strategists and creative professionals for today will be easily, nearly instantly and at almost no cost be handled by AI.

So the implication of this is that 95% of marketers or 95% potentially of knowledge workers or the work that we're doing as knowledge workers could totally be handled by AI.

So that means that people who are professional services, who are knowledge workers and experts, AI is coming for us first, which is interesting and challenging.

So we need to think about that.

So that's the kind of the context to all of this. There's been some recent developments and I just picked a few at random here.

There's so many that it's, you know, there's so many things happening, but here's some recent developments that have

Happened.

There's a company called AlphaFold and what they've done is they've used an AI to generate all possible proteins.

So it used to be the case that researchers, they would need to try and test creating proteins and it would be a process that would take months and years.

So AlphaFold have already generated every possible protein that can be created is modeled and mapped out, which means that researchers just now go and look up the directory to see is a

protein possible. And that is revolutionizing science and the capabilities with that.

Microsoft came out with a tool recently where they could clone a voice, but that's been done before, but they could clone a voice realistically from three seconds of audio,

which is incredible. And also scary in the sense of, you know, creating fakes and all of that kind of stuff as well.

So when you think about that, WHO, the World Health Organization, has talked about how they have found more accurate medical diagnosis.

So that's a real positive because misdiagnosis is an issue.

And so having more accurate medical diagnosis, there was one thing that they mentioned, which was they could diagnose COVID-19 from the sound of the specific sound of a

Cough.

So they were able to tell whether it was a COVID-19 or just a regular cold or flu from from the audio of a cough. So that's pretty incredible as well.

There's a tool called Sora from OpenAI, which they haven't released the general public, but it looks very powerful where they can generate realistic videos from a simple text prompt.

And that's going to be something else that's going to be coming down the line. And that will be both pretty cool, you know, as a user, be able to generate videos.

Imagine being able to create, you know, ads for your products for Facebook, you know, creating videos. Let's say you could create a video for your kids about, you know, their bedtime story.

You could you could all this like the possibilities are endless, you know, creating educational videos about products. So many different things that we can do with that.

So those are all kind of the stuff that's happened recently, stuff that's coming down the line. There are new models and ChatGPT-5 is probably already

Ready.

So GPT-4 is the one that we're using at the moment. It was a big step up from GPT-3.

GPT-3 was the one that kind of opened everybody's eyes to the fact that, wow, this is actually a big deal here. GPT-5 is the next version after GPT-4, which is quite advanced.

Like everybody else is being measured against GPT-4 and they say, well, this is, you know, this is a GPT-4

Equivalent model.

For example, the latest ones from Google, people like that. The fact that GPT-5 is going to be a big advance, I think that they're holding it back because it's so advanced.

So they're kind of just getting us ready for it, really. And that's something that Sam Altman has mentioned before that, and I'll give you a quote from him, but that, you know,

they sometimes deliberately slow down the pace because they want to allow society to adapt to the capabilities of these things.

AI agents are something that is going to be, you're going to hear an awful lot more about that over the next year or

Two.

And agents are, so the way we're using AI typically right now is we're using, we're having a chat with a chat bot.

So we're typing in or speaking, and you can speak to it, by the way, if you install the app on your phone, you can have a conversation literally with it.

But AI agents are these more autonomous AI where you can set it up to do multi-step actions.

So, for example, an agent, I might be able to say, I want to travel to Mallorca and stay in Palma. So I want you to book me the flights and the hotel.

And here's access to my email and do all the verification stuff that you need to do. Just go and do that. Here's the dates that I want to do or pick the dates off my calendar.

Now, the agents are going to be able to do that at some point in the future where we'll be able to just give them tasks and get it to go and do those things.

So you can see you'll be able to automate complex tasks and

Repetitive tasks.

Robotics is also getting a big shot in the arm of investment because of the development of AI.

And OpenAI have actually partnered with a company where they've put ChatGPT and the audio version of ChatGPT where you can have a conversation.

They've put that into a physical robot and they were able to get the robot to pick up an apple and, you know, put put a plate into a drying rack and things.

So so the the technology in robotics is getting it's getting added. AI is getting AI reasoning is getting added to robotics.

And then AI, sorry, robotics itself is also getting a big investment because of this, because there's there's tr illions of dollars being put into all of this development.

Ultimately, down the line, there's a thing called AGI, artificial general intelligence. And that's the thing that is both incredibly scary. And the potential is very positive as well.

The issue is this could this is so artificial general intelligence or AGI is where an AI gets to the stage where it is as smart as a human being and can reason in the same way.

And we don't know when it'll get there, but it probably is going to get there. And so the issue is that that could lead to a complete change in how we think about work, education, society.

Because, for example, if computers and if AI can do all of the knowledge work and can do everything that we do, like, how does that impact our education system?

How does that impact our society? Like, do human beings do we sit about on the on the couch, you know, scratching our ass and looking at TV?

You know, is that is that the future of our of our race? You know, so there's all sorts of really scary things about

What can happen there.

And then the flip side of that is, well, maybe that will, you know, maybe it will create a lot of it'll create a lot of new jobs. It'll create, you know, people will be able to work less.

There's all sorts of different implications of that as well

So one example that OpenAI put out as a case study.

So I don't know why that screen is flashing.

I hope it's not flashing for you guys. One example is Moderna. Moderna is the company that make the vaccines. And so they have implemented ChatGPT and OpenAI in their systems.

And so they're finding that a team of a few thousand can perform like a team of 100,000.

So they've got a team of, I don't know, maybe three, four or five thousand employees, something like that. And they're saying that they can have the same productivity as a team of 100,000.

So this is pretty incredible in terms of productivity. And after two months of adoption, so only two months, after 60 days, employees were having 120 conversations per week on average.

That's each employee is having 120 conversations per week. So that's these chat conversations with the chatbots. So that's around one conversation every 20 minutes that they're having.

And these, the bots that they're using are helping them to do their job better.

Now, for example, one of the things that they used as an example in the case study was they have a compliance, a legal compliance bot,

their own version, which has been customised just to scan legal documents.

And they give it a contract and they ask it to give them a summary of the contract and call out what the things are they need to look for. And this is stuff that we can all do, by the way.

This isn't just doesn't have to be Moderna. You can do this right now if you want to. But that's the power of these tools in terms of productivity.

And also then, well, do you need to call a lawyer?

Well, that's a good question.

So, you know, the issues are like for lawyers and for all knowledge workers, a lot of the grunt work that we do as knowledge workers,

as experts may be taken over by these systems as they learn more and get smarter. So here's another quote from Sam Altman.

We've been through massive technological shifts and a massive percentage of the jobs that people do can change over a couple of generations. And over a couple of generations, we absorb that.

So as a society, we absorb that. Every technological revolution has gotten faster and this will be the fastest by far.

And the part that I find potentially a little scary is the speed with which society is going to have to adapt.

So, you know, like we have seen, for example, you know, we changed like humanity has changed over time when we saw the development of, you know, the development of cars.

You know, there would have been farriers and blacksmiths and carriage, you know, drivers and, you know, there'll be people managing stables.

They all lost their jobs because of the introduction of

Cars.

And so in the same way, this introduction is going to change. The only issue is this is happening very quickly.

And I think that's the thing that people are both fatigued by and scared by, you know, how like how does this impact on us? So it's it's interesting.

So I started a podcast called the AI Powered Thought Leader and it's interesting in its impact on thought leaders in particular.

And I'm probably going to rename this, by the way, to the AI Powered Business Leader, because I think that the general implications in business are very interesting.

So I've spoken, I've spoken with a lot of thought leaders and really smart people. I've spoken, I decided I wanted to talk to a lawyer about the legal issue.

So I spoke to a lawyer about the legal perspective and she's an IP lawyer. So she had a good perspective on that. I spoke with people like Mark Schaefer and Chris Doe and Jonathan Stark.

Mark Schaefer is an author of, I don't know, 10, 15, he's 15 books. He's a leader in marketing. Chris Doe has, I don't know, maybe four million people following him on YouTube.

So that's the type of people I've been talking to about this just to get their perspective on this.

So here are some takeaways that I've taken from speaking with some really smart people about this, about how we can implement this in our business.

So AI is increasing our productivity and it's doing that by helping us to automate repetitive tasks.

It can help us to do things like analyse research, reformat content, and it allows us to focus on our core expertise. That's the really interesting thing about this.

And so that's the positive side of this is we can increase our productivity and do more and not have to work on the gr

Unt work.

So the repetitive tasks, that's a big one for me personally

The analysing research is really interesting.

So I did a survey of over a thousand consultants a couple of years ago and I pulled out, like I read through all the data and I pulled out all of the insights manually myself originally when

I did it in 2019, 2020. Now I took all of my survey data and I just fed it into Google's AI studio.

Google's AI studio is interesting because there's a version of it, a beta version of it that we can access that can take up to 1 million tokens. So that means that it can read a lot of data.

So one of the big limitations right now with these systems is the amount of information you can feed into them. The more information you can feed in, the better the output

Usually.

The Google AI studio is really interesting because you can put in a million tokens, which is about the equivalent of three or four business books.

So you can give it three or four business books worth of input and ask it to process that.

So I was able to give it all of the survey data that I had from this big survey and asked it to pull out insights. And it was really interesting.

I was able to pull out the same insights that I had spent hours, days, weeks doing manually. It did it in 25 seconds. It was incredible.

So doing things like that, analysing research and reading documents, all of that stuff, reformatting content. So stuff that we've already done.

So, for example, I'm talking to you now and I'm recording

This.

And so I can take the recording. I can take the transcript of that and I can ask it to reformat that as a blog post, for example.

And so I find that really interesting that it can just instantly reformat content. And that gives me more time back. It allows me to take something that I've done and reuse it

In different ways.

So that's the productivity side of things. One of the issues that I found, particularly in terms of thought leadership, is how do we stay authentic?

So one of the things is if we're creating, if we're writing, we have to be careful that we don't compromise our own credibility.

So, for example, if you post something up on LinkedIn that you got AI to write for you and people see that it's clearly written by AI because sometimes it's easy to tell by the word choices

and things like that. The formatting even that could potentially compromise your credibility as an expert because people see that you didn't write it yourself.

So where is the line for using AI to generate stuff for us or when we add our own thoughts into this? So where is that line lie? And it can come across. It's clearly not in your voice.

So you have to be careful about how you use it to generate. And then there's also potentially a danger of becoming over -reliant on these systems.

One of the things that I've noticed, and I've written a couple of books, I've got two regular-sized books and then I've got four or five booklets. And they're all up on Amazon.

And one of the things that I've noticed is that the process of writing is how I develop my own thinking.

So when I write, that helps me to formulate my thinking and it helps me to think better because I'm trying to explain a concept.

And so if we're using AI to do more of our writing for us, we have to be careful because it may take away from that thinking that we normally do.

So be careful of the over-reliance of it as well. Another issue is authorship, credit, and intellectual property. So first off, the concept of being an author is being redef ined.

And so this is something, like to say that I wrote that. So my friend Joe Casabona, we had a fun conversation about this actually about six or eight months ago.

He said that using ChatGPT to write a book, saying that you wrote a book using AI, is like saying that you used a car

To run a marathon.

And it's an interesting kind of comparison. So where is the line in claiming credit for writing something?

So if I sit down and I type out and I write something, then I've clearly written it.

If I use a dictation tool and I'm driving to Dublin and I dictate and record that dictation, and then I say, please transcribe that,

and it takes my words verbatim, then you'd probably say that I wrote that.

What happens if I dictate it and then I say, I want you to tidy that up and pull out the key points and then reformat it as a chapter of a book? Now the line is kind of getting blurred.

Did I write that or did I not write that? Because I provided the ideas, but I didn't write the specific words that are there.

So the line gets more and more blurred the more you use this assistance. And then there's legal and ethical issues around ownership.

So in the US, the Supreme Court ruled that works that are generated by AI are not copyright protectable. They're not copyrightable.

And so it's, and again, all of this, it's not as simple as it sounds on the surface.

And it's worthwhile listening to the conversation I had with Aaron Austin, if you're interested in that side of things.

But the lines, so if we put, if we put ideas in to AI and then get it to give us back, then it may be somewhat protected by copyright. But it's, it's not a straightforward thing.

And then claiming credit. So like, do we say, like Joe said, you know, you can't claim credit if the AI wrote it for you. What's the definition of wrote it, you know?

Because if you, if your input is, if you're, if you're putting a lot of stuff in, I'll give you an example.

So I have a custom ChatGPT bot, a custom GPT, which is when you create your own version and you put your own text into it.

And so in one of the ones that I've created, I put the text of all of my books and all of my blog posts.

So I put all of my content in there and I taught it how to speak, how to write in my voice, in my writing style

Now, if I use that bot to write something, can I claim ownership for what it writes? Because I provided the input or, you know, where, like, where's the line there?

It's, it's, it's interesting. And it's like, this is all challenging. There are also ethical issues.

So for example, there may be inbuilt bias, biases, stolen training data, that should say not date. And then there's privacy and GDPR. So for example, an inbuilt bias.

So one of the issues is the, what are these trained

On?

So these models, these large language models are typically trained on billions of pages of text and there are built inbuilt biases.

So for example, when these systems came out first, I think these have been fixed.

If you said, for example, give me an image of a beautiful woman every time, 100% of the time, if you ask that over and over and over again, it would always give you a blonde white woman.

And that's just an instinctive inbuilt bias because what it was trained on and what the, what our media consists primarily of is the idea that a beautiful woman is a white woman.

And that is, and that's just because of, you know, cultural

Reasons.

So if there's inbuilt biases like that, and that's only one example there, you know, there's so many other, you know, potential racism, all of those political leanings.

All of those biases that could be present in training data.

And so we need to be aware of, there could be potentially biases in the systems that we're using and just, be careful about how we use that.

A second thing is there's a lot, the stolen training data.

So basically these systems, what, when they, when they trained them, they just took entire libraries worth of books and use them to train.

they took, for example, the entire archive of the New York times and put all of the articles in there and the New York times sued open AI for it.

And, I think, I think that lawsuit is complete.

I think they were found guilty.

I'm not sure. but those are just examples. So if you're an author and you know, that, that, like let's, let's say, let's say Terry Pratch ett or something like that.

So if you said, you know, write a story in the style of Terry Pratchett, and here's what I want you to do now, Terry Pratchett is no longer with us,

but his estate could say, Hey, you know, you can't use, like you can't train on all of Terry's books and then use it to create a book based on that, because that's his intellectual property.

And then the question is it, and so that's what the lawyers are fighting about. There's also privacy issues and GDPR is one of the reasons why in Europe, I can't use Google AI studio.

For example, the tool I just mentioned, unless I VPN. So unless I pretend I'm coming in from a US IP address.

And one of the reasons for that is because we have a lot of privacy protections in terms of GDPR. And, so the AI, companies are trying to figure out how to, how to do that.

So right now they're just blocking access on some of the

Tools.

We don't get the same access as if we were coming

From the US.

We have more access when, in using these tools and that's because of privacy and GDPR.

So for example, I use an AI note taker quite often when I'm doing, calls with my clients, when I'm doing, you know, calls with people.

now the AI note taker that I personally use has a notice. So when somebody joins, they see Alastair McDermott is recording this, for note taking purposes.

Now that note taker takes the conversation that we have, it processes the audio, it transcribes it and puts it into its database,

which I can then, ask it to give me, you know, a summary of the call.

Now, for example, if I'm having a call with a client about their website and I say, look, here's, here's what we're going to do.

And they tell me about their website issues and maybe they tell me something about their business. Maybe they tell me something personal on that call.

All of that is gone into the AI's, and being processed and gone into the AI's memory. The AI companies may train their next models on that.

there are serious privacy implications in terms of if we record, conversations and put that information in there.

for example, if I got a, an email from a client where, for, let's say it's a website inquiry and I copy paste that email into the, into the AI system.

And I say, I want you to give me, I want you to process this and I want you to give me a response.

I want to give them a quote, or let's set up a meeting or tell me what the important points are that they really care about.

I can, I can do lots of different things with that information once I put it in, but if I don't take their name or their business name off that email before I feed it into the AI,

the AI may have a copy of that. and, that's something that we need to be careful of is the privacy implications of this. So those are all things that we need to think about.

so here's what an AI ready business might look like. one thing I suggest that every business does is to become AI ready in terms of culture and structure.

So, kind of best practice for this, I think is to pick people in different teams in your business and appoint them to say, look, you are the AI team.

we want you to explore how we can use AI in the business and give them, give them the tools to do that promote AI literacy.

So this is one of the most important things for all of us, whether we're solo business owners or whether we've got a large business is investing in education for training for ourselves.

And for employees to have a basic understanding and understand what the potential applications are and to create this culture where being innovative and being responsible about how we use AI

and being aware of the things like the privacy and ethical issues.

and a place you can start is to look at use cases for specific things and you can look at, you know, where can we be more efficient?

How can we, increase our productivity or how can we give a better customer experience using AI?

So start looking at case studies and ways that you can use AI today and start adopting its use. And I would strongly suggest having a policy.

So having guidelines about how you use these tools. And this is particularly important where we've got things like GDPR and privacy issues.

So, and, you know, one of the issues that, Aaron Austin, the lawyer talked to me about is when we're creating content.

there is a big difference between creating content where we intend to sell that content versus where we don't intend to sell it.

So for example, if I was to use AI in helping me to write a book that I want to sell, that's a very different use case in terms of the,

the implications, the legal implications of ownership versus if I was to use it to write a LinkedIn post. So I would be much more comfortable using it to write a LinkedIn post.

I would be much more careful about using it to write a book that I wanted to sell, for example.

So, creating policies around how we use it and think and talking to people about those policies and then continuous evaluation.

This is really important.

AI space is moving rapidly. So we have to continuously evolve and continuously evaluate an experiment. So like, there's no place for head in the sand here.

Like we have to continuously, evolve and look at these things and, and talk to people about it. So, that's it for me. I would love to talk to anybody.

I'm particularly looking to, to do some small case studies with some businesses who are interested in doing, AI. And, I'm happy to talk to anybody about it.

that's my email address, amd at website, doctor.com. If you scan the QR code, it will set up a virtual meeting with me.

And I'd love to chat with anybody who's, well, I'd love to chat with anybody anytime, but specifically about AI

I'm really interested in talking to people about how they

Might implement it.

So thanks for listening. And if you would like to chat with me about this or any other issues, I would love to chat with anybody who has

Listened to this episode.

You can book in on my calendar for a virtual coffee at or E C dot N Z slash coffee. That's or E C dot N Z slash coffee. That's the short URL I use for the recognised authority.

And I would love to chat with anybody who has listened to this episode. If there's anything that you want to talk to me about AI, about marketing, about business, I'd love to chat

With you.

Thanks for listening and see you next time.