娇色导航

Maryfran Johnson Hello.

Good afternoon and welcome to 娇色导航Leadership Live. I'm your host, Maryfran Johnson, the CEO of Maryfran Johnson media and the former editor in chief of 娇色导航magazine.

Since November 2017 this video and audio podcast has been produced by the editors of CIO.com and the digital media division of Foundry, which is an IDG company, our growing library of past interviews, all of them openly available on both cio.com and CIOs.

YouTube channel includes more than 150 chief information technology and digital officers from mid sized to large companies across every industry joining that esteemed lineup of CIOs today is a long time friend of the family who has been interviewed in 娇色导航magazine and on ci.com a number of times over the years.

Richard Wiedenbeck, he's the chief AI officer at Ameritas, based in Lincoln, Nebraska. Ameritas is a mutual based financial services company with annual revenues of 3.4 billion.

It serves some 6 million customers, many of them in the small to mid sized business space, and it serves them with a broad array of life annuities, retirement, disability, dental and vision insurance plans.

Rich has worked in business and Senior Technology roles for more than three decades across multiple industries, including defense, manufacturing, consulting and software.

He joined Ameritas in 2010 as the vice president of it, moving up into the CIOs chair in 2013 in 2020, he was inducted into our 娇色导航Hall of Fame, which every year honors an elite group of outstanding business technology leaders.

Then last year, in January of 2024, rich joined yet another elite group of leaders who hold the newly minted and still relatively rare title of Chief AI officer.

According to our 2025 state of the 娇色导航survey, only 14% of mid sized to large companies have caios, and another 21% of companies are out there actively looking to hire one the responsibilities of this emerging CI C suite role which are being covered by a lot of our cio.com reporters these days.

Those responsibilities range from setting a company's overall ai ai strategy and overseeing how and where the AI tech is being used to developing an AI skilled workforce and to establishing a new enterprise governance that integrates with existing corporate cultures.

It is no small task, as you're going to hear about during this conversation with rich, and there's some really great expectations around this role. So we have a lot to talk about here. Welcome rich. Thanks for joining me today. Richard Wiedenbeck Thank you.

Maryfran, always a pleasure to be chatting with you. Totally. Maryfran Johnson

Alright, let's start out with let's talk first about a broader picture of how the broader business picture about how Ameritas has been doing during these last few challenging years, and the role that it has been playing in the business success you have been having, Richard Wiedenbeck yeah, absolutely.

So, I mean, Ameritas, I always like to say, if you look at our kind of growth, right, our growth has been relative, has been really good, relative to the industry, right?

We're classified, even with that broad range of diversified products, we still get classified in the life and annuity space, or the life insurance space.

If you look at that industry, or sub part of the insurance industry, it's been growing at about one to 3% a year, and we've been growing at about seven to nine so we're clearly outgrowing our industry, which is a good sign.

But by the same token, if you look at our expense structure, our expense structure seems to be holding pace with our top line, right? So top line growing, bottom line going, or top expense structure growing at the same rate, right?

So, so the challenge to do that cost curve. And then around 2020, we took a look at that and said, Hey, we really, you know, we really need to modernize, you know, I mean, a lot of the standard stories. We need to modernize our systems.

We need to really look at how we're getting things done. We need to look at the interactions and the digital advancements we're having, and then we need, we need to look at this kind of cost curve bending thing.

And we took on a transformation project, an enterprise wide transformation project, we call Pepi, everybody. Everybody gives it a name. Everybody gets an acronym. You know, you always, Maryfran Johnson

everybody loves a good title on a program, right? Yes, Richard Wiedenbeck always, um.

And so we started that journey and and we're, you know, we're obviously, you know, four years into it, we've, you know, like any transformation journey, you're going to say, these things went well. These things didn't go as planned. These things.

We wish we could go back and do a little differently, but I think all in all, we've made meaningful progress on that, on that journey, and then we started to see the AI frame come in and and we didn't want to lose sight of that, and we didn't think it was something to wrap into that.

It was something to really start to pay attention to a little bit differently.

But I think a lot of firms are on that broad, transformative journey, whether you call it Age of the Customer digital, you know, all of those are pieces of the puzzle and Ameritas certainly, just like other firms in our industry, and even our industries, have been actively pushing to make progress on that, not just kind of doing it as a Hey, here's our portfolio, let's prioritize.

We actually chose to drive our investment levels up for a period of time to really try to make meaningful progress on it. And now we're kind of coming on the tail end of that saying now let's get into that standard.

Still continue to make investments, but But where are we pushing those priorities, and how do we bring this kind of AI frame into it. Yeah, Maryfran Johnson

well, because when we talked previously, you said that, and this didn't surprise me, because I think a lot of companies discovered this when they start their big transformation projects, that whether you're spending 50 million on it or 500 million on it, you discover that you haven't been spending enough.

So there's a reinvestment thing that happens, but you mentioned that it also has a lot to do with a concept that we were both smiling about, because it sounds so 1990s it was all about engineering, straight through processing. And re engineering is what I meant to say there.

And you were suggesting when we talked that re engineering is making a big comeback, driven in a lot of senses by the artificial intelligence products and the whole AI wave that may or may not be engulfing us now. Richard Wiedenbeck

Yeah, I and so, you know, I, I've said this a few times, and I think we're going to hear it come back into frame even stronger in the next year or two, there's a few new names that are being thrown on it. I'm hearing reimagination.

I'm hearing, you know, you know, rethink. But the reality is, if at its core, it is business process, re engineering, and quite frankly, that was very popular in the 90s, right?

The 80s, 90s, even maybe early 2000s and I think the reason it died out is there's five techniques that have been used in re engineering, you know, since hammer and top Scott or whatever kind of popularized and wrote the books. You know, you got straight through process.

You got automation. You got, you know, streamlining your process. All those pieces AI puts a new tool in the toolkit. And I think it brings a new tool in and it allows you to kind of really rethink processes, rethink them, reimagine them, work them.

And so I think we're going to see this emergence of the value that you can get now, and a kind of a revisit of that kind of re engineering frame, because that's the way you're truly going to get what I would call that exponential lift on your on your cost structure, on your ability to get things done, on your speed.

And I think in some ways, quality, I think AI is going to bring a quality play, right? It can pay attention to things across a vast amount of knowledge and bring those into a specific a transaction frame.

That you know, humans are smart, but it is almost impossible for a human to know everything about everything that it, you know, knows about, and memorize that and put it to use, whereas AI is actually really good at that, you know, it's like, I you give it all that information, it's like, I know it so I can apply it.

So, yeah, well, and it's interesting too. Maryfran Johnson

I think that of all the different technologies that end up being brought to bear on business re engineering and business reinventing, and, you know, whatever buzzword of the day we want to call it AI, is unusual in several ways that I know we're going to be talking about more, but it's very unusual as I've been watching this.

I've been involved in the IT industry for 30 plus years, as you have, and this is the first one that people seem genuinely both curious about and fearful of, and I don't know that we had that with the re engineering of the past.

When you talk about modernizing tech and moving from many computers to laptops to smartphones, people didn't seem freaked out about those technology moves. Have you before we get into the whole. Meat of our conversation here today.

Are you getting that kind of feedback and reaction from your compatriots there at Ameritas, or is this happening more in, you know, like in the tech industry, among the companies that are struggling to keep up? No, Richard Wiedenbeck

I I think there is. There is both reactions, right?

And it's human nature, you know, we, we, you know, we've also been indoctrinated into the various versions of movies, you know, everything from how to the Terminator, in terms of, you know, AI gone AI gone bad, or rogue, or I Robot, you know, and those types of things.

So I think people have a both healthy and unhealthy fear and and then they people have both an excitement and desire to capture opportunity, okay, and quite frankly, you know, yeah, AI is going to be very powerful.

You know, we could argue the internet was a game changing, very powerful thing. And, and the internet, you know, has been used for good and has been used for not so good. And so I think you know, the key here is, what did we learn from that?

And where can humans provide their role of ethics and guidance? And but the reality is, there are people who are going to use it for ill, and there are people that are going to use it for good.

And so I think that healthy that healthy skepticism, that healthy fear, is important to have, but not to the point that you don't go capture the value. And so we, I am seeing it. And so to me, there's a big How do you embrace people around it?

How do you get them comfortable? I think we're still, we're still on that stage where, and I love to use the internet analogy, when we were, you know, go grab some YouTube and look at, I think it was Bryant Gumbel and Katie Kirk, or somebody, right?

And they brought people on their show, and they were like, explain this internet thing to us? Yeah, I mean really. And it was this, can somebody come in and just explain this to us and our listeners, you know?

And and what we thought on that side of it was very different than what we thought as we got in it, and what we learned as we got in it.

And so I really think, and we've been doing this at Ameritas, pushing people to go live in the world. Let's just get in the world, right?

So it's hard to make a good decision about it sitting on the outside of the world, looking in, whether you're looking in with beer or you're looking in with opportunity, you're still not in it. You're not going to make a good decision.

And so there's a little bit of let's get in it, and let's start it. Let's start understanding it, and let's start experimenting with it.

And, yeah, let's put some guard rails around that, so that we don't get, you know, we don't get off our skis, or we don't and, you know. And then how do we want to guide that? Get in, learn, but then also go get some practical things done.

I think, look, we've seen this in data projects, and we've seen this and, you know, websites and portals, it's like you're spending all this money and nothing's happening, you know, or I'm not getting my value. And so AI's got a very similar flavor to it.

We want to make sure we're doing that combination of Learn, learn by experience, but also go get some practical that builds the desire to do more, to do more, to do more.

And you know, we're probably a good six, eight months into that phase, yeah, um, it's starting to play with some of the future phases.

But that, that I think is important to the to the cultural and to the fear side, is to, you know, get your feet wet, right? Yes, you're afraid of something you don't understand.

Once you understand it and you have a fear, then we can probably talk about it in a more meaningful basis. And yeah, I mean, a company of our size, 2500 you know, you know employees? Yeah, I could.

I could probably put them in the three camps, those that are like, how fast can we go? Those that are actively and passively resisting and afraid to the end, and those that are on the fence going, which way do I go? Right, yeah. Well, Maryfran Johnson

a 娇色导航friend of mine some years ago said that he looks at any kind of introduction of a new technology, no matter what it was. He said you're going to get those three camps.

You're going to get the people that are enthusiastically into it, because a lot of it is about change management. And there are the humans who are like, yeah, change, bring it on.

And then there's the people that let the wait and see, you know, and what's everybody else doing? And then there's the group that's actively opposed to it.

And he was making, you know, the and we were talking about, I think, developers at that point, and he was making the case that you want to figure out who the people really working against it are, and then do what you can to get rid of those people, because they're going to freak everybody else out.

So you know, when you mentioned the age of the customer, it made me think about one of the the you had mentioned, the provider portals, and some of the really practical things that Ameritas is already doing with. AI technologies has to do with those provider portals.

Let's, let's just pivot over and talk about those a little bit. And I know that this is work. This is all internal to Ameritas.

You're not doing anything where AI is out there in the face of your 6 million plus customers because of that, still, that reaction about AI. It's like, oh my God, what's happening here? Richard Wiedenbeck

Yeah, and our decision, and we see people leading up using AI more in a customer interaction or producer interaction, we just decided, hey, if we're going to learn and stub our toe, let's do it to ourselves, yeah, and not do it to our customers.

So I certainly don't want to fault a firm who is out doing that. I think that was a conscious decision on our part. Yeah, that doesn't mean we have AI.

We don't have aI behind somebody who's interacting with a customer or assisting somebody who's helping one of our producers. But, yeah, we've been doing algorithmic and machine learning AI in a few places.

For five or six years, we've, we've been using it to read dental X rays and help provide the call on, is that a crown? Is that a billing is that, you know, is that a bridge? What is that?

And we found the AI, you know, after we got it kind of honed in the machine learning models understood, it does a wonderful job. It does it fast. It's consistent.

We have a panel of dentists that pay attention to how it's doing and yep, and they've actually said, you know, hey, it's doing a better job than we would be doing because it's consistent. It applies that rule the same every time.

Whereas, you know, you get a panel of dentists, and they might disagree or agree or whatever, but we do have humans checking on it, and any escalation does kick back to them, but it, it does an amazing job.

And right now we've got it looking at somewhere close to 26 procedures, and it speeds up claims, and it makes it very consistent, and the and the dentists like that, you know, they're like, look, we know, you know, when we submit this, we give them an answer instantaneously, on, yeah, no, we agree with you.

That's, you know, two centimeters, and that's, you know, that's a crown, right? We're good, or millimeters, or whatever they operate in. I do not want to appear as if I am good at dentistry, right? Maryfran Johnson

You're on you're on a board or two that has probably a lot of dentists on it. I do believe so. No, Richard Wiedenbeck

there I sit on the Advisory Council of over jet, which is the AI firm that does a lot of this. Alright, they have a few dental experts on it. The focus there being on dental care.

But, yeah, no, I, I never want to pretend to be an expert in the space, in a space I'm not. But yeah, so we've been doing that. I think the Uber moment with Gen AI has showed up for everybody.

And that's, you know, that really is kind of the next place that, you know, the place we're actively doing stuff. You know, we've got it helping our life. Underwriters read attending physician statements. I mean, it's really good. I mean, it's the statements, the handwritten notes, the the medic.

I mean, it's phenomenal at that. And it reads it all, it ingests it, and the underwriters interact with it and ask their questions.

And it will say, yeah, here on this page, I found this, and it points to that on that page, and I correlated those together and heads it back.

The AI is not making the underwriting decision, but it is certainly the amount of time the underwriters were spent reading all of that was like, you know, they're going from 80 hours of work down to 20 minutes in order to look at something make a call.

So we see, we see AI really helping do things that it's good at. Maryfran Johnson

Yeah, do you find, I know you, you had mentioned also, when we talked earlier, that you have aI processes.

Perhaps it's probably machine learning processes helping out with the contact center, like when people call and contact either through chat bots or through the telephone, and they get in touch with Ameritas that you have them is that, where they're mining for the right answers and the information and delivering that, Richard Wiedenbeck What?

What? So there's a couple points on that, and I know when we talked earlier. So again, we don't have it doing the chat bots five, but we do have it helping the contact center rep, right? Okay, so help the contact center rep find the information. What?

What was really interesting about that was, what was the right information set to give the AI, right?

Yeah, and this is a really great example when we first started talking, and you know, all the work that we're doing, and what I really like about the the 娇色导航role is you really are you really are working with business leaders and the tech folks and the kind of AI team, because you can't do something without all three of them coming together so that, that perfect world we all talked about in technology, of having everybody work together and collaborate to.

Get to an objective, you know, AI is kind of forcing us to do it that way, because it's the only way to get it done. But we were having this conversation.

It was like, Hey, let's go grab all 10 years of the call logs, you know, because we've answered this question, you know, we've answered this question 100 times and and we'll go grab all the call logs, we'll give it to the AI, and then we'll have the AI help the CSR, because we've answered the question 100 times and and what we uncovered is yeah, we've answered the question 100 times, 60% of the time we answered the question wrong, and 40% of the time we answered the question right, yeah.

And we're asking the AI to discern between which one is right and which one is wrong. So we had to back up and say, Wait a minute, that's the wrong information set right. That's not the information set the AI needs.

The AI needs the information set of what is the right answer, yes. And we said, so that's the admin memos and the policy docs and the things that aren't the truth. Give it that.

And then when the CSR asks the AI, the AA is going to say, Well, according to, you know, the way this thing is supposed to work, according to the policy, this is the answer to that question.

And you're not asking the AI to discern between what even five times answering it right and five times answering it wrong. It's like, well, the last two times you answered, you said this, and the three times before that, you said that.

I mean, the AI is going to go, I don't know which one's right. I guess if you tell it over time, but Maryfran Johnson

let, let me serve them both up to you, and you right, and you decide Richard Wiedenbeck

which really didn't, didn't speed anybody up. So no, no, no. So we have learned through that that not just what you're having it do, but What? What?

What are you giving it as the knowledge and information set in order to have it help you do something is equally important, you know. And then how do you think about all the things the person does and integrate those into to drive the automation up?

Because I think AI assistance is one piece, but AI enable automation is another, and that gets you, you know, getting back to this custard change and getting work shifted from people to AI automation in the right ways is, I think, where some of the power is, yeah, Maryfran Johnson

well, I think it's a good cautionary tale for everybody, from regular, random consumers on up through IT pros To keep pointing out that AI is a tool there trying to please you, trying to serve up the information it thinks you want, but it may be dealing with faulty data.

You used a great early use case, and I'm not sure if you did this at Ameritas, or if you read about this the 100 Years of credit transaction, yes, yes. I love that story. I had not heard this.

Maybe this is really well known in the industry, but this was new to me. So tell that story, yeah. Richard Wiedenbeck

So, you know, this is back to people talk about bias and AI, and it's like, well, the bias might not be in the AI, the bias might be in the data itself or in society, right? Yeah, and, and it really was the credit card industry.

They they get the credit gear. There was a particular credit card processor who went and took 100 years of credit card transactions and gave it to the AIS and information set, and they found that the AI was biased against women.

And so everyone was like, Well, why is it biased against women? That doesn't make any sense. And so 1973 or 74 whatever year that was, and most people don't remember this, because partly we're all like, you kidding me.

But in I think it was 1973 or 1974 was the first time women were allowed to have a credit card. And so prior to that, maybe they had their husband's card or something, but women did not have credit cards.

And so if your data went past that, it looked as if, well, you know, women weren't good credit people and and that wasn't the data itself was biased because society had been biased, because, right?

So they found, hey, we probably can't use 100 years worth of data, even if we want to, we need to use 50. You know, it's going to have to be good enough, because 50 is more representative.

In fact, they had to pull that up even more, because in the first few years, it wasn't like every woman ran out and got a credit card overnight. So no, you know, there was this peak up.

But it's, it's an interesting story, because I think people have a an interesting reaction to really, you know. I mean, I, when I heard it, I was like, really, is that true? Really, you know, are you kidding me?

It wasn't until the 70s, really, we're that far behind, you know? And you're like, yes, yes, we are. So when you think about bias. You have to think about both.

You know, is the AI really biased, or is the information that it's using actually got an inherent bias in it?

And you want to make sure you're paying attention to getting that you know that that's not the bias that you want to be driving your your AI to help you, Maryfran Johnson right?

Well, and. You probably you're you and I are a similar Baby Boomer generation. You probably remember when, if you traveled, you had travelers checks. Do you remember those?

Go to the bank, they made sure you had enough money, and they gave you a bunch of checks that you could write, and they were basically like having $100 bill, I know, and I'm running around on my smart watch now I have, like, a little thing.

It's my MasterCards on there, and I just, like, put it up against the machine. And I can imagine when millennials and Gen X are well, Gen Z find out. And they're like, You did what? Because why?

Let me see, I want to pivot over to the kind of tech architecture that Ameritas is either settling on or experimenting with for your AI production. I know you, you've got large language models underway, multiple large language models, and agentic AI is also in the game.

So kind of give us an idea what the tech architecture producing this is now, Richard Wiedenbeck

yeah, and I think, you know the good, the good news is there were firms ahead of us, and we, we paid attention, right of what they had learned. And I think one of the first learnings was build a multiple LLM architecture, yeah.

And I think the second lesson was, make your LLM the smartest it could possibly be, you know, because it just, just make sure it's as intelligent as possible. And then I think the third piece is and then give it the information sets to know you.

Because if, if you're not doing that, then you really that makes no sense to have an in house version. Just use, you know, chat, GPT or Claude or whatever, given that, whatever's out there.

So, you know, I mean, I would look at it as you kind of got the three layers. You've got the you've got your LLM in your model layer, and what you are, what you are giving it as information sets.

You've kind of got your interaction layer, your rags, your interpretations, your pieces like that. And that's kind of the Gen AI side of the coin we're also looking at.

Because, you know, this is the next new place we're going, what I would call the agentic AI system that works with the Gen AI, you know, understands the data, works with the Gen AI, because, and we've heard it, look there's AI agents and there's agentic AI.

I think those definitions are still playing out, but the difference is, an AI. Agent is just an automated AI that's doing a task, and it's usually very task oriented. I know how to plan. I know how to schedule.

I know how to do this, yeah, agentic AI is how I string all that together with workflow in order to actually emulate human work, right?

So can I get agentic AI to act like a BA, can I get a genetic AI to act like an expense analyst and and we are. We talked to a lot of firms. We're trying to not go just use AI to help it.

We want to go use AI to help real business functions, right?

You know, can I, can I teach AI to act like a CSR in my in my back office, the customer service rep or back office rep, and can I teach it how to do the job and have it ask the questions, and then, okay, once it knows how to do that job, I can replicate that.

And you almost end up with this army of little AI agentic AIS and people working together. But that's new.

So we have a platform that we're learning on, and we've kind of assembled that multi layered architecture, you know, you've got, although there's a lot of pieces inside that, Gen AI, your LLM, your vector databases, your rag models, your, you know, I would say that's going to become, I know, the mic, the Microsofts and the that's going to become commodity at some point, right?

It's like, because it really isn't that. It's what are you using it for? Yeah, and I think what you're using it for almost looks like a business app, right, right?

But it's a business app that is written to support a process or to do work as a human so it's a very different business app than what you're used to, right? And so you're going to have to think about that.

But you've got that kind of three layer architecture, I would say, your gen AI piece under it, your interaction layer, your agentic AI hop on the side, trying to figure out how to be more, be more like human work.

And then, you know, what are you actually using it to do? Is kind of your, you know, your business process, layer, your business app, layer, right? I'm, I know, I'm the Caio. But if you said, Oh, rich, you know what, what?

Which vector database are you using, and how are you synthesizing that data, I'm going to be like, I got some really smart people on my team, and they actually get up here. Today and make sure we do that, right? Yeah, yeah.

But I would say in general, you know, and I've heard a lot of firms are taking this kind of tiered architectural you know, I need an LLM that's smart about this to use it in this business process. I need an LLM that's smart about that.

So I see people doing a combination of large language models small language models. I don't know if that flushes out at some point. It just becomes the biggest, best, large language model, and it's just smart about everything. Or do you still need the specialties?

But we build an architecture that allows us to have that flexibility, so that we can navigate that until I would say that final answer is reveals itself in the marketplace. All Maryfran Johnson

the all the truth will be revealed. This reminds me of years ago we had, I think it was the 娇色导航at general it was General Motors or General Electric.

One of the big generals was on stage, and we asked him, Well, you know, you've been talking about your transformation journey and, and it's a great, big trans man, and when are you going to be done with transformation.

And he laughed, and he said, You're never done with transformation, because the minute you stop, everybody else gains on you, and you're dead in the water. Do you think we're going to end up with a lot of different AI types and approaches that again?

CIOs caios, maybe Chief Data Officers are going to be responsible for making it all work together and integrating it. I remember Charlie Feld, one of the most famous CIOs, probably, and he was in one of our original 娇色导航Hall of Fames.

He pointed out that 娇色导航often meant chief integration officer, because you had to essentially negotiate between all the different pieces that the vendors were the tech vendors were selling you, and every tech vendor wants their product suite to be the one you use.

Do you see that same sort of it's almost, it's almost like you're arguing with yourself about some of this stuff. Do you see that happening with AI technologies now? Or is there a better, a more, a more mature level of integration abilities between them already?

Or no, you're making a Richard Wiedenbeck face.

Yeah, I'm making a scrunchie face. This I, you know, I, I love the utopic, Maryfran Johnson yeah, dream, right?

Not a bridge to sell me in Manhattan, right? Richard Wiedenbeck

I mean, you want, I mean, we want it. We all want it, you know, which is why we listen to the vendors, because they come in selling that they've got the solution to everything, their road maps, yeah, you know, on a practical basis, right?

And I would say, just like you and I talked about that, that answer is probably still revealing itself. I think you've got the big, the big, we'll call it LLM folks open AI anthropic. AWS is Bard, you know, Google's Gemini.

They're, they're trying to set out a basis, and they're moving into the agentic side.

You have a lot of the data science products and the data management tool kits that were a little AI, and they're, they're moving into that, I think everyone's moving into this AI agent this agentic AI space.

And they're all offering, it's me, come to me and I will get you there. Yeah, so I think, you know, there's a little bit of that answer will reveal itself. And where are you placing your bet?

And how are you setting yourself up so that you're you have the flexibility to pivot if it changes. You're not in such a vendor lock in spot that you can't, you know, you're basically going to spend the same amount of money to just go sideways.

So, you know, we're seeing that. And when I talk to other folks, I think we're seeing the same thing. Everybody's trying to figure out, how do I navigate that?

Gen AI is settling down a little bit, but there's a couple layerings in there that you're trying to you're trying to work through. So I, you know, I guess the answer is, yeah, you're, I don't see the world of master integrator going away anytime soon.

Okay, whether you're a CIO, Maryfran Johnson

a chief AI officer, Richard Wiedenbeck

a chief transformation officer. I mean, all those titles are out there, and you got to get in and do it.

I think the hard part is we're moving into a world where the business process that we are enabling, yeah, with the combination of AI automation and underlying systemic tools, is is now got a level of how do we monitor it? How do we pay attention to it.

How do we understand it? Because the speed at which AI can go after that objective learn from itself, right? AI is not a smart robot. Ai learns by doing. AI can be self correcting. AI can learn from itself and adjust continuing to focus on. That objective, right?

And you're going to have to pay attention to when is it within your guide rails, and when is it veering off? Yeah, and I think it's going to push us. It's going to push our discipline around how we manage work.

It's going to push our discipline around how we monitor and put controls in place in the areas where we're using it, it can really go fast. It's going to push us to a level of discipline that we have. Some firms have always been there.

Some firms have been like, yeah, we're okay with we're okay and we're going to have to up that game. And some firms have just been like, yeah, it's never been a problem before. And be like, now it is, yeah, yeah.

And I love your comment on transformation that's never done. I always have two reactions to that, right? My first reaction is, well, of course, you're always on a journey of change, but that cannot be an excuse to not get something done.

You know, yeah, for oh, you know, change as ever. It's we're never done with transformation. So don't deliver anything. Nothing gets across the finish line.

I think, you know, I have that kind of, probably won't say, visceral reaction, but allergic reaction to, you know, it almost feels like agile, you know, it's like agile was supposed to be this dream that solved everything. And Maryfran Johnson

blockchain was another one that was going to solve everybody, Richard Wiedenbeck

you know, it didn't. It didn't do it. It didn't do it. You know, it, you know, the number of projects that are successful in business terms have fundamentally not changed in the last 20 years, right?

So if I use that as my measuring stick, and I go back and say, agile was supposed to give me better delivery, but do I think Agile is a good technique?

Do I think iterations and being focused on Absolutely, but it's also become an excuse to just spend money and never have an outcome? Yeah, and I think, okay, so you ended up with both versions and your net, your net score of did these things actually deliver?

The number of the number of initiatives that fail, is the same number, right? So, Maryfran Johnson yeah, I know.

I mean, I think we've done at 娇色导航magazine, you know, which is was in print until 2015 and then we went to cio.com so it's been online since then.

I'll bet we number in the 10s of 1000s of stories about it delivering value and all that, all that sort of and but at least we're not talking anymore about it aligning with the business goals, because now we got, I you know, the CIOs are at the table, which I want to get a little more specific about how you're approaching this job change.

You were 12 plus years as the 娇色导航at Ameritas, and now you're the chief AI officer. I often wonder when I hear about all of the data and the importance of the good data you know, like, because you don't want the garbage in to be garbage out.

Why are chief data officers not the natural candidates for the chief AI officer? And I know you have an opinion on this, so I'm challenging you to share Richard Wiedenbeck

I have an opinion. I don't know how popular I'm going to be after I share that opinion. Okay, but I have an opinion. All right, so one i i think Chief Data Officers have always struggled with, how do I go show battle? Okay?

And I think it's always been about, well, it's all about wrestling the data and making the data as an asset, and getting right and and that's the right discussion. It's just the value has never been about the data.

The value has always been about what is the business outcome that I'm trying to achieve? Right? Yeah, and it usually doesn't happen by data alone. It happens by It happens by insights and analytics. It happens by automation. It happens by algorithms.

It happens by systems, it happens by process.

So I think the reason that that's not happening is, you know, and I don't want to pick up, I mean, I know some really good, cheap data officers that look like business people this, this is going to get back to the evolution of the CIO, right?

The 娇色导航earned a seat at the table by starting to actually have a business dialog, by starting to have a outcome dialog, by starting to say, I'm focused on achieving business success, and I understand how we bring technology into that brain, right?

And I think Chief Data Officers are either a early in that journey or have struggled to emulate that right. And we know, look, I know CIOs that are still in the cost bucket and all of that, but way more of them are now Right.

Like you said, we're not having this business alignment conversation, so I think it really. Really, is that issue of, look, you're focused on the data, and that's the wrong focus, right the focus?

And a lot of it will say, I've got business cases and use cases, and I'm driving value, but you're really, you're really the lens is, I think, too narrow of a lens, and it's, I think, I think it's both hard to win from that seat, and it's hard to have people look at you and say, you're a business leader, right?

I mean, the conversation that we had around me in this role was they were like, look, it wasn't just that. You were a CIO, you were a business leader who ran it, and we need a business leader here who understands tech business, right? Richard Wiedenbeck

You know, collaborative culture, collaborative Richard Wiedenbeck culture, you know.

And this is going to be about transforming the workforce on top of that. So how we need to bring all that together. We cannot lose sight of that. And Hey to my to all my 娇色导航colleagues who I still feel I'm in the camp with.

One of my comments was, as I step into this role, I do not want to become the bad actor that I spent 13 years trying to make sure didn't show up in the business. So I am Shadow IT.

You don't want to be desperately trying to not be that individual, right? Yeah, yeah. And work very closely with our current 娇色导航to make sure we build that, that strong partnership. But the partnership with it is one piece of my puzzle.

The partnership with all of the components of the business and our Enterprise Services functions is the other part, right?

So we're and we can't be a group that just sits in the middle, back to your transformation that's never done and it we can't be a group that sits in the middle and doesn't own an outcome.

It's like, No, we look we put a we put an objective on us collectively. And I gotta go make that objective real. Of taking 240,000 hours of effort and moving it from people doing it to AI automation doing it Maryfran Johnson

this year, and that's a hard that's a hard number too. Richard Wiedenbeck

That is not an insignificant number right now. That says we're predominantly focused on productivity and efficiency, but that is one of the things we want to go focus on. I don't want to ignore revenue. I don't want to ignore quality. But we're saying, Hey, that's a real thing.

Gets people, gets people hyper focused on, hey, how do we go do something that drives some value? Yeah, that's a real number, you know? And it's like, yeah, and we, we own that. We own making it, own it with this. We own making business. Own it with this.

But you cannot go after this without something tangible and needy. So I know I baked a lot in there, and I, didn't, you know, I didn't hit. I didn't want to punch the chief data officers in the nose.

But I do think, I think it's hard to win from that seat over to this seat, because I don't think leveraging AI is a tech play only, and I don't think it's a data play.

I think we talked about data and it's like, this isn't about data is a piece of the puzzle. Yeah. And I will argue data in all forms, right? Yeah, AI can look at a document just like it looks at a structured database.

That's really good at reading documents, really good. Yes. And Chief Data office historically haven't looked at Word docs and PDFs, PowerPoint, you know? It's like, they're like, Oh, I'm looking at No. Maryfran Johnson

That was what was that called? That was called what kind of data, unstructured, unstructured data, I know I was thinking at one point when most of my life has been producing, you know, like stories on in, for print media and then for online.

And I was thinking, My God, I've never been in a structured data. Richard Wiedenbeck

But video is another, right? Video? Oh, exactly. It's a form of unstructured data. Yeah, now, Maryfran Johnson

and I know underlying all of this, and this is something I know you're very focused on, is how we prepare, not just the workforce, for the future, because this can't be in the hands of a bunch of data scientists and AI engineers.

Because, for one thing, most companies can't afford to have that many of them. They make pretty good money, right? So we gotta have the general workforce has got to be on board with this, but so do the managers who themselves.

None of those business you know, those business leaders you have working now, they're not going to go back for an MBA in data, or an MBA in AI or whatever.

So you're going to speak about all this, I think in a couple months, you told me at a conference, and you're so you're thinking about this a lot.

Do you have any any sneak peeks for us about what you're going to say when it comes to preparing the workforce and your management colleagues for a future that is maybe not controlled by AI, but AI is much more front and center than any other technology I think we've ever talked about in this industry very Richard Wiedenbeck much.

So yeah, so in June, if I can plug, you know, there's a few. Chair of insurance conference that Reuters is hosting in Chicago. I will be speaking on this subject. You know, please come make it thoughtful, purposeful and engaging.

I'm happy to give other opinions, but as you think about AI evolution, and this gets back into you know, are you afraid of it or not? But I'll just ask people to walk with me on a journey for a minute.

So we're going to move from Ai assisting us, to AI starting to do the work, to AI actually running entire end to end processes, right? And maybe even providing oversight of it, monitoring of it, management of it. So alright, so that's the journey we're on.

Then let's think about how the workforce starts to evolve. And AI is really, really good at intelligence and knowledge, right? And it's actually good at stringing together. You know? I mean, ai, ai doesn't learn by being trained anymore.

Ai learns by doing and and that's kind of like how a human learns, right? AI learns by doing. Humans learn by doing. But it's really good at the intelligent potion, and it's really good at the knowledge management side.

It's less good at the emotional the EQ side and the social cue, right? Those are what people are good at.

So I think, you know, look, there is a stage of evolution where I call the professions reset, where, where we are going to have to let go of our profession identity and move to a higher valued enabler, right?

So you're no longer a developer, you're a technology enabler, you're no longer an expense analyst. You're a finance and accounting enabler, right? And maybe that stays, but think about that. Okay, so what is that skill look like?

That skill does not look like I went to school and I learned this, right? It looks like I have to work across that. I have to have more critical thinking. I have to have more Indian process. I have to have How do I use the tech and understand?

I gotta have a broader understanding of that. Yeah, and if workers are shifting up that chain and working across those then what is management doing right? Management has to start going well. I need to be thinking about, how am I managing a combined workforce of AI and people?

I need to be looking at how is, how is this staying within the guardrails of my ethical boundaries, my my decision, you know, here's, here's what I wanted to do.

And again, how am I paying attention to those and so I think management is no longer going to have the luxury of learning the NBA playbook. They're going to have to have it and be very skilled at it.

So how are we growing management to get up every day and work at that elevated level? And how are we evolving the workforce to let go of that, you know, profession identity that they have?

And I think that's just as we're moving from having AI assist us to AI starting to do the work, right? Yeah, I think there's a future, future where you actually have this human, AI, synergistic environment.

And I'm not sure I've got fully framed what that what are the characteristics of a worker there, and what are the characteristics of the workforce? But, you know, hey, interacting with a super intelligence can be fun until it's not, but, or Maryfran Johnson

it can be intimidating and frightening. Yes, right, exactly, Richard Wiedenbeck right.

But I think we have to start. We have to start thinking about, how do we prepare people to work and live in that world? Now, I'm very much focused on what I call the knowledge the knowledge worker, because that's kind of where insurance is.

I think other industries, I'll have a little bit of a I think there's a jagged edge to this, but other industries might have a different brain.

So my my picture and my framework right now is probably way more office worker based, knowledge worker based, insurance focused, which insurance companies are predominantly the land of the knowledge worker, right? You know, that's where the knowledge workers probably showed up first.

But I do think we're going to see that, right, and, and there's probably, there's probably a stage in between that synergistic and that profession reset that you kind of call the the new utility infielder, you know, or the new utility worker, right?

And it's like, now you're now you're not just staying within your functional brain, but as reimagination and re engineering comes in, and you're looking at things end to end, are you going cross functions very differently? And then how does, what does a worker do?

What is their job profile? How do you how do you comp them? How do you train them? How do you skill them up? How do you develop them? You know, I think we're going to be, Maryfran Johnson

yeah, right, change in their day to day. We're going to be pushed Richard Wiedenbeck

on a lot of fronts there, yeah, yeah, and, yeah. And I think there'll be jobs we talk about in America. Just, it's like we think that, you know, the number of employees will have five years from now be the same. No.

Employees, but what they do and how they work will look very different. Yeah, right. And I think that's the key that's Maryfran Johnson

good to hear, because a lot of times you, you know, you have I'm thinking about, well, I'm thinking about boards of directors and the misery these days of making a three and a five year future business plan, when you've got this enormous it's almost like a cloud of tornadoes coming at you with AI, and it's the climate change of AI, if you will.

And being able to talk to boards about that, I imagine you're going to get a lot of questions at that Reuters conference about that. Yeah, Richard Wiedenbeck yeah.

Again, I, you know, it's funny. I'll say this, you know, because I you and I have really great dialog.

It's like, look, hey, that is either going to be a wonderful success and highly engaging and interactive, it'll be a complete flop, and I'll go back to the drawing board and have to rewrite everything and figure out what the new answer is.

I said that to the writers folks, and they were like, Oh, please. Can it be the first one? I really, we really don't want the second one. And Maryfran Johnson

you know, you're almost going to have to one of these, because I've run many, many events over the years, and I'd suggest, if you have a way of doing it, find ways to show a couple of places where AI helped you create your presentation.

I'll bet you're all over that. I mean, yeah, Richard Wiedenbeck

I use it every day. I mean, I use it every day Maryfran Johnson

because everybody loves a show and tell. So if you've got away, here's what AI did for me here. And you might also want to talk about how you created. You you've taken a center of excellence approach to creating.

I see you making a note there, so I'm very Richard Wiedenbeck

Yes, you always give me a good idea. Maryfran Johnson

Maybe that was worth your time for this interview today, you have a small team reporting to you, and you took a center of excellence model approach to it.

I want you to talk a little bit more about that, because I know you from your days as the Chief Information Officer with, gosh, a staff of seven or 800 people that were you know, your responsibility. So who's working with you now?

And how are you putting some of these ideas about this workforce of the future at all into practice with these? Are they your new guinea pigs? Richard Wiedenbeck

Yes, very much so. And some of this is just size, right? How big do you want to start? I know a lot of firms that build big departments. They loaded it with guy. I saw the announcement.

I'm trying to think of who the firm was, but they said they had 250 data scientists. Oh, and I was like, what? A lot of money? Yeah, yeah, that's a big investment. That's a huge investment, huger.

My question is, are you because we kind of, we've kind of said, Look, data science feels a little bit specific right now. And we wanted to say we, we think it's more of an AI consultant or AI engineering job.

But again, relative to our size, we wanted to one. We didn't want to overload our cost structure before we could value right? So we did take what I would call a center of excellence.

How do we get a group of people that know how to understand the AI, help you use the AI and start to put it to use?

And we kind of call those AI engineers, or, you know, AI consultants, AI engineers, you know, we want to rely on it to basically learn all the underlying AI technologies and how to operate them. But in the beginning, I got people that know a little bit of both.

Because you're getting it off the ground, I feel like I'm doing an internal startup, right?

Okay, and so when you're doing an internal startup, you might have to do some things to get it off the ground, but then you got to move it more operationally to the places that should be caring for it.

But we think that that kind of COe model is a good model to work with. It's better rated, right?

I have a team that focuses on this line of business, and a team that focuses on that line of business, and a team that focuses on these functions and, you know, and a team that that works with the tech folks to make sure the platform is there and that they understand what we're doing.

So you end up with that highly collaborative, let's all work together. Let's go accomplish these things. Environment. It's a, it's a, it's a cheaper model.

I think it's also very effective in the engagement side, you know, because we're dragging people in and and then you got to get good at the prioritization of the opportunity. What's the opportunity to work on?

So that helps to have whatever our our North Star metrics are in place, what is, Maryfran Johnson

what is an outcome that we could realistically get to? Because I need to go talk to the board about this, you know, yeah, Richard Wiedenbeck yeah.

Or I gotta make sure management's on board with it, of all levels, right? All my colleagues, I want them on board.

So we do look at we and we built some cost diagnostics and revenue diagnostics so we have top down diagnostics so that we're not spending, you know, $200,000 to go save 20 hours worth of work, right? You know that that doesn't play out, right? Mm.

So we want to make sure we're we're putting our into where we can move some needles to prove some points.

We're also very sensitive to one of our filters right now is, are we ready to do that, both technically ready, culturally ready, you know, are we ready to take on that?

And if not, maybe it's still got a great benefit case, but it it's got the wrong profile of readiness. Yes, and believe it or not, we even have a decision criteria coming in the side that I call management intuition.

It's like, you know, management, watch some some layer man just walks in and says, you know, if you can help me this, I'm pretty sure I could save 50,000 hours worth of work over a year. It's like, Hey, I'm going to go lean into that, right?

I'm going to rely on the fact that you kind of know your area, and that's real. Now we're going to put some discipline under that, and if it comes out that you're slightly over inflated your belief system, we might readjust that priority.

But top down diagnostics, kind of readiness, management, intuition and that bottom up, hey, I think there's something here to do. All of that goes into the sausage of deciding what to go work on.

And so our COE takes on that process, but then divides it up into the teams that are working with people. And we, we think that model is really helping us get going and get off the ground and get traction and focus on things that we need to do.

And it's a good model if you're dragging everybody into the room and saying, Hey, I need you to come be part of this. And because you're going to, you're going to need to know this part, when you take it on operationally, right, right?

You have a lot of the like the agentic side that's new to everybody. So, so we're going to take that on the COE, and then as we mature it, we'll go figure out, okay, well, where does it belong, right? Where does it live? Where does it belong permanently?

That part maybe goes back to it. That part needs to sit up in the business. And so I think that COE model, with the frame of we may take it on initially, but it needs to land in the right spot.

Is a good model when you're doing something very different right across those planes, is that, does that make sense with that getting what you were after, or was there some piece you wanted me to not leave out there? Maryfran Johnson

Yes, no, I that was a very thorough and really interesting answer to the question. In fact, I found myself hoping a couple times that you were taking notes for your Reuters conference speech. It was coming up, because there's some good stuff in there.

And this leads me to my I feel like we could talk about this for hours on end. It really is interesting.

I had a question lined up where I was going to say, What do you miss about being a CIO, but I'm not really detecting anything that we can plumb the depths of there.

I think that you've taken all of your experience as a business leader and a CIO, and you have funneled it into this whole new challenge.

And it can't hurt that you've been there for 12 years, and they know you, and you know, I mean, I noticed too that your successor in the 娇色导航role has also been with the company for nine or 10 years, and I have developed a real admiration over time for companies that value the experience of executives that have been there a while.

I remember years ago, we tried doing a cover story at 娇色导航magazine about long tenure CIOs, and we had a heck of a time finding anybody that was in the role for more than five years.

And you know, when you're there for a while and really enjoying yourself and you've got all that trust going, it's kind of hard to imagine a chief AI officer, some brand newbie, coming into the company and being able to get all this collaboration and so forth together, Richard Wiedenbeck

so it would certainly make it harder. I think, you know, I think there's two versions of long tenure right. There's the Unknown Speaker

lifers, right, Richard Wiedenbeck

and then there's the career. Right? I built a career off of delivering value, providing success and showing that I can pick up a challenge and do it, and then I understand enough of the company to maybe get that moving a little quicker, right?

I think you can come from the outside. It's harder because you got to figure out and navigate the culture and and build the relationships and work through it. I will throw this in. And you said, No, I don't. I don't miss being this CIO. I loved it.

What I can tell you that I definitely do not miss is the burden of the run and my Maryfran Johnson hat's off.

80% of the budget that has to go to keeping the lights on and everything, Richard Wiedenbeck

yes, and hats off to all the CIOs. If there was, it took me probably about four months, and I was like, I felt like I was lighter on my feet and I was more agile. And I was like, wow, why do I feel this way? Right? You know?

And it dawned on me that I don't have the burden of the run, yeah, that every 娇色导航has, every 娇色导航and. It is part of the job, and it is part of learning that job. And you know, you got to own it.

That is part of that job, but it is, it is a it is a big burden on every CIO, and you can't take it away from them. It is their job. But I don't have it.

And so yeah, when you see this little pep in my step, it's like, yeah, and do I appreciate that my my successor does have that and has taken that on? Absolutely. Yeah, no, I I bring him all of my run problems every day.

You know, there you go, Maryfran Johnson

:32 and you promise not to become a shadow. It product for Richard Wiedenbeck

:35 him, and I promise I will not become the version that I didn't want to have. So I think those two things certainly keep me in the good graces of the new CIO, who it was also with us for a while.

You know, he was part of my org, and, and, and I think we'll, we'll do great things. We'll do, we'll do great things I didn't do, you know, because that's what you want.

You want someone who's going to take it to the next level, do something different, go where it needs to go and Maryfran Johnson

:02 and he'll enable you to do some of the things you want to do with the absolutely, yeah, Richard Wiedenbeck

:06 I feel like, you know, he's got the hard job of, he's got to go cut a whole different path, but, but not throw me under the bus in the process, right?

You know, which is funny, and that's tough, because it's easy when somebody's gone to say, Ah, well, that was all their fault. Number one, blame your predecessor, right? You know. And you know again, people don't, you know again. There's great CIOs who don't do that anyway.

But you know, sometimes, sometimes it's real, you should? Maryfran Johnson

:37 I know, and, and sometimes it's great to be able to say, well, I don't want to say the previous 娇色导航was an idiot lot, you know, he he can't really get away with that, because you'll find out about Richard Wiedenbeck :50 it.

Well, then the problem with long tenured is, well, you are part of the org, so you could throw it on me, but you were there too, you know, we both own it, right?

And it's like you own the sins of the past, whether you've created them or not, and and you have to, you have to, you know, you have to be honest about them. But I always have this other friend, future good doesn't have to equal past bad good, right?

You know, you could say there's just a better way to go do this. It doesn't mean what you were doing in the past was wrong or bad. It just means there's a better way. So let's just go through the better way. Yeah, and let's try this.

Yeah, let go of that. And I use that phrase a lot. Future good does not equal past bad. That's let's just go to future Maryfran Johnson

:31 good motto, and it's a good way to get along with all your other corporate compatriots that you've been working with for 12 plus years.

Yeah, final question for you, because I know you, you, you know you seem to be having a whole lot of fun, but you do have a very real job. So I gotta let you get back to it.

CIOs, who are listening or watching this interview with you, may be thinking, you know, there's something there about that Chief AI Officer role that really is drawing me, I can feel this pulling me. Chart.

What do you wish you'd known a year ago when you took this on, that that you've learned that you want to leave as brilliant 娇色导航Hall of Famer type advice for for any of your compatriots that are watching this and thinking, ha, you know, I wouldn't mind leaving the run burden at this train station and hopping on, you know, the bullet train to Chief AI officer.

What would you recommend to them? Richard Wiedenbeck

:34 You know, I think there's always a little bit of, there's two sides to that coin, right? There's side one, which is, you want to you're always looking for fun, exciting, challenging things to go do. This clearly has that written all over it, yeah. But it is.

I don't care how big your company is, it does have a little bit of that startup feel, yeah.

And so my words of advice would be, if you're not willing to roll your sleeves up and get in and get your hands dirty and do the Trunch through the mud, don't take it on it, right? Because this is not to me.

This is not a think about it. I'm a little, I'm a little. Coe, right? Yeah. And I came from, you know, I do miss having 800 people that I can say, shoot, I can find a way to get that done, right? You know, Maryfran Johnson

:24 please go see to this. Yeah, yeah. Richard Wiedenbeck

:28 And so that it's, it's both sides of that, right? It's the yes, there's this fun, exciting thing, but be prepared. And if that's what you want, I always tell people, Look, if you're looking for that kind of work. This is an awesome role for that, right?

It is, roll your sleeves up, get in, solve problems in ways that are very creative work to operate at 10x the speed you were operating before, right? You're going to have to crank that speed dial up at whatever it was in your firm. You're going to have to.

Push on that envelope. I had it. One of the SVPs in the company actually said to me, it took the law. They said rich, if you operate at Ameritas speed, you fail. You need to go, you know, 3x 4x our normal speed.

You have to keep pushing our speed battle. And I know you want to do that, so I would say it's fun, exciting work, but be prepared. Draw your sleeves up, get dirty, get in the weeds, do the hard work you're going back to that. There's fun in that.

Yeah, there's frustration in that. There's opportunity in that. But it's both. It is not just a one sided I got this cool title, and look at me. And to your point, I'm getting a lot of airplay.

I'm talking to a lot of people, but you're absolutely right this, this is, you know, whatever time we took is an extra hour I'm going to spend at the end of the day to make sure I get my stuff done.

Because we've gotta keep moving and we gotta do it. And I would tell people just, you know, eyes wide open, right? Go, go into it, knowing you've got both of those at your disposal. But, boy, never be afraid to take on a new challenge, right?

I that is something that's career advice I've given to every mentor I've I've mentored, right? Is, go look for those. If it's this great, if it's something else great, do not be afraid to and don't wait for somebody to handle table. Go, go offer it.

Hey, I can help with that. I can help with that. I can help with that. No, that's not in my wheelhouse, but I know how to go do it. I can help with that. Those are the things that I tell people go do.

And when you go do enough of those, somebody will walk up one day and say, you know, I'd really like you to come do this for me anyway. So great if I can offer my sageness with the gray hair that's starting to show up there. There it is.

It's probably too many platitudes. Mary Fran, but I really think those things are real, and that's what I will offer up. Maryfran Johnson

:52 Good Well, it's been a lot of really great specific, specific info, and a lot of wonderful examples within your kind of characteristic good humor. So it's, it's really, it's always fun to talk to you rich. And I'm, I'm glad you're enjoying yourself out there in Nebraska.

I hope there's no tornadoes coming into your neighborhood. I think of that entire Midwest area as constantly having to Richard Wiedenbeck

:18 big winter storm coming tonight. So we're go, alright, 70 degrees today. Winter storm tonight, it'll be 60 in another day or two. We're just Maryfran Johnson

:28 welcome to the Midwest. That's right. Well, batting down the hatches and get working on that Reuters conference presentation, and I am looking forward to reading and hearing more about that. So thank you.

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