In-Ear Insights: 2023 Year in Review

In this week’s In-Ear Insights, Katie and Chris look at 2023 in the rearview mirror in their 2023 year in review. What a year, from GA4 to Generative AI – what did we learn? What’s changed, what stays the same, and what can we look forward to in 2024? Tune in to find out!

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Machine-Generated Transcript

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Christopher Penn 0:00

In this week’s In-Ear Insights, In-Ear Insights holiday special as we round out the last show of 2023.

And as we coast into 20/24, okay, it’s been a heck of a year.

What are the things that for you really stood out about the absolutely zany in this? That was 2023? We definitely

Katie Robbert 0:21

didn’t end where we started, for sure.

Well, you know, it’s funny because where we were, you know, so we can sort of go from a different few different layers.

In terms of Trust Insights, we started the year strong, doubling down on everything, Google Analytics 4, because Google Analytics 4 was the thing we were all talking about.

Everybody was nervous about it.

With Google had set these arbitrary, you know, deadlines and all kinds of stuff for like moving the data.

Now the data is not going to be available.

Now you have to use this new system.

So that’s we were all in on Google Analytics 4.

Fast forward 12 months later, and I don’t remember the last time we’ve talked about Google Analytics 4, with a client or just in a conversation, it’s been all generative AI.

Now here’s the interesting thing.

Generative AI is not new, but it’s new, in a mainstream way.

And like somewhere along the line, I would say maybe, end of q1 beginning of q2, people are like, well forget Google Analytics 4, what is this generative AI thing I need to worry about.

And now that is all consuming.

And that is all we talk about to the point where we’ve even pivoted the services in our own business to keep up with what people care about.

So sure, we still do Google Analytics, 4 audits, but primarily what we’re doing is educating on generative AI.

So that’s a big part of it.

The the industry that we’re in, mostly B2B marketing and other industries are completely just overwhelmed by everything generative AI, so it’s it’s been interesting.

It’s different from things like NF T’s and Bitcoins, which were basically like passing fads.

This will not be a passing fad.

This is all consuming because the major technology players like Google, like Microsoft, like IBM, are all in on this tech.

And that, to me is a good way to know like, Okay, I probably should not ignore this thing.

But I can still continue to ignore NF Ts.

Christopher Penn 2:30

That’s true.

Yeah, it’s been a crazy year.

So if we look at for example, the macro economic data this year is the third year in a row of record corporate profits, right.

So companies have more money and earning more money than ever before, to the tune of at least in the USA, an extra trillion dollars after taxes, which then makes all the people saying we don’t have budget for x&y, really confused from a marketing perspective, hiring has been a hot mess this year, the number of open positions fell mid year, two below pre pandemic levels by about 25%.

So companies are hiring much, much less, even though they’re making more money.

And when we look at, like you said, the landscape of what people are talking about generative AI is certainly is the technological top topic.

But the big picture perspective on that window, and we’re seeing a lot of really, really bad short term decisions on this is sort of what the future of work looks like, as people adopts generative AI, but like we can get you can replace our entire marketing team with this.

Like it’s a word prediction machine.

It’s not what it’s good at.

So you’re right, we’ve pivoted a lot of our services towards educating people just trying to get people to even understand what this stuff is back.

We just released a new course about a week ago now go to trust insights.ai/ai course, if you want to see what that’s all about.

But this, this conversation about what work means and how we do it is one that I think, certainly we didn’t have any answers this year, we’re probably not going to have an answer as next year.

And the technology continues to change so fast that I’m not sure we’re going to have any solid answers for quite some time.

Just two weeks ago, the French company Mistral released its new mixture of experts model that is like having a miniature committee inside of a language model.

So they there’s like eight little people inside this thing arguing with each other all the time as it produces results, which apparently is generating state of the art results on a thing that you can run on your laptop, which 12 months ago was impossible, did not exist, conceptually, not even a real thing.

And so that’s kind of the weird, the weirdness of 2023 is so much technological change has happened so fast, that certainly individuals have struggled to keep up.

Businesses have definitely struggle to keep up And governments have are way behind.

So there’s there’s this like, bizarre leading edge of technology, no one other than the technologist is keeping up with and that’s probably not a long term healthy thing.

Katie Robbert 5:11

Well, and you know, it’s, you keep saying this word future and, you know, predictions, and we don’t know that we don’t.

And I think that that’s the problem that a lot of companies are gonna get into is that they’re not managing for what’s happening right now what’s right in front of them.

They’re too focused on where do we want to be in five years? That’s a great question.

But, you know, if, if you asked us when we started where we want to be in five years, the answer we gave is not our reality.

And so it’s not a good, it’s not a good idea to only focus on questions like that, and to only manage for things that haven’t happened yet.

That’s where we want to make sure that we are advising and educating on what’s happening right now.

Because what’s happening right now, is that people are scared, people are fearful, they’re unsure.

And companies to your point are making very fast and loose short term decisions that aren’t going to pan out for them in the long run.

You know, and so that sort of missing in the long run is contradictory to the point I’m trying to make.

But that’s exactly what it is, is.

They’re not managing for what’s happening today.

And today, they need to be educating their teams on the existing technology of generative AI.

Rather than focusing on well, some point AI is just going to take it all over and it’s, you know, going to be sentient, it’s going to be all the we don’t, it might, but it might not apply to our businesses, it might just be for entertainment, or it might just only be attainable by companies like Google, it may not apply to smaller or midsize companies.

So for them to be focusing on that and freaking out about that is such a poor use of time, which means that they’re not then spending that time getting educated on basic prompt engineering, which is always that’s an evergreen skill set.

That’s basic business requirements.

You know, regardless of how the technology changes, you need to know how to do that, it might look a little different, the structure might change the way you input, the data might change.

But knowing how to write a prompt is basic business requirements.

That’s the stuff you need to be focusing on all of those foundational things.

Yep.

Christopher Penn 7:38

And looking at your current roster of platforms of technology to see how the prompt based interface to computing is being introduced.

So if you are a graphic designer, hey, the Adobe Creative Suite, there’s a prompt out in parts of Photoshop, there’s a prompt in Illustrator, there’s going to be a prompt in Premiere very soon, there’s going to be you know, if you use Microsoft Office, and you’ve subscribed to Office co pilot, there’s a prompt now in Word and Excel and outlook and PowerPoint.

If you’re in Google workspace, and you have duet enabled as a prompt, Allen, Google Docs and Gmail, and so on, so forth.

So the the adaptation of using natural language to control software is a big deal that, to your point, no one’s paying attention to right now.

It’s happening right now.

It’s not the future.

This is today.

And as these technologies start weaving into everything that we do, it’s going to be harder and harder for companies to sensibly think about what to do with as the technology arrives, it’s kind of like Google Analytics, right? It’s there, whether you want it or not, except that in this case, general AI is sticking into every software package.

It’s not just your website analytics, it is literally everywhere.

And you know, there’s a piece, we talked about last week’s live stream about how something like 70% of employees are trying the technology with or without their company’s consent.

And so it’s it that’s now that’s that’s not the future.

That is that it actually is last week, getting a handle on that sooner rather than later is really important.

Katie Robbert 9:14

Well, and if you think about what prompt engineering is, it’s just expectation setting.

And so expectation setting is also not a new thing.

We have been trying to set expectations with our team members, for as long as teams have existed.

And so that’s probably basically since the dawn of time, hey, I need you to get this bucket of food into the cave before it rains.

Why? So the food doesn’t get wet, and we can eat it and we can survive that setting an expectation.

So that’s as old as man.

And it’s funny when I talk with other teams and other companies.

The biggest challenge I always hear is, you know, I struggle with delegation and Don’t know how to delegate, you know, I don’t trust anyone besides myself to do this thing.

Using tools like generative AI are no different, it’s just a matter of, you’re not going to get the snotty tantrum pushback that you would from a team member, the machine just gonna say, Well, this is what you asked for, period.

And you’re gonna say it either is or isn’t what I want.

And for people not to recognize that it’s the exact same kind of interaction is mind boggling to me.

So, you know, when we’re talking about the year and review, I’m looking at it like, well, who cares what the technology is, the problem is still the same.

You don’t know how to delegate, you don’t know how to set expectations, whether it’s a person or a machine, that’s not a new problem.

So focus on that if you can fix that problem.

If you can scale up there, it doesn’t matter what the technology is, you will be able to work with it.

Because you will know how to say, this is what I need.

This is why this is how I want you to do it.

And this is how I want you to deliver it.

It sounds overly simplistic.

But it is so hard for some people to really put those pieces together in a coherent way that they’re able to get what they need back.

Christopher Penn 11:22

And a big part is because a lot of the time so this is stuff that you know, and we’ve talked about this in the past, this is essentially, in a lot of ways, it’s requirements gathering.

And it’s something that a lot of people don’t have a lot of experience with, or in people like me, like who tried to avoid it for a really long time.

And it turns out that that also is not a recipe for success when when you using language models, though, using generative AI, haven’t all that written out is not just a good idea from our best practices, it’s actually the literal stuff that you feed to the model.

This is an example in part of the 12 days of data, I’ve been using the Python script that ChatGPT Use GPT-4 model helps, right.

And the first thing it it now proactively does is it says let’s do some requirements gathering.

And so we do that, and then I have summarized the requirements.

And then that actually becomes part not only of the software, but the prompt itself to say these are the requirements over and over and over again to keep it aligned.

Just like if we were working with people, we’d have to remind people like, hey, that new feature idea you had is cool, but this is what we’re doing.

Katie Robbert 12:28

Right.

And it really comes down to, you know, so to your point, you know, people just want to skip over it, it comes down to it’s a little bit more work, you have to have some patience, you might not get it right the first time.

And that’s why a lot of people don’t do it.

You know how many times Chris, have you both heard and said, Just do the thing.

When someone says I don’t understand what it is you’re asking you just just do it.

Because you just want the thing done.

So like, and that’s where that, you know, frustration and friction comes from of well, then I’ll just do it myself, I don’t have time to explain it.

You’re not being given the choice this time.

You have to learn how to explain it.

Christopher Penn 13:07

It’s really interesting, because it has actually changed how I interact with humans.

You know, we had this past fall, we had we had an intern auto team temporarily.

And I found myself speaking to the human intern, exactly like I speak to language models, like writing out literal prompts.

This is this is what I want this thing to be, here are the dimensions.

here’s this, here’s that and I realized that that that in reverse actually worked better for me is to say like, you know, treat humans like machines and explain things out write out the requirements be detailed set expectations, it’s just that I had to learn it in reverse from machine to human as opposed to what most people do, which is learn it first with humans and then apply it to machines.

Katie Robbert 13:53

Which is what you know, to be fair, this is why we never put you in charge of people.

Because and that’s not you know, it’s not a knock at you.

It’s just not your, you know, area of expertise.

It’s not your comfort level.

No, I was laughing because, you know, I think we’ve told this anecdote before but we were at a client meeting in California and I was sitting at the table across from you.

And you started talking to me as if you were dictating to your phone.

And I was like, Oh, hello, I’m a human.

You don’t have to tell me to you know, add a period and then you know, a page break and whatever it was that you were saying to me and I was like you know you’re not dictating to machine you’re actually talking to appear but okay.

But what it what is the point being is that so when we think about the year and review, the technology has changed, but the problems have not.

And I think we will be able to say that every year because the problem is with people and that is what we are working hard with our five P process to try to fix So even a little bit, and give people the tools and we, you know, we go through the five P’s, we have the purpose, people process platform performance, it from people on the outside, it probably sounds like just another framework, but you’re never going to be able to master all of these new technologies without the foundational skills.

And you know, we teach this to our clients, we teach the seminar course we teach us in our workshops.

You know, it’s interesting, when you really break it down, we don’t spend as much time on the button pushing, as we do on how to actually think about pushing the buttons, why the

Christopher Penn 15:39

button pushing is pretty easy.

And the thing about the evolution and particularly in AI, the evolution of the tools is that the tools make button pushing easier and easier with every iteration, right? When you when you go from feed forward networks to mixture of experts networks, they’re smarter, they can infer more about what you mean.

So they don’t require as much, you know, convoluted button pushing.

And so the performance you get really is based on the people and process side where the platform is trying to reduce that friction for you.

And that’s a trend we’re going to see well into 2024, as more as more model makers adopt these more hybrid models, these technological changes that make using them easier, that will, that will create uneven results, right for companies that have the purpose the people the process of performance nailed down.

As the platform’s improved, their results will exponentially increase.

For companies where purpose people process and performance is not buttoned up.

The models will only offer incremental growth and not exponential growth, because you’ll still be bogged down by the same things that have been the problem all along.

Katie Robbert 16:51

Right.

And so if we transition from urine review to predictions for next year, I predict that companies will still be focused on platform first, which is absolutely the wrong way to think about it.

Because to your point, the button pushing is getting easier.

But that doesn’t mean that you’re doing it correctly, it’s just easier to find the button to push, you know, they’re making it, you know, bigger and brighter, and there’s less buttons, but it means that you have unless you are doing purpose people processing performance, in addition to the button pushing of the platform, you don’t know what you’re gonna get.

And it’s going to be a big old expensive waste of time because these technologies, whereas right now, they’re somewhat affordable, if you take them piece by piece to integrate a full artificial intelligence system into your enterprise size company is not an inexpensive undertaking.

Christopher Penn 17:49

It’s not and we saw that this year, we saw this past year, a lot of companies trying to figure out like, hey, there’s a new appliance, how do we fit into the what we’re already doing in the kitchen? Some companies again, they have great processes and adaptable people.

Easy to do that other companies are like, Hey, Joe axon, just put his head in the blender, I believe it did not go well.

But that’s again, that’s something that is likely to continue into the new year.

I think the last thing I would say for looking at the year in view, the crazier that was is that people are not spending enough time looking at what we call orders of effects, right.

So orders effects are the ripples that come from a change.

So general AI is a zero word effect, hey, there’s nothing ChatGPT Boom, drop it in the water.

First order effect is things like okay, well, I can write blog posts faster now, or, and so on and so forth.

second order effect is something like a company manager saying, Well, if we can write 10x more blog posts, we don’t need 10x.

But we can reduce the number of people on our team, bye bye this way, that would be an example of a second order effect.

That’s as far as people have gotten this year, there are going to be third and fourth and fifth order effects a one and companies are not thinking about this, but society is not thinking about this.

The third order effect is structural unemployment, right? That is a third order effect where you have okay, if you were if you were making rash decisions, and you’re just firing lots of people because you can because you think AI is going to do it all.

And this happens at a societal level.

You have structural blunt, the fourth order effects of that is civil unrest because you can’t have 40% of your workforce suddenly idled and not have, you know, like guillotines.

That happened at like a three to 5% structural employment change back in the late 1700s.

So part of what people should be thinking about looking at this your interview and looking at your head is what are the consequences, the chain of events that are going to happen from the decisions you are making today? If you’re making decisions that are just, hey, I need to save a few bucks on this quarter’s numbers to hit you know, what are investors expect? What are the longer term impacts of that going to be? And how are you planning for that? How is your business planning for that? We this past year, we saw a lot of changes in what people were asking Trust Insights to do for them.

And like you said, at the very beginning, for the first half, it was all Google Analytics, then poufs, like a light switch, people just stopped, right? Yeah, that was just went away.

What are the ordered effects from that? And so I would encourage people as you’re, as you’re enjoying the eggnog just to think about the consequences of the decisions we make, and to spend some time to not rush into them, particularly around eighth is technological, because the technological stuff, it’s easy to change the platform, it’s much less easy to change, things you’ve done with processes and people.

Katie Robbert 20:50

And I think that that’s really the takeaway is, don’t panic, don’t rush into things, you know, take a minute to really think through, okay, what is it that I actually need to do? What is the problem I really want to solve.

And that’s where the five P’s come in handy, because they’re designed to not only gather the information you’re looking for, but to give you just a second to breathe, and think through do I have all the information I need before making big decisions.

And so if you want to learn more about the five P’s, you can reach out to us trust insights.ai/contact.

We teach the five P’s in our new course, which is trust insights.ai/ai course or you can join us in our free slack group.

Trust insights.ai/analytics.

For marketers, where we talk about it a lot.

Christopher Penn 21:42

We do we talk about it a lot.

And it’s a great place to just stay in touch with what’s happening with marketing in general, lots of lots of other changes this year that are less notable, although you will see them in our 12 days of data.

As of the day we’re recording this, we’re putting up the posts on alternatives to Twitter based on the year that was the thing.

So that will be by the time you see this that will be out and available as well.

So as Katie said, if you want to talk about this or just share your holiday plans go to trust insights.ai/analytics for marketers where you win over 3000 other marketers are asking and answering each other’s questions every single day.

And wherever it is you watch or listen to the show.

If there’s a platform you’d rather have it on instead, go to trust insights.ai/ti podcast we can find us on most major platforms.

And while you’re on your platform of choice, please leave us a rating or a review it does help to share the show.

We wish you a happy holiday season a happy, healthy and prosperous new year in 2024.

We will be back in July I believe January 3 will be our first podcast episode of the new year.

And that will probably be likely talking about what we think will be ahead in 2024.

And how ideally we can work together to make your that your 2020 for the best it can be thanks for tuning in.

I will talk to you next time.

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