Episode 296 Transcript:

 AI in 2026 isn’t something you get to experiment with on this side anymore. It’s part of how work gets done. Now Your team is using it, whether you know about it or not. Now, the risk isn’t for many people actually, that AI replaces their job. That is an sort of easy fear and for many people actually kind of far out, but the real risk is a bit more subtle ,

the bigger risk is actually that you keep your job, but that you are losing your thinking skills, your cognitive abilities to ai, because you start to outsource your judgment. You stop exercising the mental muscles that actually made you good. At work makes you good at work right now, but the more that you use AI and the more that your team starts to use ai, you may start to realize that while work and tasks move faster, thinking becomes more shallow. So today in this episode, I wanna walk through three things that actually work and are incredibly important for leaders and managers in 2026 when it comes to ai.

We’re gonna talk about how to run AI experiments as a team instead of people doing random stuff.

We’re gonna talk about how to think in workflows so that you can automate instead of just speeding up one-off tasks. And then lastly, we’re gonna talk about how to protect your and your team’s thinking skills and cognitive abilities while still using AI effectively.

Now I wanna be clear. This isn’t an episode about AI trends or the latest model instead, this is about how leader should approach AI adoption in 2026. At least what we know today. Now what an ideal outcome would be for any leader working with AI is that we make better decisions. We have faster execution, we make fewer mistakes. We are maybe even more innovative and creative. And the critical part here is that our thinking actually becomes stronger, not weaker.

So our human capacity continues to grow with the use of AI and does not become weaker. If you achieve that by the end of 2026, to me, that would be a success.

Here are the two questions. This podcast answers. One, how do you successfully transition into your first official leadership role? And two, how do you keep climbing that leadership ladder and continuously get promoted, 

although the competition and the expectations get bigger. This show with a manager track podcast will provide the answers. I’m your host, Ramona Shaw. 

I’m on a mission to create workplaces where work is seen as a source of contribution, connection and personal fulfillment. And this transition starts with developing a new generation of leaders who know how to lead. So everyone wins and gross. In the show, you’ll learn how to think, communicate and act as a confident and competent leader. 

You know, you can be. 

 Let’s kick off with the first topic.

This is about AI experiments. Now, an AI experiment is a small time-boxed test tied to a real workflow with a measurable outcome or a real project or initiative. Ideally we can measure it, we can track it, we can quantify the outcome, but it may also be something that’s a bit more creative, depending on your job.

When you try something, you really don’t know what to expect, but it will be part of your learning curve. You’re doing it intentionally to learn something new. You might notice that your team is spending a lot of time every morning looking through certain files, or that it takes quite a while to go through the notes from all the past client meetings in order to be prepared for a client update. Now, you may not know yet how to solve this, but you’re going to start to iterate and experiment to see, can AI help improve this process, increase quality, reduce time, or make it more efficient.

Those are the AI experiments. Now if you work in a tech company and everyone’s talking about AI and everyone’s on it, you’re probably just learning on the job, which is great ’cause the discussions happen organically and naturally in the conversations and the meetings that you partake.

If you are in a job where your work has not much to do with ai, and in fact, maybe your leaders or your executive team don’t yet have a clear and articulated strategy. On how to incorporate ai, and your colleagues aren’t that familiar with it.

Maybe you’re in the hospitality sector or you are out in the field, or you work in a brick and mortar company, or in a construction firm, or manufacturing, whatever that might be. If AI is not something that is naturally integrated in the conversations.

Then even though you might know that this is important, you have so many other things that you need to take care of that that, while important doesn’t seem urgent, and so experimenting with ai. Sounds good, but might just fall the bottom of the list, and I totally get why urchin important. You probably have things that are not just important, but also urchin as you.

You prioritize those, but it’s only a matter of time until AI experiments, AI workflows, and your. Integration of AI into your job is no longer optional, and then it becomes super urchin and super important. Now, if you as a leader wanna build a reputation that you are at the forefront, that you are tech savvy, that you are one of those leaders as it comes to AI adoption, you can’t wait for that moment.

The moment was last year, two years ago, and it’s most definitely now, if you haven’t started, this is now what should fall into your list of urchin and important things. So doing AI experiments both on your own with what you notice and what you.

Find like, huh, I could see and try to figure out how I could use AI for this. And you can, by the way, ask Gemini or Chachi bt, or Claude or whatever you use to say, here is what I am doing. Help me figure out how I can leverage AI to make this faster or improve the quality. And so using it actually as a thought partner, now we get in a moment to how to use or prompt an AI so that you are not delegating and abdicating all your thinking to the machine.

Uh, but instead you’re still the driving force. But that is one part, AI experiments that you run, you think through as you go about your day. The second part though, is Doing AI experiments with your team because the chances are high, you have people on your team who are tech savvy, others who are not.

Some people. Have, you know, spare time maybe in the evenings or even at work to actually think through how to leverage ai while others don’t. They’re either too busy at work, they have other commitments, kids, other things that occupy their time, and so they’re not able to learn or invest as much into developing those skills as others are on your team.

So for a range of reasons, there’s going to be a, a range of different. Skills and knowledge levels when it comes to AI on your team. If you sort of set the tone that everyone’s out there to learn on their own, you’re going to quickly see that you have some people who are leading and other people who are not able to catch up.

It’s going to be a problem in the future. So a way to bring that back and trying to make this a more equal playing field is to deliberately create those AI experiments or working groups where you bring people together so that they think about different processes or integrations between work, uh, or ideas on how to leverage it.

And they work as a group and then they share it with the rest of the team. You are combining different strengths and different expertise, but you also make sure that no one’s left behind. Everyone feels like they’re part of the learning curve and they’re learning not just with each other, but also from and through each other.

So that was the first one around ensuring that you spend time and deliberately invest in experimenting with ai. Now The second one is about workflows and workflow thinking more specifically which in essence means that you’re seeing your work as a repeatable sequence instead of one off tasks, and you start to take that mindset or that you’re looking at everything that you do, constantly being on the lookout.

Where are the workflows and what are the workflows Most people think in tasks, unless, again, workflow is really something that’s ingrained in in the work that you do. If you’re an engineer, you might be naturally gravitating towards thinking in workflows. For others, it may not. But when you shift into thinking about workflows, regardless of what you do, something starts to unlock as it relates to AI leverage and adaptation.

For example, in my work of working with leaders, a lot of what I do for clients is customized. What I do for one client looks different than what I do for a different client. How I coach one person is very different from how I coach another person because it’s personalized and customized to a company’s needs and their values.

I love that about the work that I do, and it is one of my core beliefs that when it comes to delivering strong results, being great at leadership development, cookie cutter solutions just don’t work. So for me, in order to do my best work for clients, I need to be able to customize and personalize.

So naturally, while on the operational side, there are things that. I always had automated or had workflows and SOPs in place. A lot of the work I did on the front end or with clients I was not looking at it from a workflow perspective. Now, thinking about how I wanna leverage ai, I had to be really disciplined to look at everything that I do to try to see what are the common factors, what’s the process when I prepare for a client call? What is the research that I do? What are the questions that I ask? Where is the information being stored? And then how does this information get processed?

What are then the outcomes that I’m looking for? And so this is a simple example, but having the discipline to map out your work in workflows and to stop thinking that everything is a one-off, or that everything is customized and can be repeated.

That’s gonna be the thing that holds you back. So shifting our thinking into workflow thinking . Setting the foundation to actually understand

how agents and workflow automation can support our work, be this team development, one-on-one conversations, feedback, performance reviews, or actual technical work in your profession that you do. It’s also the necessary shift to start to look at everything that you do when you open, let’s say chat, GPT, and you type in a question to not think about your use of AI as one-off questions, one-off prompts, one-off tasks. But every time you do this one-off thing to say, hold on a second. How am I doing this on a repeated basis?

How often is this prompt gonna come up when you create onboarding plans, you scan resumes, all those things you won’t just do once.

You will do on a repeated basis, and that is where the opportunity lies to experiment with AI based on the workflows that you identify, and then document and then try to optimize and automate. 

As a leader, understanding and harnessing the power of AI is not just an advantage anymore. It’s becoming a necessity. If you want to rise in your organization and you have. 5, 10, 20 more years to go. There will be no way around. But to get involved and to embrace AI as a way to achieve business objectives, and that applies to the vast majority of departments and professionals.

And if you are in a role where you think, yes, this is likely going to impact me too, get started and download our 10 AI Quick Start Guide. For Leaders, you will not only learn more about AI as a technology, but more importantly, will address how it’s going to impact you as a leader, what skill sets you’ll want to develop in order to be well equipped during this time and ultimately become AI fluent.

And AI savvy. This is an easy way to get started. Familiarize yourself with the concepts. Learn some practical tools and frameworks to leverage it. Right now in your role as a manager. Head on over to our covid.org/free-toolkits or check out the show notes or captions for the link. We’ll see you over there.

So we talked about AI experimentation and how you built that into your team. We’ve been talked now about workflow thinking and why that is so key regardless of your job and how much AI is already integrated in the work that you do.

And now the third point I wanna make here is that We need to protect our cognitive abilities. You wanna make sure that you create habits

where the hardest thinking stays with you, at least at first. You can augment that and improve and iterate on it if you don’t have the capacity, like data analysis for example, or research or statistics where you do need additional tools. But if you do have the capacity to do the thinking, do the thinking first.

And the research here is really clear. When you generate ideas or thoughts yourself before using ai, you remember more. You understand more,

you stay engaged more, and your cognitive abilities stay strong. Now, when AI goes first, your brain will go passive, and over time that will show. now as I mentioned earlier, in order to prompt where you still are actively engaged and the prompt incorporates your own thoughts or builds on it, here is the framework. Instead of just asking a question or saying, Hey, take on the role as a. Product manager and then work on X, Y, Z.

That is how many people prompt, they give a bit of context, they define the role of the task, and then they send it out and they see what comes back, but in order to start off with your own thinking, you have to make it tweak to the way that you prompt these tools.

It starts with context. This means you add backgrounds and any constraints. You wanna sort of answer the question what does the AI need to know about the situation, the audience, or what’s at stake.

Then you talk about the role that means like what’s the expert that the AI should emulate? Is it being an editor, a strategist, risk officer, an technical interviewer? What is it there? You wanna be really specific. Now, here is where it gets key. This is what’s different to that example where I said just.

You’re typing something and you’re pushing it off, and you see what it comes back with. After providing context and talk about the role, you are now going to, insert a section that we label as interview, and this means that the AI is going to ask you questions before producing output and this is the step that most people skip and it’s the most important one for 2026.

Because it forces clarity and it protects your thinking by requiring you to articulate what you want before you delegate it. This is what’s going to keep your cognitive abilities alive. Then the last part is the task.

This means the deliverable and the format that you’re looking for. So context, role interview, and then the task. Now you can lay that all out in one prompt. But for the interview section, do mention that the AI should ask you questions before giving the answer and then hit send and see what kind of questions it comes back with so that you can fine tune your thinking and

add more context and information to the tool so that you’re actively staying engaged. The tool is building on your own thoughts and will therefore provide a better outcomes.

Okay, now onto the third, and I’m going to frame this. Carefully,

because I don’t want to sound like I’m fearmongering or I’m painting like this dark picture for the future, but the evidence is real and it’s important for us to really have our eyes wide open. Cognitive offloading is a real phenomenon when it comes to ai. For example, people remember and understand more when they generate information themselves instead of passively consuming it. Right? It’s fairly intuitive to know like when we just read something or we are passively consuming something that’s very different than we are actively co-creating the information or we are the ones sort of saying it out loud or writing about it.

We know a lot more when we do this and so evidence from neuroimaging clearly shows that when we work. Based on our thoughts first, and then use ai. We have broader brain activity than when we’re just passively consuming it,

Another example from the past is when people use a GPS on a regular basis, their ability to orient themselves in a city actually declines. So as we outsource certain skills or even just sort of mental exercises, yet the tool gets better at it.

We are declining. It’s like we’re going to the gym and then we’re telling the robot to lift the dumbbells. The robot probably gets better or more coordinated, even if they don’t have muscles. We don’t. Our muscles don’t grow. In fact, they decline.

Another example here,

doctors who used AI to detect cancer actually got worse in their ability to detect cancer without AI after using it on a regular basis compared to the doctors who’ve never relied on ai. Their ability to detect cancer stayed stable or slightly increased while again, the doctors who used AI in the process of detecting cancer, their ability.

Declined. So the broader issue is skill atrophy when we use technology as an assistance, and that becomes the default.

So maintaining your cognitive ability is the third important factor to pay attention to when it comes to managing and leading with AI

in 2026, we talked about AI experiments, workflow thinking and protecting your critical thinking and cognitive abilities as you continue to integrate and leverage AI tools.

I hope this planted some food for thought. We’ll be back with another episode of The Minute to Track Podcast next week. I’ll see you then.

Bye for now.

If you enjoy this episode, then check out two other awesome resources to help you become a leader. People love to work with. This includes a free master class on how to successfully lead as a new manager. Check it out@ourcova.org forward slash masterclass. 

The second resource is my best-selling book, the confident and competent new manager, how to quickly rise to success in your first leadership role. Check it out at our cova.org/books or head on over to Amazon and grab your copy there. 

You can find all those links

In the show notes down below.