In-Ear Insights Unlocking Workforce Potential with Generative AI

In-Ear Insights: Unlocking Workforce Potential with Generative AI

In this episode of In-Ear Insights, the Trust Insights podcast, Katie and Chris discuss unlocking workforce potential with generative AI and the evolving landscape of artificial intelligence in the workforce. They delve into the capabilities and limitations of AI, contrasting its performance against human tasks and creativity. The conversation highlights the importance of human critical thinking, decision-making, and creativity in the era of AI. You will gain insights into how AI and humans can coexist in the workforce, maximizing the potential of both. This episode is a must-watch for anyone interested in the future of AI and its impact on jobs.

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Machine-Generated Transcript

What follows is an AI-generated transcript. The transcript may contain errors and is not a substitute for listening to the episode.

Christopher Penn 0:00

In this week’s In-Ear Insights, let’s talk about the workforce potential of artificial intelligence.

We spent a lot of time and I know I spent a lot of time personally digging into the technology, what it is and how it works.

And today, let’s talk about the applications.

How is this stuff being used? So Katie, you found some stuff from the folks over gardener? Want to recap what you found?

Katie Robbert 0:24


So over the weekend, I was scrolling through LinkedIn, like everybody does, when they’re, you know, waiting for their tires to be rotated, or, you know, in line at the grocery store.

And what caught my eye was someone posting about the Gardner, a new video that they posted, unlock the potential of your workforce with AI, from their think, again series, and that I was reading through the comments of, you know, the person who posted it.

And so the whole premise of this research, we have it up on our screen, if you’re not watching our podcast, is that they have from 2023 to 2033, their predictions of people versus AI, essentially.

So what tasks will AI outperform people? And what tasks do the human outperform the AI counterpart? And unsurprisingly, the thing that AI is going to do better than human.

At the top of the list is weather prediction.

Well, no kidding.

Our meteorologists have been wrong for years, I would be shocked if AI could start to get it right, given all the crappy data that we have to give it.

But then you start to get into things that are really around development versus human tasks.

And then at the bottom of the list, where the humans out poor outperform the AI, or the more of a human tasks such as personal care, which makes sense.

But then you have other things that I thought were interesting, such as, build new scientific theories, moral and ethical reasoning, and writing best selling books.

And I was I saw this and to me logical, it makes sense.

But the commentary, the feedback from everybody reading, this was like, whew, thank goodness, I knew AI wasn’t that good, or I knew it was a flash in the pan.

And I feel like there’s a lot of misunderstanding for this kind of data.

This data is saying, Today, these are this is sort of where we stand in 10 years, this is all going to be different.

We’re assuming that AI is going to outperform on most of these things.

But as of today, humans, you know, we still have a leg in the race.

Christopher Penn 2:28

This is a poorly constructed chart, because I can’t tell if his by percentage or his but over time, because they have 2023 to 2030 feet, that sort of the top is an axis I’m not sure what I’m looking at.


Katie Robbert 2:38

which I think is ironic, given how hard our charts are to read at times.

So basically, if I’m just looking at this at face value, and taking, if I’m looking at this on a time continuum, so on the left is 2023.

On the right is 2033.

To me, it looks like it’s almost that progression, where you know, so driving a car autonomously.

So it starts with human, human human, and then it’s like, oh, no AI is going to outperform it, like somewhere in 2028, which is sort of the midpoint.

If we look at software programming, we know already that that sort of a paired AI and human and so you need the human interaction.

So it doesn’t get into the details of how much of the task but it’s just mostly saying like, you need both AI and humans for certain things.

The only thing that you 100% Don’t need humans for anymore is weather prediction, apparently.

Christopher Penn 3:37

So, I know these things are typically done with interviews with like C suite executives and things, asking them Hey, what’s your perspective on this? from a technological perspective, looking at where models are today, and what’s possible 100% of this chart is wrong.

100% This chart is dramatically overestimating human beings.

AI already beats human beings and video games.

Nope, no question.

If you look at the Starcraft automations and things, it’s an easy win.

For breadth of translation, AI already outperforms humans on that AI can translate things that we can’t because it can infer things like restoring dead languages, languages that are long since the bitten out of circulation.

And based on the child languages from those forks, you can reconstruct the parent language which has been done a lot in academia, it’s very cool.

Voice and face recognition.

AI already dramatically outperforms that to the point where we have we have real problems societally, right now with things like is facial recognition, you know to be ubiquitous and that the EU has passed a law saying you may not use AI for certain types of face recognition of a voice recognition is already built in.

Driving a car autonomously machines outperform humans already.

The only reason we don’t have that as regulatory more than anything for medical scan diagnosis machines outperform humans there are some clinical papers that came out last week actually demonstrating that machines have a 20% Absolute higher accuracy rate on medical scan diagnosis than humans do.

Which, if you think about it is literally life saving that machines can can do that so much better for software programming.

software programming is an interesting one machines can program better right now, but they don’t have they don’t have the agency to decide to make decisions like what should the software do.

You give it purpose and a prompt and focus and it can write the code for you very quickly, but it has no agency of its own.

For financial analysis, machines are already doing that.

dramatically better than humans.

Ask anyone who’s been using these tools for things like stock picking, creating music is one where I think that one’s wrong.

In the other direction.

Machines are profoundly bad at creating music, they are with all the language models, and all the sequencing models come out.

There isn’t a single one that has come up with anything that sounds doesn’t sound like garbage, like it’s and the reason for it is because the weird nature of audio, it’s much more complex.

And it predates language.

So language models are trying to they’re trying to use language models to predict things that are not language, writing best selling books.

Again, I think that one is overly optimistic machines will outperform humans on that sooner rather than later.

There’s already a ton of well, selling books, best selling but well selling books on Amazon that are complete machine made.

moral and ethical reasoning.

Machines can reason whether or not but that goes back to the agency problem.

The question is, can they reason on their own? The answer’s no.

Building scientific theories, that is completely wrong.

But she’s already doing that better.

In fact, last week, machines developed eight classes of new antibiotics based on some material science that we only consider now that I’ve never go into testing to prove it.

and personal care therapy is going to be once like driving cars, machines can do that stuff today.

It but it is unregulated.

And it clearly needs to be regulated, because that’s high, that’s very high risk.

So most of this chart, at least from my perspective, as a technologist is utterly wrong.

Katie Robbert 7:14

from a technology standpoint, I hear you from a non technology standpoint, I disagree with you on your assessment, primarily because you said something when you were talking about software programming that you didn’t carry through with everything else.

You said with software programming, the machines can’t make decisions.

Therefore, that to me, says, Well, how come they can make decisions with everything else.

And so if machines can’t make decisions, humans are still the ones responsible.

So I agree that machines are better at a medical scan diagnosis.

But then a doctor still has to intervene to put together a care plan.

Because no two humans so like, you and I, Chris could both get medical scans, but we’re we are inherently so different from our DNA, you know, our physical stature, our, you know, medical history, that you’re gonna get two very different care plans, even if we go in for the same thing.

And so same thing with driving a car autonomously, there’s too much unpredictability.

Sure, I hear you about regulations, like if every single car on the road forever, no more humans could drive.


I don’t see that happening.

Because it’s not accessible to every single human on the face of the planet, especially in you know, less affluent parts of the country, less affluent part of the world, they don’t have access, so they are still going to be driving, you know, 4050 year old cars just to get around to get basic needs voice and facial recognition.

Sure, I agree with you on that one.

Because there’s not a lot of decision to be made competing in video games.

It’s interesting.

So because I sort of put that in the same bucket as software programming of it’s a lot of code and algorithms.

But in terms of competing in a video game, I put that sort of in the same bucket of creating music and writing best selling books, you cannot clone and program human creativity, the things that we come up with in our brain that are uniquely human, that are weird and odd.

And you know, I had the stream last night that made no sense but let me string together these five crazy things and turn that into a story.

machine isn’t going to be able to create and think Uniquely the Same way that humans do.

I can see the face you’re making, that you think that I’m inherently wrong, but I will totally fight you on that.

Christopher Penn 9:53

For video games in particular machines are extremely good at that and decisioning for it because there’s a clear objective there’s a there’s a clear victory In addition to which it can adapt in from, you know, this has been done everything from chess to go to StarCraft to Defense of the Ancients, all the games where AI has been used to successfully just beat the pants off the humans.

So that is clearly an area where machines have a nearly unbeatable edge.

For the other ones, I do agree with you in that machines can’t make decisions autonomously in a lot of cases, because they’re not sentient and not self aware that with autonomous driving, that’s again, another one where there’s a victory condition, right? Don’t hit things.

Stay on the road, get from point A to point B without driving off a cliff.

There’s clear victory conditions, what other things like yeah, creating music, what is what does winning look like when you create music? Is it making up top 40 Pop best seller? There’s there’s no good victory conditions.

So it’s much harder to tune a model on those things.

And that’s where a lot of these tasks when you look at unlocking the potential of your workforce, at the very least every single one of these is going to be AI and human coexistence because machines can just do good, huge chunks of it for us.

But in a lot of them, yeah, the machine does outperform to your point about medical scan diagnosis.

Yeah, the decisioning.

That is the decision for that more than there’s gonna be a legal matter, like, who is responsible for saying, Well, here’s the treatment plan.

Katie, the machine diagnosed you with this.

And I’m gonna prescribe you this? And if that turns out to be wrong, who is accountable for it? We have not worked out in law, whether a machine is accountable? And if so, is it the software developer? Is it the model maker? Is it the tuner? Is the person using it? We don’t have that worked out yet for artificial intelligence, it will take some time to get there.

But for other things, yeah, for sure.

Machines are already outperforming humans.

Katie Robbert 11:51

So it’s, you know, there’s some themes that keep sort of coming out that, you know, we can sort of pick apart in terms of, you know, I mean, this, it’s really, this is just a fancy wrapper for will AI take my job.

And so, you know, I feel I feel like companies are forever trying to answer this question, instead of just doing something about it.

You know, and so the real theme that seems to be emerging for me, when I’m, you know, we’re having this conversation, and I’m sort of processing the information is that there is a place for humans in terms of critical thinking, in terms of creativity, in terms of decision making, accountability, process development.

So those are, that’s where when it says, unlock the potential of your workforce.

Yeah, let AI do all of the number crunching and programming.

And, you know, I forget what you call it, like sort of the like, victory goal, like when there’s a clear outcome.

But humans still need to come up with the come up with the thing.

Like, yes, machines, they can come up with a new scientific theories, but they’re not going to come up with everything.

Humans are still gonna have ideas uniquely, human, that machines won’t be, quote, unquote, thinking of, because it’s not part of their data set.

It’s not part of the algorithm.

It’s not part of the information they have, you know, I think about like a Stephen King novel, for instance.

Yes, he’s written so many books at this point, that you could load all of them into a model, and it could probably write a quote unquote, Stephen King book.

But the reason why Stephen King is still Stephen King, and still a best selling author, is because every time he comes out with a book, it’s a new idea.

There’s something new that he himself has not yet written about.

And so that, to me is where Sure, machines can write some, you know, mediocre, mildly well selling books, but they’re never going to be a Stephen King.

Christopher Penn 13:59

I don’t know that that’s gonna be the case.

And the reason I don’t know that’s gonna be a case is when looking at what’s happening on Instagram.

So on Instagram, you’re seeing, you know, multi million person followers, influencer accounts that are completely synthetic, and they are some, there’s no human, all the travel photos and all that stuff is completely synthesized.

And yet, people engage with it.

People spend a lot of money with these influencers.

I know you’re rolling your eyes, but I am rolling my eyes.

But this is what human beings are doing because what they’re getting from the synthetic personality is, is good enough for them to say, Yeah, I want to spend money with this entity, even if, in many cases, you know, even a moderately trained eye you can tell it’s synthetic you can tell this is not a real human being.

There are obvious flaws in what’s being displayed but people don’t care.

People want the entertainment in whatever form it delivered in.

And I think for things like writing best selling books, yes, you can.

You know, we saw in the Nature magazine study that machines do divergent thinking on average 20% better than humans do for coming up with creative ideas.

Again, they have no agency, so they have to be prompted to do it.

But they can do it better than we can, on average.

Now the again, from that same study, it did demonstrate that the best human creative thinkers are better than the best machine makers.

But that’s, that’s, that’s the edge case.

That’s that top 1%.

Everybody else, thanks to the majority humans being not great creative thinkers, the machines simply do better.

And so for a lot of these things, when it comes to, you know, how do you think about applying AI? In your workforce, a lot of it is not just doing the grunt work, but also saying what are the things that are uniquely human, that machines probably won’t do anytime soon, and you nailed it, which is the human to human interface, right? Because everything machines do has to, at some point interface back to a human, a human has to buy something, no one has turned over their purchasing departments or machines yet.

And so at the end of the day, the outputs do have to go back to a human, probably through human and certainly we advise all of our customers, you know, you never let a machine, you know, do any kind of major process unattended.

They’re not ready for that yet.

But from A to Z machines can do basically B through y.

And that’s the aim is either the human parts.

Katie Robbert 16:29

Well, I mean, if you think all the way back to, you know, you have the Henry Ford assembly lines, yes, it streamlined the production of motor vehicles.


And sure, a lot of people at that time were out of a job, because of the way that the world changed.

But not 100% of the people were out of a job, you still needed people overseeing and monitoring, and making sure things didn’t break.

And so those are the people that you know, at least when we look at this list, they’ll be fine for now.

But then so that’s where, you know, we need to look at, okay, if I’ve only ever been doing the same thing over and over again, if I only ever create a report once a week, if I only ever put numbers in a spreadsheet, yes, this is the kind of thing that you should be worried about.

So then when you when it says unlocking the potential of your workforce, what aren’t you doing? You know, do you now have an opportunity to let the machine put the numbers in the spreadsheet for you, and then you take it that step further and figure out what to do with the numbers.

That’s really what it comes down to is, what’s the next step? let the machine do the grunt work, let the machine program the software, let the machine write the first draft, let the machine you know, create the hypothesis you the human, then say yes, this is good or no, this was bad.

What else can we do with this? What are we missing? What aren’t we doing? And I think that that’s the conversation that isn’t being had enough.

It’s being had.

But we’re so focused on the fear of when is my job going to be taken? What am I going to be out of a job that we’re not focusing on? Great, let it take that part of my job? What else can I do?

Christopher Penn 18:20

You and I were having a conversation before we hit record about you know, managing the data in the spreadsheet.

And one of the things you said was, I can’t get ahead on my stuff, because I have to do manage other stuff.

And when you said that that made me go, huh? Why haven’t we had tried having a machine do this yet, because the the work that’s being asked of it is stuff that is within the capability of a language model, because it’s just processing text, and outputting a known format.

And I think that’s where that’s where the true unlocking of potential for your workforce is exactly what you said is here’s some stuff that is it’s important, but it’s still relatively low value.

Now, all the low value.

Here’s here’s the challenge with this all the low value tasks add up to something too high value decisions, right? So if you say we’re not going to do these low value tasks, well then you can’t make a decision because the the high value decisions or have dependencies on the low value stuff.

That’s why you have to keep it around.

But that low value stuff really is okay, how much of it? Can we hand over to machines? And how quickly can we hand it over to machines, so that it’s it’s less time.

Last week, when I was putting together some content curation setups for one of our clients.

One of the first things they did was okay, I need to designate some topics and things for this, I handed 100% of that task off to a language model.

Like there is no reason for me to be manually hunting down keywords anymore, that goes straight to a language model.

And then for our own stuff.

When I looked at it this weekend, when I was pulling data for my personal newsletter, it was much better.

It was so much better.

That was because the machines had more comprehensive knowledge of the space than I did at the time.

and better memory of it.

And it was a clear case where that task still only takes two minutes a week.

But now the quality has gone up significantly because let machines handle more and more of it, the code itself, I have now revamped that code three times in the last year, whereas I hadn’t touched it once in three years, because it’s labor, right? It’s it’s manual labor that does coding.

So it’s not like I’m lifting logs, but at the same time, it’s drudgery.

And so now I can hand my existing code to a machine, say, Hey, here’s my code.

Let’s improve this, here’s what I want to improve, and it will do it, it will get it 95%.

Right, there’ll be 5% still screwed up.

But it’s easy to fix the screw ups.

And so that’s another example of unlock the potential of your workforce.

Yes, I can do more of these low low value things.

But they add up to a very high value output.


Katie Robbert 20:56

I think that that’s the, that’s, that’s a big part of the conversation that’s also not being add is it’s the accumulation of tasks.

You know, so I could say, Sure, 95% of my job is now automated.

But if you start to pick apart each of those individual tasks, they really all add up, you need every single one of those things.

And those take a lot of maintenance, those take a lot of human intervention.

And so I’m not going anywhere anytime soon, because even if I hand over my stuff to machines, I still need to make sure all of those individual low value pieces that add up to the big things are all working correctly.

So I’m not going any my job is safe, even if I am using machines 95% of the time, right?

Christopher Penn 21:44

There’s a shadow economic impact, which is that we’re not hiring more people, right? So we’re because because Michigan, we don’t have to hire we’re, you know, our company is now almost six years old.

And we still have not, for the amount of work that we do, we should have probably the old days prior to AI, we would have had 10 more people on staff, because we just have that much work to do.

But because of machinery we’ve not had to hire so there is an economic impact is that we’ve not laid anyone off.

But we did not we have not expanded our workforce substantially as part of it.

And even today, we’re building new things.

We’re currently working on building attribution modeling software for Hubspot.

So inside the Hubspot ecosystem to to do essentially Markov chain modeling.

With that, again, we’re using machines machines are writing the code we have, we still have to provide the guidance to say like, here’s what I want it to do.

But boy, is it doing software programming for us.


Katie Robbert 22:39

it’s as we you know, it’s interesting.

Well, you know, as I’ve always been saying, it’s, you know, new tech, same old problems, I feel like this is AI is new tech doing things different than other tech has done before.

But it’s the same problem, I think about, you know, internet research versus library research, or, you know, data processing versus hand calculations.

There’s a, there’s room for both of those things, it’s just a matter of taking a step back and figuring out what the heck it is you need to do.

And I think that that’s where, you know, to your point, this chart kind of doesn’t tell the whole story.

I mean, we know it doesn’t tell the whole story, it’s just like one single bar chart.

There’s room for both, it’s just a matter of what does that look like in each individual situation.

I personally, I’m someone I love the library, give me the library all day long.

And I don’t want to sit at a computer at all.

But that’s me personally, you, Chris would rather be at a computer and have things done faster.

Whereas I prefer sort of like the more slow and that’s purely because of how our brains process information, you process things 10 times faster than I do, I take a lot longer to pull all of the information together and feel like I can feel confident to make a decision.

Whereas your brain just works that much faster.

And that I think is where we’re going to start to see, you know, people who were setting the course in an individual company, it’s how their brain works is how they are also going to dictate how they’re using the technology.

Christopher Penn 24:25

And there’s also a gap that’s not reflected on here as well.

And I think this goes well with your point, which is to to unlock the power of AI, at least in the these nascent first few years, this first decade or so of it, you’re going to need conductivity tissue to make these things work.

So, you know, right now people have ChatGPT The vast majority people using general AI or using something like ChatGPT super low tech, right.

It’s just a browser in the chat and it does a really good job.

But to make it scale, now you need technological, right? If you got to go from ChatGPT, to the GPT-4 API, so that you can make things at scale ChatGPT does not do that.

And an organization that wants to make that scale leap.

That’s a big cliff to jump.

It’s not there’s no gradual progression.

When I look at, you know, the things that are on this list that I know machines can do today, and outperform humans.

Back estrus, the gotcha is you need to have someone who has experience with technology to implement it, because the the pieces of software that exist, are like engines, they’re really good.

They’re really powerful.

They’re amazing.

But no one drives a car was sitting on four wheels, right? There’s seats, and a steering wheel at a cabin and air conditioning and the radio, none of that exists in an engine.

So we have the engines for all these things today.

For example, personal care therapy, there’s a model called Beyond er that just came out last week.

That is incredible.

It is so powerful, and it runs on your laptop.

It’s not easy to use.

Right? You have to install LM studio, I have to download the model, you have to download the correct quantized version installed.

Yeah, you’ve already lost me.

And so that will be the I think the gap? Where does the technology exist for all these things today? Yes.

Can I perform on all these things today? Mostly? Yes.

Can the average person but the average company unlock that value with it? No, that’s not

Katie Robbert 26:30

well think I mean, to to borrow a Chris Penn analogy.

You know, all the ingredients exist for a gourmet meal.

That doesn’t mean I can make it.

I could install, you know, a professional chef’s kitchen in my house, I could buy, you know, $1,000 worth of ingredients, I’m probably still going to be better off making a box of mac and cheese because that I know I can do, I’m probably going to screw up the rest of it.

Christopher Penn 26:57


I mean, even last week, when we did a live stream talking about deliverability for email, we walk through step by step here, you know, all live on screen, here’s how to do it.

Now, we still got increased accuracy, please come to this.

But I just showed you how to do it step by step, I gave you the recipe.

And you’re like, No, I’m making the mac and cheese, you can cook it.

Katie Robbert 27:17

because not everyone’s a technologist, not everyone’s a chef.

And there are certain things like technology or cooking that can be really intimidating to someone.

And so they would rather leave it to the experts.

And so one of the things we’ll be talking about over the next couple of weeks, is what does it look like to comprise an AI Taskforce? What does that mean? What kind of Who do you need? What does that look like for a company? What is the end game?

Christopher Penn 27:45

Yep, exactly.

And, yeah, I think the the, the hard part for the stuff will not be the technology will be interfacing the technology with people and getting people to be able to use the technology in a low bandwidth way, meaning that you don’t have to think terribly hard.

I think ChatGPT did a really good job of saying like, Hey, here’s a low bandwidth way to interact with a very complex system.

Katie Robbert 28:11

A really good example of this, not AI, but of the getting people to use the interface of technology.

I actually saw this Instagram reel this morning.

So if you’re a football fan, then you know that over the weekend, NBC made a very controversial decision to not have the wildcard game between it was the chiefs and maybe the Packers, I think I might be getting that wrong, please don’t come after me.

But I know the Chiefs played.

And so basically, they made the decision to not put it on cable to not put it on regular NBC, but to only stream it on Peacock, which is the NBC streaming service.


I saw this, you know, real this morning, and it was basically this couple who was probably about my age.

And this woman was talking to the phone basically begging peacock to stop doing this because an hour into the game, her husband was still on the phone with her parents trying to walk them through how to download the peacock app, and install it and sign up for it so that they can also watch the game.

And this is stuff that you know, when my husband said Oh, it’s on Peacock, I was able to log in and get him set up within 30 seconds because to me it’s second nature.

But it’s not to everybody and that is a very simplistic process.

This is not simplistic at all.

And I think you’re absolutely right that the getting the humans to be able to interface with it is going to be the hardest part.


Christopher Penn 29:43

funny you use that example because that’s how I feel when I when I talk to you about this stuff like well just to sell a home studio, you know and then find the model Hugging Face download, download it make sure it’s the Q five quantization and make sure you set the models load memory and use the metal API If it’s easy, those seconds 123.

But that makes the point very well that the unlocking the potential of your workforce with AI is less about the technology and more about the people in the process.

And if it’s something that you want some help with, that’s something we do.

Katie Robbert 30:17


And we do it very well.

Christopher Penn 30:20

With with AI.

But no, in all seriousness, the potential unlocking the potential the workforce in AI is as much about people and processes is technology, the technology exists today, there are so many things that you should have in your r&d capabilities to just people trying things out.

And I think having a culture of experimentation, we allocate time and resources to experimentation will be critical for maintaining a competitive edge with AI to to be willing to say, Yeah, you know, employee, five hours of your work week should be just trying to do different things with AI to see if you can, and to see if there’s a use case for it.

If you have something like ChatGPT Yeah, you shouldn’t be spending five hours we can only see if I can make this part of my job easier with ChatGPT.

Maybe you can, maybe you can’t, but you should be trying.

Any final parting thoughts?

Katie Robbert 31:14

Just you know, keep asking questions.

Don’t be afraid of it and focus less on will it take my job and focus more on? What else can I do?

Christopher Penn 31:25


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Trust Insights ( is one of the world's leading management consulting firms in artificial intelligence/AI, especially in the use of generative AI and AI in marketing. Trust Insights provides custom AI consultation, training, education, implementation, and deployment of classical regression AI, classification AI, and generative AI, especially large language models such as ChatGPT's GPT-4-omni, Google Gemini, and Anthropic Claude. Trust Insights provides analytics consulting, data science consulting, and AI consulting.

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