Yorck F. Einhaus
I think most insurance companies know, if we do not get on this train, we will not be here in a couple of years. Because it will be a game changer. If you miss that boat, you will potentially fall behind the pack and eventually be irrelevant.
You can have as much data as you want. If the people don’t trust it and don’t use it, it’s worthless. If I can’t see the impact on the top or bottom line, then I haven’t been able to scale it.
Push yourself out of your comfort zone. Volunteer for the work no one wants to do. They have a reputation of doing the stuff no one else wants to do. And they’ve been successful at it.
Saket Saurabh
Hi everyone. Thanks for listening to another episode of Data Innovators and Builders, this is Saket. And today I’m speaking with Yorck Einhaus. Yorck was formerly the CDO at Liberty Mutual and Farmers. Yorck, great to have you here.
Yorck F. Einhaus
Nice to have me. Thank you.
Saket Saurabh
Yorck, let’s start a little bit with your background and your work in the data space.
Yorck F. Einhaus
Yeah. So it’s probably a little unconventional. First of all, I’m born and raised in Europe and worked for Allianz and Zurich Insurance, two big carriers in the past in Europe, and then relocated to the States and started working at Farmers.
I came actually from the business side as a business process engineer and then on the tech side, program management, and then eventually Chief of Staff and CIO and then CDO and then shifted over to Liberty Mutual and was global CDO there as well for the global risk solutions.
So it was a bit of a journey. It wasn’t a straight line. I did a lot of movement from business to tech and within tech and then to data.
Saket Saurabh
Great. Thank you for sharing that. And having worked for some of the largest global insurance companies, maybe give us a high level of what are some of the key lessons you learned working on the data side of things and AI in these complex enterprise environments?
Yorck F. Einhaus
Yeah. Well, one thing that people need to think about when it comes to insurance is that insurance is nothing else but a data company. Everything they do is based on data they can gather to assess a risk and create a price for a claim that is somewhere in the future. So I think the DNA of an insurance company is very much tied to good data and data that they can use for the actuary and the product development.
And nothing in the economy actually works without insurance. You can’t drive a car, you can’t fly a plane, you can’t start a train, you can’t build a house, you can’t employ people. So insurance is really across the board necessary in our economy, and it is a very data-heavy industry, but it’s also an old industry. Insurance companies are over 100 years old, and some of their systems are very old. You still have mainframes and many, many legacy systems.
And then in addition to that, there was a time in 2005, 2006, 2007, at least in Europe, where there was a lot of acquisition. So many insurance companies bought other insurance companies. You had this phenomenon of bringing different systems together under one umbrella, and many of them did not take the time or have that vision of modernizing and standardizing and going to one platform.
So if you look at the architecture of some of the bigger insurance companies, especially those that have grown through acquisition, it is everything under the sun, many hundreds of systems, hundreds of applications across the value chain. So that obviously introduces complexity, it introduces data that flows from its source to its destination through many, many different pipes. And they all manipulate the data. So there’s a lot to work through.
Saket Saurabh
Yeah. Absolutely. Insurance is the original sort of data application. Actuarial science, actually one of the first applications of taking data and actually building a business on top of that, or a product on top of that. And you’re right. Data variety is a big challenge in large enterprises. It’s often hard to appreciate if you have not been in one of these big enterprises with that much history. Every merger and acquisition creates new silos.
So one of the things that is great about your background is you’ve been able to actually accelerate the pace of innovation in some of the places that you’ve been. Tell us a little bit about, on one hand, with all this complexity and silos, yet you’re moving fast and it’s not easy.
Yorck F. Einhaus
Yeah, so it was a very focused attempt to say, if we want to embrace and challenge ourselves and think innovatively, we need to create a space that allows that. And so we really tried to not only create a physical space, but also a technological space where you can experiment with emerging technology, where you can do fast prototyping, and where you can build without all of the legacy tie-ins that might slow you down.
So that was an environment that we created because we needed to be separate from the mothership, from that monolithic sort of environment, to be able to move fast. But once you can prove out that certain innovative products work, you need to be able to show that it can scale. And then it can scale in parallel or even embedded in the existing environment. And that’s the hard part.
Because that’s really when it gets complex and might cost a lot of money. So you need to know, is it worth it? Can you get your ROI to work? But you will never know if you don’t have a space where you can experiment. And if you already know all the problems that are going to come, you need to get rid of that mindset to be able to think creatively and innovatively and come up with something that is truly new.
Saket Saurabh
Yeah, I think that’s great advice in terms of moving fast and managing the different aspects of that. Can you maybe share a little bit about this innovation lab that you created at Farmers? Maybe share some examples of specific innovations you were able to bring about?
Yorck F. Einhaus
Yeah, so we had a small but very powerful team that focused on creating this innovation lab. As I said, it was a physical space that we ripped everything out that looked old and we created a very modern innovation lab that was able to interact with the environment with the newest technology. And that attracted the right talent. When people came to interview for this team, they saw an environment that was more along those lines that they were hoping for than the cubicle space. So to attract talent, you need to create an environment in which they want to thrive.
And then I’ll give you an example of how we did it. We were experimenting with emerging technology at the time. Obviously, one of them was virtual reality and augmented reality, which now is no longer so emerging. But at the time, it was new. And no one really could think about, well, how are we going to use that in insurance? But we said, let’s first see what it can do. And then maybe we can solve a problem that no one thought was solvable with a new technology.
You know, just like Henry Ford always said, if I ask people what they want, they’ll say faster horses, but they’ll never think of a car because it’s just not something that they can imagine. So we experimented with that and we realized that there was a use case for it, which was training.
We know that there is actually a University of Farmers, like in the commercials, and claims adjusters get trained there on real life claims and losses. So broken cars or water damage in a house. And there’s only so many hours they can spend learning these and then they go out on the job and they learn on the job. So what we did was create a virtual house with I think 16,000 different water damage scenarios. And with the virtual reality goggles, they were able to go through this house and kneel down and open a cupboard and look inside and see if there’s mold and they could really see what was going on.
So from a training perspective, there were two big advantages. We could increase the number of training scenarios significantly. We were able to gamify it a little bit to keep people interested. And it was much cheaper than flying people in. They could do this at home. And then when COVID came, of course, that was a big thing. We were ahead of the game there.
Saket Saurabh
Very cool. I think looking ahead, there comes a situation where COVID kind of made you almost ready for it, in the scenarios that you were in.
So I think one of the things I see with this sort of concept of Innovation Lab is that you had also actually created a physical space. It’s not just getting into that frame of mind of innovation. It’s not just picking a few people, but the whole environment around that.
And you mentioned to me that these days you’ve also been advising people who are at the leading edge of innovation in startups and venture and so on. Maybe tell us a little bit about that part as well.
Yorck F. Einhaus
Yeah, I think it’s a completely different environment. There are very bright people that have great ideas and they come up with a concept in the fintech space. And it works for different types of potential clients. But sometimes there is a broad spectrum of insurances. There’s life insurance, health insurance, personal lines insurance, commercial insurance, and they are very, very different. And the companies that provide these products can be very, very different.
What I’ve noticed is that sometimes these fintech companies have a very interesting product but they do not have a go-to-market strategy for the variety of potential clients. Knowing what a big insurance carrier needs and what their priorities are and how their funding mechanism works versus a life insurance that is regional in southern California, those are very, very different. So helping fintechs understand the potential of their product and understanding what features are interesting for which type of audience and potential client might be different.
The common thread I think for all is that right now everybody wants to be smarter than their competitors. So data and analytics and insights that tell them something they didn’t know yesterday is where you get the advantage and where you get the hook. That’s what the decision makers want. They want to know something the others don’t. And so insight creation with data that helps you interpret not only a dashboard but actually understand the data, understand the background, and interpret it and be able to get insights that you didn’t get yesterday will give you that competitive advantage. And that’s really what I think most of them are looking for.
Saket Saurabh
And I think I’ve heard you talk about something called decision intelligence. Is that kind of what you’re referring to?
Yorck F. Einhaus
Yeah, I think that’s what I’m talking about. Decisions, I think, there’s a balancing act as well. You need enough information to make a decision, but not too much either. You need to understand before you make a decision what are the key elements I need to know to be able to make an educated and good decision. And if I don’t get those, I shouldn’t make a decision. And if I get more than I need, I should not necessarily need them for the decision. So get as much as necessary but actually as little as possible.
Saket Saurabh
I see. So just being exactly zoned in on what really matters.
Yorck F. Einhaus
Yeah. Because there are so many opinions and so many people think you should know this before you make a decision and you should know that before you make a decision. And it often sounds very reasonable. That’s why you need to take a step back and say, for this decision, what do I really need to consider? What do I need to know? And what is sort of noise and distraction?
Saket Saurabh
And understanding that, we’re talking a little bit in the insurance context as well. Does this sort of thing come down to just leaders or does it come down to everybody, maybe a claims adjuster or any role?
Yorck F. Einhaus
I think it should be a cultural thing. It should be embedded in the culture of the enterprise and the company. Everybody makes decisions. Let’s not pretend that only the executives make decisions. They make maybe fewer decisions than the others, but maybe a little more important or more impactful decisions. But everybody makes decisions in their daily job all the time. And I think understanding how much do I need to know to make decisions smart and secure is applicable to any role.
Saket Saurabh
Got it. And could you share maybe an example for me to grok on this? Like applying this sort of approach, have you seen that sort of create, let’s say in the insurance space itself, better policy structure or targeting the right sort of audience or market or having the right pricing? Where does it create some competitive advantage?
Yorck F. Einhaus
I think an example that doesn’t necessarily only apply to insurance is when you are talking about your strategy. If you have a north star, a vision that you want to work towards, you need to take actions to get there. And it’s not a leap. That means every day you need to try to make progress. These are the little decisions you need to make almost incrementally to get towards your north star.
And the path won’t always be a straight line. So having that vision and that north star as an orientation but knowing that the path is not as clear as you might think will require you to make decisions on the fly sometimes, on a regular basis. And you need to know, okay, what really matters to get to that point?
Sometimes there is so much information that you forget about your goal and you start drifting off the path because that also makes sense. And I like this term that says sometimes we leave good ideas behind. That means there are often very good ideas that we should not do because they are right now not helping us stay on track towards our goal. And those can cost money and they can cost time and they can often be a distraction and slow you down.
Saket Saurabh
Yeah, makes sense. You know, as we’re talking about this, I think of course it’s essential to talk about AI as well. And you were in a global CDO role at Liberty Mutual right at the thick of when generative AI had started to come in. And you’ve driven a lot of innovation there on that front. Maybe give us a little bit of perspective of how generative AI applies within the domain itself.
Yorck F. Einhaus
I think every CEO and every leader is talking about AI and they want to know what the others are doing and are we falling behind or are we also doing our fair share to stay with the pack. So there’s a lot of buzz around it. But I think there are also different topics that get co-mingled. AI, Gen AI, and Agentic AI are all a little different. So you need to understand what the differences are to be able to make educated decisions on your AI strategy.
And my point there is, AI strategy on its own I don’t think makes much sense. If you have an AI strategy, what I would mean by that is a plan to activate AI throughout the enterprise that supports and accelerates your business strategy. So if your business strategy is we want to become more profitable, and you’ve identified that within the value chain of your process, these are the three elements that really make a difference when it comes to profitability, let’s say pricing, or marketing, or claims handling, that is where you should focus to activate AI to make sure that the piece that helps you achieve your goal is what you are supporting.
Now, of course, there are often things like, oh, we could automate this and automate that. But sometimes those use cases are interesting and they could be seen as POCs and pilots that help you learn. But if you really want to make a difference, you need to scale in those pieces of the value chain where you want to achieve your strategy. So if it’s profitability, let’s say pricing or underwriting might be the key elements. That’s where you need to really scale the AI capabilities. And maybe not so much in accounts payable or billing, where you could do it as well, but they’re not the differentiator and they’re not necessarily supporting you in getting more profitable.
Saket Saurabh
Yeah, absolutely. So basically aligning your AI strategy to your business strategy. And instead of going in all different directions and doing things which may not necessarily align with what makes your business profitable, you’re focused on where it really matters.
Yorck F. Einhaus
I think, again, both technology and data strategies are intertwined, and they both need to support the business strategy. The business strategy needs to be clear. And then you can identify which capabilities does the business need and how can I accelerate them with AI use cases and eventually where can I scale.
I think that’s really what many are struggling with. They have many use cases and they’ve done POCs and they have 15 different AI agents running around within their process. But are they really scalable to a degree that you see it in the top or bottom line? In the end, that’s what it comes down to. If I can’t see the impact on the top or bottom line, then I haven’t been able to scale it.
Saket Saurabh
Yeah, it’s the difference between a cool idea and what actually impacts the business.
So actually, on that front, you’re executing your AI strategy, you’re looking at the business strategy. We did talk about earlier how in the world of insurance specifically, the data environment can be extremely complicated. How is that playing a role in taking some of these use cases to production, assuming it’s aligned to business strategy? What else are the challenges coming in front?
Yorck F. Einhaus
Well, I think the biggest challenge is data governance. And what I mean by that is almost more data quality and reliability, the veracity of the data. Can I really use it? You can have as much data as you want. If the people don’t trust it and don’t use it, it’s worthless.
So I think the biggest challenge is to have the right data adhering to the right quality standards at the fingertips of those users who want to use it on a daily basis. And trusted. When I talk about data governance and quality, I mean you need to decide what is important. Is it important that the number is accurate? Let’s say social security numbers. Do they need to be accurate? Do they need to be timely? Phone numbers could be old or new. Is timeliness important? Is completeness important?
So there are many different dimensions of data that you need to know. Which ones are the most important so that I can say this data is good and you can trust it? Because there’s always the feedback that comes, yeah it’s accurate but it’s old. Or half of it is empty. Or there are four zeros for a zip code, that can’t be true. Or 20% of our clients seem to have their birthday on the 1st of January 1999 because it’s easy to type and people just put something in. So you know it’s not true, but it’s complete. It’s theoretically accurate. But it’s still not something you will use or trust.
So that really I think is the biggest challenge in this overflow of data. Being able to say which pieces of data do I need and can I use and trust? Those are the ones that we really want to make sure are available.
Saket Saurabh
True. I mean, I think governance and data quality seems to be one of the more complex areas, especially in the kind of data environments we’re talking about. Legacy systems, mergers across multiple companies and hence multiple systems, many many copies of data and so on. Is there something you were able to identify, strategies that we can learn from?
Yorck F. Einhaus
Yeah, I think at Farmers, we did a migration from our on-prem data environment to Snowflake, in the cloud on AWS. And we used that migration to make sure we set up an architecture and a governance model that helps us provide data in a way that is more reliable and takes less time to massage and fix.
And that really means you have to go all the way upstream to your systems of record, transactional data, and all kinds of different flavors of data and determine with the business, how do we want this to be? Because sometimes they don’t agree. The actuaries and the underwriters have a different understanding of the same data element. And then sometimes you have to say, well, if you’re not talking about the same thing, then maybe it isn’t the same. And we need to create two values, one for you that you use and one for you that you use. And it’s both coming from whatever source we might have, but we need to agree on the terms and create that.
So those are good opportunities to take a look at what you have and how you can make it more usable in the end.
Saket Saurabh
It’s a great way to look at it. One of the things as I’m thinking about AI specifically, and my experience working with some of our customers in insurance, has been that there’s a new variety of data that can be leveraged that was harder to leverage earlier. Looking through documents or being able to query documents along with data. More recently I’ve seen images and also videos, so it could be images of an inspection or videos and so on. How is that changing things?
Yorck F. Einhaus
Oh, I think that is already largely implemented or being used already. So in the claims process, people take videos of the damage instead of pictures and they can send it in. Some documents are handwritten or we used to use OCR, but now we can use AI and go much faster and more accurately. And you have all this unstructured data that can now much more efficiently be analyzed and transformed into structured data that we can use. So that is already happening.
I think in many places, calls are being recorded and analyzed, videos are being taken, photos. And you know, it’s also created a challenge around AI-generated photos that we think are fraud. You can create such great images today of a damaged car that’s actually not damaged and send them in. So now there’s already that counter-action that you need to take. Are these AI-generated pictures that we use for our claims assessment? And how do we catch that now?
Saket Saurabh
Oh, wow. Yeah, it’s a benefit but it’s also a security challenge then.
I think broadly, my understanding of the industry, you know, insurance being both regulated and also many of the companies being big with very secure businesses, the pace of innovation if we go back to five or ten years ago wasn’t that high. But it seems like, would you correctly say that insurance has become one of the faster innovators in the post generative AI space right now?
Yorck F. Einhaus
I would not say the insurance industry is extremely fast at anything, but that’s not because they wouldn’t want to be. It truly is because of the history and the regulatory environment. Every state is different in the US, every country, every continent has its own regulations and it’s very hard to move fast when you have to jump through so many hoops.
But I think that AI and Gen AI and Agentic AI are just so obviously helpful within the insurance industry. And there are some that have been really investing billions of dollars in this space that it has created a sense of urgency across the board. I think most insurance companies know, if we do not get on this train, we will not be here in a couple of years. So I think that’s a big part of the future of AI and Gen AI.
And I think in insurance in general, yes there’s a lot we can do with AI, but in general the way we work will change significantly with AI across the board, not only in the insurance industry. But if you miss that boat, you will potentially fall behind the pack and then eventually be irrelevant.
Saket Saurabh
Yeah. And I think talking about work being done in industry, one of the things you mentioned earlier was that you used AR technology to train people virtually in ways that weren’t possible at the time. And that became an advantage later on. So as you’re thinking about AI coming into work within the industry, thinking ahead, how things might change, what are some of the interesting or cool things that you have seen in work being done?
Yorck F. Einhaus
Well, I think we touched on it briefly. I don’t know if it’s cool, but it’s definitely something I’ve seen, which is using AI to read and interpret unstructured data that comes in all kinds of shapes, forms, and formats. That is definitely already happening.
I think that within the broader catastrophe space, when we’re trying to analyze where the next fire is going to be in California or where the next hailstorm is, using third-party data that you can buy from across the board and a good model, you might be able to predict more accurately. And that’s what insurance has tried to do, predict the risk. And the more accurately you can do that with a good model using trends, instead of looking only backwards which we used to do for the most part, I think it’s changed so much that we need to leverage it to be able to predict more accurately how we see the risk landscape changing.
Saket Saurabh
From being inside the industry to being outside and helping founders, boards, and figuring out how to take their innovation back into the industry, you’re getting a bit of an outside view. I’m curious, maybe it’s giving you a different perspective. How is that helping you advise folks who are getting in? Because you were an innovator and a leader, but it’s a lot harder when you’re on the other side going to somebody who may not be in the same frame.
Yorck F. Einhaus
Yeah, I think people are scared of the complexity and the slowness and the reputation that the insurance industry has. I mean, it seems to have a bad rep, you know, even worse than banks, it seems. Because people interact with their bank much more frequently. So they feel more comfortable with it. But with insurance, sometimes you don’t interact with it for years. So you don’t really know how it works.
I think what I really try to advise people on is how can you understand an insurance carrier and the industry without all the perceptions that you might have as a personal user of insurance? And it’s all manageable. I think if you explain to people what the significance is of an insurance company and what they do, then you will know that data is important.
At the same time, yes, they have grown through acquisitions over many, many years, so that’s a problem. Yes, they are heavily regulated and they’re different across the board, yes that’s a problem. But it’s all solvable. So the advisory piece that I bring to my current clients is more about helping them see the art of the possible in an industry that seems a little scary.
Saket Saurabh
Yeah, you’re very right that most of us do not maybe understand how insurance works on the inside. And it’s complex data decisions that are happening there, but we don’t often end up interacting with it. It’s almost like, if you think about it, very few people understand how credit scores work. This is kind of another order of magnitude complexity because you don’t even get to deal with that.
Cool. One thing I want to go back to is sort of people who are building careers in data. And while you’re advising founders, CEOs, and boards, how would you share some advice to people who are building their career in data? Especially given that you’ve had several lateral moves in your background as well.
Yorck F. Einhaus
Yeah. So this is a topic that I have been sharing regularly with people that are trying to grow their career or who are new to the work environment. It’s, push yourself out of your comfort zone, try new things, and lateral movement.
I think people underestimate what lateral movement can open up for you. People are very focused on moving upwards vertically. And there are only so many jobs up there ahead of you. But if you move side to side, suddenly there are five jobs that you could potentially do and then more and more. So I think lateral movement is sometimes frowned upon, but I think it is actually a career accelerator because suddenly you’re the person that can do this and that and that. You’re no longer focused only on one area.
And especially if you are thinking of being a successor to your leader, your leader manages six people. And if you can do four of the things that those six people do because you have experience with four of those areas out of the six, you have an advantage over those that have only done one or two.
So lateral movement, pushing yourself out of your comfort zone. And the last one is volunteer for the work no one wants to do. And I’ve said this also to my kids. Someone needs this to get done and no one wants to do it. The advantage there is that you probably have more autonomy than normal to just get stuff done. That means you can try things out, you can do it your way, you can be more efficient and effective because you don’t have as much red tape to jump over.
But at the same time, you can build a reputation of being the person to go to when you need to get something done, or when there’s a little bit more urgency or maybe even a bit of a fire drill. I remember in my career when we had fire drills, it was always the same group of people that we would call together because we knew if we get these 10 people in a room, they will figure it out. They will make it happen. And they have a reputation of doing the stuff no one else wants to do. And they’ve been successful at it.
So those would be the three things. Push yourself out of your comfort zone. Keep your eyes open for lateral movement because that broadens your potential. And don’t shy away from the uncomfortable tasks.
Saket Saurabh
Yeah. And I think this is great advice at a time when a lot of people, including kids and people who are early in their career, are thinking, how is AI going to impact my job? And I would say that being the person who can take somebody’s problem and say, you know what, I can get this problem solved for you. Take that one thing off the plate. I think it’s a great place to be. And that sort of agency and initiative and getting stuff done will always be needed. In what shape or form, we don’t know, but that’s a human element that I think will always get valued.
When you talk about lateral moves, help me understand. Are you basically saying that, hey, you may be in customer support and you’re seeing a lot of customer questions and issues, and your lateral move might be like, hey, I’m going to become a product manager, because I really understand what customers ask about, what issues they’re facing, and I’ve built that empathy for them. And I can translate that as a product manager into somebody that defines what the product should do. Is that how you mean it?
Yorck F. Einhaus
Yes, I think that’s a good example. But there are also examples even within your immediate environment. Maybe you’ve been an application developer for the sales tool. Maybe you should switch and try to understand the underwriting tool, so that you get pieces of the value chain. Or yes, you’ve been working in the customer service space, you know what the customers are dealing with, come and help us create products that are better.
And sometimes what I mean by lateral is you’re not going to make more money, maybe even less in some cases, and you’re not going to be higher up in the hierarchy. You’re moving laterally and rotating maybe even through roles. But then you have a much bigger arsenal of knowledge that will help you then take up one or the other job that might eventually open up.
Saket Saurabh
Yeah. And I think this again maybe goes to thinking that basically the more places you sort of work, the more context you create, the more places you’re able to connect the dots together and sort of become more valuable.
Yorck F. Einhaus
Yes. I think I’ve had discussions with friends that are very focused on being a specialist, and then I’m more focused on thinking that being a generalist is better. Now, there are pros and cons to both. I think you will always have your specialty. And if you want to be great at something, it’s going to be something where you’re naturally gifted. So if you are not good at doing presentations, you’re never going to be great at it, but you can get better at it. But if you’re really good at coming up with innovative products, that’s where you can be great.
So you should have your specialty and your strengths and you should focus on your strengths to make sure that’s where you can become great. But if you have other tools in your arsenal, it just broadens your potential. And my career has been impacted by being more of a generalist and being able to help out in many different areas rather than having only one specialty that goes very, very deep.
Saket Saurabh
And on the question of career advice, I think one of the questions I’ve heard from some of the CDOs that I’ve spoken to is, the CDO has been a critical role that has grown in the past five to ten years or so. But with AI becoming so front and center and data of course being a core part of AI, how should CDOs think about their career? Like, how do they get to play that role in AI where it may not be just up for grabs for them?
Yorck F. Einhaus
I think there will be different types of future CDO roles. And I think it will have a lot to do with what the person in the role is capable of. In the end, it’s only a title. But are you really focused on data? Are you someone that can do data and AI and analytics? Are you more of a tech person or more of a business person?
So I think titles will sound similar. It will be CDO, CDAO, or whatever. But the actual role will be impacted by what you can do. And that’s where I come back to, the more skills you have, the more you can take on and have impact on your work.
Saket Saurabh
Yeah, yeah, I think very true. And CDOs traditionally have been successful not just because they know the data, but because they also understand how the business works and how it operates. And I think that’s the key.
And I think that again becomes, in my mind, a critical part of AI as well, because AI can do a lot of things for you. You can tell it to write code, but what code are you trying to write? And what is the outcome you’re driving? And are you able to leverage AI yourself and in your org to get to those outcomes? I think that becomes the critical part.
Yorck F. Einhaus
Yeah. I mean, in that sense, I think AI as a tool is going to change the way we work on a daily basis. I remember when the computer came out, people had to switch and they couldn’t even imagine what a computer could do. And then the internet and email came and people had no idea what that could actually do. Same thing with the smartphone. Same thing with AI. I think we don’t even know exactly how it’s going to impact our lives, both professionally and personally.
But it’s going to have a huge impact. It’s going to change the way we work. And the next generation, they already use it. They’re going to come to work and they’re going to be disappointed if a company doesn’t offer them the tools that they have on their phone at home. And if there’s no AI tool that they can use to generate whatever work output they need, they’re going to be like, this is antiquated because they’re already used to it.
So that is definitely going to change and be an accelerator. I think it’s going to create a lot of new jobs. I think it’s going to make the work we do easier and help us focus on the things that really only a human in the loop can do. And that means create focus, which means create more efficiency. And hopefully I don’t have to do all the stuff I don’t like doing anymore and I can focus on the things that I really have fun with.
Saket Saurabh
Very well said. And I think I was having this conversation just earlier today about there are cases where people are able to use AI as a thought partner, as an assistant, be able to get their work done better. Also some topics about using AI or prompting AI to challenge you, and rather than just asking it stuff because these models are trained to please you, so you have to force them to do that. But then you can get a counterpoint and you just operate better if you leverage it the right way.
Yorck F. Einhaus
Yeah, yeah, absolutely.
Saket Saurabh
So before we wrap up, I’d love to maybe hear from you, what are one or two AI tools that you’ve been using and enjoying? Maybe share that with us, or maybe learning resources as well.
Yorck F. Einhaus
Yeah, well, I’ve actually tried to compare them. So ChatGPT and a couple others. Gemini. I try to see which one works best for what prompt. And so I feel like Grok has some strengths, I think they all have different strengths. So I’m trying to figure out, okay, for this stuff I’d rather use ChatGPT, for this I’d rather use Perplexity. And so those are the tools that I’m experimenting with, and my kids as well. And we’ve identified that some are better for certain things and others are better for other stuff.
When it comes to accuracy, I still prefer to double and triple check any source myself.
Saket Saurabh
Yeah, that’s a good reminder. It’s not to trust, especially when I’m telling my kids, it’s like, don’t believe what is being said there and just take it at face value.
But yeah, I think this has been super exciting. Totally agree with you on some of the approaches to leveraging AI. I think it’s one of those things that you can only learn by doing. It’s only when you try out all these different tools that you realize where are they good at and where are they not. And you sort of start building up your own way of operating in this world. And it may be very unique to you versus the other person. And the only way to get that is to use it, try it out, and keep up with it as well.
Cool. It’s been a pleasure talking to you, Yorck. I truly enjoyed the conversation. Thank you for covering all these aspects of AI and giving some really good career advice to folks who are listening. Thank you.
Yorck F. Einhaus
Anytime. Thank you. Thanks for having me.
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