Latest / The Adults In The AI Room / Henrik Hahn on innovation & strategic business development - Episode #17

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Henrik Hahn on innovation & strategic business development - Episode #17 · The Adults In The AI Room

0:36
SPEAKER_01: Welcome to today's podcast.
0:39
SPEAKER_01: I am Seth Early.
0:40
SPEAKER_02: And I'm Chris Featherstone.
0:42
SPEAKER_02: It's good to be with you.
0:43
SPEAKER_02: Hey, so nice to see you, Chris.
0:46
SPEAKER_02: Let me ask you something because I had a good friend of mine send me a link from LinkedIn that you're now one of the top 50 influencers in AI.
0:57
SPEAKER_02: What?
0:57
SPEAKER_02: That's awesome.
1:00
SPEAKER_01: It was a surprise to me.
1:02
SPEAKER_01: It says I'm a top global global AI influencer.
1:05
SPEAKER_01: I don't know how that came about.
1:06
SPEAKER_01: And I don't know if I deserve it, but I'll take it.
1:10
SPEAKER_02: Yeah.
1:10
SPEAKER_02: I mean, you know what?
1:11
SPEAKER_02: You you absolutely deserve it because there's, you know, just in terms of, you know, I know how you interact with customers as well as the community at large.
1:21
SPEAKER_02: And I I, you know, when you look into these things, most of the time it's, you know, I mean, if it was like Gartner or Forrester or something, then I would say you probably paid them.
1:32
SPEAKER_02: But I know that's not the case.
1:34
SPEAKER_02: So congrats.
1:35
SPEAKER_02: I'm I'm privileged and honored to be with one of the top global guys.
1:42
SPEAKER_01: Like I say, I don't know whose criteria it was or how, but but I'll take it.
1:45
SPEAKER_01: And you know, it's certainly this podcast helps, right?
1:48
SPEAKER_01: And and I thank you for your being the catalyst to get this started, right?
1:52
SPEAKER_01: I mean, of course, our partnership is wonderful.
1:56
SPEAKER_01: So uh let me go ahead and uh uh introduce uh today's uh guest.
2:01
SPEAKER_01: Uh he is a chemical engineer by training, uh, but he's now the chief digital officer, a chemical giant Evonic Industries.
2:09
SPEAKER_01: His interests include AI, the digital transformation, innovation, and strategic change management.
2:15
SPEAKER_01: And when he's not busy with all of that, he uh renovates uh classic cars.
2:22
SPEAKER_01: And we'll hear a little bit more about that.
2:24
SPEAKER_01: And uh please welcome Henrik Hahn.
2:26
SPEAKER_01: Henrik, thanks for joining us today.
2:27
SPEAKER_01: We're looking forward to uh having a great conversation with you.
2:31
SPEAKER_01: Uh let me ask you about your cars.
2:33
SPEAKER_01: What uh tell tell us a little bit about that that hobby of yours.
2:38
SPEAKER_03: Uh first of all, congrats uh to be uh honored as a big influencing voice.
2:45
SPEAKER_03: And uh it's really a privilege uh to join uh this podcast.
2:49
SPEAKER_03: Uh hi everyone.
2:50
SPEAKER_03: Uh yeah, if you ask my son, uh he he says uh this is basically junk.
2:56
SPEAKER_03: Uh so he has a completely different view.
3:00
SPEAKER_03: Uh no, actually, I'm uh very much uh into um classic cars German brands uh from the uh 60s and 70s, uh and of course, uh as always good German brands always uh trace back uh to famous uh US American producers like GM.
3:19
SPEAKER_03: So Opel, a German former GM brand, is uh part of uh uh you have called it collection, and uh also some uh German FAT models uh as well as also Japanese uh custom certainties.
3:37
SPEAKER_01: And this is a labor of love because uh I know that the the renovation and the painstaking uh effort behind that uh rarely leads to a monetary uh yes, I can tell you it's not an investment, it's just for pleasure.
3:54
SPEAKER_01: Right and uh yeah, yeah, and I told you I was a uh chemistry major, so uh I did not go the chemical engineering route.
4:03
SPEAKER_01: Um, and Chris, uh, you actually do some, you said you have uh renovation going.
4:08
SPEAKER_02: Yeah, I I've I classically do um motorcycles.
4:13
SPEAKER_02: So I'll take you know uh different style motorcycles and make them into scramblers or crude or uh bobbers or something like that.
4:21
SPEAKER_02: I have an affinity towards like 50s style hot rods and you know things that are blacked out with a pop of color in some way, shape, or form with white walls, right?
4:31
SPEAKER_02: So yeah, and you know, Henrik, the uh the crazy part is this is when you learn to know how good you really are.
4:40
SPEAKER_02: If you're good with the braking system and bleeding them, or you're good with the, you know, with the um the engine and you know, and everything from the engine back, or you're good at textiles and interiors, or you're good at paint.
4:55
SPEAKER_02: You know, it's very rare that you're good at everything, but I'm gonna give you the you know, immediately the benefit of the doubt and say, you know, I definitely want to see what you have.
5:03
SPEAKER_01: Yeah, what are you good at?
5:04
SPEAKER_01: What do you what's your favorite part of renovation?
5:07
SPEAKER_03: I think it's uh the mixture, and uh I would not say that um particularly good uh at any uh subject matter because in the end, of course, you also I would say in former days when I got the time, I don't get the money, and now I may have kind of money, but not the time.
5:29
SPEAKER_03: So you have to know when you should rely on uh also expertise uh and source out uh topics.
5:36
SPEAKER_03: Uh but I literally did everything, and yes, I have to admit, of course, I also own a motorcycle, and uh I'm in BMW uh motorbikes, and uh yeah, I fully got you, and you really hit my nerve with what you have said.
5:53
SPEAKER_03: So it's it's amazing.
5:55
SPEAKER_03: Uh but uh to be honest, uh it's really very hard to really be good at it if you do not practice frequently on a daily base, and uh yeah, being now a CDO in a chemical company, unfortunately, there is this lack of time, which prevents to really be good.
6:19
SPEAKER_01: Right, right.
6:20
SPEAKER_01: Well, let's talk a little bit about what you're doing this and how did you get to where you are?
6:24
SPEAKER_01: So you started as a chemical engineer, and how did you kind of move into this particular role?
6:29
SPEAKER_01: And tell us a little bit about Ivonic as well.
6:32
SPEAKER_03: Just to the Yeah, actually, uh indeed, I spend more or less my entire professional uh career within Ivonic and predecessor companies, and I always worked at the interface between technology and innovation uh on the one hand and uh strategic business development on the other hand.
6:52
SPEAKER_03: And uh indeed uh uh for whatever reason that more and more brought me also into uh these uh more digital topics.
7:02
SPEAKER_03: Um so I am indeed a chemical engineer, process engineer by my first education, but I also uh hold a degree in business administration and specialized in information economics and uh mathematical game theory.
7:17
SPEAKER_03: So there is, let's say, at least it's close uh to the digital world and um uh and being uh along in the company, uh being part of our strategy group.
7:30
SPEAKER_03: I proposed uh that we have to do something about it, about digitalization, about what is called in Germany has been coined as a technical term, industry for zero.
7:42
SPEAKER_03: Uh so embracing uh the entire spectrum of digital technologies, and with my technology and innovation background, for whatever reason, our former CEO said, Well, Henrik, that's your turf.
7:54
SPEAKER_03: Find about it and go about it.
7:57
SPEAKER_03: Right.
7:57
SPEAKER_02: Well, there's no doubt though, taking nostalgic things like good architectures are um are definitely repeatable, but applied in different ways provide more enablement, right?
8:10
SPEAKER_02: So repeat, you know, so things that we used to do in the prem environments and things that we used to do for security or or you know, math, you know, like equations.
8:18
SPEAKER_02: There's only so many ones and zeros that can be manipulated in so many different ways.
8:23
SPEAKER_02: But when we apply it to a different thinking, right, then we get you know, um, a needed outcome that addresses a specific, you know, problem in this point in time, right?
8:33
SPEAKER_02: It's probably why you work on cars, you know, old nostalgic cars, right?
8:36
SPEAKER_01: Same, same renovation idea in there somewhere, right?
8:40
SPEAKER_01: And there's a and there's a specialization idea, and there's the idea of what do you do yourself?
8:44
SPEAKER_01: What do you where do you get experts?
8:46
SPEAKER_01: You know, what do you try to build in terms of your own capabilities versus where do you outsource or where do you hire the right experts, right?
8:53
SPEAKER_01: I mean, that's kind of kind of the analogy that I can see, but help me understand how you so you started as a chemical engineer there.
8:59
SPEAKER_01: First of all, what is the scale and scope of Ivonic?
9:02
SPEAKER_01: It's a large organization and you have a lot of different business units, and you're looking at the digitization across this from a global perspective, right?
9:11
SPEAKER_03: Yeah, actually, we we are indeed one of the world's leading specialty chemicals companies.
9:16
SPEAKER_03: Uh, we're active in more than 100 countries around the world, and uh uh let's say our 7,000 plus uh products uh, for example, makes uh uh make make tires fuel efficient, mattresses uh more elastic, uh medications make mattresses more mattress, yeah, yeah, yeah, yeah, yeah, yeah.
9:38
SPEAKER_03: Yes, sorry, sorry.
9:40
SPEAKER_03: Medications more effective and uh animal feed uh healthier.
9:45
SPEAKER_03: Just to mention a few uh examples what specialty chemicals is all about.
9:49
SPEAKER_03: And while we often uh uh contribute only small amounts of material, those contributions really make the difference.
9:57
SPEAKER_03: And uh lipids for messenger RNA-based vaccines are a very recent example.
10:04
SPEAKER_03: And uh, by the way, a world without chemistry would be really dull, dark, and simply disastrous.
10:14
SPEAKER_03: Uh, so no iPhone, no LED lighting, no almost everything, including classic car restoration, uh, and including this podcast.
10:24
SPEAKER_03: And so basically, everything in our daily life uh is centered around uh chemistry, and that's really a super exciting topic uh from the food we eat uh to personal or healthcare to mobility.
10:36
SPEAKER_03: Yeah, in essence, uh the chemical industry is the industry of the industries, and as uh the industries are more and more about to transform, uh of course, uh also our industry has to transform.
10:51
SPEAKER_03: And to keep it very simple, uh, one could say we just make stuff for stuff, but uh, when the others that do the stuff uh the end consumer uh consumes uh are changing, then we also have to change.
11:04
SPEAKER_03: And uh also uh classic car restoration has a lot to do with transformation, and uh, being uh also within uh uh our process technology group, uh, we always tried uh to make uh our production processes more efficient, more sustainable.
11:22
SPEAKER_03: That means that we also have to rethink uh processes that have been established over decades.
11:30
SPEAKER_03: And uh the industry is pretty successful as a whole, and so this is in particular the challenge why should anyone uh change anything?
11:43
SPEAKER_03: And that's uh actually also uh the beauty of my current job uh to think about how one could do things differently than before, right?
11:53
SPEAKER_01: Right, right.
11:53
SPEAKER_01: And so, in terms of uh the size of Ivanik, you said 100 countries, uh employees more than 33,000 employees worldwide.
12:02
SPEAKER_03: So it's I would not call it a giant, but we are a fairly large company.
12:06
SPEAKER_01: It's a good sized company, but there's a lot that's going on in those different business units.
12:11
SPEAKER_01: And when you start thinking about the amount of organizational change, I understand another area that um uh is really uh first and foremost in your mind is is really the the cultural sciences.
12:24
SPEAKER_01: Tell us a little bit about that.
12:26
SPEAKER_01: I understand you're you're you continue your studies and you're continuing to to uh uh educate yourself and add additional uh degrees.
12:35
SPEAKER_01: Tell us a little bit about the work that you're doing in that area.
12:38
SPEAKER_03: Yeah, actually, being uh also part of our own innovation landscape, uh for me it was very clear that uh you have to broaden your own horizon, you have to tap in new perspectives and uh uh indeed uh cultural sciences that is a field uh uh of uh new from an engineering point of view, theoretical and methodological perspectives and practices.
13:08
SPEAKER_03: And I believe very much that you can transfer uh very much also learnings from humanities, social history, and even media history, and those are perspectives uh that uh cultural studies are centered around, and indeed, in my spare time, uh I'm trying to earn my B bachelor degree, and this is uh really very enlightening, very uh um much uh also a matter of um tapping into new territory and also better understand uh also what culture is all about, and uh you could also say digitalization that is indeed a very good candidate uh for culture.
13:59
SPEAKER_01: And the cultural issues around artificial intelligence are, I think, uh trump and supersede a lot of the other issues around the technology and even the data sources, right?
14:12
SPEAKER_01: I think the data is more important than the algorithm, but the culture is more important than anything, right?
14:16
SPEAKER_01: Because without that culture change and that acceptance and that understanding, these things are going to stall, right?
14:22
SPEAKER_01: So talk a little bit about that.
14:23
SPEAKER_01: You also, when we were when we were chatting before, you talked about AI being augmented intelligence as opposed to artificial intelligence.
14:30
SPEAKER_01: So maybe you can talk a little bit about those topics.
14:34
SPEAKER_02: That's the fun, that's not a debate, but it's a fun topic.
14:36
SPEAKER_02: So yeah, I'd love to get your take.
14:39
SPEAKER_03: Yeah, and and we all know about this famous Peter Drucker statement that uh culture eats strategy for breakfast, and uh, there's uh much uh truth about it, even I'm not 100% sure whether he really said it, but uh indeed when we talk about uh uh a technology topic like artificial intelligence, uh even how we call it might change the way we think about it.
15:07
SPEAKER_03: And therefore, for me, uh artificial intelligence intelligence is really meant to augment our human intelligence rather than substituting the human intelligence, and in order to do so, you not in the first instance uh have to deal with technology, uh you have to do everything centered around the human being, the employees, uh the managers, uh the leaders within the company to explain uh what are the benefits, what are the threats, and what are indeed also material risk uh related uh to uh uh uh artificial intelligence.
15:54
SPEAKER_03: First, AI can really help also companies coming from traditional industries to improve the bottom line.
16:03
SPEAKER_03: And uh, this is uh basically holding true in all key functional areas uh of a business, uh, from procurement to the operations to marketing and sales, research and development, uh let uh to finance.
16:18
SPEAKER_03: Uh and here we'll talk about efficiency gains and and and using uh advanced mathematical methods.
16:26
SPEAKER_03: So when I joined the company, of course, we were applying the same uh mathematical methods uh which are now coined as being AI.
16:37
SPEAKER_03: Uh um this is it's a rebranding and for good measure, yeah.
16:44
SPEAKER_03: And uh so we very much more uh refer uh to cognitive solutions, uh, and here indeed uh these cognitive solutions help to transform data into insights and the and and predictions, and these insights and predictions they indeed augment human decision making in a more or less targeted manner, right?
17:13
SPEAKER_02: Or reducing the cognitive load on the human, making it easier for them to do their job, reduce that you don't until I'm glad Henrik you bring this up because when I was I was at a um a former company that produced a lot of the um we'll say mainstream democratized AI services for people to use and consume as infrastructure, right?
17:35
SPEAKER_02: Um there was you know, in our in our uh team, I focused on the human augmentation side of AI, and then we had the business augmentation side of AI.
17:46
SPEAKER_02: And I, you know, purposely defined it as the augmentation piece, as opposed to there's some AI models, but you know, and we'll get into this because this is this is where um Seth just likes to really spin up on the right data at the right time and the right amount to actually make a decision or insight from it.
18:09
SPEAKER_02: But that's exactly what we're talking about, is and a lot of people get so spun up and hung up on the ideas that these AI things are silver bullets, and if you just put it in place, it'll solve all problems.
18:23
SPEAKER_02: And the nirvana of that is you know, people miss it's like what you know, watching a movie.
18:29
SPEAKER_02: You miss all the work that went into making something a masterpiece, and they don't consider and or you know, really um, I guess I don't know, I don't know if care is the word, but they don't they don't they can't appreciate how much work goes into the upfront needs to in order to get the right the the designed outcome that you're looking for.
18:52
SPEAKER_02: Does that make sense?
18:54
SPEAKER_03: Absolutely, and uh and and here uh I think uh we have to complement uh uh human creativity.
19:04
SPEAKER_03: Yeah, and so human is really, I would say that's the key uh ingredient.
19:09
SPEAKER_03: You need to also uh when I was while I was just referring to to the bottom line effects, of course, there's also top line growth.
19:17
SPEAKER_03: Uh and uh of course you can also think about how to unlock new revenue streams, uh, which is I think in traditional industries not that easy, but yeah, here it's clearly a must uh to take advantage of human creativity.
19:33
SPEAKER_03: Yeah, exactly.
19:34
SPEAKER_01: So quick question when when you are looking at your initiatives and your those scale and scope that you're talking about, and the number of business units and the number of um processes, you know, how are you how are you managing or catalyzing this innovation that leverages some of these emerging technologies as well as older technologies, right?
19:58
SPEAKER_01: I mean, we still need to, everything seems to be AI powered and AI driven these days, right?
20:04
SPEAKER_01: That's just part of what people talked about.
20:06
SPEAKER_01: And as you say, a lot of things we've been doing for many years, text analytics, predictive analytics, all of those things have been rebranded under this umbrella, this rubric.
20:15
SPEAKER_01: So when you look at the practical part of the organization, how are you determining where those priorities should be, how you're really fostering innovation, where you should be focusing your efforts?
20:26
SPEAKER_01: I know there's a whole bunch of stuff packed into that, but maybe talk a little bit about how you're building that strategy across the organization and how you're kind of deciding where to focus your energies.
20:39
SPEAKER_01: And, you know, how do you think about readiness of the organization?
20:43
SPEAKER_01: How do you think about the opportunities for innovation?
20:46
SPEAKER_01: Because the field is so vast.
20:49
SPEAKER_01: Is that a fair question?
20:50
SPEAKER_03: Is that a fair question?
20:51
SPEAKER_03: Yeah, that's I would say a very hard to answer because it's not a fair question.
20:56
SPEAKER_03: It's not a fair question, but uh to keep it brief, of course, you uh have mentioned all the relevant uh aspects, and indeed uh uh take the example of our company, we are a super decentralized company, so one could think how here we clearly need a purely centralized approach to tackle all these obvious challenges.
21:21
SPEAKER_03: I see it completely differently.
21:23
SPEAKER_03: I believe uh it's uh smart to develop a framework methodology, and uh this is much more helpful than to try to force the organization to apply something which they don't even understand.
21:42
SPEAKER_03: And uh, in order to uh uh be able to leverage a framework, then you need to uh follow up uh with a cross organizational approach.
21:53
SPEAKER_03: And that was exactly what we tried to do.
21:56
SPEAKER_03: We set up a cross organizational team, we set up A uh what we call cognitive solutions agenda where we from the very start involve not just uh only, let's say, all colleagues that can be regarded somehow as solution providers, but we also try to involve the beneficiaries, the potential beneficiaries.
22:20
SPEAKER_03: Yes.
22:20
SPEAKER_03: From the very start, and then we try to derive a kind of use case methodology.
22:26
SPEAKER_03: So, first of all, it's not that easy to answer what a use case is.
22:32
SPEAKER_03: What's the definition of a use case?
22:34
SPEAKER_03: So we try to follow uh also a well-known uh um reference uh for use cases uh uh coming also from the late 90s from object-oriented programming, uh, and so it was clear use case is not a let's say uh a candle you burn down, it's really meant to be there for a long time, right?
23:00
SPEAKER_03: And then you have to see uh uh how to evaluate the feasibility.
23:06
SPEAKER_03: Is it really a good idea?
23:08
SPEAKER_03: And here, of course, classic uh POC thinking uh comes into place.
23:13
SPEAKER_03: And if you take advantage of the cross-organizational expertise, then you then you can come up with uh let's say a good evaluation, uh a feasible evaluation uh approach, uh that you're more or less relatively certain that you're not just doing the right the things right, but you are doing the right thing.
23:38
SPEAKER_03: And uh here if you do the right thing, then you have to think about how to do it.
23:44
SPEAKER_03: And here one also has uh to pose the question is it something you can entirely do on your own behalf?
23:52
SPEAKER_03: Or, and here we come up with a make or buy decision, wouldn't it make sense uh to involve also external partners, service providers, uh, solution providers, technology companies, startups, giants, uh, academia.
24:08
SPEAKER_03: And uh, here uh we have limited resources within the company, and we have to be very clear about uh where to do things on our own and where to rely on an ecosystem approach.
24:21
SPEAKER_03: And uh ecosystem approach means that uh the organization, first of all, has to learn that we are indeed part of an ecosystem, right?
24:30
SPEAKER_03: And then things more and more can be shaped, and uh hopefully we're on the right track.
24:37
SPEAKER_02: We're also talking about too that hopefully the organization has, like you're saying, hopefully they have the right attitude, but part of that too is having the aptitude to do it, right?
24:49
SPEAKER_02: So, you know, so many times I I would you know see organizations that would either do one of two things, think that a vendor could provide the silver bullet and then not use any outside thinking to um validate or invalidate that, or they would do the opposite and then they get down the rabbit hole and then things were predefined, or they would get you know go down the rabbit hole of doing it themselves, miss some key steps, create some models, create some um, you know, some outcomes, and then uh also feel like they didn't get down the right path, and then bring in a vendor to or something outside to validate or invalidate, right?
25:29
SPEAKER_02: And usually course correct.
25:31
SPEAKER_02: But as you know, each one of those without, let's say, intermittent steps of validation are super expensive in terms of the switch and the redo and the switch and the redo without trying to do it up front.
25:47
SPEAKER_02: So I'd love to get a take from you, Henrik, on you know, on those ecosystem discussions, especially within your organization, how you actually help get people to the right level of thinking so that they don't, you know, spend, like you said, there's only limited resources in time money people, and and then we have too many processes.
26:10
SPEAKER_02: So, you know, how do you how do you go about your you know on your daily to get initiatives through like this that actually get people on board to make sure that they're thinking correctly?
26:20
SPEAKER_03: Yeah, it's not me to argue who thinks correctly or not.
26:26
SPEAKER_03: Uh but one important idea of this cognitive solutions agenda is to inform, inspire, and educate our employees.
26:37
SPEAKER_03: And uh this is, I think, uh a must to drive forward uh the relevant uh application.
26:45
SPEAKER_03: And here we introduced a four-step training program, for example, and that has been designed uh to provide learning journeys for starters as well as for pros.
26:58
SPEAKER_03: And these entail uh these trainings entail interactive awareness and basic trainings as well as dedicated uh data analytics and data science courses.
27:09
SPEAKER_03: And I think that was the foundation where we more and more build up a community of uh colleagues that understand uh also and do not just uh yeah follow these myths, for example, I just need an AI black box because I have this and that problem, and I want to push a button and uh have uh the answer uh right on my desk.
27:35
SPEAKER_01: And and here I think that was uh indeed uh a couple of years ago, really uh a problem that there were simply unrealistic views about what's possible based on a lack of understanding, based on a lack of knowledge, because there's so much hype, there's so much noise, they're getting bombarded by all these different perspectives.
27:58
SPEAKER_01: And at least you can say, here's the ground truth, according to here's our perspective, and this is what's generally accepted in this industry, and here's how you need to think about this, and then you cut through that noise.
28:11
SPEAKER_01: I think that's a fantastic approach.
28:13
SPEAKER_01: I also love the fact that you were really truly basing this on libraries of use cases, right?
28:18
SPEAKER_01: When you start thinking about what you're gonna do, it's where the rubber meets the road, it's what's going to happen.
28:24
SPEAKER_01: How does this impact a process?
28:26
SPEAKER_01: How does this impact an individual?
28:28
SPEAKER_01: How does it impact a department?
28:30
SPEAKER_01: And to me, when we build use cases for organizations, we build libraries of them.
28:34
SPEAKER_01: There'll be hundreds or thousands of use cases, right?
28:37
SPEAKER_01: You know, and they get broken down at different levels of granularity, but it comes back to what is it you need to do, right, in order to support your process or your outcome.
28:47
SPEAKER_01: And then with that, we can have baselines.
28:51
SPEAKER_01: And when you talk about, you know, deciding what's really working and what's not working, you have objective evidence.
28:57
SPEAKER_01: So maybe you could talk a little bit about that because I know we we've talked before about you know being able to make data-driven decisions, right?
29:05
SPEAKER_01: And how is that kind of tying in to these efforts and this innovation?
29:11
SPEAKER_01: You know, are you able to, and one of the big challenges, of course, is are you well instrumented to collect baselines?
29:17
SPEAKER_01: And then can you measure the impact of what some of these things are happening?
29:22
SPEAKER_01: So maybe you could talk a little bit about, you know, that kind of feedback loop where you're you have an intervention based on a use case, you have a baseline, and then you're you're measuring the impact and then doing course corrections.
29:36
SPEAKER_01: You want to talk a little bit about that thinking?
29:38
SPEAKER_03: Yeah, yeah, this this uh what we are um typically doing.
29:42
SPEAKER_03: Uh we are applying the OKR methodology, and that helps uh to not just set to set the course, but also to uh check uh whether we are on the right course.
29:54
SPEAKER_01: And uh for people who are not familiar with that, do you want to just describe for sure, for sure.
30:00
SPEAKER_03: It's it's also a well-renowned uh methodology, uh coming from Intel and Grove.
30:06
SPEAKER_03: Uh uh it's also uh ages old, uh, but indeed uh it changes the thinking from output to outcome by articulating a clear objective and uh um key results that can be measured in a more or less quantitative way, and uh we have uh you can also regard this uh being a kind of agile uh uh methodology because uh this also uh works in cycles, and it helps uh to always uh have also a clear understanding within the teams whether they are on the right track or not.
30:56
SPEAKER_03: Exactly, exactly.
30:57
SPEAKER_03: And and this is really a relatively simple uh methodology.
31:01
SPEAKER_03: Of course, it needs discipline, and of course, it's not that easy uh to uh uh formulate uh those key results, so it needs a little bit uh of practical doing, but I believe that everyone uh can uh apply this, and there are great videos uh available on YouTube.
31:22
SPEAKER_03: Uh, for example, how Google Ventures is uh uh employing the methodology, and it's really very, very simple, and it's uh I think one of the uh key ingredients uh uh to uh also work in an agile environment uh and applying also agile methodologies.
31:42
SPEAKER_01: And those key results need to be measurable, right?
31:45
SPEAKER_01: Those key results exactly, yeah, right.
31:48
SPEAKER_01: And that's where when when we met at uh that conference and uh you had a presentation called our metrics layer cake, right?
31:54
SPEAKER_01: Where you start looking at the data, then the process, then the outcome, and then the alignment with the organizational objective.
32:00
SPEAKER_01: And you can do that across the entire customer lifecycle and all the departments and all the accountabilities, and that is the key to that mechanism for doing that continuous improvement.
32:10
SPEAKER_01: The biggest challenge I have found is when executives have a hunch or direction or an objective that they want to show uh metrics around, but they don't want to look at the data, right?
32:22
SPEAKER_01: So the data may say one thing, but their their intuition or their objective or their judgment says another thing, and they try to manipulate that data.
32:32
SPEAKER_01: So you can get a little bit of um, you know, you can get a little bit of success theater around that, right?
32:38
SPEAKER_01: So are you able to do this in such a way that you do get that buy-in?
32:43
SPEAKER_01: And what if it goes against kind of that intuition or insight?
32:49
SPEAKER_03: And you and you can nail down the OKR formwork basically to two simple questions.
32:54
SPEAKER_03: Where do I want to go?
32:56
SPEAKER_03: And how do I know I'm getting there?
32:58
SPEAKER_03: And uh, how do I know here?
33:01
SPEAKER_03: Data comes into play.
33:02
SPEAKER_03: Right, right.
33:05
SPEAKER_02: For those, for the yeah, for those listening for OKR's objectives and key results, there's a great book out there called Measure What Matters.
33:13
SPEAKER_02: It's simple and it goes through exactly what Henrik's talking about.
33:16
SPEAKER_02: And you know, and Seth are talking about where you you literally go through those measures and those metrics.
33:22
SPEAKER_02: And I think that the key to Henrik, you brought up, you know, people will game incentives always, especially you know, to get well, humans will game everything, but um, but you know, in order to get there, I think that's it's it's really key and critical to make sure everyone's fingerprints are all over it and it's socialized in the right way so that you can go back to and say, hey, listen, this was the point in time, this is what we bought off on, and this is what it's enabling us to learn, understand, and grow from.
33:50
SPEAKER_02: And the beauty of an OKR is there's no set time frame on it.
33:53
SPEAKER_02: You guys set the time frame on what that looks like so that you know, people often get misunderstood on what an objective is as opposed to what a goal is, right?
34:02
SPEAKER_02: And what can be measured.
34:04
SPEAKER_02: And if you find that those measures are not mapping to what you set with your objective, then you switch it quick.
34:11
SPEAKER_02: And then you course correct quickly, and then you're back on track.
34:14
SPEAKER_02: Hopefully, the the key with a lot of this too is if if you don't have something or a process that you can course correct quickly, you will never learn from what you know what happened because you need to fail fast in order to learn.
34:28
SPEAKER_02: And a lot of these silver bullet technologies or buying it from a vendor, you know, take away the education process and learning curve that needs to happen for these organizations.
34:39
SPEAKER_02: And then they get something put in place and they have no idea what the hell it's even doing for them if it's not, you know, giving them the output that they're looking for, or like you said, Seth, the output that they desire.
34:52
SPEAKER_02: And so guess what?
34:53
SPEAKER_02: It just dies on the vine because it wasn't their incumbent cultural thing that created it, it was something they bought.
35:00
SPEAKER_01: I do want to take a pause here.
35:02
SPEAKER_01: Uh, we're a little more than halfway through our time, and I wanted to make sure reintroduce our guest.
35:08
SPEAKER_01: Uh, we are speaking with uh digital transformation expert, uh Henrik Hahn, who is the chief digital officer of Evonic.
35:16
SPEAKER_01: We'll be right back.
35:18
SPEAKER_01: We'd like to thank Marketing Artificial Intelligence Institute for their support of this podcast.
35:23
SPEAKER_01: Marketing AI Institute is a media, event, and online education business that makes AI approachable, actionable, and accessible to marketing leaders around the world.
35:32
SPEAKER_01: They run the world's leading marketing AI conference, MACON, which brings together hundreds of marketing and business leaders to better understand, pilot, and adopt AI.
35:43
SPEAKER_01: Today's podcast is brought to you by Simpler Media Group, owner of the Customer Experience Decoded Podcast, covering the latest in customer experience and marketing.
35:50
SPEAKER_01: CX Decoded is a semi-monthly podcast designed to deliver insight from leaders driving the evolution of customer experience with an emphasis on digital touch points, customer journeys, respectful data management, and voice of the customer.
36:05
SPEAKER_02: I want to give a big thanks to Early Information Science in support of the podcast.
36:09
SPEAKER_02: Early Information Science is a professional service firm with proven methodologies designed specifically to address product data, content assets, customer data, and corporate knowledge bases.
36:19
SPEAKER_02: Their information architecture expertise has allowed clients from all over the world to efficiently organize, access, and utilize their data, ultimately resulting in better business outcomes.
36:28
SPEAKER_02: They deliver customer-driven solutions that scale and adapt to the needs of a business as it evolves.
36:34
SPEAKER_02: Founded in 1994, Early Information Science has proudly delivered result-driven solutions for more than 100 brands across the top industries.
36:42
SPEAKER_02: You can get more information about Early Information Science at early.com.
36:47
SPEAKER_02: E-A-R-L E Y dot com.
36:51
SPEAKER_02: Working with customers in AI, the number one question that I get usually is where do I start?
36:56
SPEAKER_02: What data do I need?
36:57
SPEAKER_02: How do I gather it?
36:58
SPEAKER_02: And that's great questions.
37:00
SPEAKER_02: Well, if you want to catch the wave of AI, not be crushed by it, you really do need to read a book written by Seth Early called The AI Powered Enterprise.
37:08
SPEAKER_02: It gives you the ability to look and understand how to harness the power of ontologies to make your business smarter, faster, and more profitable.
37:16
SPEAKER_02: Seth goes into the intricate details of how to build an ontology, develop and employ an ontology as a secret weapon for the basis for how to actually utilize AI as a powerful competitive advantage.
37:28
SPEAKER_02: This book is really the first one to combine sophisticated explanation of how AI works with a practical approach of applying AI to the problems of the business.
37:37
SPEAKER_02: And it's applied to, you know, various use cases from customer experiences to business operations to product development.
37:44
SPEAKER_02: It's a powerful, powerful tool in the arsenal of understanding how to deploy AI to make your business better.
37:49
SPEAKER_02: The AI Powered Enterprise by Seth Early.
37:52
SPEAKER_02: Harness the power of ontologies to make your business smarter, faster, more profitable.
37:55
SPEAKER_02: Out on Amazon.com today.
37:57
SPEAKER_02: And now back to the show.
37:59
SPEAKER_01: So, Henrik, one of the things that, you know, is super important for organizations is really thinking about, you know, speeding up that information metabolism of the organization, getting the right information to people in the context in which they can apply it.
38:13
SPEAKER_01: And of course, organizational learning is part of that, right?
38:17
SPEAKER_01: Where you learn things and then you have to apply them.
38:20
SPEAKER_01: That's what's giving us uh sustainable advantage.
38:23
SPEAKER_01: Do you want to talk a little bit about knowledge processes and how that fits in when we think about cognitive AI?
38:31
SPEAKER_01: That's usually about helping humans do their jobs.
38:34
SPEAKER_01: Talk a little bit about knowledge, knowledge management, knowledge processes, and and really how um those problems need to be tackled in the organization.
38:45
SPEAKER_03: Yeah, I think in indeed you're uh uh referring uh to a topic what I call organizational design.
38:54
SPEAKER_03: So everything that has to do uh with the knowledge assets that an organization has at their hands, at their fingertips.
39:06
SPEAKER_03: However, uh, we at least in our industry uh very much followed uh the need-to-know principle, which means that uh it was never intended that everybody knows everything.
39:20
SPEAKER_03: And of course, that doesn't really make sense, uh, no doubt about it.
39:23
SPEAKER_03: But uh bringing the knowledge assets to the people that can exploit them, that's something where indeed uh novel digital technologies uh can help, um, uh be it in form of uh uh I call it intelligent databases, but also utilizing uh machine learning algorithms simply to uh sort out uh and help to organize uh your knowledge assets, and uh fortunately most of our knowledge is still um being on paper.
40:03
SPEAKER_03: And so the first uh issue was to get it digitized, right?
40:08
SPEAKER_03: Not digitalized, but really digitized.
40:11
SPEAKER_03: And we are talking about uh uh plenty of unstructured data.
40:16
SPEAKER_03: So to exploit a knowledge asset, you first of all have to um yeah shape it and th free, let's say, the knowledge from the asset, uh so to say, because it's hidden somewhere, and it's uh sometimes a matter of uh consistency, uh, and sometimes it's a matter of availability.
40:38
SPEAKER_03: So tapping uh into the knowledge assets a company and organization has that is really a challenge.
40:45
SPEAKER_03: And once again, it's not that much a matter of technology, it's a matter of trust, it's a matter of people, and how people are willing to share knowledge, to share information.
40:58
SPEAKER_03: And uh, here I think uh indeed uh not uh everyone needs to know everything, but uh one should uh uh think of an organization as an information processing system, right?
41:13
SPEAKER_03: And then, as you say, it's all about to move the right information to the right people in the right time, and uh, this is uh also a very uh old uh uh concept of information processing as an integrating uh organizational concept, um, even also has been developed uh like the OKR methodology in the in the in the 80s, uh, but this is exactly what we need to foster change related to AI topics, to data topics.
41:44
SPEAKER_03: And uh in the past, so when I joined the company, everyone was talking about knowledge management systems, but these knowledge management systems were really um super static, and nobody was really willing to uh also spend the efforts to put in the information, the data, and even sometimes was hesitating.
42:08
SPEAKER_03: Is really a good idea that I put in my individual expertise, my individual knowledge into a system.
42:17
SPEAKER_03: And what about myself?
42:20
SPEAKER_03: What about my reputation, my expertise?
42:22
SPEAKER_03: Uh, and and so uh many, many loss of control.
42:27
SPEAKER_03: That was one fear, and uh, I think um that was basically a major roadblock.
42:34
SPEAKER_03: Uh which and and so at least also in our case, I never got the impression that our knowledge um knowledge uh uh management systems really ever did take off.
42:50
SPEAKER_01: But those are critical, right?
42:52
SPEAKER_01: Those are critical, especially for cognitive uh AI, right?
42:55
SPEAKER_01: When you start losing that uh human expertise when people age out or or you lose them through attrition, you know, you do need to have that stuff, that tacit knowledge converted into explicit knowledge to some degree in order to support, you know, uh the organization's growth and evolution.
43:13
SPEAKER_01: And of course, that's where those centralized standards with decentralized execution comes in, right?
43:20
SPEAKER_01: Um, so I mean it's a struggle that everybody does deals with, but I think when you start looking at, you know, building question-answering systems or building reference uh architectures or building cognitive uh agents or conversational access, you know, those things are just channels to the knowledge and the content.
43:39
SPEAKER_01: Right.
43:39
SPEAKER_01: So we need to have that.
43:41
SPEAKER_01: And uh and I think we're gonna see a lot more emphasis on that in the coming years as uh these uh cognitive assistants and and um uh you know virtual assistants become more powerful and more useful and more valuable, right?
43:55
SPEAKER_01: It's it's going to be an arms race, you know.
43:58
SPEAKER_02: Henrik, do you still find in your organization?
44:00
SPEAKER_02: That you have information gatekeepers?
44:03
SPEAKER_03: Sure, sure.
44:04
SPEAKER_03: Right.
44:04
SPEAKER_03: And how do you approach that?
44:06
SPEAKER_03: For good reason.
44:07
SPEAKER_03: And when we're talking about information, by the way, we have to be also aware of the fact that we're not just talking about formal information, also informal information is sometimes very, very important.
44:18
SPEAKER_03: I think, of course, there has been good reasons to follow this need-to-know principle.
44:25
SPEAKER_03: And we have to now deal uh with uh these old habits and explain uh to to also the beneficiaries that uh and we have to listen also to skeptics because there are skeptics that we uh uh we may lose our competitive advantage if we uh simply let uh informal and formal information uh flow in the entire organization.
44:53
SPEAKER_03: So it has to be action, yeah.
44:56
SPEAKER_03: Exactly, exactly.
44:57
SPEAKER_03: So I think we have to uh see where we have to be strict, yes, and where we can indeed generate insights by sharing.
45:07
SPEAKER_03: So we have to categorize, we have to classify information and data, and we have to be clear about that sometimes bringing together different pieces of information may change the categorization and the classification of bundled data and information.
45:26
SPEAKER_03: Right.
45:26
SPEAKER_01: Exactly.
45:27
SPEAKER_01: Yes, it's funny because there was an organization we had dealt with that had a very, very specialized product on the market that was kind of a leading product.
45:38
SPEAKER_01: Very, very few people in that large enterprise knew the formulation for that product.
45:44
SPEAKER_01: Very, very few people.
45:45
SPEAKER_01: And so that IP was guarded, you know, very heavily because it was a huge competitive advantage.
45:52
SPEAKER_01: And then you have other situations where, you know, there may be uh parts of your organization that are dealing with competitors, right?
45:59
SPEAKER_01: And there's IP in each of those competitors that is known within your organization, but cannot be shared across different parts of your organization.
46:09
SPEAKER_01: So those are those are the IP and security and information security requirements that can get very complex, uh, but are necessary.
46:18
SPEAKER_01: And of course, the categorization and classification, as you say, is really the uh uh the final determinant there is making sure that you have that protection of that IP.
46:28
SPEAKER_01: So that is a huge balance.
46:29
SPEAKER_01: You want that information to flow, but carefully, you know, in a defined way.
46:36
SPEAKER_03: However, I think uh, of course, the famous Coca-Cola formula.
46:43
SPEAKER_03: Yes, uh, that's something you do not really want to share with everyone.
46:47
SPEAKER_03: Yeah, but uh as an organization, you have to uh uh ask yourself how many Coca-Cola formulas I have in my walls.
46:56
SPEAKER_03: And uh I think sometimes we overestimate uh um the importance of a single piece of information and uh also the value of information.
47:06
SPEAKER_03: And this is something, of course, I think it needs facilitation, it needs uh good data stewards and custodians, uh, and it's again a matter of change management.
47:18
SPEAKER_03: And if you can explain that there's much to win, but not that much to lose, uh, then it's uh maybe the right thing.
47:27
SPEAKER_03: And as I say, you have to listen to the skeptics, right?
47:30
SPEAKER_03: Because the skeptics, if it's it's not a black or white, uh, there's a reason, yeah.
47:34
SPEAKER_01: And that's where those centralized standards and policies and uh and reference architectures and taxonomies and metadata, all of those things become a corporate asset, but then the the execution has to be distributed, right?
47:49
SPEAKER_01: They have to follow a process, follow a standard, but then decentralize how people are executing on that in those business units, in those uh areas where they're closest to the information, closest to the problem solving, closest to that uh that that solution and that IP.
48:06
SPEAKER_01: So that makes a lot of sense.
48:08
SPEAKER_01: So when you when you look at these the kind of emerging applications for AI across, you know, RD, marketing, sales, give us a sense of where you're seeing kind of the biggest bang for the buck.
48:21
SPEAKER_01: Are there classes of tool or classes of application that you're seeing getting greater traction in those different departments?
48:29
SPEAKER_01: Uh, if is that something you can um can share with us?
48:32
SPEAKER_03: Yeah, I think um, of course, for our industry uh application in the RD space, uh, that's really something where, because that is, I would say uh knowledge management uh par excellence, uh if you say so.
48:50
SPEAKER_03: So here in the RD uh uh space, uh it's not that much about efficiency gains, it's really about uh uh to be uh um at the forefront of also innovation and uh here coming up with novel ideas.
49:08
SPEAKER_03: And I think um this is uh uh something uh where we can use AI for the development and the production of new materials, uh where we can model new processes, uh also in light of sustainability, and where we can even better understand uh the needs and requirements of customers because in the end, everything we're doing, we're doing for our customers.
49:33
SPEAKER_03: And here uh we have a couple of uh examples.
49:40
SPEAKER_03: I'm I'm happy to share, be it uh um use cases that are already productive.
49:48
SPEAKER_03: Uh for example, here also first-level language assistance uh to certain industries, for example, to the pain industry, uh, where we uh offer a virtual assistance that makes it easier also uh for our customers to find the right additives, or uh and uh also more a kind of internal uh knowledge grave graph uh technology uh that uh gives our internal experts uh also centralized access to the know-how we have accumulated uh uh through our in-house research, or uh in the area of compound formulations uh also uh sort of uh patent classifiers.
50:34
SPEAKER_03: That's really a practical tool, and uh we even have a strategic collaboration uh with the um uh uh MIT IBM Watson AI lab, where we are looking also into new uh uh technologies uh where we are indeed uh uh looking into also graph modeling, hybrid modeling, even the link uh to quantum computing uh in order to uh look for applications fields like materials discovery, right?
51:09
SPEAKER_03: So not classic lab uh discovery, but uh uh in silico discovery, formulation technology, yes, and market analysis.
51:18
SPEAKER_03: Right, right.
51:18
SPEAKER_02: Be careful, Henrik.
51:19
SPEAKER_02: You said knowledge graph in there, so just get ready because there's there's saying I love you to somebody, especially like Seth, and then they're saying knowledge graph, and they're very close to each other.
51:29
SPEAKER_02: So he's he's gonna start getting spun up, and uh you you you struck the the for him, right?
51:37
SPEAKER_01: Well, well, I guess I would I would I would only when that was the make for my presentation with that conference that that you facilitated.
51:46
SPEAKER_01: So yeah, so we're we're on the same page there.
51:48
SPEAKER_01: Well, I guess I would ask the question more generally, not dig digging in, but you know, what were the lessons, what were some of the lessons learned, you know, from you know, if an organization is going down that path and wants to use graph data, knowledge graphs, wants to tackle some of the things you just talked about with text analytics and you the facilitation of discovery and and getting through those.
52:09
SPEAKER_01: What are lessons learned?
52:10
SPEAKER_01: What are some of the obstacles people want to look out for, or you know, uh pitfalls or or challenges that that they might run into that that they could um anticipate?
52:21
SPEAKER_03: Of course, we are talking about uh sometimes extremely complicated stuff, and I would say it's absolutely okay to be misunderstood for a while, yeah.
52:36
SPEAKER_03: But uh you have to uh uh be aware of the resistance to change, even if you have a good idea, of course.
52:45
SPEAKER_03: And I think that's something uh which uh is frequently underestimated, change is stressful, and and people try to avoid it.
52:54
SPEAKER_03: And if we're talking about how to exploit uh uh knowledge assets, uh company already has, uh you will find many people that don't like the idea because it would mean that they have to change their habits, that they have to change their daily uh job.
53:09
SPEAKER_03: And uh, in order to avoid the pain, anguish, and frustration, lack of confidence that goes along with change management, effective change management is absolutely important.
53:21
SPEAKER_03: I would say that's for sure the most important uh learning.
53:26
SPEAKER_03: And this, of course, also holds true for any make or buy decision, because uh, of course, our own engineers, our own data scientists, of course, know much better about everything than any external uh uh expert, as you can imagine.
53:41
SPEAKER_03: Uh, and that's also I would say quite in the human nature, right?
53:47
SPEAKER_03: And uh, we have to somehow deal with our human nature for good reason, and therefore involve the ones involved, bring them in early on, and don't try to surprise people with a great idea, yeah.
54:06
SPEAKER_03: Educate, involve, and uh listen.
54:09
SPEAKER_03: Lead them, yeah, yeah, and and let them find the right way.
54:16
SPEAKER_02: Yes, let me uh let me ask you something quick, Henrik.
54:18
SPEAKER_02: So, in terms of obviously you're in a very heavily regulated industry, um, there's also all of the besides the socioeconomic stuff that you talked about, you know, personally, there's also all the world economics, you know, that that you have to deal with as a um as an organization.
54:39
SPEAKER_02: Is the organization asking you to help come up with like prediction models and things for you know what this looks like for the future?
54:47
SPEAKER_02: And how does that look?
54:48
SPEAKER_02: Because there's you know, there's world stage things happening, right, that that affect you.
54:54
SPEAKER_02: There's your upstream supply chain that affect you.
54:57
SPEAKER_02: There's all these, you know, these factors that are coming into it that we haven't seen in a long, long time.
55:03
SPEAKER_02: What does that look like?
55:04
SPEAKER_03: And what are they asking you to you know provide them so they can make rational predictions, uh even uh uh uh referring to the macroeconomic uh uh situation, overall situation, that is of course highly desired, uh which is of course also uh uh not that easy, as you can imagine, because if there would have been someone who uh would have found the silver bullet bullet, uh then obviously uh everything would have been in place already.
55:34
SPEAKER_03: So I think uh these predictions uh when it comes uh to economic aspects are very uh uh uh highly requested.
55:43
SPEAKER_03: But also I would say, even, and that's also a reason that we do not uh that much talk about artificial intelligence, but even process statistics also here uh are part of the cognitive solutions in general.
55:57
SPEAKER_03: Sometimes statistical process control is really very, very viable from a practitioner's uh point of view.
56:06
SPEAKER_03: So sometimes it's not that we are uh uh having a super algorithm in place, sometimes it's really good old known uh advanced mathematical models that can help uh to indeed uh uh predict uh conditions uh within our manufacturing uh landscape, for example.
56:27
SPEAKER_03: And uh as I say, these in silico considerations about materials discovery, that's of course a super hot topic because here the current computing power can fully play out.
56:42
SPEAKER_03: That's great.
56:43
SPEAKER_03: Pattern recognition is of course also always a topic.
56:47
SPEAKER_01: Yeah, of course, right, right.
56:49
SPEAKER_01: Well, I know we're right at the top of the hour.
56:52
SPEAKER_01: I I wish we had more time to explore this, but some wonderful lessons learned, you know, about uh and takeaways, about the cultural issues, about building use cases, about measuring results, about understanding your objectives and your key results and being able to course correct and evolve, decentralization of execution, bringing people into the fold, education, that foundational education for both less uh people newer to the topics and people who are the pros.
57:21
SPEAKER_01: I think those are all fantastic takeaways.
57:24
SPEAKER_01: Uh, you know, I Henrik, I really want to thank you for your time today.
57:28
SPEAKER_01: Uh, if there are any other final thoughts, uh please share them.
57:32
SPEAKER_01: If uh if you have any other takeaways for uh executives listening, uh, in any other thing that you want to kind of lead people with in terms of final thoughts?
57:41
SPEAKER_03: I'll leave it to the top influencing voice during this round.
57:45
SPEAKER_03: And thank you so much.
57:47
SPEAKER_03: It was really fun.
57:48
SPEAKER_03: As I say, in the end, uh it's there is no magic, uh it's just work.
57:55
SPEAKER_01: I love it.
57:56
SPEAKER_01: The blocking and tackling, right?
57:58
SPEAKER_01: It there is no magic, it is work.
58:00
SPEAKER_01: That's a wonderful, that's a wonderful summation of all of this.
58:04
SPEAKER_01: Thank you again, Henrik.
58:05
SPEAKER_01: This has been a pleasure.
58:06
SPEAKER_01: Chris, thank you so much for your time.
58:08
SPEAKER_02: You know what, Henrik?
58:09
SPEAKER_02: Yeah, again, you know, I'll echo Seth's sentiments.
58:12
SPEAKER_02: Thank you again for you know spending time with us, especially being across the pond and it being late your day.
58:18
SPEAKER_02: Um, very much appreciated.
58:20
SPEAKER_02: And uh look forward to you know, ongoing relationships with you and see, you know, what what you know the cool stuff you're doing at Ivonic.
58:28
SPEAKER_02: It's a it's in you know, chemical industry industries are so interesting just because of of all of the core things that you're producing, the measurement, the instrumentation, the processes.
58:40
SPEAKER_02: You know, it's it's phenomenal.
58:42
SPEAKER_02: So it's it's super intriguing.
58:43
SPEAKER_02: But uh good luck with all the cultural sciences, right?
58:46
SPEAKER_01: And your professors hearing the renovation and the uh car renovation.
58:49
SPEAKER_01: Good luck with the car.
58:51
SPEAKER_01: Like getting your parts and stand in one of your warehouses burned down.
58:55
SPEAKER_02: So that's that was gonna be the the next thing is is uh Henrik on the back channels.
58:59
SPEAKER_02: You and I will collaborate on what's going on with the absolutely, absolutely.
59:02
SPEAKER_03: Would love to do so.
59:04
SPEAKER_02: Need to compare notes.
59:05
SPEAKER_01: Exactly.
59:06
SPEAKER_01: All right, and make it so much guys.
59:08
SPEAKER_01: All right, guys, have a wonderful day and a great weekend, and we will talk to everyone soon.
59:14
SPEAKER_02: Thank you.
59:19
SPEAKER_00: Thanks for listening to the Early AI podcast with your host Seth Early at Chris Featherstone.
59:25
SPEAKER_00: We hope you enjoyed the program and took away some ideas that you can start to implement in your organization right now.
59:31
SPEAKER_00: If you do need help, check out www.early.com for information about early information science and of course check the show notes for links to any resources we mentioned today.
59:44
SPEAKER_00: That's all for now.