This week on the Be Epic podcast, Brent sits down with Varun Grover, Distinguished Professor of Information Systems and George & Boyce Billingsley Endowed Chair in the Sam M. Walton College of Business at the University of Arkansas. They explore the profound effects of AI on individuals, businesses, and society. Varun highlights areas where AI can enhance lives through applications in healthcare, education, and more. However, he cautions that without understanding how AI reaches conclusions, trust remains elusive. Brent and Varun discuss improving AI through specialized models, explainability, richer inputs and contexts. Varun asserts that augmenting human skills rather than automation ensures AI uplifts roles and fuels innovation. Listeners gain insight into AI's trajectory and its role partnering with instead of replacing humans.

You never have innovation through automation,

you will only have innovation if there are ways in which the AI

and the technology can work together.

Welcome to the Be Epic Podcast brought to you

by the Sam M. Walton College of Business at the University of

Arkansas. I'm your host, Brent Williams. Together, we'll

explore the dynamic landscape of business and uncover the

strategies, insights and stories that drive business today. Well,

today, I'm lucky to have with me Dr. Varun Grover, and Dr.

Grover's the George and Boyce Billingsley Endowed Chair and

Distinguished Professor of Information Systems in the

Walton College. Varun, thank you for joining me today.

I'm delighted to be here. Thank you.

Well, I think we're going to touch on some

interesting topics today. But maybe before we do that, I would

love for our audience to get to know you better. I've gotten to

know you over the last five years, I think you've been at

the Walton College since 2017. But tell us a little bit about

you your background, and before coming to the Walton College.

Sure. So. So I'm a techie at heart in the sense

that I did my undergraduate degree in engineering,

electrical engineering, and, and that's where my interest in

technology started coming about. And I had the good fortune of

getting into a very competitive engineering school that allowed

me to think systematically about technology. So when I, when I

graduated, I kind of felt that I was in a very narrow silo, I

understood technology gyrations I understood, you know,

technical concepts, but I didn't really understand the world, the

broader context. And so I decided to do an MBA and then go

into my PhD in information systems. And then after that,

I've been basically in academic jobs, I was at USC, and then I

was at Clemson. And my primary goal or responsibility in those

jobs was to, to build the PhD program, and conduct research

and set up centers, and have some outreach, dealing with

important problems in the Information Systems domain. So

I've been integrally involved in this in this field, and the

field has evolved, because the technology catalyst is kind of

driving changes. So if you think of technology in the 1960s, and

70s, and 80s 90s, and now, now we're in this digital world,

that is a lot of people really don't know how to deal with it,

and companies are still struggling. And now we've got AI

coming in, that's adding a new wrinkle. And so what I like

about the field in general, is that we don't have a dearth of

problems to study, there are lots of challenges faced by by

companies faced by individuals, and now the impacts transcend to

society. So there's a lot of really, really interesting

issues. And that keeps me excited about the field.

Yeah. Well, you know, Varun, you're, you are a

researcher, you're a teacher, you know, you serve the

University and the College through the normal mechanisms.

But you have spent the majority of your career really focused on

research. And as you say, the your work and PhD programs are

really advancing the research of these institutions. Just when

you think about business research, maybe more generally,

how, how does it impact the world? How does it impact

others? And how have you viewed that over time?

Yeah, that's a really interesting question.

Because even business research has has evolved. If you think

about in the 1950s, business research was not much of

anything, it was more, you know, go out maybe some outreach and

case studies that you did with with businesses, but then the

scientific aspect of business research came in the 1960s,

1970s. And we kind of went overboard with the concepts,

scientific concepts of theory and became somewhat

disassociated with practice. So my thinking is that in in a, in

an applied business discipline, it's really important that we

create interventions to practice. Any research that we

do, should not be esoteric and isolated from the real world.

And so when we talk about when we think about research

projects, traditionally our approach would be, you know,

look for research gaps in the literature and then try and fill

those gaps. I think the more productive approach is to To

identify real problems faced by practice, and then to convert

those practical problems into research problems. So it's more

like engaged scholarship, you engage with practice at a deeper

level, and try and look at their problems, and then translate

those problems into research problems so that you can create

interventions, as a result of your research that actually make

a difference in practice.

That's right, what a good way to explain that

I just couldn't agree more about at least in my own research that

that's where the research questions that I've been most

interested in then that ultimately, I think the research

that has the biggest impact for my own career comes from same

for you.

Absolutely

Well, I'll, I'll brag on you a minute, because I

don't think you would brag on yourself in this way. A couple

of the interesting statistics about you as a researcher, I

think you've published more than 400 articles in your career,

over 100 in the top journals, and you are listed as one of the

100 most cited, not just information systems, but

business researchers in the world, what an accomplishment.

And so you have truly over a career made a real impact in the

academy and in practice.

Thank you. Thank you Brent.

Well your research, yeah, I told you this

before, I'm kind of borrowing from your bio here. But you

know, at a real broad level, you know, as I saw it described

focuses on the effects of digitization on individuals on

businesses and organizations and even society, tell us a little

more of a high level before we get into some of the more

detailed questions.

So I've always been interested in how

technology creates value. And information technology is

particularly of interest to me. So when we look at computing

technologies, in a business context, what are the aspects of

these technologies? And the and the context around them, that

lead to value value at the individual level? So how can

technology enhance individual productivity? How the value at

the group level, value at the organizational level? How do you

actually create competitive advantage through these

technologies? How do you create greater profitability, more

revenues? And then at the market level? And that is how do you

make markets more efficient through technology. And then now

even more so at the societal level. So with technology, and

particularly digital technologies, penetrating every

aspect of what we do, there are issues of value, and the value

could be positive or negative. So now we are seeing you know,

this, there's two phases of technology, you have the

intended consequences to create positive value for companies.

And then you have sometimes unintended consequences that

create problems for society. So I like to study at every one of

these levels, I like to study different questions regarding

the value proposition of digital technologies.

Well, this has to bring probably to anyone's

mind, you know, when you talk about the enter the the

intersection of society and digital technology, as we sit

here today, the conversation around artificial intelligence,

AI, is everywhere. All right, you know when chat GPT popped on

the scene, it really just brought it to the consciousness,

even though this has been developing for years, if not

decades, it really brought it to the consciousness. You're not an

AI researcher, I know. But but I do think that you're probably in

a unique position to think about this intersection between this

technology, and people, businesses and society. So maybe

just I'll just I'll stop right there and kind of say, all

right, as you're observing this phenomenon of AI, what's kind of

going through your mind and what are the observations and

questions that that are becoming apparent to you?

Yeah. As many people are fascinated by AI, so

am I, as it's not, it's not my primary area of research, but

clearly it falls within the domain of digital technologies.

And like you said, ChatGPT came on the scene and it blew away

people because not because it wasn't necessarily the most

accurate or more most interesting output, but it gave

people the sense of the possible if this is the starting point,

and it can generate such interesting outputs, then where

is the world going. And I remember the first time I played

around with GPT, I asked it to write a story, I said, you know,

here's a little girl walking up a hill, and there's a, there's a

big rock at the top of the hill, write a story for a five year

old. And it wrote a pretty compelling story that I actually

read to a five year old. And then I said, okay, redo the

story for a 10 year old and it reformulated the story, change

the language, and again, fairly compelling story for a 10 year

old, then I asked her to do one for an adult, and it did it. And

then I pushed it to be more creative, in the ending, make it

more interesting, make it more controversial, make it more

enigmatic. And every time I did it, it came up with interesting

changes. And I said, wow, you know, this is actually doing it

from the corpus of data it's been trained on, and yet, it has

this layer of creativity that we don't quite understand how it's

getting there. And this was the early version of ChatGPT. More

recently, I was playing around with now there's many, many of

these chatbots, AI based chatbots. And I noticed one of

the differences. So I was playing around with a chatbot

called Pie, which now I believe has been acquired by Microsoft.

And it I found it to be extremely conversational. And

what was different about the earlier versions in this version

is that it had empathy. It actually could relate to me, and

it was taking cues from my text, and putting that emotional

response back to me.

And that was very interesting. So it's like, it's

Interesting.

Interesting. Well, you mentioned profound and scary. You also,

almost like we're coding emotions into these training

sets. Now, to provide far more conversational far more personal

AI, personal intelligence reactions. And I stopped

thinking about this, it is absolutely fascinating where

this can go. Because if you can have that level of

you talked about a wide potential set of potential

interactivity, and conversational and empathy with

with individuals, you can put this front end on pretty much

any intelligence, and you can access the world. So customer

interfaces, so providing excellent customer service, that

impacts on people business society at large. I'll, I'll

make it make it conversational, so you can align with what the

customer wants. Personal learning it for in the education

sphere, companionship for old people, you know, if you

actually put a physical manifestation, like a robot, and

kind of dive into two and ask you a question. So you mentioned

then you have this AI back back end, you can you can maybe

alleviate some loneliness in the world. And then so clearly,

there's this tremendous implications. And then you see

the applications of AI. Some of the things that we've often

wealth management, and you mentioned health care. And as I

talked about is in the medical field, in the medical field, you

can feed in radiology reports, and it can find abnormalities,

that's kind of low hanging fruit. But then when you have

this kind of conversational AI, you can put that in front of

was sitting here listening, I was thinking, oh, those are two

medical knowledge and you have a doc in a box. And when you are

considered the shortage of primary care physicians, this

would be a very interesting way to layer our medical

interactions, where you have the first layer of interaction where

pretty personal topics to me, like, one, you know, how am I

you can feel comfortable talking, even though it's not a

person, you can talk talk to the box, and that can do a diagnosis

and write up a report. And then it can go to the next level for

verification with a human and then go to the next level for a

managing my money and two managing my health? And I know

referral to a specialization. It can change the medical field

quite quite significantly. And we've seen applications of AI in

supply chain and manufacturing with robotics, in the financial

sector with wealth planning. And so when you have these

that I think that a lot of your research in digitization and how

interfaces, you basically can interact and get it more aligned

with what you want in terms of your wealth goals. And, and

recently, I was reading an article that described IBM's

Watson that was working with a fashion house, and they actually

trained the AI on videos of models walking down runways, and

it affects people in organizations and society looked

social media included, and the AI was quite adept at

identifying fashion trends, and new fashion clothing designs,

based on what they observed through this corpus of data. So

at least at trust at some level. So what have you learned about

it's remarkable at the diversity of areas that AI can have have

an impact, which is why it's so profound. And in some sense,

also a little scary.

the way that trust, I don't know, I don't know if it

mediates those two. Tell me what you've learned and how you think

that that's going to play out with AI?

Yeah, this is a really interesting question of trust.

Because historically, when we looked at information systems,

we programmed these systems, we could actually code them, we

said, so if we had a, we had to make a computer play chess, we'd

say, okay, this is the way the queen moves. This is the way the

bishop moves. These are the rules of chess, we put it into a

program, and then it will play chess. So the earlier AI systems

were actually programmed systems, they will rule based

systems. So there was AI in the 1970s. But it was usually

referred to as a snake oil of the 1970s and 80s. Because it

over promised and under delivered, you could only have

some gaming applications. So yes, you had medical diagnostic

systems, but they were based on rules, the rules were coded in.

So if a patient had temperature over 98.6, the patient had

fever, if their white blood cell was over a certain level they

had an infection, and it would, you'd kind of give it the

symptoms, and it will pull out the rules and come up with the

diagnosis. What's changed now is we have had the perfect storm of

processing power. With these GPUs, graphic processing units,

Navidia is the big player there, we have massive amounts of data,

and low cost of data storage. And we have with this process of

processing power and data, we can kind of look for patterns in

the data that are and, and identify these patterns and make

predictions based on these patterns. So the large language

models that we see of today, are really next word predictors, if

they although now, we can actually feed them videos and

images. So they're multimodal models. But the language models

are, really next word predictor, so they don't have any raw

intelligence of their own. But they're kind of looking for

these patterns in the data. And so while the old systems were

programmed, the newer systems are digging out these patterns,

almost like your neurological connections in the brain. It's

actually creating these connections. But we don't really

understand how they're creating these connections. It's almost

like asking a person, say, How did you come up with that

decision? Yeah, and what what neurons were connected in your

brain to come up with that decision. So we don't really

understand it. And so if we don't fully understand it, it's

like Warren Buffett says, Never invest in anything you don't

understand. So here, we are relying on the outputs. And we

don't really understand how the outputs came to into existence.

It's massive amounts of data, millions of parameters in the

model. And so without understanding it, why should we

trust it? And if we can't trust it, then why should we use it?

And so I think it's a fundamental question of

technology and value, which is my major stream, that we need to

be able to trust these systems. Because another way to think

about this is typically, if you look at scientific research, the

idea of actually looking at patterns from data was

considered voodoo science for many years.

Yeah

This induction you don't look for patterns.

Yeah

You come up with a theory or a statement and then

you go on to get data and test it.

Yeah, that's right.

But what are large language models doing? What is

Yeah. So it's just thinking exactly the same question.

AI doing? It is really creating and testing theory

simultaneously without any scientific underlay. So when I

think about this, I think, you know, I've always grown up

through my information systems career, thinking about data,

information and knowledge as a trichotomy. So you have data,

which are raw facts. And then you put the data together, and

you get information which informs you. So you get

interesting reports that reduce uncertainty or informs you. And

then when you apply it, and you use organize the information,

with experience, you get knowledge. What we've done with

AI, is we've removed the middle layer, we've gone data is

knowledge. And so we're basically taking massive amounts

of data about the world. And we are proclaiming that this is

actually creating knowledge. So we don't really need to

understand the world to know it. It's like we're just take, so

that layer has gone. So that's why this idea of trust is so

fundamental, because we don't really understand how these

connections are made. And so I I was thinking about, you know, as

you why do people trust other people?

And so so and do the others elements actually translate to

Well, that's what I was gonna ask you, you

AI. And I think people trust other people, because they may

have a connection with them, they actually relate to them,

they have empathy, or they relate at some subliminal level.

know, how do you see that changing and evolving? The one

So they say I trust you. People may trust people, because they

understand where they're coming from. So if they make a

decision, if you talk to someone, they make a decision,

that I could see that you just mentioned. Well, the model is

you can ask them. Okay, what's the basis of this decision? And

do I trust you to explain to me how you came up with this

decision. People trust people, because they are predictable,

getting more reliable, let's say less erratic. That one may

they are reliable, they may give accurate outputs. So if someone

is erratic, we probably won't trust them. But if someone is

more stable and predictable, and comes up with good, good

change, you know, but and you even mentioned the ability of,

outputs, we tend to trust them. And people trust people because

we might share the same social experiences. So I think these

elements of we trust people, because we can connect with them

or relate to them. We trust people because we can understand

of maybe I don't think you said it this way. I think you were

where they're coming from, we can ask them, we trust people

because they're reliable and not erratic. And we trust people,

because we have a shared social world, is generally why people

mentioning Pie, the the product you were working with, that it

trust people. These elements are not very prominent in AI. And

so, so I think that's why trust is a kind of an important issue.

Although they're changing.

had a feel of empathy, which might mean you could emotionally

connect to it more easily. So some of these things seem to be evolving,

I think so I think that from whatever little

observation I've had over the last year and a half, it's not

been that long since ChatGPT was removed, released. But this

idea, I can see improvement in empathy, I think that element is

there. So we are starting in on that trajectory in terms of

understandability, there is a whole field or subfield of AI.

That is called Explainable AI. How do you get take this neural

connections? And how do you actually convert it into a

language that humans can understand so they can explain

the rationale for their decisions? Now, it may not be

that important for radiology reports. But if we're dealing

with a business discipline, you know, often you want to

understand the rationale for the decision. So Explainable AI is

actually being worked on. And and it's important, and it's

it's particularly important because AI is all about data and

connections and patterns. AI is not particularly good at

reasoning. So if we tell an AI that Look, if they're looking at

a pattern, x is the parent of y, it will not naturally infer from

there or deduce from there, that y is the child of x. That

reasoning ability will only come about if it's in the corpus of

data. So the question then becomes, you know, how do you

get this reasoning out of the AI, and that's where a lot of

the research on Explainable AI is, give you another example. We

can put millions of chess games played by world chess champions

and feed it into the AI and it'll figure out the rules of

chess and what works and what doesn't work. So that it will

basically be a really good chess player. But if we change the

board from an eight by eight board to a nine by nine board,

then the training doesn't really help because it needs more than

that it needs some kind of reasoning ability to play chess

well on a nine by nine input. So I think that that idea of how do

you get that reasoning to come into you know, the box or the

corpus of intelligence of AI is is something that is being

worked on through Explainable AI. So empathy? Yes, we're

seeing improvement there. Explainable AI, we're not quite

there. But there's certainly a direction there. And like you

said, absolutely the corpus of training data. So most of the

models, we had an AI foundational models, like from

open AI. And from Google and Microsoft, these models are

trained on massive corpus of data from the internet, the

internet data is noisy. And it is, you know, it has a lot of a

lot of problems, which means it can be a pretty good general, it

can provide general advice on a lot of things. But if you're

looking for very specialized advice, sometimes these models

hallucinate and create these arbitrary outcomes, because

they're not fully trained in those specialized areas. So I

think one of the trends is how do you actually curate data for

training and make these models more accurate and better? And

how do you move away from the big tech companies which have

the resources to train these large foundational models, to

smaller models that are done through open source where you

can, individual companies may not have to invest millions of

dollars in training these models, but they can train them

on their own data,

Right.

And, and become, it can become more more

personalized and more accurate within that domain. So I think

that's the third component of trust, where these models are

being trained on better data. And sometimes even instead of

the large models, we're getting smaller and more specialized

models that are more accurate. And I think that's a trajectory

that will improve trust. And finally, in terms of context. So

I think that context is also improving, because the earlier

models had very little that you could put into the model in

terms of input. Now, some of the newer models, you can put entire

books and and say, you know, so your context window, the number

of tokens that go into the context is becoming larger and

larger, which means you can provide a richer context to your

decision making. And then you can have conversations to get

the alignment of get what you want from the AI. And so that

alignment can continue by probing and interacting with the

AI. So I think on the third aspect, the context aspect,

we're also seeing improvements. So I will trust the box, because

I can connect with it. I can trust the box, because I know

where it's coming from. It can explain its reasoning to me, I

can trust the box. Because it gives me good outputs, accurate

outputs, and I can trust the box because it's grounded in my

world. And so all those aspects are advancing. And so I think

that on the technological front, we are seeing a trajectory

that's positive for trust.

I'm gonna I'm gonna ask you about to human.

Maybe let's start in the business context, right. So

let's say the models are improving, they're becoming more

trustworthy. They're certainly becoming more commonplace.

They're still probably some of the like, there's the

excitement. There's some of those some people who think it's

scary. Probably each of us land somewhere on that continuum

differently. But in the business world, you know, the role of the

human in business, does it, change it slightly? Does it

change? Fundamentally? You have any thoughts to share on that?

Yeah I think that's, that's, that's a million

dollar question, or maybe a billion dollar question. or now

given, given the market of AI, maybe a trillion question.

If you can answer this, we can raise a lot

of money for the Walton College.

But I think I so, how should AI, so if if we are

using AI, to automate jobs, if we're using AI to, to automate

and replace jobs, so you can, you know, you can do an

accounting job better, or you can do a legal job better. Then

what is essentially happening is we are devaluing labor. We're

devaluing human skills and labor, because we're essentially

replacing labor with the technology. And that's going to

bring the cost of labor down. So substitution of jobs with

technology is probably not the most innovative direction, it's

probably appropriate. And probably the incentives for

corporations to make money are in the cost cutting sphere. So

we're certainly going to see a lot of that. But I think that

where we're going to see the human and, and the technology

work better is through augmentation. You never have

innovation through automation, you will only have innovation,

if there are ways in which the AI and the technology can work

together. So if you automate a lot, you're basically devaluing

the human if you augment you're up valuing the human, because

when you when you automate, your competition is between the AI

and the human, when you augment your competition is between the

human with the AI, and the human without the AI.

Right.

And so you're upskilling. So I think that we

are at a point of inflection, where we are making 1000s of

decisions regarding AI. And it's really important that if

corporations are going to invest in AI, they should consciously

think about how to augment human skills through AI, rather than

only automation. Because automation will probably give

you the short term bang for the buck in terms of reduction in

costs. But the longer term impact on innovation will only

take place if you give people the discretion to leverage AI

and become and use it innovatively. So if there's

there's a series of tasks in your job, and there's a few that

can be automated, that's fine. But then think about what are

the tasks in your jobs that can be augmented. That's where the

innovation comes in. And that's where the value is created.

Well, Varun, thank you. Thank you for sharing

your knowledge and that knowledge that comes from

decades of work and research and I know I learned a lot and I'm

sure our audience did as well. So thank you for joining me

today.

Thank you, Brent for having me.

On behalf of the Walton College thank you for

joining us for this captivating conversation. To stay connected

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