Participants
Hosted by Isaac Sacolick, CEO of StarCIO
Special Guests
Digital Trailblazers
Summary
The episode focused on the impact of AI on Level 1 expertise and entry-level roles in organizations. Isaac, the host, shared statistics on declining employment for early career workers and the need for companies to adapt their hiring and training practices. Participants discussed how AI is being used to automate tasks traditionally performed by entry-level employees, raising concerns about the loss of critical skills and knowledge. The group explored strategies for companies to retain and develop Level 1 expertise, including retraining programs, apprenticeships, and a shift in how roles are defined in an AI-driven world. They emphasized the importance of critical thinking, context awareness, and decision-making skills in the new AI landscape. The conversation also touched on the need for organizations to adapt their change management practices and redefine roles to maximize the benefits of AI while minimizing disruptions to the workforce.
See the blog post on 4 Ways to Boost Entry-Level Talent in the Gen AI Era
StarCIO Research
Sources
- Constellation Research – A Stanford study using ADP payroll data finds a 13% relative decline in employment for early‑career workers
- CNBC – Postings for entry-level jobs in the U.S. overall have declined about 35% since January 2023
- Institution Labs – AI’s impact on graduate jobs
- ComputerWorld – 77% of early-career and 67% of tenured workers believe AI raises expectations for entry-level roles
- Gloat – Gartner’s future of work analysis confirms that 39% of the workforce is expected to experience significant disruption in the next two to five years.
Transcript
[00:00:02] Speaker A: Hello everyone. Welcome to this 157th episode of the Coffee with Digital Trailblazers. We meet here every week on Friday at 11:00am Eastern Time to talk about areas that digital transformation leaders are facing in technology and leadership and practices as they guide their organization. So through all the changes, whether it’s AI, whether it’s quantum computing and everything in between, today we’re talking about a very practical conversation around the talent crunch and developing level one expertise in the Gen AI era. Knowing that so many organizations are looking for productivity improvements, looking for ways to drive efficiencies using AI capabilities, deploying AI agents and getting even into some agentic AI capabilities.
The groups in our companies, the employees that are most impacted by these changes are our Level one employees. Those are on the front lines in customer support and IT support and information security, handling the socks and IT and the network operation centers in marketing and handling content and SEO and everything that AI can actually drive a lot of value in, but also is impacting the people’s jobs here. So what I’m sharing here today with you is just some of the latest statistics that I grabbed from the Internet around the impact on Level one expertise.
First one is coming from Constellation Research. It’s quoting a study based on ADP data. 13% decline in employment for early career workers. You can see that in the charts on the right hand side postings for entry level jobs reported by CNBC. About 35% decline since January 2023.
A really good article from Institution Labs had just a good number of data points in there. 66% of enterprises are reducing entry level hiring.
According to the Institute of Student employers in the UK, there’s been three expect they’re expecting a 53% drop in graduate hiring in 2026.
And in the US this was actually covered in the Wall Street Journal.
They broke out the unemployment numbers here in the US and age 20 to 24 that number rose to a high of 9.5% in September 25th.
So that’s really scary numbers to make the numbers worse.
Even as you look at that from the bottom up as somebody who is a Level one person coming out of school, the expectations of the skills that you have to be able to get a job in this climate has also increased. This is coming from Deloitte and reported by Computer World. 77% of early career and 67% of tenured workers believe AI raises expectations for entry level roles. And gartner is focusing. 39% of the workforce is expected to experience significant disruption in the next two to five years. And what that means is in addition to all the entry level folks to 22 to 30 year old folks, there’s going to be a backlog of people who are laid off or are have to retool themselves or move from one part of a country to another to get a job. And that’s going to create competition for everybody getting jobs over the next two to five years. It’s a, it’s a real mess.
And Joe will remember this.
We were at a CXO Spark conversation I think. Was it December, Joe? I think it was roughly around that time. And talking about this impact of the level one employees that are going to be most impacted.
We came to the conclusion at that meeting that a lot of companies are simply not going to care about this in the short term, that they need the cost savings to offset all the investments they are making in a AI. But this is going to have some mid and longer term impacts if we don’t hire the people in critical roles in entry level positions today. They don’t have the expertise that’s required for them to gain more leadership and managerial roles going into the future. And there’s a lot of CIOs that are worried about this. So to kick off our conversation today, let’s, let’s just go around the room. I’m going to start with Joe.
I think Kevin, who is our special guest has, has made it onto the floor, but we’ll start with Joe. And you know, I just ran off a whole bunch of numbers and they all add up to the same thing. At a gross level it’s going to be harder to get level one expertise in companies. I just, you know, of those different metrics that I shared, which are the ones that are scaring you the most?
[00:05:22] Speaker B: Well, I think you alluded to the Spock conference in New York City where we heard that, you know, that trend toward allegedly cost cutting in taking credit for AI having substituted for those entry level positions. And so we’re saving money and we’re smarter now. We’re going to make more money and everybody’s going to be happy. And, and it’s a fool’s paradise because of the reasons that you, you’ve cited.
There is no, no training going on at the lower level.
I’ve always bemoaned the fact that I, I feel like people, people don’t have the skills already to figure things out.
And, and you know, our education system is failing now. The entry level positions and companies aren’t there.
This is a disaster waiting to happen. I think the long Term prospects are sad.
[00:06:24] Speaker A: You know, there’s an article, I think it’s in the Times, New York Times, just today or yesterday talking about how schools are now partnering with Microsoft and partnering with OpenAI about bringing ChatGPT and Copilot into the classroom in a more proactive way.
I don’t know. I don’t. Do you see that as a way of closing the gap or do we have a lot of room to.
[00:06:51] Speaker B: I think there are two issues. One is critical thinking, which has been lacking for years in my view and secondly, they’re addressing the issue of the need for skill sets in either building or using AI.
They’re not addressing the issue of understanding how business functions, how things are done in my company, how to work with other people and learn from your mentors or from your management. How things are done.
That’s all vaporizing. And boy, I think a few years down the road we’re going to really feel the effect of that.
[00:07:31] Speaker A: Oh boy.
Kevin, can you just say hello to the group and maybe weigh in on some of the things and the impacts that without level one expertise, some of the things that were you.
Kevin, you’re on mute right now.
Let’s jump to Martin. Martin, you’re raising your hand. Go for it.
[00:07:57] Speaker C: Well, I think that there’s a couple of things I posted, kind of a slightly tongue in cheek old joke in the, in the comments.
Yeah. What is the most common phrase uttered by recent graduates? Would you like fries with that? And yeah it from previous rounds of this type of thing happening that kind of. That joke was going around and unfortunately it’s fairly true and it’s quite sad really. And I’m kind of.
It does concern me. I think what we are seeing is a lot of knee jerk reaction to companies saying they’re going to save a load of money from using AI and therefore in order to satisfy the street they are cutting back on things like new hiring, other things like that.
Even though AI may not be making those savings, they are having to kind of basically for the shareholders benefit, they’re having to actually demonstrate that they are reducing headcount by, by using AI. So I think you’ve got a kind of a false, a false positive, if you, if you want to put it that way from doing that. And I think that’s kind of very concerning. I agree with, with what others have been saying, what Joe was saying, etc, that this is very, very concerning because if you’re not building the basic skills then how do you get the more advanced skills? And there’s. Yeah, we Talk about the kind of silver tsunami of a lot of people kind of waiting to retire. And if you haven’t got the skills coming through in order to actually replace those things, then you’ve got problems. And how much of that can I actually do?
And I suppose the other kind of thing I’d throw in there is I was talking to somewhere in higher ed at one point about a couple of years ago and they said to me, yeah, this was talking to it undergrads and things like this. And I said, what languages should the undergrads be learning? And I said, well, by the time they graduate and get into industry, whatever languages they need, what it was going to change.
So what you really need is critical thinking, the ability to understand business value, the understand to logically think through problems and structure responses and answers. And those types of skills can be applied to any situation, any programming language, AI or anything else. You need those core skills. And if you, if you understand the concepts of programming, you understand the concepts of logic and things like that, you can apply that in so many different situations.
So I think it’s all of those pieces coming together to say if you, if you’re missing some of those kind of key chunks, then things start to fall apart.
[00:10:51] Speaker A: Mark, we’re going to go deeper around that. I want to hear from maybe like Derek and John. We talk about technical skill sets and you know, we’ve always, every generation of new technologies allows us to go upstack a little bit.
And so, you know, we don’t learn tcpip, we don’t learn, you know, assembly program for the most part anymore. And Fortran has fallen off the wagon. But I have a hard time believing that our next generation of software engineers never learn how to, I don’t know, never learn distributed computing, never learn best practices around securing an application, never learn about object oriented programming and go straight into vibe coding and using English and just hoping that what the machine spits out is what our application is actually going to do. Derek, I just want you to hold off. I think Kevin is with us right now. Off mute I want to. Kevin is our special guest today. So before we jump into technical skills, Kevin, just say hello and your thoughts around the impact on entry level roles and hiring from AI.
[00:12:01] Speaker D: Yes. Hello everybody. Welcome.
Well, I’m glad to be here and greetings from a rather wet, cold London, but I’m sure it’s the same over there.
I think what has been said so far is absolutely on the money.
There is a disconnect between what companies are doing and what they should be doing, they are looking, as has been said, to try and save money. And rather than augmenting the Level 1 resources and giving them the tools to do their job more efficiently, they’re seeking to replace them with AI, which is a very short term plan, as Joe mentioned.
And we’re seeing what has been referred to as a broken rung in the ladder. So in the ladder that people would normally climb up on their journey through the company, there are rungs missing because they’re not getting the grounding, they’re not putting in that thousand hours of, of learning on the job to actually understand how things work so that they, they can become subject matter experts. And if the knowledge is only within AI and not within their brains, then they will not be able to proceed. So it may save costs in the short term, but in, in the five year time frame when those guys have moved up the chain, they won’t have the knowledge and the organization will suffer accordingly. So I agree with everything that’s been said and it’s a very worrying situation, whereas it could be a cause for great optimism because we can be increasing the efficiency of these people considerably. But by doing it in the way they’re doing, it’s having the opposite effect.
[00:13:43] Speaker A: Yeah, we’re going to get into some of these details about some of the roles that we think are, you know, we need to have at that level, one entry level area and some of the skills. And that’s where I’m jumping right to Derek. We’ll go to Joanne and John after that. Derek, you know, you’re a ciso, you know, are you getting rid of the society? Are, you know, what are some of the skills that you think are just critical for, you know, beyond just critical thinking, beyond the being able to do analytics and evaluate the AI. Let’s get into, you know, what should somebody who wants to get into security really home in on to be employable over the next few years?
[00:14:23] Speaker E: Yeah, I mean that’s a great question. And as far as getting rid of the SoC, the socks not going away, but the SOC will become more efficient. So for those socks that may have had 12 analysts, you know, analyzing different threads and things coming through that may be cut from a 12 now to maybe 3 or 4 where they use an artificial intelligence now to do the threat intelligence monitoring and seek out those anomalies that they need to pay attention to. And those four remain will be humans in the loop. The statistics that you mentioned earlier about the automation of these routine things that are taking place, you know, it’s staggering. You know, the people that are in college now coming out of college and trying to figure out what kind of job opportunity am I going to have. This is scary stuff. When you’re looking at, you know, a lot of the companies now, when they see these tasks like the soccer, they look into other things such as the IT help desk, a lot of these triage endpoint hygiene, the user education and training, they’re now automating these processes now because they realize they can use an agentic AI and these bots to actually allow them to replace what might have been a human in the loop to do so. And they, once they train up the system, it can become more efficient. So when you’re looking at these particular numbers and the jobs that are available, yeah, it’s, it’s crazy. I mean, you know, just the one of the things I was reading the other day about the ISC Square and some of the World Economic Forum, they’re talking about 39% of the jobs between now and 2030 will shift due to core skill changes. Which means now those people that are coming out of school, they need to understand, as Joe said, not just the business piece, but they also need to understand what are the skills that I need now that are really going to be adaptable to what I need to do to get a job. Because if the jobs are being taken over by generative AI and the AI bots and stuff, they need to redesign and think how they get involved in the workforce, what kind of things based on governance and, and some of the things you mentioned earlier, Isaac, about, you know, understanding networking services, some of the basic applications to an extent, because you still need the AI to run on these tools. They, they have to run on some sort of system and database. So you still have a small subset of people that need to do that, but eventually that will go away. But at the bottom line is, you know, these leaders and looking at these jobs that were coming in for entry level or internships, they are going to be reshifted, they’re going to be rebranded, they’re going to be removed or upgraded to a different skill set requirement that you need to have coming in the door. And that’s something that’s going to take time to mature.
[00:16:46] Speaker A: Thanks, Derek. Joanne, your thoughts not just on it, but maybe even getting into operations.
How do we think about what roles are most important to preserve at entry level so we don’t lose the knowledge and we build up our next generation of workforce?
[00:17:03] Speaker F: Well, I think that there’s two things. One is a semi contrary contrarian point of view to what’s been said. The early adopter companies, those that really jumped on the AI bandwagon at its earliest availability, have seen the light and they’re beginning to realize that they need to create specialized cohorts around process and specialized cohorts around what would be niches in the business that require in depth process knowledge to be able to promote people up the food chain. So they’re starting to look at how do we leverage critical thinking skills for sure, which are always a mandatory. But they’re coming at it from the point of view of I can teach people how to prompt, I can teach people how to write, you know, build knowledge graphs and build all the other tooling that’s required for AI or buy it because it’s becoming readily available.
It’s the process, the nuance, the, the stuff that used to be called on the job training about the business and how it operates. That’s what they’re focusing on training people for. And if I had a word to the wise of not only companies that are looking to lay people off, I would think long and hard before you lay those people off, that the tribal knowledge or institutional knowledge that’s going to go out the door with them is not only invaluable but extremely hard to replace. And from that perspective, if I was a new grad or about to graduate, I’d be looking at various operational issues, whether it’s on the plant floor or in manufacturing of some sort or, or just in the business aspects, true business processes of corporations and looking to develop skills in that area.
And that’s where I think they’re going to be far more employable in the near and midterm and then they’re going to get the rest once they land on their feet. In some companies, I think we’re also going to start to see people staying in jobs longer at the level one to gain that knowledge, to be able to then kind of leapfrog up the stack to go from a level one to let’s say a first level manager or maybe even higher because they will acquire the business knowledge that they need and companies will begin to change their tune and value those, those skills more than the actual how many, you know, how many ways can I write a prompt and how many ways can I build an agent?
Because that will be automated.
[00:19:49] Speaker A: Joanne, I want you to think about your action plan around this because I mean, you advise CIOs and CIOs. We’re going to get to that after our midterm Break. But I do want to hear from John and who else is here. We haven’t heard from Liz yet. Your thoughts on, you know, just some of the roles, some of the areas that SEA leaders should be concerned that without entry level expertise it will impact building the next generation of subject matter experts and other hands on knowledgeable leaders. What are you focused on, John?
[00:20:23] Speaker G: Well, I see AI is something that’s being used by everyone. Anyone that has access to the Internet is using AI. Anybody that’s doing Google searches right now, they’re getting zero click results back that have AI in it. And so I think it’s just our whole society, people in every country right now are using AI and what we really need to do is get people proficient so that they’re able to really use AI.
And I think what we see is that almost everywhere it’s able to bring the people at the lowest level up a level. But I think what’s really important when you’re building applications or you’re doing marketing or anything else in the business or operations or security is you have really, really highly skilled people. And so that, that really takes time to build. And so what we need to do is make sure that we’re always having people that are, you know, that at the lower levels are benefiting from the AI, but we need to make sure that we have the people that are still the subject matter experts apart every, across every part of the company.
[00:21:25] Speaker A: And so John, John, let’s role play a little bit, right?
CIO is telling you you got to cut headcount by 10 to 15% in this software development group.
And you know, he wants to first know what skills you’re going to preserve so that you can continue being an excellent software development shop. What are some of the skills that come to mind that you’re going to say, you know what, I’m not going to lose my Level 1 experts in A, B and C. Yeah.
[00:21:57] Speaker G: And so what I would do in that situation is really understand what we need to run our current systems and understand like what do we need to run our current systems and then what do we need to run in the future. And based off that, understand what people are really important to retrain and what people are really important to keep in the company and build a plan off that. And what happens is unfortunately the programming languages of the year change every year. And so when you go to build an application in a couple years from now, you’re not going to be using the languages that you use right now.
The one common language is actually SQL that seems to be number two, language for people learn for the last 20 or 30 years. And so SQL is always one of the most important things for people to learn as a skill.
But after that I would really start being in a skills inventory, make sure we really understand what we need in our company to keep the current applications running and look to see which way are we going in the future and make sure we have people with those skills.
[00:22:51] Speaker A: I’m going to add so you’ve got knowledge in the platform. So if I’m running Salesforce or Workday or whatever I’m running, make sure we’re not losing that expertise. Number two, the basic building blocks that are, you know, you could say called it SQL, I think data skills in general. And I’m going to add a third one, John. I’m going to say my testing people.
Yeah, I have not met companies that invest enough in testing in general. And so if you lose your testing people, I don’t know how you upgrade your applications.
[00:23:22] Speaker G: Testing is going to be so much more valuable in the future because generative AI is able to write code. But when we’re plugging code that’s written into existing systems, we can’t change everything. We have to make sure that code works with what’s already there that’s been built over the last 30 years. That’s where the testing skills, where we are, we call them sset, system development, engineering and testing. And those are going to be some of the most important skills to make sure that with whatever is being built in by the AI and the younger people and new people and the new systems is playing nicely. And then the other thing is, is anybody that’s using an AI service in their application, what you get today may not be what you get tomorrow. They’re constantly changing those things. And so if you’re having a production system, you may be getting changes from your AI providers and you may not be, you know, be given a heads up on those things. And so just to keep the systems running, you know, you need good testing, monitoring.
[00:24:17] Speaker A: Thanks, John. Let’s go to Liz.
I really, I’m kind of curious where you’re going to take this conversation. You’re advising the cio, Same question.
You’re facing headcount reduction and you know, she or he asks you the question, what skills do we need to preserve?
[00:24:36] Speaker H: Okay, well, so first of all, if you think back initially when you talk about new hires, there was two kinds of tasks that you gave to new hires. One was grunt work and one was true apprenticeship work where they’re learning the business, learning how you think as a leader, you know where you’re growing them into the next level Grunt work is now AI. You offload grunt work to AI. I would say testing is something in the same list of brunt work.
I would not say testers are that important. Maybe testers to someone who can think about operational impact, who can think about the overall systems impact.
They need to understand how to set up the AI so it can do the testing, but they don’t need to do the testing.
So I would say that apprenticeship, you want to keep the people who you can see in the future, can grasp your role, can understand and grow into your role and who you can then double up in terms of leadership. Not necessarily people who just do a good job.
[00:25:53] Speaker A: Liz, you gave me my topic for a future one is QA grunt work or is it not? We will have that as a future episode and you and I will debate that. Let’s go to Joe and Derek. Joe, what skills are you hiring and preserving as a cio?
[00:26:12] Speaker B: I want to build on what Liz said and circle back to something we talked about a little bit earlier, if I may.
You know, Joanne is always saying human in the loop.
And that same Times article that you read, I believe, talked about the human in the lead.
And if you’re going to lead the use of AI, you have to understand it, as I think Derek has pointed out in the comments stream, and understanding it. Now to circle back to some earlier comments, it’s, it’s about understanding what’s under the hood. You know, I started thinking about an analogy here to the automobile. The automobile has become so smart and so sophisticated that most mechanics can’t, can’t fix it. Right? You have to be a, a, an automotive computing technician using all sorts of very sophisticated equipment in order to troubleshoot and fix a car.
But it doesn’t take away from the need to understand how the internal combustion engine works or how an electric car works. What are the fundamental elements of it.
I see an analogy here to business in general.
If you don’t come in at the bottom and understand the spark plugs fire, the pistons and the gas and the oxygen igniting is what causes the compression and turns a crankshaft and so on and so forth, how are you ever going to understand how a car works?
We need to get back to fundamentals. Bring back shop in high schools, bring back fundamental technology, understanding in higher learning, higher institutions.
And I think that will go a long way towards solving these problems. Sorry I didn’t answer your question, but I Had to get that off my chest.
[00:28:02] Speaker A: No, I mean there is an equivalent. I mean, you know, when you talk about how we recover from an incident, you know, Verizon had a major incident last week. The network was down for a whole bunch of different places and I still haven’t seen the RCA that. I don’t know if anybody knows around what it is, but you could think of, you know, we’ve all been through this. What happens when a system goes down and do you have enough monitoring to figured that out before it’s catastrophic? Do you have automation in place and most important, do you have observability in place so that you can really find root cause and address issues proactively?
And that’s a tremendous skill set. You just don’t walk into IT operations or anywhere else and know all the bits and bolts and how things are connected to be able to do that. We’re going to go to Derek. Derek, same question for you on the security side. I will take my break and then I want to go back to Kevin and Joanne. I have a question about the Industrial Revolution for them to be able to make some analogies for us before that. Derek, before our break, what are CISOs.
[00:29:13] Speaker E: Preserving so they really need to look at? There’s still a few things that they need to look at. The GRC portion of it, although some of that can be automated, there’s still some AI strategies that really require critical thinking to make that happen. You just can’t say, I’m going to plug it into a machine and have it spit out something to what I need. That’s not going to work. Well, the human in the loop is still going to be possible.
The privilege access. You still need somebody to double check if a machine is actually doing this. Double check to make sure it’s doing it properly. These are things that you can’t have been overlooked because the machine doesn’t know any better that it’s not the right thing. If you don’t have the people in there when it comes to marketing and other things and use a technology to look at the other business units, you’re still going to have people that need to evaluate the marketing paths that need to take place. All these different things require visibility and eyes, people, people in the loop to be part of the process. And then, you know, the operations thing and looking at the customer support, you see a lot of that being now being using artificial intelligence. But there’s some cases you just want to talk to a human to get a response quicker because you don’t want to have to go through the process that’s already been predefined in the LLM to try to get your answer. It still takes time. So there’s still things as people are still trying to figure it out, you know, and it makes it work. And I think Joe brought up a great point earlier, you know, and mentioned in the chat that this critical thinking is huge. I remember going to a movie theater a couple years ago and their cash register was down and I said, well, you can count it back as far as how much money. So well, I I need to figure that out. So you already see where people are using and relying too much on technology and artificial intelligence, where they’re not using their own brain to figure things out. And these are things that are going to be imperative. As you said before, you you know the cars nowadays that you plug them in, you figure it out. You need to understand what happens when it doesn’t work. And the example you mentioned with the Verizon is a perfect example. How do you get around it?
[00:31:03] Speaker A: Thank you, Derek Folks, welcome to this week’s Coffee with Digital Trailblazers. Our 157th episode today we’re talking about level one expertise in the generative AI era. We’re looking at it from the employer’s perspective.
What are we doing to make sure that we retain the talent at level one expertise, continue to hire that talent so that we don’t lose industry, business knowledge, subject matter expertise, and tribal knowledge. Another topic that we’ve covered here several times here at the Coffee with Digital Trailblazers, we meet every week here at 11am Eastern Time.
Just about every single week. And our episode next week will be on AI First User Experiences Planning for the evolution of Generative AI Enabled Customer journeys. I have not set a schedule for February, so if you have ideas for topics or you want to be speaker on this, do reach out to me on LinkedIn, send me a quick message and say I have an idea around this. Liz I don’t know if we’ll do QAs not grunt work or is it next month, but we will see and I’ll have all the announcements of February’s topics sometime in the middle of next week.
For those of you trying to find how to join this couple links that you can remember. Starcio.com Coffee will always redirect to the upcoming episode and if you want to watch or listen to previous episodes you can go to drive.starcio.com coffee that has a bunch of episodes there. I also publish a bunch on Spotify and Apple Podcasts and you can always visit LinkedIn to these URLs and find previous episodes if you want to listen to them. Lastly, we are doing a new push this year for people to join the Digital Trailblazer community. If you have not checked that out yet, please visit drive.starcio.com community. You get access to all the past episodes, you get access to experts and I’m adding some new capabilities this quarter.
So please consider joining our network of Digital Trailblazers. Back to our episode Today we’re talking about level one expertise in the Gen AI era. I want to bring back our special guest, Kevin Wallace. EAD Kevin, you and I had a very interesting conversation around the fourth Industrial Revolution and drawing analogies from there.
I want you to just share a snippet around that. We been down this rodeo before.
What are some of the things we can learn from the Industrial Revolution that say let’s not make those mistakes again this time around? AI Hello Kevin.
[00:33:52] Speaker D: Yeah, hi.
So absolutely right, we are clearly in the fourth Industrial Revolution. Now terminal, that’s been coined and used probably over, over overused by many people.
But it, it requires a complete change in mindset from the third Industrial Revolution in the same way as going from an agrarian society into the first Industrial Revolution required a change in mindset.
Except we’re doing it probably a hundred times faster. Things are 10 times faster and happening 10 times more quickly.
And I’m finding that a lot of companies are still working in a second Industrial Revolution mindset. So automation, standardization process and so on, which is all fine and well and good, but it doesn’t fit very well with the new way of working with AI. So companies have to go and I think rethink how they can implement AI. Because trying to implement AI into old fashioned processes which do still have their place by all means. But trying to implement AI into those older processes will maybe give you a 5 to 10, 15% maybe enhancement in productivity. Whereas if you invert the whole way of thinking and approach the the process that you need to complete from an outcome point of view and look at It from the AI’s perspective, you can see significantly larger gains and possibly, you know, more than 100% efficiency improvements. But it does require a considerable amount of thinking. Before you go and do that.
Just to use an analogy, what with what I’m seeing is companies will say, oh, look at this wonderful new AI and it’s like somebody going out and buying a whole bunch of very high performance racing cars, dropping the staff into them, not hiring professional drivers, sending them out on the new on the track saying, you know, isn’t this wonderful? And of course, all the stuff spin off into the gravel trap on the third or fourth corner because they haven’t been given the opportunity to learn how to drive those vehicles. And I think there is an assumption that everyone can use AI. Yes, everyone can use AI, but not to the levels that they need to, to be efficient.
And I think a lot of companies, not all of them, are just sort of kicking the tires at the moment.
We mentioned earlier some of the early adopters and they are starting to get it and they’re having these thinking processes, but a lot of companies aren’t in that. Aren’t in that phase yet. They’re bringing in a few tools, but they’re not thinking. The culture of the business and how it affects the culture, the people, and even the values of the company may need to change as well.
[00:36:47] Speaker A: I’m going to go straight to Joanne now. I lost connectivity while Derek was speaking. I think I’m back.
Joanne, do you want to continue?
[00:36:58] Speaker F: Sure.
I see it slightly differently. It is definitely a mind shift around AI. It was previously a mind shift around Industry 4, and there were a lot of epic failures in Industry 4 as companies tried to roll them out, because from a early adopter all the way through a laggard, we were seeing repeatedly that the workforce was not brought into the picture until far too late in the game. So you either had resistance from the workforce or you didn’t have enough planning. But where the real gap is and where a lot of companies are using AI now is what’s called the execution gap. And what they’re not realizing is that as part of the mindset shift, you really have to look at what is the AI doing. And it’s not about bots and it’s not about generative in the agentic world. It’s about how are you bringing things like judgment into the equation. What are you using for your baseline, for purpose in the AI? In other words, are you trying to solve a business problem or are you just trying to basically shortcut around some of the processes that seem to be out of sync or out of whack in some way? And really, it comes down to the company looking at things from the point of view of what KPI is being used to measure things? That’s number one. How are you institutionalizing that KPI through agents or through even generative to a more limited extent? And then how are you bringing in things around context? And context is not just what is happening.
Generally speaking, context is a very specific thing, it’s very purposeful and it gets you to judgment. Is the system going to make the same judgment that you as an executive would make if it’s running autonomously? The answer to that is probably going to be no. And how often are you actually going to trust an agent to do that kind of critical thinking for you? So those are things that the mindset shift has to bring into play and doing it in a way that’s either based in evidence, based in provenance, or based in lineage. Where did the data come from? How reliable is that data? How clean is that data and what is the context around that data? How is that bringing you to a better determination? Because the whole name of the game is not to replace the human, it’s to augment the human to make better business decisions faster, more safely, and in a way that is risk.
Not averse necessarily, but weighted trade off management will be the next big thing that we’ll see because it’s always been the killer for every system out there. And a lot of people believe that AI is the way to go to get that trade off management.
Only with human in the loop, however, does it work.
[00:40:14] Speaker A: Joanne, you have some really important words here for people to just, just keep at the back of their mind around context, around judgment. I’m going to add, and I put in the comments here around being able to recognize that the tea leaves are changing, right? Your, your objectives are changing and so the decisions that your AIs may have been programmed to need some remodeling around this. I, you know, I think about the early days of robo traders and then all of a sudden there’s a structural change in the market and they’re just flying off the cliff.
I think about the bounds of the early bots and what they were able to solve for and what they couldn’t solve for in a rules based oriented solution. Even in an agentic solution, they have limited context compared to human experience.
And that’s what we need to teach people, right, is how to develop that context, how to develop strong judgment skills and when to recognize when the world is changing and we have to make decisions differently than we’ve done in the past. Go ahead Joanne. I’ve got everybody’s hands raising here, so we’re going to hear.
[00:41:18] Speaker F: Sorry, I just want to be very quick.
It really comes down to how the critical thinking of the individuals is being applied through the AI. Now I’m not, you know, I don’t want to go on a long thing, but I posted about this earlier in the week and I will be posting about it more, more often because it really comes down to perspective and how you apply the perspective, which is the empathy, the expertise and the experience of the individual. And that is one of the key aspects that makes AI succeed where other areas may fail.
[00:41:58] Speaker A: Oh, I, I, I’m really loving this idea of perspectives. I, I just have to figure out how to coin the phrase, but I’m not going to go there now. I got Liz and John raising your hand. I’m looking for action plans. Go ahead, Liz.
Liz is on mute.
[00:42:18] Speaker H: Sorry about that.
Action plans for apprenticeships. I mean this judgment, context, perspective, these are all things that can only be learned with maturity.
So as we’re going forward, you want to the folks who have the ability to maintain context or grasp, grasp context or shift context, these will be the best people to, you know, bring up the food chain and actually create as your leaders. And the biggest indication of someone who can grasp these things are good listeners.
Listening and being able to really comprehend and shift context and shift perspective based on listening. Those are the people that you can, you want to target it to keep and grow.
[00:43:09] Speaker A: Thank you, Liz. Let’s go to John.
[00:43:12] Speaker G: Thank you. Yeah, I love hearing Joanne speak about this because she’s, you know, founded a startup that uses AI and it’s just like the way that she describes things is absolutely, you know, the best way to do things. Unfortunately, most companies that are trying to adopt AI, they don’t have much of a strategy on anything. And they have started adopting AI largely by the employees bringing and using AI into their daily activities, often through the commercially available ones. And so I think that the first thing any company wants to start using this stuff is that one is they have to figure out what kind of company they are. They have to figure out their core values.
They really need to create a strategy on how they’re going to use this new general purpose technology.
AI is a fundamental technology that’s like electricity, it’s like the Internet and it’s such a important thing that transforms everything. And so I think people, people really have to figure out like, where are they going to use this and what’s their strategy and where are they not going to use this stuff?
And then I think they just have to, they have to start doing the general change management activities. They have to actually have tools that are blessed that people can use. Because right now if people don’t have blessed tools, they’ll just use any tool that they have and who knows what’s happening to the data of the company. Right. And so I think those very, very fundamental things on how you roll out technology apply that same thing to AI. And that’s, I think, the very initial steps that people have to take.
And it’s really neat. And unfortunately, when I’m reading the news over the last couple days, I’ve been reading a ton of stuff about how people aren’t getting value from AI. And when I look at how they’re doing change management, I’m not surprised. I’ve seen so many computer systems rolled out without change management and get zero value.
And I mean, Martin has articles on this stuff, and so it’s no surprise that people aren’t getting value when they’re not doing change management.
[00:45:07] Speaker A: You want to comment on that, Martin?
[00:45:10] Speaker C: I referenced it before. Don’t finish your implementation.
A full adoption is where you’ve got to get to. And that’s the big gap.
Now, I was going to say, just to John’s point there, if you only have a hammer, everything looks like a nail.
So you need the right tools. And he talks about tools being blessed or whatever, but you need the right tools for the right jobs.
I, I was just going to say, I was going to go back almost the first principles, which is, at the end of the day, AI is just a tool.
And right through history, right through all the industrial revolutions, we have improved tools, improved capabilities, and the ones that win are the ones that work out how to use those tools most effectively.
So, yeah, the action plan has to be looking at, okay, what are we going to use the tools for and how we’re going to use it?
And then how do we approach using it? How do you make use of it? And that then comes into those skills we’ve been talking about the other day. I won’t repeat all the skills, yeah, critical thinking, logical thinking, all those types of things.
But you have to go to, this is a set of tools. How are we going to use those tools? What are our end goals that we’re trying to achieve?
[00:46:31] Speaker A: Joe, maybe comment on this a little bit. I was just trying to put the comment in here.
I agree, Martin, AI is just a tool.
But that’s not what our board and CEOs are hearing from big tech, from McKinsey’s, from, from a good number of very influential people that are basically saying, you know, 39% of the workforce is going to be changing over, be disrupted over the next two to five years. That’s coming from Gartner.
So, Joe, you know, how do we set a pragmatic tone around Our action plan. When there’s kind of two different extremes of what boards are hearing. AI is just a tool versus AI is going to change everything.
[00:47:17] Speaker B: Think one has to set expectations accordingly.
I think it was John who was talking about, you know, managing change and this, this is a big part of change management.
Managing the expectations and developing the understanding.
I love analogies and I would posit that AI is like the mechanization of the construction industry. You know, when all we had were shovels, it took 20 men to dig a ditch. And now I could go in there with one steam shovel in 10 minutes and dig the equivalent ditch.
But it, it means that I have to have a guy who understands how to pull the levers and make that machine do what we wanted to do and not go hog wild and, you know, rip up everything willy nilly.
It also doesn’t eliminate the need for somebody who understands how deep the ditch has to be, how wide it has to be, and why it has to be that way.
If you’re asking for an action plan, I’d also suggest that this is a two pronged problem. There’s a problem of what companies should be doing internally and that is retraining. Again, something you’ve heard me say numerous times, almost as much as communication. Right? Retrain the workforce. Turn the workforce into leaders of the AI technology.
Get them to understand how to manage the AI technology in the same way that they would manage people.
So it’s management training in a sense.
I’m not going to be managing a human, so don’t train me on how to speak to it politely and not get into all sorts of issues. But, but make sure that the task is defined well and clear and measurable and all those things that we teach in management structures. And then there’s the societal, which we could go on for hours, but our education system has to be changed and adapted to today’s requirements. Anyhow, sorry for the long winded response, but there you go.
[00:49:26] Speaker A: No, it’s great.
I agree with you about the retraining part and it’ll be very interesting to see how companies interpret that because, you know, when there’s cost cutting moves, training actually is one of the first budgets they end up cutting. So this is something that I’ve been, you know, talking about quite a bit. If you’re going to be retooling, you got to be investing in retraining. Go ahead, Derek.
[00:49:52] Speaker E: Yeah, I mean, Joe’s spot on and some of the other things that the others have said as well. But I think when you look at this, you know, the apprenticeship, the thing that Liz mentioned earlier, the AI augmented apprenticeship, getting people in there to pair, understand and go through the routines and understand what’s taking place. But I think the biggest thing we need to look at, you’ve got all these entry level positions asking for all these years of experience which is like it’s, you know, you put the cart before the horse type thing and that’s not realistic. We need to change the mindset, as Kevin mentioned earlier, and change it from years of experience based hiring to task and skills, outcomes based hiring. And by doing this, you’re going to really force people to look at what they need to do to become more knowledgeable, really understand what it takes to understand the AI system. But take that AI system and make it so it can be productive and make it tangible so it can create that return on investment within the workspace. And when you do that, are they going to have better outcomes? Because right now the way it’s set up with the way these AI agents are scanning these resumes and stuff that are out there, people are missing out because they’re not being able to comply with the way they’re looking at the requirements. Change it to skills based. Look at the things that are going to be important, that are going to be be essential for that business to move forward in an AI world. In AI ecosystem that’s going to be key. So things such as governments, that’s not going away, security, that’s not going away make it, those things are going to be tangible and compound over the years. They’re going to be escalating, that it continue to evolve, that people can actually work their way up into and not be considered obsolete before they get started.
[00:51:25] Speaker A: You know, Derek, you’re touching on another topic here.
I have a few people I know who’ve given me feedback about how companies are overloading on the skill testing they’re doing as part of their hiring process.
And it’s intimidating and scaring a lot of people off.
It’s ridiculous, it’s just absurd. And even a bunch of years ago I wrote a blog post about the job ads that people are putting out there and it’s like, like, you know, looking for CTO level knowledge in a, you know, a senior software developer, you know, and it’s just gotten out of ridiculous. So it will cover that. Another topic I want to hear from Kevin. I see Liz, Joanne and John. So we’ve got about eight minutes. Let’s keep our comments quick. Go ahead, Kevin.
[00:52:14] Speaker D: Yeah, picking up on Joe’s comment about staff becoming managers of a team of AIs. Totally agree. That’s going to happen. This will bring in a new concern called decision fatigue, where if someone’s dealing with 10 AI is moving at the speed of AI, they’re going to be asked to make a lot of decisions being referred back to them by the, by those AIs. And we will have to train people how to cope with that decision fatigue because at the moment people will get burnout very quickly because they’re not used to making that many decisions a day.
[00:52:50] Speaker A: Not only are they going to have to worry about burnout, we can’t just keep having meetings after meetings after meetings to deal with all these decisions. I think organizations are going to have to think about how they’re defining roles around decision authorities. Go ahead, Liz.
[00:53:03] Speaker H: Yeah, I hear that these McKinsey’s and the Gartners are all about AI. AI. Everything is AI. But you know, if you remember, I mean not just the industrial revolution, but Also the.com era and everything it there was always like, oh, we got to like rush to the shiny new object. It’s not about the shiny new object. It’s about making sure you’re making good business decisions, focusing on what’s the right thing for you to do as a firm, for your customers and your operations and then seeing where you can apply AI there. And when you’re looking at talent, it’s about making sure that you’re looking for that potential and looking for how people think and how people relate to and how people absorb again the context so that they can actually be leaders in your field.
[00:53:49] Speaker A: Very interesting. Go ahead Joanne.
[00:53:52] Speaker F: I think the, you know, from a planning perspective, one of the things that we’ve kind of tested with a bunch of C levels is the ability for first levels to start to learn the tricks of the trade that senior MBAs, you know, and senior executives learn in terms of trade off management, in terms of stochastic versus probabilistic versus, you know, all the things that you have to face from not an academic perspective, but on an applied level you are constantly making decisions at certain management levels and up to the top start teaching those skills to those first levels trainees, those that are just coming in because it requires time, it requires skills, it’s not quite an apprenticeship, it’s more like a MBA in a box for you know, first years to give them the beginnings of how do you actually do trade off management? How do you actually make decisions? What is involved in the decision making process that’s not workflow based but get absolutely the critical thinking skill in the sense of decision trees, what’s important when, and that’s what they really have to learn. And so a lot of people are adding that to their action plans around AI because they see the gap coming, maybe not this year, but definitely by next year.
[00:55:21] Speaker A: I’m just going to plug a book here I read at the end of last year. It’s about China’s quest to engineer the future. It’s by Dan Wang and he talks about the impact of what China was able to do when we outsourced most of the manufacturing in the US and the process knowledge they were able to build up over generations of manufacturing.
I don’t know, Joanne, I’m getting the sense we’re going to end up in the same situation around knowledge fields.
[00:55:54] Speaker F: I would absolutely agree.
The thing that people seem to forget about is the small language models that are being built specifically to direct AI, to use process knowledge, to use context, to use all of those critical thinking skills that we’ve been talking about, including the mathematics behind it. It’s not enough anymore, you know, even for the frontier models. They, they’ve trained on so much data and so much information, but so much of it is, is extraneous to what people actually need. And this is where small language models excel because they focus on a particular process, a business operation and the leadership and skills and the actual applied skills that are used for those processes. And that’s what the future is.
[00:56:57] Speaker A: Interesting. Joanne, I have a sense that we’re going to have to cover this topic from a different number of different angles going into the future. John, you had your hand up. I’d love to hear your comments.
[00:57:08] Speaker G: Yeah, the Industrial revolution came after people that were doing manual labor. And AI is different. It’s really coming after the knowledge workers. And I really don’t like the term industrial revolution applied to this because this is, is, this is really a general purpose transformative technology that’s like electricity, it’s like the Internet. And it, it’s going to really transform society. And so it’s, it’s not just going after what I would describe in, in as industry.
And because of this I think it’s really going to make us look at every role in every company if we want to do this right. And I think it’s really going to redefine roles and if, if we’re going to have to do this, we have to, in every company and every role in society we have to see like, like how does this new technology impact this role. And I think from that I think companies are going to start having new ways to work. And it’s going to take people and companies a lot of time to use this technology, figure out how to use it, right? And the people that figure out faster, they’re sure going to have a massive advantage.
[00:58:07] Speaker A: John I think some folks would argue that coding is software’s manual labor and maybe there are some analogies that we can pick up from the Industrial revolution.
But I agree with your last statement, John, and that is, you know, I actually agree with the Gartner numbers that it’s going to that AI is going to drive a significant restructuring of how we think about roles and what skills we’re hiring for and what people’s jobs are all about.
But I don’t think it’s going to materialize in the same way people are talking about. I think the biggest big shift is in the last generation we rewarded people who knew how to solve problems. And I think in the next generation we’re going to have to reward people about knowing what problems to solve and is the AI and how to make good decisions based on what the AI is providing us with knowledge. So where should we dig and how far should we dig to use John’s analogies? And I’ve got a really good action plan out of this, right. And I think the number one I’m going to take away from this is like, you know, how should organizations build apprenticeship programs, particularly for areas that require a combination of both skills and understanding outcomes and understanding their industry and business process. And without that, you know, we’re going to have a situation where we, you know, we lose too much knowledge. And that’s my takeaway from this conversation. Folks, thank you for joining this week’s coffee with digital trailblazers. Our conversation next week is going to be about my favorite topic. Honestly, it’s about AI first ux. We cannot change our companies just by becoming more efficient or by investing in productivity. We are going to start seeing in 2026 how companies are evolving their customer journeys using generative AI capabilities. I want to hear about examples. I want to hear about programming models. I want to hear about the operating changes between product, user experience, marketing and technology to enable companies to build the next Ubers and the next Airbnbs that are built from AI from the ground up. So that will be our conversation next week. I hope you will join us. Everybody who is being affected in the US by the snowstorms.
Please stay safe and warm.
Enjoy the time with family as the snow is coming down. I’m going to do my best to get home from Tucson to help my family out. Everybody have a great, great weekend. See you here next week.
