Latest / The Adults In The AI Room / Thomas Blumer on The Role of AI in Business Decision-Making and Governance - The Earley AI Podcast with Seth Earley - Episode #043

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Thomas Blumer on The Role of AI in Business Decision-Making and Governance - The Earley AI Podcast with Seth Earley - Episode #043 · The Adults In The AI Room

0:01
SPEAKER_02: This is the Early AI Podcast, hosted by Seth Early and Chris Featherstone.
0:06
SPEAKER_02: Join us as we delve deep into the passions, expertise, and experiences of thought leaders and practitioners to talk about what's possible with artificial intelligence.
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SPEAKER_02: The Early AI Podcast is sponsored by Early Information Science, your digital transformation journey with design and deployment of innovative technology solutions, as well as CMS Wire.
0:25
SPEAKER_02: Now, enjoy the show.
0:32
SPEAKER_01: Okay, well, welcome to today's podcast.
0:35
SPEAKER_01: My name is Seth Early.
0:37
SPEAKER_01: And I'm Liam Koonig.
0:40
SPEAKER_01: And our guest today has decades of experience in knowledge management and technology adoption.
0:48
SPEAKER_01: He's been a specialist in knowledge management.
0:50
SPEAKER_01: He's done a lot of work in AI and machine learning, digital adoption, data strategy.
0:55
SPEAKER_01: He has a proven track record of saving millions of dollars for companies like Google in their Auto ML implementation.
1:04
SPEAKER_01: He currently leads digital transformation efforts.
1:07
SPEAKER_01: And we recently had him on our webinar on data governance in the age, information governance in the age of AI.
1:15
SPEAKER_01: Welcome, uh Dr.
1:16
SPEAKER_01: Thomas Moomer.
1:18
SPEAKER_00: Thank you so much, Seth.
1:19
SPEAKER_00: I'm really glad to be here and talk a little bit more about all these exciting uh elements we just addressed.
1:27
SPEAKER_01: Absolutely.
1:28
SPEAKER_01: Absolutely.
1:29
SPEAKER_01: So before we uh start, what I'd like to do is I'd like to get a sense of um what are the things that you're running across that are misconceptions and misunderstandings in this space?
1:41
SPEAKER_01: And I know there's lots of them, but why don't you articulate some of those?
1:45
SPEAKER_00: Yeah, you know, it's like I've been around the blog for a while, as you just mentioned, and whenever there's a new technology, we always feel that will save all our troubles, you know.
1:55
SPEAKER_00: And I think we have to be realistic.
1:57
SPEAKER_00: It is a tool in our tools that, you know, and yes, that the tool sets start getting better, but it's not that it will solve all our problems.
2:05
SPEAKER_00: It is, you know, like the traditional thing, you know, is people, process, and technology, you know, and they have to be kind of in balance and aligned, otherwise, the best tool will not solve it, you know, as you mentioned at the webinar.
2:18
SPEAKER_00: It just makes a mess, a bigger mess, much faster if you don't have a clear governance underneath, you know.
2:25
SPEAKER_01: Yeah, one of the things that we have seen is, you know, when people have content operations for AI for a specific group, that ends up with more knowledge fragmentation than if we do this in a more um thoughtful manner using something like Create Ones Publish Everywhere.
2:40
SPEAKER_01: We're not going to talk a huge amount about that today, but we'll talk a little bit about content operations and content processes.
2:47
SPEAKER_01: Um so it kind of comes back to basics, right?
2:50
SPEAKER_01: When you're when you're looking at these tools, it comes back to business problem and process and data sources and so on and data hygiene, right?
3:00
SPEAKER_01: I think the big misconception we come across is that you don't need that stuff, you know, you don't need taxonomies and ontologies, you just need the tools, and the tools will do it.
3:10
SPEAKER_01: And tell me, is there a little bit of truth to some of that?
3:13
SPEAKER_01: I mean, again, people are putting this forward, but it's not completely right, right?
3:18
SPEAKER_01: We know it's not right, but it's not completely wrong.
3:21
SPEAKER_01: Do you want to talk about where the tools will help with things like quality data hygiene?
3:27
SPEAKER_00: Yes, you know, there are certain things AI or machine learning can't do because of the algorithm which is applied.
3:34
SPEAKER_00: For example, if you have a whole stack of information, you know, PDFs or over PDFs and PDFs, you know, it's like if you have no structure at all, you could let AI start actually put them in context to each other and do its thing, what the LLM will do, and you will get some information back, which in the past will have spent a lot of time uh to kind of fine-tune.
3:59
SPEAKER_00: But you know, you know, as we say, a rubbish in, rubbish out.
4:03
SPEAKER_00: If the data you provide is not clean, you know, don't expect it is suddenly cleaned by by AI.
4:10
SPEAKER_00: It is still there if you have bad data and you haven't sorted it out.
4:14
SPEAKER_00: So it may help getting you some work, but you still need to do the legwork on the knees to leverage it, you know.
4:21
SPEAKER_00: But something I was really impressed is like uh when I wrote my dissertation, it took me weeks to code all my interviews.
4:31
SPEAKER_00: I had 600 pages of interviews and I had to tag them very closely, and then I pressed a button, and it took literally 12 hours of computing time to come up with something like how these terms relate to each other.
4:47
SPEAKER_00: I did a very similar thing last month with a knowledge graph.
4:52
SPEAKER_00: You know, I had 600 pages, I pressed a button in in minutes.
4:56
SPEAKER_00: I had a knowledge graph which I could turn around, dig deeper, get out.
5:03
SPEAKER_00: It even told me where I had holes in my thing.
5:06
SPEAKER_00: You know, it's like, hey, there's some information missing in that.
5:09
SPEAKER_00: And it's like, wow, I mean, we came a long way, but you know, if I don't provide the right information, don't expect uh that it comes out clean, you know.
5:19
SPEAKER_00: But uh, it is a really fascinating how how far we came.
5:23
SPEAKER_01: Absolutely.
5:24
SPEAKER_01: And I think one of the critical things that you mentioned was or implied was that you have to have a reference architecture, right?
5:31
SPEAKER_01: Even for the systems to do something with, you have to tell it what to expect.
5:35
SPEAKER_01: You know, you have to tell it the types of content, the terminology you care about, and so on.
5:39
SPEAKER_01: And when you did that initially originally, you tagged that stuff and you tagged it with a reference architecture.
5:45
SPEAKER_01: Now we put in the reference architecture where we can get something um that is machine generated in terms of a knowledge graph or reference architecture, but then we have to have some human intervention to fine-tune that.
5:56
SPEAKER_01: And that's been a challenge for you know 20 years when it comes to machine learning and artificial intelligence.
6:03
SPEAKER_01: Back in the days of Lotus Discovery Server in the late 90s, when I co-authored a book on that uh tool that became omnifine from IBM, that DNA went into Watson.
6:14
SPEAKER_01: And it was the still the same core algorithm.
6:17
SPEAKER_01: And the big challenge was generating those taxonomies and generating that uh knowledge architecture, because you could tell what a machine-generated taxonomy looked like, right?
6:27
SPEAKER_01: It made sense to the machine and it had clusters, but it didn't make sense to humans.
6:32
SPEAKER_00: Yes, absolutely.
6:34
SPEAKER_00: You know, that the two have to be aligned to to get something out of it.
6:38
SPEAKER_00: Uh go ahead.
6:40
SPEAKER_01: We're gonna say something.
6:41
SPEAKER_01: No, no, no, that's fine.
6:42
SPEAKER_01: Go ahead.
6:44
SPEAKER_01: So here's what one things that I want to understand is, you know, my guess is that the boom in in generative AI is going to catalyze funding for for knowledge processes.
6:57
SPEAKER_01: What is your perspective on that?
6:59
SPEAKER_01: Where do you think organizations are in their realization or understanding?
7:03
SPEAKER_01: And then how soon do you think that's going to actually lead to investment in that area?
7:08
SPEAKER_01: And talk about why that's important.
7:11
SPEAKER_00: Yes, that's a good question.
7:13
SPEAKER_00: I I think 2023 were kind of it was the big awakening for many companies, like, whoa, we are behind the curve, you know, like ChatGPT really was a wake-up call for many companies, you know.
7:25
SPEAKER_00: So 2023 was the year where people just played around a lot of experimentation.
7:29
SPEAKER_00: But as you mentioned earlier, it's like, where's the business value?
7:33
SPEAKER_00: And you know, I went to Silicon Valley, I had a lot of interesting conversations with really smart people, and they could do everything, but they didn't know where to apply it.
7:45
SPEAKER_00: It kind of like I can solve every problem, but I don't know the problem to be solved, you know, because there was a real dissonance between business and the technology.
7:54
SPEAKER_00: I think the gap is closing.
7:56
SPEAKER_00: I think we will see 24 more deployments, you know, kind of people who have played enough and know like this is how we actually can use it for good, you know.
8:08
SPEAKER_00: Uh, and you know, it's like many software companies of now AI baked in.
8:13
SPEAKER_00: I mean, Salesforce has Einstein, uh Adobe has their tools.
8:18
SPEAKER_00: So we see all these AIs now built in.
8:20
SPEAKER_00: But where I always talk about where are the AIs of the AIs?
8:24
SPEAKER_00: You know, it's like I don't want to have just a little piece optimized here.
8:29
SPEAKER_00: I want to have the big changes and the big changes that are not coming by.
8:33
SPEAKER_00: Well, I have an automatic summary is that well, good, I saved two minutes, but the quantum beliefs are on a different level.
8:40
SPEAKER_00: And um I think companies still have to get there.
8:43
SPEAKER_00: Yeah.
8:44
SPEAKER_01: I I mean, what I've researched and what I've seen and what I've heard and what the analysts say, first of all, that the whole market around you know, retrieval augmented generation.
8:55
SPEAKER_01: And for those of you who are not familiar with that, it's not relying on the language model for the answers because the language model contains knowledge, right?
9:02
SPEAKER_01: It understands relationships.
9:04
SPEAKER_01: And when you're using Chat GPT and you're asking questions, it's basing that on what's in that model.
9:10
SPEAKER_01: But it doesn't know your secret sauce, doesn't know your processes, your IP, doesn't know the specifics about your differentiation, right?
9:19
SPEAKER_01: Your knowledge.
9:20
SPEAKER_01: And so we need to use that knowledge as a source to retrieve information that's retrieval augmented generation.
9:27
SPEAKER_01: And that's what people are starting to look at.
9:29
SPEAKER_01: That does require that we have some curation of the knowledge, right?
9:33
SPEAKER_01: And I believe I'm a big believer in the uh intersection of knowledge engineering and prompt engineering, right?
9:39
SPEAKER_01: I think there's going to be a lot of work in that area because the knowledge engineering is structuring the information for retrieval, the prompt engineering is structuring the queries in some way, right?
9:50
SPEAKER_01: Because you can see when you have good prompts and they're very detailed, that's actually metadata, right?
9:56
SPEAKER_01: There's metadata in there.
9:57
SPEAKER_01: And then we're lining that metadata with the metadata in the content through the knowledge architecture.
10:02
SPEAKER_01: Do you want to make uh some comments about that?
10:05
SPEAKER_00: Yeah, that's a really good point, says uh you know, it's like we have on one side the people who are just kind of they know they need to do something but haven't started, and then you have the companies uh who already pretty deep, you know.
10:16
SPEAKER_00: And I had some discussion with uh consulting companies who we don't even talk about prompt engineering, they have entire prompt libraries already for the different verticals, you know.
10:27
SPEAKER_00: So they have these all these more people who already start building that.
10:31
SPEAKER_00: And now the question which is really important how can you transparently uh enter that in your work stream?
10:39
SPEAKER_00: You know, you don't want to have another silo.
10:41
SPEAKER_00: It's like, oh, and here's the AI silo.
10:43
SPEAKER_00: You want to bring that right in your process where it's like, oh, I have a question.
10:46
SPEAKER_00: I'm in this vertical, I I work for this uh area, and it brings you an entire library, which you then can still change the prompt.
10:54
SPEAKER_00: It needs this flexibility, but it's 80% already there.
10:58
SPEAKER_00: And I think another fallacy um I forgot to mention, which I see, is like if you have no concrete idea what you want to get out of AI, AI blows you away.
11:11
SPEAKER_00: You know, you you go to meet your and you say, Hey, I I want to see whatever Apollo 14 on Mars, and you will build something, and it's wow, that's really good.
11:22
SPEAKER_00: You know, as long as you're very vague, uh AI is super, super good.
11:27
SPEAKER_00: Now, the more precise you get, the harder it is.
11:30
SPEAKER_00: You know, it's like uh if you're familiar with the Penrose uh stairs, you know, from an Azure drawing, you know, the illusion which just goes around in circles.
11:40
SPEAKER_00: I I try with every release of the latest uh AI or text to image to build one, and so far it could not build one.
11:48
SPEAKER_00: And if anyone of the audience would figure it out, please let me know.
11:51
SPEAKER_00: I'm really interested.
11:52
SPEAKER_00: But I could not figure it out.
11:54
SPEAKER_00: I put in a challenge on a LinkedIn page.
11:56
SPEAKER_00: It's like, does anyone know how to do that?
11:59
SPEAKER_00: So you see limitations, and when it comes to LLMs, the same thing is true.
12:05
SPEAKER_00: Like, yes, if I want to have a summary, that's not a problem.
12:08
SPEAKER_00: But now think about the tech company.
12:10
SPEAKER_00: You know, it's like I have support, it's like this is my problem, and AI will give you an answer.
12:16
SPEAKER_00: But now it's like in a tech, it's like, well, on which operating system are you?
12:21
SPEAKER_00: Which patch level do you have?
12:23
SPEAKER_00: Uh, which operating system?
12:25
SPEAKER_00: Now you have far more dimension you need to take care of.
12:29
SPEAKER_00: And now it's like a generic model can't do that without being trained to that level.
12:35
SPEAKER_00: So that's where you know taxonomy and apology needs AI.
12:40
SPEAKER_00: And it's a little bit more challenging than if you just play around, it's like, wow, it's so powerful.
12:45
SPEAKER_01: That's such a great, that's such a great observation because it can be so impressive on its surface.
12:50
SPEAKER_01: But then when you try to get into those very specific use cases and scenarios, you realize that the large language model doesn't have the context.
12:59
SPEAKER_01: Yes, it doesn't have the knowledge, it doesn't have the context.
13:02
SPEAKER_01: So we have to build that in.
13:03
SPEAKER_01: You know, I use my example of error code 50.
13:06
SPEAKER_01: Uh I need troubleshooting steps.
13:08
SPEAKER_01: Well, okay, what product do you have and what model and what's the configuration and what's the operating system and all of that stuff.
13:16
SPEAKER_03: Yeah.
13:21
SPEAKER_01: Right?
13:21
SPEAKER_01: It has to have all that stuff.
13:23
SPEAKER_01: And without that con without that context, it's not going to be able to answer those questions.
13:27
SPEAKER_01: So you hit the nail on the head, and that comes both from the signals about the user, right?
13:32
SPEAKER_01: A customer or employee identity graph, right?
13:35
SPEAKER_01: First party data, you know, who's your customer, who's your employee, what's their technical background, what's their knowledge, what's their history here, what's their configuration, what uh is the as-built installation, all of that stuff are contextual clues that will help the LLM get to what you want.
13:52
SPEAKER_01: But then you have to also have the content tagged so that it can make use of those contextual clues.
13:58
SPEAKER_00: Oh, absolutely, absolutely.
14:01
SPEAKER_00: You know, it's like uh generic is generic, which is good for generic questions, and the more precise you need the answer, um, you you need to engineer that, you know.
14:11
SPEAKER_00: So it's uh that's where you I think many companies start realizing like, oh, it's not just so easy as to get an LLM inside your company.
14:21
SPEAKER_00: It's like um, you know, we we started with uh Confluence uh many, many years ago, and people loved it.
14:28
SPEAKER_00: It's like wow, it's so good, and the search is great.
14:33
SPEAKER_00: Well, you know, the same people now 10 years later coming like the search is horrible.
14:38
SPEAKER_00: It's like it's not the technology has changed, it's just the amount of data in it because you have now 10 years of different versions, and you you type something in, and instead of one result, you get now 20, and you have to figure out well, which one is it?
14:54
SPEAKER_00: And that's the same thing for an AI.
14:56
SPEAKER_00: It's like that's right.
14:57
SPEAKER_00: I don't know which version you're talking about, and if you use a generic one, it's even bigger challenge, you know.
15:03
SPEAKER_01: Yeah, you can say what's the status of the project?
15:06
SPEAKER_01: What project?
15:07
SPEAKER_01: You know, if you're in a single project database, fine, but if you're across multiple projects, you got to give me a little hint, right?
15:14
SPEAKER_01: It's kind of like walking up to the to the counter at Home Depot and say tools.
15:19
SPEAKER_01: Yeah, what do you want to know about tools?
15:23
SPEAKER_01: Power tools, you know, there's yeah.
15:26
SPEAKER_00: Yes, and uh, I mean, we mentioned it uh during the webinar that the Boston Consulting Group did a really fantastic study.
15:32
SPEAKER_00: It's like if you use a AI for the right purpose, it's really powerful, but it's also really challenging to identify when not to use it because the LLM was not trained for it, but it still gives you an answer.
15:47
SPEAKER_00: And if you take that, it will actually be worse than if we wouldn't have used AI.
15:52
SPEAKER_00: And so making this distinction is still a challenge.
15:55
SPEAKER_00: I mean, if that's a problem for the Boston consulting groups, which I would say pretty high up uh in the knowledge level, yeah.
16:03
SPEAKER_00: Uh think of just for the average person, it is a challenge.
16:07
SPEAKER_01: The mere mortal, the mere mortal.
16:09
SPEAKER_01: Exactly.
16:11
SPEAKER_01: Yeah.
16:12
SPEAKER_01: So when you start thinking about you know readiness for this stuff, like we we actually have a TM for AI readiness assessment, and we'll put that in the uh show notes.
16:22
SPEAKER_01: Um, I think there's a there should be an easy URL we should be able to rattle off, but you can you can put it in the notes, uh Liam, when you edit this, right?
16:30
SPEAKER_01: Or uh, but but the point is that you know you need to start looking at these different dimensions and start thinking about what you're ready for.
16:38
SPEAKER_01: And Thomas, do you want to talk about your perspective on what makes an organization ready for uh uh AI and generative AI, especially with regard to uh knowledge, knowledge processes, governance, data governance, those types of things.
16:51
SPEAKER_01: What what makes an organization ready?
16:54
SPEAKER_00: Yeah.
16:55
SPEAKER_00: Well, they're obviously different layers.
16:59
SPEAKER_00: Uh I normally say keep it simple and start with a nicely contained area to to just get a proof of concept, proof of value.
17:10
SPEAKER_00: You know, like uh you know, and you learn so much just by going through because uh every company is uh unique, at least they think everyone is unique.
17:21
SPEAKER_00: So try out what works for you.
17:23
SPEAKER_00: And like what we did is like um there's so many things you could tackle, and we just we saw a problem with our translation, which we did.
17:33
SPEAKER_00: Translation of a product is very expensive, especially if you want to translate an entire help system, you know.
17:38
SPEAKER_00: And so we had like over 4,000 pages we want to translate into 14 languages, so we we talk about a lot of money, you know.
17:47
SPEAKER_00: And so rather than go the traditional way, reaching out to a translation vendor, getting it back, which takes weeks, we actually start leveraging 20 years of translation memory and start loading them into the model from Google.
18:04
SPEAKER_00: We use Google Auto ML, and so we leverage what we had, and now we have our translation model based on our own translation, our language, our terms, and now it took it took days.
18:18
SPEAKER_00: Uh what uh actually actually the translation took one day, the you know, like uh uh the cost came down about 95 percent, and the quality output, depending on the language, was on par.
18:32
SPEAKER_00: You know, it's always challenging translating uh technical documents, but um it was as good as someone who doesn't know our product and translating.
18:41
SPEAKER_00: So this was a pretty clear use case.
18:44
SPEAKER_00: Um, it was isolated and it just was tremendously valuable and now set to move for the future and open the door.
18:53
SPEAKER_00: So, like, oh, now that we have that, should we start translating our training documents?
18:58
SPEAKER_00: What about uh other labels?
19:00
SPEAKER_00: You know, it's like a uh it really opened the door, but you don't want to have it confined.
19:06
SPEAKER_00: Uh figure out what works, what doesn't work, uh fine-tune it before you start scaling it.
19:12
SPEAKER_01: Right.
19:13
SPEAKER_01: And that's one of the things that we do when we do that assessment is we we identify a very narrow set of use cases, right?
19:20
SPEAKER_01: So what problem do you want to solve?
19:22
SPEAKER_01: And then let's make sure that those processes that those use cases uh comprise or are supportive of uh are measurable, right?
19:33
SPEAKER_01: You have a baseline.
19:34
SPEAKER_01: If you had a baseline, you said it'll take X or dollars to do this translation.
19:38
SPEAKER_01: Now with the new process, it's a very defined process, very narrow scenario instead of use cases, clearly defined knowledge and information sources, and then you had the measure, right?
19:50
SPEAKER_01: So you had the baseline.
19:51
SPEAKER_01: And then when we talk about proof of value versus proof of concept, it's really about scalability, right?
19:57
SPEAKER_01: It's the proof of concept.
19:58
SPEAKER_01: Many proof of proofs of concept don't get out of POC, right?
20:02
SPEAKER_01: Because you know, the data is different, the algorithms are different, the uh the the environment, the hyperparameters, um, you know, uh all of those things are um uh uh you know different in in a way that you can't readily translate into production, right?
20:20
SPEAKER_01: You don't have the luxury.
20:21
SPEAKER_01: So one of the things we want to do is use production data, you know, to make sure that we're satisfying that use case.
20:28
SPEAKER_01: Um Leem, you had a question related to this.
20:31
SPEAKER_01: So go ahead.
20:32
SPEAKER_02: Yeah, um, you may have just answered it, but I was gonna talk a little bit more about the proof of value or proof of concept and proof of value and um seeing what are kind of the common challenges that companies face when they kind of get past that concept of value stage, they want to expand it to other parts of their company.
20:50
SPEAKER_02: Um, Dr.
20:51
SPEAKER_02: Thomas Bloomer, if you don't mind answering, what are those common challenges that a lot of these companies kind of run into?
20:57
SPEAKER_00: Yeah, well, a really good question.
20:59
SPEAKER_00: Um, you know, there's obviously the vertical and the horizontal um integration you can go.
21:07
SPEAKER_00: You can go deeper what you already do, or you can actually go from silo to silo, you know.
21:12
SPEAKER_00: And while for both, they're pretty a lot of experience already, you know, it's nothing new.
21:19
SPEAKER_00: And where I get really excited is really to start thinking about end-to-end processes, you know, like combining the different silos.
21:28
SPEAKER_00: Because what we are really good in, we we we squeeze out minutes out of a process, you know.
21:34
SPEAKER_00: This is a process, it's well defined.
21:37
SPEAKER_00: How can I squeeze in uh or out a minute out of it?
21:40
SPEAKER_00: You know, and yes, sure.
21:41
SPEAKER_00: That that obviously for big companies still millions of dollars.
21:45
SPEAKER_00: But where I get excited is like so you squeeze here a minute, and then you have a handover, and the process is stale for a day, a week, or even a month, you know, and nobody manages in that.
22:00
SPEAKER_00: Right now we have the technology to start tying these pieces together.
22:05
SPEAKER_00: You know, I call it like a lean dab, you know, for digital adoption platform.
22:10
SPEAKER_00: You know, it's like for lean manufacturing to really start thinking how can we manage the wait time?
22:16
SPEAKER_00: Because the wait time is normally you know like uh between 70 and 90 percent, depending on the company, the industry.
22:23
SPEAKER_00: But this is where a lot of that time in the company is between the process, and I think that's the era I really want to start addressing in, and I think technology is slowly ready to actually do that.
22:36
SPEAKER_00: What you know, the the total quality management gurus talked about in the 70s, you know, it's like we need to manage it that way, and now we we we slowly have the tools to do that electronically as well, and that's what I get the most excited about.
22:51
SPEAKER_01: Right, that's great.
22:52
SPEAKER_01: Have a bigger impact on the business.
22:55
SPEAKER_01: So, um, one of the things that uh I like to say, and I think you agree with it, there's there's no AI without IA.
23:04
SPEAKER_01: And what does that mean to you, and how would you explain that to a business person?
23:10
SPEAKER_00: Um, is the call.
23:16
SPEAKER_00: Yeah, I mean it can go really deep to explain that, but um, if you don't organize your content you know, like uh in a way, then you can't I normally explain it as like if you have a foundation built on sand, you don't really want to build a skyscraper on top of it, you know.
23:37
SPEAKER_00: And this is uh information architecture is for me the foundation, you know, like if that thing is not a good foundation, don't try to build something on top and expect it to work very well, you know.
23:50
SPEAKER_00: It's just whatever you have, you replicate, and you know, you have a kind of a gut feel how good or how bad it is.
23:57
SPEAKER_00: And if it's good, okay, we can play around and see what comes out.
24:01
SPEAKER_00: But if you already have a bad feeling, it's like, well, clean the house first before you replicate, otherwise, you just have a bigger problem and uh more to finally clean up.
24:11
SPEAKER_01: So and it'll break in unexpected ways when you have built on top of it.
24:16
SPEAKER_01: Taking the analogy of building, I think it's a great one.
24:19
SPEAKER_01: You know, uh, if you were going to build a house or a building of some sort, you wouldn't just start digging holes and pouring concrete, right?
24:28
SPEAKER_01: You would have an architecture, you'd have a plan, you'd have a, you know, and you'd have multiple plans because you'd have a foundation plan and you'd have a framing plan and you'd have an HBAC plan and an electrical plan and a and a uh frame for all of these different things because they're different ways of looking at that information structure, right?
24:50
SPEAKER_01: The information has to be built based on an architecture.
24:54
SPEAKER_01: And just like you'd hire an architect to build a house or you'd take a plan that you know closely aligns to what you're looking for, you of course would want to modify it in some way, shape, or form.
25:05
SPEAKER_01: But but especially for a large complex organization, you really do have to build that information architecture.
25:11
SPEAKER_01: And it's not off-the-shelf stuff because you know, think of standards.
25:16
SPEAKER_01: Standardization will give you efficiency, right?
25:19
SPEAKER_01: But it's differentiation that gives you competitive advantage.
25:23
SPEAKER_01: So if you just take an off-the-shelf plan, great, that'll give you some efficiency.
25:27
SPEAKER_01: And maybe you need that efficiency between organizations or within your organization, but it's not going to give you that differentiation that you need in the marketplace.
25:34
SPEAKER_01: You differentiate based on your knowledge, your expertise, your IP, your knowledge of customer needs, your knowledge of the competitors, of the technical problem and solution, and so on.
25:45
SPEAKER_01: So that comes back to managing and uh monitoring and curating and structuring uh the knowledge of the organization.
25:54
SPEAKER_00: Oh, absolutely.
25:55
SPEAKER_00: And that's I think everyone agrees with what you just said, but it it is also very hard to differentiate where you should actually focus on the efficiency and where on the differentiation.
26:07
SPEAKER_00: Because what I see often, companies they always say we are different, and everything is different.
26:13
SPEAKER_00: And so, like every process needs to be different.
26:16
SPEAKER_00: It's like, no, you don't differentiate here, you just you don't even use best practices, leverage best practices, and what are really the areas where you differentiate and you need to be different, and people, it's just so much more expensive if you customize every single screen from an ERP system, but it's like that doesn't add any value, it's back to the value proposition.
26:41
SPEAKER_00: You know, you really need to have this hard discussion because everyone thinks they're so unique.
26:46
SPEAKER_00: It's like, no, you are not.
26:48
SPEAKER_00: Here, here is not the differentiator.
26:50
SPEAKER_00: Leverage what someone else already sought for you, and here is your secret sauce.
26:56
SPEAKER_00: Yes, here it makes sense, you know.
26:58
SPEAKER_01: That's a great, great observation.
27:00
SPEAKER_01: And it really is the the the framework, framework, or ref frame of reference that you need to look at that stuff.
27:06
SPEAKER_01: You're right, you don't need to differentiate everything.
27:08
SPEAKER_01: Maybe sometimes off-the-shelf standards will work for you, but it's understanding when they'll work for you versus where you do need to have your secret sauce, as you say.
27:19
SPEAKER_01: So that's a great, that's a great observation.
27:21
SPEAKER_01: Um, let's talk a little bit about um data governance, which was our topic uh of our webinar just the other day.
27:29
SPEAKER_01: And um, you know, just maybe you could talk a little bit about, you know, kind of a metric sharing governance approach and how that applies to AI and AI implementations in a in a corporate environment.
27:42
SPEAKER_01: You want to expound on that a bit?
27:44
SPEAKER_00: Yeah, I I want to address it from two different angles.
27:47
SPEAKER_00: One is like you need data and metrics to manage any project.
27:55
SPEAKER_00: You know, it's like you want to know do we hit the milestones?
27:58
SPEAKER_00: Are we within the parameters?
28:00
SPEAKER_00: You know, can you fine-tune it?
28:02
SPEAKER_00: For that, you need obviously data.
28:05
SPEAKER_00: On the other hand, what I have found through discussion with many, many people is, you know, like it's kind of a new term.
28:11
SPEAKER_00: Well, I don't know how new, but everyone wants to be a data-driven organization.
28:15
SPEAKER_00: You know, it's a kind of yes, we are data-driven, we have data analytics and all that great.
28:20
SPEAKER_00: But when it comes to hard decisions, data is good as long as it's aligned with the gut feel of C-level people.
28:28
SPEAKER_00: We are not data-driven organization if it goes against your gut feel.
28:33
SPEAKER_00: Then Zal is like, Well, I don't know what you did.
28:36
SPEAKER_00: I'm pretty sure I can pull different numbers.
28:39
SPEAKER_00: I know how analytics works, you know, and statistics.
28:42
SPEAKER_00: You know, you know, people always make fun about statistics and liars, you know.
28:46
SPEAKER_00: It's like, um tell us that old story.
28:49
SPEAKER_01: Tell give us that song.
28:50
SPEAKER_01: What is that again that they say?
28:52
SPEAKER_00: Oh, how is it called again?
28:53
SPEAKER_00: Like a statistic, statistic and liar.
28:55
SPEAKER_00: I I I forgot about, but you know, most people in the audience will most likely remember that.
29:01
SPEAKER_00: So it's still a challenge, even if you have data and proven data, it's like, hey, this is where it goes.
29:08
SPEAKER_00: And it's like, well, I don't believe it.
29:10
SPEAKER_00: Uh, I still think we should do this.
29:12
SPEAKER_00: So there's still an opportunity to for change management, you have solid data where people really believe.
29:19
SPEAKER_00: And you know, I always say uh trust needs to be earned, you know, it's like you you showcase and then you you deliver, and then like, well, I didn't expect that to work.
29:31
SPEAKER_00: Okay.
29:31
SPEAKER_00: And every time you have a uh a value added incidence and you can show it, it's like, oh, you know what?
29:39
SPEAKER_00: Okay, I think these guys know what they're doing.
29:42
SPEAKER_00: So, you know, I I I don't fault anyone, but it's not as easy.
29:45
SPEAKER_00: Don't think because you have the data, people will just follow that.
29:48
SPEAKER_00: You know, it's like, no, you need to prove your value.
29:51
SPEAKER_01: I I could not agree with this more.
29:55
SPEAKER_01: And um, we did we did go into uh this in the in the in the webinar.
29:59
SPEAKER_01: So for those of you who want to see more detail, definitely register with the webinar and it's recorded.
30:05
SPEAKER_01: But the the story I like to tell is we had a uh a data-driven recommendation for an executive team, and it was saying, here's how you should think about your product hierarchy, right?
30:19
SPEAKER_01: But they were like, no, they tried to manipulate the questions and the survey and the and the testing that didn't seem to work right to get their answer, to get the answer that they had in their gut, right, that they wanted.
30:33
SPEAKER_03: Yeah.
30:33
SPEAKER_01: And so they still, even though manipulated the uh uh the questions and and the uh uh and the testing, uh it still gave us the answer that you know was data driven that said, no, do it this way.
30:46
SPEAKER_01: Uh and they said, hmm, yeah, we don't care.
30:50
SPEAKER_01: Do it this way, right?
30:52
SPEAKER_01: They said, do it the way we want to do it, rather than the way that all your data tells us.
30:56
SPEAKER_01: And guess what?
30:58
SPEAKER_01: The results were as we expected.
31:00
SPEAKER_01: It was the wrong decision.
31:02
SPEAKER_01: And then when you got the results, you're like, okay, this is what the data told us.
31:06
SPEAKER_01: And now here's what the results are telling us, right?
31:09
SPEAKER_01: So you're right, you know, you're 100% right that that it they will believe in it if it aligns with what the gut says.
31:16
SPEAKER_00: I know, and it's really funny, it's not a new phenomenon.
31:19
SPEAKER_00: You know, I'm pretty sure you're familiar with the Monte Carlo simulation, and you know, like uh they lost some bombs uh in in the Mediterranean, you know, and they're like, How can we find it?
31:32
SPEAKER_00: And there's a mathematician who was uh appointed to find them, and he just started asking what to find what I mean a bomb from the second world war, okay.
31:42
SPEAKER_00: And so they had to to find it, and instead of going to look for it, he starts just asking all the people, like, where would you think it is?
31:50
SPEAKER_00: And and then he started doing his mathematical thing, and they say, I think it should be over here.
31:56
SPEAKER_00: And people like, how did he come to this conclusion?
31:58
SPEAKER_00: Like, no, no, that's not how we do it in the Navy.
32:00
SPEAKER_00: We just go and search the whole thing, you know.
32:03
SPEAKER_00: And months later they found it, and it was pretty much exactly there where he was, you know.
32:10
SPEAKER_00: But it's uh, you know, believing the numbers is like, no, that's not how we do it.
32:15
SPEAKER_00: And it's like it's a it's a change which has been there for a very, very long time, you know.
32:21
SPEAKER_01: Right.
32:21
SPEAKER_01: And I think you're right.
32:22
SPEAKER_01: The way you had described this is you have to build that trust over time, right?
32:27
SPEAKER_01: People have to be able to see the result.
32:29
SPEAKER_01: They first of all they have to understand it, they have to understand what the inputs are, they have to understand how you're getting to the outputs, what they're expected, even if you can't, you know, understand a large language model per se, you understand the inputs and you understand the outputs and you validate the outputs.
32:44
SPEAKER_01: You have to validate the outputs with a gold set of standard use cases, right?
32:49
SPEAKER_01: But but when it comes to any of these things, it's it's trust that's built over time and it's based on explainability to the degree you can, evidence, right?
33:00
SPEAKER_01: Evidence and and results, right?
33:03
SPEAKER_01: So the evidence of what you're modeling, what you're predicting, and then what are the ultimate results?
33:07
SPEAKER_01: And it's hard, you know, if you don't get that trust at the beginning, or you or you exploit that and you build mistrust, it's very hard to regain that.
33:18
SPEAKER_01: So that's why you have to be very careful to pick and choose your use cases and and really you know bring people along on that.
33:27
SPEAKER_01: But I I it's I totally agree with you that that's a really critical thing.
33:31
SPEAKER_01: And you know, being data-driven is not just about data.
33:35
SPEAKER_00: No, it's not.
33:36
SPEAKER_00: I I think it's all about the business impact, the business value, and and you know, like uh whatever you do, you you need to set you know the translation.
33:46
SPEAKER_00: We didn't say, like, well, we we saved the translation process.
33:49
SPEAKER_00: It's like, hey, we can save you 1.7 million dollars and we can decrease the process from weeks to a day.
33:58
SPEAKER_00: It's like, oh, that sounds great.
34:00
SPEAKER_00: Okay, can we have more of it?
34:02
SPEAKER_00: You know, the driver was the business value, and uh, you know, like that's the problem I see I saw in 23.
34:09
SPEAKER_00: That there was a lot of activity without business drivers.
34:13
SPEAKER_00: You know, it's like it feels like someone in 90 tried to solve a business problem without any guidance.
34:19
SPEAKER_00: It's like, oh, that's really dangerous.
34:21
SPEAKER_00: Uh a lot of song costs is that, you know, like uh it reminds me a little bit of the Texas Sharpshooter mentality, you know, like if you don't have a clear goal, if you just start shooting at a barn, and then you go to the barn and start drawing the bull eyes around your and like wow, I'm so good.
34:41
SPEAKER_00: Right.
34:41
SPEAKER_00: But golf up front, yeah, a little bit a different story, you know.
34:47
SPEAKER_01: That's very funny.
34:48
SPEAKER_01: Um, I'm just looking over some of the questions we had from the webinar, but uh, Liam, did you want to uh ask some specific questions about um digital adoption?
34:58
SPEAKER_01: Or what would what did you want to cover, Liam?
35:01
SPEAKER_02: Yeah, um, I saw that you had some experience or a lot of experience when it comes to like digital adoption and digital transformation.
35:07
SPEAKER_02: And um when it comes down to it, I mean digital adoption, artificial intelligence, and knowledge management, they're kind of like this magic triangle of things.
35:17
SPEAKER_02: Yeah, and um, especially when it comes to building, you know, like technology solutions, how do you see the relationship to all those three things kind of I guess mesh together and create these solutions?
35:28
SPEAKER_00: I I love the question.
35:30
SPEAKER_00: You know, and like I'm totally biased because I've worked so long in knowledge management and really uh very engaged in dApp and AI.
35:38
SPEAKER_00: But so from how I see it, is like what we try to do from knowledge management is to bring the right information to the right people at the right time, you know, at the right place.
35:49
SPEAKER_00: You know, that's a kind of what we try to do, and now we have different tools, as I mentioned, up front, you know, to do that better.
35:57
SPEAKER_00: Like what we have learned like 20 years ago, is like when we started with the first knowledge base systems, you know, in support, it's like nobody cared about it.
36:07
SPEAKER_00: They felt like, wow, it's an additional step I have to do.
36:11
SPEAKER_00: And it it really was once we integrated the knowledge base right into the the flow of the support engineer in their um we implemented in the CRM system, you know, it was just a field, and suddenly people went there and like, oh, actually, that cuts down my time.
36:30
SPEAKER_00: Is I'm actually foster, you know, like when using that, and the same thing is true now, you know, you don't want it like, well, here's your work, and now go over here to this AI tool to do something you don't understand, and then bring it back.
36:44
SPEAKER_00: And that's where digital adoption platform is a kind of the glue between the systems, you know.
36:49
SPEAKER_00: You can't put it on top of a Salesforce or uh SAP or whatever system you need, you just put a launcher and it brings it there and brings the information back without having to go to IT and like, can you build me an integration here with APIs?
37:06
SPEAKER_00: And well, we don't have access to the source code, it's just an overlay on top of any application, and then you can do whatever you want.
37:13
SPEAKER_00: So that's why I think these three pieces just come so nicely together to finally integrate what you need to do right into the process.
37:23
SPEAKER_00: And I I think if we do from a knowledge management perspective, we do a good job, knowledge management is totally transparent.
37:32
SPEAKER_00: People don't even know that we were involved, it just works, and like wow, the system just got so much smarter.
37:38
SPEAKER_00: It shouldn't be like, oh, now I have to do this.
37:41
SPEAKER_00: It's like, no, we're helping you, and you shouldn't even know that we were there.
37:45
SPEAKER_00: So that's a kind of how I see these three uh connecting together.
37:50
SPEAKER_02: Wow, I think people now are really starting to realize the value of knowledge, knowledge management that's designed well and implemented well, especially with all these new tools and things coming around.
38:00
SPEAKER_01: Oh, yeah, it is it is a uh, you know, there's a a trade-off between being current and the ability to absorb change and digital adoption platforms allow or enable the organization to absorb change more quickly, right?
38:16
SPEAKER_00: Yes, and many times even without even notice, you know, you just like you you just go through the flow and you like okay, I'll that was different, but that was much faster, easier.
38:30
SPEAKER_00: You can add automation into the process.
38:34
SPEAKER_00: Like, well, how many times certain steps do you do over and over again?
38:38
SPEAKER_00: Jeez, because I can tell you like is that a new setting?
38:42
SPEAKER_00: It's like it just automatically filled.
38:44
SPEAKER_00: So I can just focus on the stuff I really need to interact with a customer.
38:49
SPEAKER_00: So you and I think that's what AI will do.
38:51
SPEAKER_00: It's like many people are afraid that it will take away everyone's job, and I think it will shift things you can repeat.
38:59
SPEAKER_00: It's like, why would you do that more than once?
39:01
SPEAKER_00: You know, like define it, leverage it, and now it frees you up to do things you never had time to do.
39:06
SPEAKER_00: It's like really value-added work, you know, like uh going deep or building a good relationship with your customer, your vendors, or whoever, and that's not what you can automate.
39:16
SPEAKER_00: So I think work gets far more exciting, you know, if you apply AI correctly.
39:22
SPEAKER_01: So it sounds the things that you're talking about related to digital adoption, uh, also sound like there's they uh they're adjacent to robotic process automation.
39:32
SPEAKER_01: Do you want to talk about how those two areas fit together or are they increasingly blurring the lines between them?
39:39
SPEAKER_00: I think over time a lot of things will converge and blur the lines.
39:44
SPEAKER_00: Uh so uh robotic uh automation uh RDPs.
39:49
SPEAKER_00: Uh I think they're just a little bit more strict, and most likely uh for high volume things you just want to automate.
39:57
SPEAKER_00: You know, it's like that's where you want to use it.
39:59
SPEAKER_00: A DAP is a little bit more flexible and most likely also smaller processes, you know, like hey, here I want to automate like three or four stack on the screen, uh, and it's right in the front end, while RDPs are often in the back end where you just have huge amounts of data you just convert from one system to another.
40:18
SPEAKER_00: So I think front end versus back end, but over overall I I see even more things converging, you know.
40:25
SPEAKER_00: Like we have also from a training perspective, artificial intelligence uh uh artificial reality, uh, AR and VR.
40:34
SPEAKER_00: And I think if you think about AR on the on really the low level, is actually you're in front of um a channel and you have your goggles, and then it tells you press here, press here, and has some instructions.
40:50
SPEAKER_00: I mean, this is the basic level of AR, you know, and it's very similar to Adap where it's just on a screen.
40:57
SPEAKER_00: And I think these two could totally converge, where it's like it's not really that you have to be in a in front of a screen, it's think about all the things now Apple is pushing, and also Meta in uh, you know, like with the goggles and all that stuff, uh Facebook.
41:14
SPEAKER_00: You know, like I I think we we will come into more virtual world where you you you you have your goggles and you see instructions, how you use the panel, and then you come back to your screen and it's one flow now all together, it's not segmented anymore.
41:28
SPEAKER_00: So I think you're absolutely right.
41:31
SPEAKER_00: These things start converging, you know, that you can't even tell which tool you use, it's just transparent and it's it's nicely put together, you know.
41:41
SPEAKER_02: Um I'm glad you brought up the Apple Vision Pro kind of new wave of AR, you know, that's coming around nowadays.
41:49
SPEAKER_02: Um we were talking about kind of like context and prompts and the importance of prompt engineering, like earlier in this discussion.
41:56
SPEAKER_02: And I was just thinking, imagine it's you wear these, you know, what are they called, the Apple Vision Pros all day.
42:02
SPEAKER_02: And it's basically taking context of your life and what you're doing on a day-to-day basis.
42:08
SPEAKER_02: And I can only imagine like the future possibilities with how AI will kind of interact with you on a day-to-day basis of knowing, oh, yeah, you get out of bed at this time and you like to make your coffee first and do this and check your calendar here to where like it'll just start autumn, it'll basically be trained based off of your life.
42:25
SPEAKER_02: And I mean, who knows the possibilities of that?
42:28
SPEAKER_02: Do you have any comments?
42:30
SPEAKER_00: Uh I'm glad you brought that up.
42:32
SPEAKER_00: Um, I attended the keynotes uh in 2000, and I I I could actually find the computer science professor who had that because he was talking about like a hey, in the future, you will talk to your computer and say, I have a homework assignment about this topic.
42:49
SPEAKER_00: Uh, tell me a little bit about that.
42:51
SPEAKER_00: And then, like, oh, um, you you make some three points.
42:55
SPEAKER_00: Can you compare and contrast point A, B, and C?
42:58
SPEAKER_00: That were really interesting, and then you know, like so the discussion goes on and on, and then it's like, oh, okay, can you summarize everything we talk uh into a paper of about 600 words uh and send it to my email?
43:10
SPEAKER_00: That was the keynote speak in 2000.
43:14
SPEAKER_00: And when ChatGPT voice launched last year, uh, I I I had to find this professor and send him a message like, Do you remember yeah what you talked about?
43:27
SPEAKER_00: Now we can do it.
43:28
SPEAKER_00: It's exactly what I can do now.
43:30
SPEAKER_00: And he's like, Oh, that do you still remember that?
43:33
SPEAKER_00: That's so that's so great.
43:34
SPEAKER_00: You know, it it took 23 years, but it you know, is it's not here, and I think it just goes on from here, you know, it's fascinating, you know.
43:44
SPEAKER_01: Yeah, I remembered also like 20 years ago when personal digital assistants were a thing that people were talking about that will be coming, right?
43:53
SPEAKER_01: And then people made the Apple Newton and how but abysmal a failure it was, but it was like stepping.
44:00
SPEAKER_01: Stone, right?
44:00
SPEAKER_01: We're going to get there.
44:01
SPEAKER_01: And you know, the I I used to talk uh, you know, in my talks about one day being able to access all the knowledge you need, you know, like whether you're an attorney and getting case files or whether you're doing something else, and it's just that's our reality, you know.
44:18
SPEAKER_01: It's it's all vices.
44:20
SPEAKER_01: And uh so it's really interesting to kind of think about where this is going to go.
44:24
SPEAKER_01: And, you know, I was listening to uh a podcast the other day, uh Hard Fork.
44:29
SPEAKER_01: I don't know if you listen to Hard Fork, uh it's a it's a good podcast, and uh they were talking about you know artificial general intelligence, which people think may not be that far away.
44:40
SPEAKER_01: I still have a hard time believing that that's coming because I think the human brain is so much more complex than anything we can do in silicon, like one nerve cell can be connected to 10,000 others.
44:53
SPEAKER_01: We have, you know, I don't remember how many billion we have, but we have a lot of nerve cells in our brain.
44:58
SPEAKER_01: And then neurotransmitters are analog, they can go at different levels, and there's a hundred different neurotransmitters, right?
45:04
SPEAKER_01: So that's a lot, those orders and orders of magnitude more complex complex.
45:09
SPEAKER_01: And you know, when you still look at what generative AI is, it's it's still an emulation of intelligence, right?
45:17
SPEAKER_01: And it's hard to imagine where that's gonna go, but it's also hard to imagine it, you know, really truly replacing people.
45:25
SPEAKER_01: Uh, it will replace jobs, obviously, and it's the people who are going to be using the uh AI most effectively and the organizations using it most effectively.
45:36
SPEAKER_01: So what do you think?
45:37
SPEAKER_01: What do you say to the companies that are banning it right now internally and afraid to do anything with uh with uh uh generative AI?
45:47
SPEAKER_00: Well, you know, like every company has a different risk tolerance, you know.
45:53
SPEAKER_00: Uh I always feel like banning is the worst thing you can do, uh, you know, because then you just open a different channel which you don't control anymore.
46:03
SPEAKER_00: Actually, having uh a very clear place where you can play with and you have control over it, I think will be much more preferred, and very clear policies what you can and what you can't do.
46:18
SPEAKER_00: Because if you ban it, you just go to your cell phone, it's like that's my phone.
46:22
SPEAKER_00: I go to ChatGPT and you enter data you should not put in because nobody told you, and you still have the same problem.
46:30
SPEAKER_00: While if you would educate people what not to do and what to do, and give them a safe sandbox, I think that would be far better because you will really behind um the wave of all the other companies who actually move forward, you know, and so you don't want to be left behind.
46:50
SPEAKER_00: So then that's kind of my approach because you can't stop it, and the more you learn and educate and uh um safely do it, I think that's the better approach than just kind of try to ban.
47:04
SPEAKER_00: Like if a company will ban computer in the 80s, it's like, well, we don't use computers, we use typewriters.
47:10
SPEAKER_00: It's like, well, good luck with that approach, you know, it's like it's coming.
47:14
SPEAKER_01: Yeah, that's very funny.
47:16
SPEAKER_01: Yeah, you are you there were there was a time when that happened, right?
47:19
SPEAKER_01: And I remember what's that?
47:22
SPEAKER_01: What's that?
47:23
SPEAKER_02: I said that was a thing, they're banning computers and replacing them with typewriters or holding on to the typewriters.
47:29
SPEAKER_00: Well, yeah, you know, every change people don't like it, and it's like, well, if you don't know how to save and how how to use work rather than typewriter, you know, like you it's slightly different, you you know.
47:42
SPEAKER_00: Yeah, it's it's change, you know, and it's an investment, you know.
47:45
SPEAKER_00: But back then it was far more expensive, obviously.
47:49
SPEAKER_00: Fair enough, fair enough, yeah.
47:51
SPEAKER_01: But um you know, I I I do go ahead, sorry, finish.
47:55
SPEAKER_00: You you brought up a very interesting point regarding the law.
47:58
SPEAKER_00: You know, it's like uh yeah, I mean, law is all written down, uh, and you could actually leverage AI for doing that.
48:05
SPEAKER_00: And uh, there's some interesting studies where it's like uh, well, if we would actually let robots or AI enforce the law, all the problems we have because we apply law still with human intelligence involved, but there's a lot of laws which are totally outdated, that they are never taken out of the law, they are actually still effective, but nobody enforce them because it doesn't make sense, right?
48:35
SPEAKER_00: But now if you tell the a robot just to enforce everything which is incorrect, we really need to clean up our law to be current, you know.
48:45
SPEAKER_00: To your point, you need information architecture, you need to clean your house.
48:50
SPEAKER_00: The law has never been cleaned up.
48:52
SPEAKER_00: We just added over 200 years more laws, and there's some really funny things which are illegal.
48:57
SPEAKER_00: We're like, What?
48:58
SPEAKER_00: That's illegal.
48:59
SPEAKER_00: I mean, it is really funny, you know.
49:01
SPEAKER_00: So just another point before.
49:04
SPEAKER_01: That's really great, a really great point.
49:06
SPEAKER_01: And I remember when I was working in an office and I brought the first computer in, right?
49:12
SPEAKER_01: They had not used computers, and I brought a computer in to you know automate uh mail uh you know, mail marketing, right?
49:20
SPEAKER_01: And it was just snail mail, but it was integrating lists and doing personalized story, you know, uh addressing personalization, and it's doing all this stuff, and we're grabbing these lists, and this guy says to me, you know, one day this this is an advantage for us now, but one day this is just gonna be the cost of admission, you know using computers in business, it's just gonna be mandatory, right?
49:45
SPEAKER_01: It's like it was one day, and I also remember when one of my clients said, our biggest problem is not having enough information online, right?
49:55
SPEAKER_01: That really changed.
49:57
SPEAKER_01: I mean, you made that great observation about you know, when people like confluence because search works so well, it's like anything new is gonna work well until you start loading it up with stuff, right?
50:07
SPEAKER_00: Yes, and I always told my team, which I think is true for AI as well.
50:13
SPEAKER_00: You can build everything, building is not easy and not difficult, maintaining it over time is it's really hard.
50:22
SPEAKER_00: And a good friend of mine made made a funny joke.
50:24
SPEAKER_00: It's like uh, well, God could create the world in seven days because he didn't have an install base he had to take care of.
50:33
SPEAKER_00: And for us, it's like it's not the new thing.
50:36
SPEAKER_00: What do you do with everything you had before?
50:38
SPEAKER_00: How you move that over?
50:39
SPEAKER_00: And I said, like that's a kind of an interesting observation.
50:42
SPEAKER_01: That's very funny.
50:44
SPEAKER_01: God didn't have an installed base.
50:46
SPEAKER_01: That's why you can make the earth in seven days.
50:49
SPEAKER_01: That's great.
50:49
SPEAKER_01: I love that.
50:51
SPEAKER_01: Um, so let's see.
50:53
SPEAKER_01: Uh, you know, we're right at the end, uh, right at the top of the hour here, just a few minutes before.
51:00
SPEAKER_01: Um, what else do you want to kind of cover up on?
51:03
SPEAKER_01: By the way, uh, tell us a little tell tell us a little bit what you do for fun.
51:07
SPEAKER_01: You know, what's what's what do you do when you're not doing DAP and knowledge management?
51:11
SPEAKER_00: Well, I I have a family, I have uh a four and a six-year-old, you know, they keep uh me on my toes, you know, like uh he's like they call me stuff like wow, uh I didn't know about that.
51:23
SPEAKER_00: Tell me more.
51:24
SPEAKER_00: It's fascinating how quickly they kind of embrace things.
51:28
SPEAKER_00: And I mean, I never taught them how to use a cell phone or a tablet.
51:33
SPEAKER_00: My son does stuff, I have no idea how he learned that.
51:36
SPEAKER_00: He kind of he takes a picture of himself or a wall and starts drawing on top of it and changes the color and start automating that stuff.
51:44
SPEAKER_00: Like wow, that's pretty cool.
51:47
SPEAKER_00: Yeah, it's an incredible, huh?
51:49
SPEAKER_00: It is, and it's like, yeah, I'm definitely not afraid of him.
51:53
SPEAKER_00: He he he's he he has drunk the Kool-Aid already, you know.
51:57
SPEAKER_00: So, but uh yeah, I I love reading, you know, keeping myself informed.
52:01
SPEAKER_00: There's just so much interesting things, you know, like uh you know, new stuff, but also what I also like is going back and see like what have we known in the past which we just have forgotten or haven't applied, and you know, yeah, we as humans are still very similar than yes many, many hundred years ago.
52:19
SPEAKER_00: And it's like, wow, this still applies to change management and all that stuff.
52:25
SPEAKER_00: So uh I love reading quite a lot.
52:28
SPEAKER_01: I love historical fiction myself, like historical, it's historically accurate, but they fill in some of the gaps and build personalities.
52:35
SPEAKER_01: But uh that's I I never liked uh history in in school, but you know, now it's like I'm so curious about everything.
52:43
SPEAKER_01: And I love science, I love reading scientific journals and yeah, all that.
52:46
SPEAKER_01: I can't get enough of that.
52:48
SPEAKER_01: Um, well, is there anything else you want to kind of sum up with?
52:51
SPEAKER_01: I mean, this has been great.
52:52
SPEAKER_01: I've really enjoyed working with you and having you on the the well, same here, same here.
52:57
SPEAKER_01: This conversation's been fantastic.
53:00
SPEAKER_01: Anything, any final uh thoughts, Tom?
53:02
SPEAKER_00: Well, something which is always a little bit uh controversial, but I always that's a kind of my go-to.
53:11
SPEAKER_00: Like, if we in knowledge management do a really fantastic job, we work ourselves technically out of a job.
53:18
SPEAKER_00: That's always my goal.
53:19
SPEAKER_00: It's like we are not here for being here forever.
53:23
SPEAKER_00: If we do a good job, things just start to happen naturally and as I say, transparent, and people have processes that work.
53:32
SPEAKER_00: Now, since there are so many changes, you fix one problem, you have the next problem.
53:36
SPEAKER_00: But the the motto should be is like make yourself obsolete and you start working the right direction.
53:43
SPEAKER_00: So I think that's something I always try to do.
53:46
SPEAKER_00: It's sometimes I was too successful, but uh most of the time, you know, like uh you just free yourself up to do some other meaningful work for the company.
53:58
SPEAKER_01: Well, I'm waiting for you to make yourself obsolete in your current your new job so that you can become uh really enjoyed this, and uh it's great to to meet people that you just resonate with and and absolutely yeah.
54:12
SPEAKER_01: Um, so that's great.
54:14
SPEAKER_01: Uh thank you again so much for your time and participation.
54:19
SPEAKER_01: This has been really great.
54:21
SPEAKER_01: Thank you, Thomas.
54:22
SPEAKER_00: It was a great pleasure, thank you so much, and uh we I hope we can continue in the future with that.
54:27
SPEAKER_01: Absolutely, and thank you to our audience, Liam.
54:30
SPEAKER_01: I appreciate your help in both uh the pre-production and the post-production.
54:34
SPEAKER_01: And now you're here on camera as well.
54:36
SPEAKER_01: So you're behind the camera, in front of the camera.
54:38
SPEAKER_01: That's a that's a good right.
54:41
SPEAKER_01: And then Carolyn, uh, of course, who's been uh instrumental in all the logistics.
54:46
SPEAKER_01: Thank you so much, and thank you to all of our audience, who will see you next time on our early AI podcast.
54:52
SPEAKER_00: Bye now.
54:53
SPEAKER_00: Thank you so much.
54:54
SPEAKER_00: Have a wonderful day.
54:55
SPEAKER_00: See ya.
54:57
SPEAKER_02: Bye.
54:59
SPEAKER_02: Thank you for joining us in another deep dive into AI innovation.
55:02
SPEAKER_02: Tune in next time when we introduce another industry expert and discuss how to maximize AI in your world.
55:09
SPEAKER_02: The Early AI Podcast is sponsored by Early Information Science and CMS Wire.
55:14
SPEAKER_02: To learn more, visit early.com.
55:17
SPEAKER_02: That is E A R L E Y.com.
55:21
SPEAKER_02: Thanks for tuning in.