3009: AI and Business Strategy: How Hyland Solutions is Shaping Tomorrow's Enterprises

[00:00:03] [SPEAKER_01]: Welcome back once again to the Tech Talks Daily Podcast.

[00:00:08] [SPEAKER_01]: Now today I'm going to be joined by Tiago Cardoso, Group Product Manager at a company

[00:00:14] [SPEAKER_01]: called Hyland.

[00:00:16] [SPEAKER_01]: And with a comprehensive background in computer science and AI plus, over a decade of experience

[00:00:22] [SPEAKER_01]: in developing everything from sophisticated web, mobile and 3D applications, Tiago stands

[00:00:29] [SPEAKER_01]: at the cutting edge of technology innovation and they are just a few of the many reasons

[00:00:34] [SPEAKER_01]: why I'm excited to get him on the show today.

[00:00:37] [SPEAKER_01]: And we're going to talk about how AI is not only revolutionising AI industries far

[00:00:43] [SPEAKER_01]: and wide from healthcare to insurance and banking, but also how it's influencing

[00:00:48] [SPEAKER_01]: computer science programmes in universities and even impacting workforce readiness.

[00:00:54] [SPEAKER_01]: It really is a cracking conversation this one.

[00:00:57] [SPEAKER_01]: And we've got so much to get through, so enough spoilers from me.

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[00:02:02] [SPEAKER_01]: Now is the moment you've really been waiting for.

[00:02:05] [SPEAKER_01]: It's time to get today's guest on.

[00:02:07] [SPEAKER_01]: So buckle up and hold on tight as I beam your ears all the way to Lisbon, where

[00:02:13] [SPEAKER_01]: today's guest is waiting to join me.

[00:02:16] [SPEAKER_01]: So a massive warm welcome to the show.

[00:02:19] [SPEAKER_01]: Can you tell everyone listening a little about who you are and what you do?

[00:02:24] [SPEAKER_00]: Hello, I'm Jack Cardoso.

[00:02:26] [SPEAKER_00]: I'm in sunny Lisbon, Portugal.

[00:02:28] [SPEAKER_00]: I'm a principal product manager at Highlands and focusing on AI, machine

[00:02:36] [SPEAKER_00]: learning and generative AI.

[00:02:39] [SPEAKER_01]: Well, it's a pleasure to have you join me on the podcast.

[00:02:41] [SPEAKER_01]: I love that you're in sunny Lisbon.

[00:02:43] [SPEAKER_01]: I'm slightly jealous because the sun's disappeared for the day here in the UK.

[00:02:48] [SPEAKER_01]: But there's so much I want to talk with you about, especially around the future of AI

[00:02:52] [SPEAKER_01]: and enterprise content management, a subject very close to my heart.

[00:02:56] [SPEAKER_01]: And can you share your vision of how you see AI, how you see it transforming

[00:03:01] [SPEAKER_01]: content management in industries far and wide from healthcare to insurance and banking?

[00:03:08] [SPEAKER_01]: How do you see all this evolving over the next few years?

[00:03:11] [SPEAKER_00]: Right. So usually content management, it's mostly unstructured data.

[00:03:18] [SPEAKER_00]: So with the advent of generative AI, I think this was a big thing in content

[00:03:26] [SPEAKER_00]: management because now those new models, LLMs, they can really leverage the text.

[00:03:32] [SPEAKER_00]: They can leverage all the information that is fully unstructured in documents

[00:03:36] [SPEAKER_00]: and get you the right information that you need at the right time.

[00:03:39] [SPEAKER_00]: And they'll start to also work for images. They already work for images pretty well.

[00:03:45] [SPEAKER_00]: They work with audio because you can even convert it to text, but video, it's coming for sure.

[00:03:54] [SPEAKER_00]: So having models that can leverage big videos and get you all the information that you need.

[00:04:00] [SPEAKER_00]: In terms of how it's useful, I think initially it's more about

[00:04:04] [SPEAKER_00]: augmenting business users and also doing some automation for content-based processes.

[00:04:13] [SPEAKER_00]: The goal is not to make users read endless pages of documents or finding, searching

[00:04:20] [SPEAKER_00]: endlessly for assets or for the right knowledge, but just asking and getting back the right

[00:04:27] [SPEAKER_00]: answer. But also when you are ingesting contents, you have the traditional IDP where

[00:04:33] [SPEAKER_00]: you digitize your content, but now you have the opportunity to not only digitize it,

[00:04:38] [SPEAKER_00]: but really to extract all the needed context, to extract the things that are relevant for

[00:04:44] [SPEAKER_00]: your business that will advance your processes and that will just make your business active

[00:04:50] [SPEAKER_01]: and productive. And there is so much hype around AI at the moment. I think sometimes we

[00:04:57] [SPEAKER_01]: lose sight of, hey, what problems are we actually solving here?

[00:05:01] [SPEAKER_01]: One area in particular that stands out is AI's impact on healthcare. So from your

[00:05:07] [SPEAKER_01]: viewpoint here, if we look at the healthcare sector, how are you seeing AI currently being

[00:05:11] [SPEAKER_01]: used to improve anything from patient care to operational efficiency? And again, where do you

[00:05:18] [SPEAKER_01]: see this evolving? What advancements do you foresee in the near future too?

[00:05:22] [SPEAKER_00]: Right. So in the past, this has been more in terms of predictive AI. So getting all the data

[00:05:30] [SPEAKER_00]: analytics, numeric data measures with the patients in the hospital, how they are doing

[00:05:38] [SPEAKER_00]: their values and getting you some indication of if a patient needs care, if a patient

[00:05:46] [SPEAKER_00]: need to return to a new consult in the future. Then you started to have deep learning models

[00:05:55] [SPEAKER_00]: and they started to do some type of, I wouldn't say diagnosis, but at least highlighting

[00:06:01] [SPEAKER_00]: some information on the patient exams and enterprise imaging. And that then went into

[00:06:11] [SPEAKER_00]: more gen AI where today you can really analyze some medical information to translate it so

[00:06:20] [SPEAKER_00]: patient can understand it perfectly. They understand what they have, what is the

[00:06:25] [SPEAKER_00]: expectation. It's also helpful for the medical, the doctor that can summarize really quickly

[00:06:33] [SPEAKER_00]: the patient history. It can ask questions about that patient specific things, not just

[00:06:38] [SPEAKER_00]: to get the summary, but how is this patient passed with some specific disease or around

[00:06:47] [SPEAKER_00]: metabolic diseases, things like that. So using gen AI really it's helpful to get

[00:06:54] [SPEAKER_00]: insights and information on the patient to be quicker, to be more productive and really to

[00:07:04] [SPEAKER_00]: spend the patient time with the thing that matters.

[00:07:08] [SPEAKER_01]: I love that. And one of the other things I love doing on this podcast is highlighting

[00:07:11] [SPEAKER_01]: so many different industries that are impacted by technology, especially areas you don't

[00:07:16] [SPEAKER_01]: automatically associate with tech. So if we switch industries for a moment, what specific

[00:07:24] [SPEAKER_01]: challenges and opportunities do you see AI presenting for something like insurance and

[00:07:29] [SPEAKER_01]: banking industries, particularly regarding data management and customer service? Because

[00:07:34] [SPEAKER_01]: there's such a wealth of data in those fields, isn't it?

[00:07:37] [SPEAKER_00]: Yeah, that's right. And one area that we are seeing, it's really the interaction

[00:07:42] [SPEAKER_00]: between the banks and insurance and the patients make all the processes quicker.

[00:07:48] [SPEAKER_00]: So if you have a claim, you want to have the result and the outcome of that claim as soon

[00:07:54] [SPEAKER_00]: as possible. And if you can apply AI and gen AI, you definitely will be able to get the decision

[00:08:00] [SPEAKER_00]: for a low risk automatically almost. And we are already seeing this. And for even

[00:08:07] [SPEAKER_00]: more complex situations, you'll be able to extract context for that claim from the policies

[00:08:13] [SPEAKER_00]: and give that information to all the underwritings and everyone that it's really

[00:08:18] [SPEAKER_00]: validating those claims. For banking, one of the biggest use cases that we see,

[00:08:24] [SPEAKER_00]: it's onboarding. So for mortgage, for lending, anything, even opening an account,

[00:08:31] [SPEAKER_00]: there's always that back and forth where you ask the customer for some documentation,

[00:08:37] [SPEAKER_00]: then you validate, then you ask for more. Then you see and evaluate the criteria. And if that

[00:08:45] [SPEAKER_00]: customer is allowed to be validated with a mortgage or so all this process is now being

[00:08:54] [SPEAKER_00]: augmented for the bank business user and even automated in some aspects by just providing

[00:09:02] [SPEAKER_00]: information directly to the customer, getting them the right expectation, also validating.

[00:09:10] [SPEAKER_00]: So you can use gen AI to validate documentations to know what are the documents

[00:09:14] [SPEAKER_00]: and what they should be according to some required list. And this just makes things very

[00:09:21] [SPEAKER_00]: and easy for everyone and gives the customer a great experience, which is the ultimate goal.

[00:09:29] [SPEAKER_01]: Another area that initially resisted technological change within the industry

[00:09:34] [SPEAKER_01]: is education. There was a fear that it could end up replacing teachers, but of course

[00:09:38] [SPEAKER_01]: that couldn't be further from the truth. It actually enhances how teachers can teach

[00:09:44] [SPEAKER_01]: students especially get rid of the one size fits all approach and offer a more

[00:09:50] [SPEAKER_01]: universalized approach to each and every student. So how are you seeing AI reshaping

[00:09:55] [SPEAKER_01]: higher education, particularly in computer science programs? And what skills should

[00:10:01] [SPEAKER_01]: students themselves be focusing on to stay relevant in this evolving field?

[00:10:06] [SPEAKER_00]: I think you're absolutely right. So the one size fits all doesn't work. And by having this

[00:10:14] [SPEAKER_00]: very large language models and rec systems where they can search the information for you,

[00:10:21] [SPEAKER_00]: you really can adapt a course or information for each student. So each student can ask their

[00:10:28] [SPEAKER_00]: own questions in their own time and get the right answers, which is something that

[00:10:33] [SPEAKER_00]: might be a challenge for just one teacher in a full class. In specifically in computer science,

[00:10:40] [SPEAKER_00]: you should learn the basic concepts, understand the technical aspects behind them. I don't think

[00:10:47] [SPEAKER_00]: you need to go too deep if you want to experiment and really code and put these things

[00:10:53] [SPEAKER_00]: to practices. If you are curious, you should, but the important thing is to really understand

[00:11:01] [SPEAKER_00]: the concepts and use it. So using it and being a power user of all these AI tools,

[00:11:07] [SPEAKER_00]: it's the best thing that you can do because you not only will be able to be more productive

[00:11:13] [SPEAKER_00]: because now you'll have an augmented tool set, but also you'll have an intuition of how they

[00:11:20] [SPEAKER_00]: work, what are their capabilities, where are they good at, what are the gaps. And this is

[00:11:26] [SPEAKER_00]: what will give you the intuition to know how to use them in the right use case.

[00:11:33] [SPEAKER_01]: And also from your perspective and everything you're seeing here, how ready is the current

[00:11:38] [SPEAKER_01]: workforce for the widespread implementation of AI? I'm seeing a lot of people jumping on board

[00:11:44] [SPEAKER_01]: and playing with it. Equally some people don't want to touch it. So what are you seeing and

[00:11:48] [SPEAKER_01]: what steps can organizations take to prepare their employees for this transition because

[00:11:54] [SPEAKER_01]: it's so important that everybody's involved here, but what are you seeing?

[00:11:59] [SPEAKER_00]: Yeah, I really think it's still early on and we see that there are some misuses of some tools,

[00:12:06] [SPEAKER_00]: but I see also most companies usually creating some sort of internal program where they try to

[00:12:16] [SPEAKER_00]: find and to set up and to decide what AI to use and what not to use and to generate

[00:12:23] [SPEAKER_00]: educational programs inside those companies so that they understand how to leverage all those

[00:12:30] [SPEAKER_00]: new AI tools. So I think it's early, but we are seeing a great deal of success in all of those

[00:12:36] [SPEAKER_00]: companies, even us internally. So we are using more and more those types of tools and learning

[00:12:42] [SPEAKER_00]: how to make them useful. It takes some time, but it's definitely useful and it's valuable

[00:12:50] [SPEAKER_00]: to do that. So what I would say to companies, it's really to organize internally, get one team or

[00:12:57] [SPEAKER_00]: more teams looking into this, how to adopt new tools, how to make people understand how to use

[00:13:05] [SPEAKER_00]: them and really go for it because this will be the best way to gain a better outcome for your

[00:13:14] [SPEAKER_01]: business for sure. And one topic that we've got to mention is the huge debate at the moment

[00:13:20] [SPEAKER_01]: around AI's impact on employment and this is something that could impact everybody listening.

[00:13:25] [SPEAKER_01]: So in your view, what kinds of jobs are most at risk and what new roles might

[00:13:30] [SPEAKER_01]: equally emerge as a result of AI adoption? Because I think it's very easy to focus on

[00:13:35] [SPEAKER_01]: some of the jobs that may go, but if we look back 20 years, there were so many jobs

[00:13:40] [SPEAKER_01]: that didn't exist just 20 years ago that are here now. So what are you seeing here?

[00:13:46] [SPEAKER_00]: Yeah, I think you're absolutely right. And even some years ago people would think that, okay,

[00:13:55] [SPEAKER_00]: jobs like that have creativity or higher level thinking, they would be saved and what we are

[00:14:02] [SPEAKER_00]: seeing it's exactly the reverse, that those jobs are being augmented. But I think it's still

[00:14:09] [SPEAKER_00]: an augmentation. We are still just in the phase of augmentation. What's happening is that

[00:14:14] [SPEAKER_00]: some of the tasks are going to be removed. It's not a job, it's not a full job, it's usually tasks

[00:14:20] [SPEAKER_00]: and some jobs will have more of these tasks that are being replaced by AI and others will

[00:14:28] [SPEAKER_00]: have just a few. But usually these tasks are those tedious tasks, those boring tasks that

[00:14:33] [SPEAKER_00]: really kill the talent of humans. So having to read lots of tedious documents just to get

[00:14:43] [SPEAKER_00]: information, searching, doing just a repeated work. That's something that humans are not meant to.

[00:14:50] [SPEAKER_00]: I think this will augment them and will allow humans to really explore their talents and

[00:14:58] [SPEAKER_00]: where they are good at, where they are motivated and everything will be much easier to do. So

[00:15:03] [SPEAKER_00]: if you are doing some type of job, your job will get easier, you'll be able to feel more

[00:15:08] [SPEAKER_00]: productive, more happy doing it. And at some point we will have more replacement. But I think we still

[00:15:16] [SPEAKER_00]: need one or two breakthroughs in terms of AI to get there. So we will have some time to adapt.

[00:15:24] [SPEAKER_00]: Even with the AI that we have today, I don't think everything was still applied. There's a

[00:15:30] [SPEAKER_00]: lot of product development to be done so that we can leverage what science has got us.

[00:15:37] [SPEAKER_00]: And so this will give us a face to adapt and to really make the right decisions.

[00:15:44] [SPEAKER_01]: And this is an ideal opportunity, I think, to introduce everyone listening to Highland

[00:15:48] [SPEAKER_01]: and Highland's role in AI advancements because you guys have been

[00:15:52] [SPEAKER_01]: at the forefront of content solutions for as long as I can remember. So

[00:15:56] [SPEAKER_01]: can you discuss some of the innovative AI driven solutions that you're developing

[00:16:01] [SPEAKER_01]: and how they help organizations to manage their content more effectively?

[00:16:07] [SPEAKER_00]: In the best, we were more about traditional IDP. So digitizing documents, digitizing paper into

[00:16:18] [SPEAKER_00]: something that is within a repository was our first approach because that's really important

[00:16:23] [SPEAKER_00]: to have everything digital. So we had a lot of AI there, classifying, improving scan material

[00:16:31] [SPEAKER_00]: and images, making sure that we can extract all the values and contents from those documents.

[00:16:39] [SPEAKER_00]: That was the first approach. Then we went into content enrichment. So making sure that

[00:16:45] [SPEAKER_00]: if you have a repository that has documents and that has some metadata, we can leverage

[00:16:51] [SPEAKER_00]: all of that using machine learning and deep learning to really create models that

[00:16:58] [SPEAKER_00]: understand your content. And by understanding your content, now we can predict and we can know

[00:17:03] [SPEAKER_00]: what are the values, those metadata values for new documents and even for legacy documents.

[00:17:08] [SPEAKER_00]: So you see a lot of times one use case is that you had, imagine a bank,

[00:17:13] [SPEAKER_00]: you had a process in the past and it was very legacy and you know that there's a lot of

[00:17:19] [SPEAKER_00]: mistakes there. So they have refactor it and now they have a very good digital process,

[00:17:26] [SPEAKER_00]: but they want to kind of improve the quality on all the past legacy process values. So they can

[00:17:35] [SPEAKER_00]: use machine learning and content enrichment to train on the new process and then apply it on

[00:17:41] [SPEAKER_00]: the legacy documentation to get the proper values, to really make it not with so many

[00:17:48] [SPEAKER_00]: errors. The other thing is really finding answers. So we are seeing with our customers

[00:17:54] [SPEAKER_00]: that they have lots of repositories, lots of sources where they have content and sometimes

[00:18:02] [SPEAKER_00]: they really want to find a document, but many times they are just after the information or

[00:18:07] [SPEAKER_00]: even, and this is the ultimate goal, they want answers. They have a question

[00:18:12] [SPEAKER_00]: that it's important for their business and they just want the answer. And so we are leveraging

[00:18:17] [SPEAKER_00]: Gen.AI to provide them with that. We just get all the information and get

[00:18:24] [SPEAKER_00]: this deal the right answer for their question using all the repositories that they have.

[00:18:31] [SPEAKER_01]: And just to bring to life some of what you're talking about now, I'm not sure if you are

[00:18:34] [SPEAKER_01]: able to do this, but can you share any success stories or case studies where your

[00:18:40] [SPEAKER_01]: Highlands Intelligent Content Solutions have made a real measurable improved outcome for

[00:18:46] [SPEAKER_01]: customers, particularly Fortune 100 companies, which I know you have a lot in your portfolio

[00:18:52] [SPEAKER_01]: there. But is there any stories you can share? You don't have to name any names. I'd be

[00:18:54] [SPEAKER_00]: interested in the kind of things that you've done. Right. The ones that I do have examples,

[00:19:03] [SPEAKER_00]: I cannot share them because we cannot always share their names, but I can give you some

[00:19:10] [SPEAKER_00]: indications. So we are releasing a new Gen.AI product in the near future and next year we will

[00:19:18] [SPEAKER_00]: be releasing a lot more services leveraging generative AI. And right now we already have

[00:19:26] [SPEAKER_00]: a pilot program for generative AI where we are doing research with customers. We are

[00:19:33] [SPEAKER_00]: collaborating with them to know where it helps. So I can give you some, some indication,

[00:19:37] [SPEAKER_00]: some insight into what are the use cases for those very big customers. So as an example,

[00:19:43] [SPEAKER_00]: we have one customer that in the Fortune 100, they have a call center, right? So they have

[00:19:51] [SPEAKER_00]: very big call centers where they need to answer things about their products that they are

[00:19:56] [SPEAKER_00]: selling to customers. And usually how it works is that they will have like a facet search

[00:20:01] [SPEAKER_00]: where they will on the call, find the right document to answer some question.

[00:20:07] [SPEAKER_00]: And by leveraging what we are building, they will be able to not have to search for the

[00:20:13] [SPEAKER_00]: document. They will just ask the question that the user is asking and get a suggested reply

[00:20:19] [SPEAKER_00]: that they can confirm. And we can then even put this directly into customers. So their

[00:20:25] [SPEAKER_00]: customers, the customers can just leverage this platform to get the answers directly.

[00:20:30] [SPEAKER_00]: This is one of them. Others in the government space, this is a different space. Sometimes you have

[00:20:41] [SPEAKER_00]: judges that have conflicts with some cases. So imagine that when you have judges, they

[00:20:47] [SPEAKER_00]: usually have people they work with in the past, people, companies that they are,

[00:20:54] [SPEAKER_00]: organizations that they were a part of where they cannot really judge the cases. So this

[00:20:58] [SPEAKER_00]: always a tedious work and humans are prone to error when they are comparing documents. So

[00:21:04] [SPEAKER_00]: one thing that no one actually trusts humans is to compare documents. Usually AI,

[00:21:14] [SPEAKER_00]: they have the trust issue sometimes, but comparing the documents, it's where

[00:21:18] [SPEAKER_00]: everyone seems to agree that they prefer AI to do it. And so here it's really to compare

[00:21:24] [SPEAKER_00]: the cases with any disclosures that the judges have gave to see if there's any conflict and to

[00:21:33] [SPEAKER_00]: validate which cases could go to which judges. So this is just a very specific use case.

[00:21:40] [SPEAKER_01]: Love that. And looking, we're talking a lot about everything that other businesses are doing,

[00:21:45] [SPEAKER_01]: what Highland are doing, but I'd love to hear more about you because I think all of us are

[00:21:51] [SPEAKER_01]: under pressure to be under a state of almost continuous learning. So I've got to ask

[00:21:55] [SPEAKER_01]: where or how do you self-educate? How do you keep on top of these trends?

[00:22:00] [SPEAKER_00]: Right, so a lot of time, it's just today it's very easy with social networks. So you get to

[00:22:07] [SPEAKER_00]: follow very incredible people and they share lots of good stuff and you can read it.

[00:22:14] [SPEAKER_00]: In the specific case of AI, it's very useful to look into the papers itself sometimes

[00:22:21] [SPEAKER_00]: because you can really use large language models to summarize, to give you the information

[00:22:28] [SPEAKER_00]: that you need in a distilled way, in the translated way. So you really get the good

[00:22:35] [SPEAKER_00]: information. But the other thing is really using. Using it, it's very important.

[00:22:40] [SPEAKER_00]: So if you want to understand how to leverage an LLM in a specific use case, the best way is

[00:22:46] [SPEAKER_00]: really to use the LLM or something similar to that use case. Because even the people that are

[00:22:52] [SPEAKER_00]: building those LLMs, the Metas and OpenAI's of this world, they don't really know how

[00:23:00] [SPEAKER_00]: some of the capabilities have emerged. They don't know if, although there are some

[00:23:05] [SPEAKER_00]: benchmarkings, but they don't really know what specifically and what an LLM can do and cannot do.

[00:23:12] [SPEAKER_00]: So by using it, you really gain an intuition which is very useful to know when to use what

[00:23:20] [SPEAKER_00]: and what is fileable and what is not reliable. I've loved chatting with you today and so much

[00:23:30] [SPEAKER_01]: more we could talk about. So I think maybe we'll have to get you back on later in the year

[00:23:34] [SPEAKER_01]: to see how things are continuously evolving. But for anyone listening, just wanting to find

[00:23:40] [SPEAKER_01]: out more information about you and what you do and contact your team or just look for

[00:23:45] [SPEAKER_01]: a little bit more information on some of the tools we mentioned there.

[00:23:48] [SPEAKER_00]: Where would you like to point everyone listening? Sure. So if you go, we have I think a new

[00:23:53] [SPEAKER_00]: website, island.com. There you can find our new platform which is Island Experience.

[00:23:59] [SPEAKER_00]: And specifically in that new platform, there's Island Experience inside. So that's our native AI

[00:24:08] [SPEAKER_00]: program. That's from where we are delivering all these new services and solutions to market.

[00:24:16] [SPEAKER_00]: So just look, we should have news and there's going to be soon Community Live near Washington

[00:24:24] [SPEAKER_00]: in Maryland. So there I think there will be a lot of news regarding AI and island using

[00:24:30] [SPEAKER_00]: generative AI and just content services with intelligence.

[00:24:35] [SPEAKER_01]: Incredibly cool. I will add all those links so people can find you nice and easily and the

[00:24:40] [SPEAKER_01]: social channels as well so people can follow you on there. And we covered so much in a

[00:24:44] [SPEAKER_01]: short amount of time today from the future of AI and enterprise content management across

[00:24:49] [SPEAKER_01]: many different industries from healthcare and insurance and banking, but also how it's

[00:24:54] [SPEAKER_01]: shaping higher education, computer science programs and also how AI implementation

[00:25:00] [SPEAKER_01]: readiness in the workforce and some of those implications for AI jobs too.

[00:25:05] [SPEAKER_01]: As I said, I'd love to get you back on later in the year,

[00:25:07] [SPEAKER_01]: but just thank you for sharing your insights today.

[00:25:10] [SPEAKER_00]: Thank you for having me. It was really a good time. Thank you.

[00:25:14] [SPEAKER_01]: So as we wrap up today's discussion with Tiago, I find myself reflecting on the profound

[00:25:19] [SPEAKER_01]: impact AI is having across a wide variety of sectors from transforming enterprise content

[00:25:26] [SPEAKER_01]: management to reshaping the educational landscape and preparing the workforce for future

[00:25:31] [SPEAKER_01]: challenges. The potential for AI in these areas just seems almost limitless, but got to say

[00:25:38] [SPEAKER_01]: that there's always a caveat with great powers comes great responsibility. So what are

[00:25:44] [SPEAKER_01]: your thoughts on the ethical implications of AI in the business and indeed educational sectors?

[00:25:49] [SPEAKER_01]: And how do you see or how are you seeing AI shaping your industry or area of study?

[00:25:55] [SPEAKER_01]: There's only so much I can learn from sitting here and having a conversation with my guest.

[00:26:00] [SPEAKER_01]: I turn to you, this is where I live my life vicariously from you and the reason I

[00:26:04] [SPEAKER_01]: record this podcast every day is to try and learn something not just from my guests, but

[00:26:09] [SPEAKER_01]: from each of you listening too. So as always share your thoughts and join the conversation

[00:26:14] [SPEAKER_01]: by emailing me techblogwriteroutlook.com, Twitter, LinkedIn, Instagram, just at Neil C Hughes.

[00:26:21] [SPEAKER_01]: I genuinely look forward to hearing from you and hearing more about your thoughts and

[00:26:25] [SPEAKER_01]: what you're seeing on this. But I'm afraid we're out of time already. There goes the bell.

[00:26:31] [SPEAKER_01]: I will be back bright and early tomorrow morning, but thank you for listening today

[00:26:35] [SPEAKER_01]: and until next time, don't be a stranger.