Everyday AI Podcast – An AI and ChatGPT Podcast - Ep 845: Human-AI Collaboration: Best practices for working alongside AI (Start Here Series Vol 4)

Starting point is 00:00:00 Welcome to the Everyday AI podcast. My name is Jordan Wilson, and for the past three and a half years, we put out more than 800 episodes. Yet, one of the most common questions I get I didn't really have an answer for. Where do I start on the Everyday AI podcast? And that's why we started the Start Here series. And with the fall now back in full swing, the Everyday AI podcast is going back to school and playing back the entire Start Here series from front to back. We've hit pause on our normal Monday to Friday programming to run back our most popular series ever for the next 30 days. We made the Start Here series for beginners and AI champions alike.

Starting point is 00:00:42 So whether you're just trying to get a grasp on large language models or grappling with the best coding harness for multi-agentic workflows, the Start Here series covers it all. Plain language, no jargon, and easy to follow along each day. So make sure to subscribe to the podcast. podcast and check back each day for new insights day by day. The series is a culmination of spending more than 10,000 hours covering generative AI over the past three and a half years, so you don't want to miss a single episode of the Start Here series. Let's get into it. Be honest. How is your working relationship with AI changed in the past few years? Are you still copying, pasting,

Starting point is 00:01:26 pasting into chatypti and hoping for the best? Are you drowning in AI generated drafts that need more editing than if you've just done it yourself. Or maybe you figured out a few tricks, but something still feels off. Here's the thing. Most people are still treating AI like a junior version of themselves with better grammar. But the game has changed. In 2023, you learn to prompt. In 2024, hopefully you learn to iterate. And in 2025, you started wondering why this still feels so manual. And now in 2026, the skill gap isn't technical anymore. It's managerial. It's human.

Starting point is 00:02:07 It's self-reflection. Because the teams winning with AI aren't better prompters or they don't have access to better models than you. They're just better orchestrators of AI. Better at throwing away the old way of work and starting fresh with AI front and center. And that human AI collaboration shift changes everything about how you work. All right. Does that get you a little fired up to reexamine your working relationship with AI? I hope it is.

Starting point is 00:02:40 And welcome to volume four of the Start Here series with Everyday AI. We're going to be looking at the human AI collaboration and best practices for working alongside AI. So if you're new here, after almost 700 episodes, we have kicked off our Start Here series. Like I said, this is Volume 4. This is essential beginner and advanced AI. I overviews to launch your year strong. So if one of your big goals in 2026 was to learn AI or to double down, maybe you've been there since day one,

Starting point is 00:03:12 this Start Here series is for you. And we have a special resource. So make sure you go to starthereSeries.com. That is going to get you access for free to our inner circle AI community. And it's going to take you straight to our Start Here series page. So you can very easily catch up with all of the Start Here series. Listen, additional resources, related episodes, anything you need to get either started or centered on your AI journey. And if you are very new here, this is part of everyday AI.

Starting point is 00:03:47 It's a daily live stream podcast and free daily newsletter helping everyday business leaders like you and me. Keep up with AI and get ahead and use it to grow our companies and our careers. So the Start Here series, we're going to do about a dozen or so of these episodes in the first part of 20. 26 that I hope is going to be a refreshing look at AI for all of us because yeah as someone that's done this now almost 700 times I understand how it can be so hard to just start somewhere so this is for you if you miss our last episode it was volume three of the start here series AI as an operating system so if you are listening on the podcast make sure to check your show notes we're going to be updating the show notes of all the start here series

Starting point is 00:04:27 so you can very easily if you're listening to the podcast just flip around that way as well. All right. Let's get straight into it and talk about, well, why? Why is there this big skill gap in 2026? There's those people that are running away with AI. And then there's people that have been using it pretty much every day or every week since the Chad GPT moment of November 22. And they're still just barely keeping up. So it's a shift. It's a shift from operator to orchestrator. And that's what I think that you have to start thinking about, right? Your relationship with AI and if you really want to stay ahead of the best practices,

Starting point is 00:05:13 because your new job isn't doing the work. It's really defining on what the work looks like, how it gets done, what information the work needs. You set the parameters, the constraint, and the success criteria. And the AI is probably going to be the one doing the actual work. Right. I talked about this literally in 2024, this concept of, you know, agent orchestration. And that's how I believe that that's where we're headed. And I think we've really seen that a lot in the last, gosh, only four to five weeks. That's really coming to kind of mainstream fruition on this big shift. And I think the winners of this era aren't those that who can effectively, you know, on board, supervise and audit digital agents.

Starting point is 00:05:57 It is really thinking it like an entire team that you are now orchestrating, right? Not an entire team that you are a player coach. You are the manager. You're not out on the field doing all of the work. So here's what we're going to cover in today's iteration of the start here series. We're going to be talking about the comfortable, uncomfortable truths of AI in 2026. and our relationship as humans with AI. And I think what so many people do is they take their current antiquated processes that

Starting point is 00:06:32 a lot of times are broken and they just find ways to put AI in it, right? In the front and the middle, oh, they find a broken joint, a leaky funnel. Let's slap some AI on it, right? Do you guys remember the infomercial, right? You slap some flex seal on it. That's what people look at AI. They find something that's broken or something, this could be improved a little bit. Let's slap some AI on it.

Starting point is 00:06:53 That's the wrong way to do it. I think that it's about unlearning, right? I'm going to be griping on a couple of these terms that I absolutely hate. One is human in the loop. I'll get to that later. One is upskilling or re-skilling. That alone, I think, has set so many companies back years. Because when, you know, the C-suite in the board room, you know, go around HR and they're looking at, you know,

Starting point is 00:07:23 AI investment. They're like, who needs upskilled? Who needs reskilled? They're looking at it almost as a proactive or sorry, a reactive measure. Right? It's almost like a course correction. Oh, who needs upskilled this year?

Starting point is 00:07:39 Who needs reskilled in AI? No, you need to unlearn, right? I've been chirping out this unlearned word for a very long time. Because that's how I really started getting the most out of AI myself. self when I started unlearning good habits that had traditionally led to success. And I think that's when you look at your relationship with AI, your working relationship with AI. That's what you have to start to do. You have to start to say, no, why would I just upskill with AI? My skills are, again, they're not worthless. They're worth less.

Starting point is 00:08:17 So if you're still holding on to those skill sets, right, whether you've been using them for two years or 30, it's probably time to let them go and to unlearn and then relearn and rebuild from scratch. Well, why? Well, because right now we have, I'm not going to say superhuman AI, but I will say above human, above human expert AI that can make millions of decisions per second worldwide. And humans can't keep up. So we think of this concept of human in the loop. And that's our job, right?

Starting point is 00:08:52 And that's a job that we thought was going to last into the, you know, into the late 2020s. It's not. Human in the loop, in my opinion, is dead on arrival. It was dead on arrival, right? Because we thought that you could stick any human in the loop and, okay, let's have this human go look what the AI is doing. No. It is dead, right? Because agentic workflows really create miles long action traces that humans cannot realistically interpret.

Starting point is 00:09:22 We can't keep up. The complexity has far outpaced what a human can review, right? When you see a large language model or if you're working with agents that have subagents and they can work around the clock, what good is a human going to do at that point? Right? A human in the loop, right? Like, I mean, let's say what it is. It's your last, you know, your last hope that you didn't get something wrong. It's your last hope that a hallucination doesn't slip its way through to production.

Starting point is 00:09:56 Right. It's your last hope that you don't make a tragic mistake from what a bunch of agents did. Because if your human in the loop can't push back, pause, or ask hard questions, that's not even oversight. That's a fail safe, probably a name only, right? No human in the loop can keep up with what agentic AI can do today, let alone, next week and next month. And yes, it does change that quickly.

Starting point is 00:10:26 My gosh. So this isn't hype. This is measurable. And the problem is, well, models are smarter than us. So why would we want to keep a general or generic human in the loop? You need to find what your expertise is or, you know, your team's expertise. What is that one thing that you are always? better than the best large language models at.

Starting point is 00:10:54 And that's what you should be focusing on and doubling down on and seeing where the overlap is between that one thing and what ultimately makes your company or your team more revenue, right? Because you have to stop comparing AI to perfection. Compare it to the human speed and accuracy for the same task. And at that point, humans can't keep up. And I'm not going to say every single use case, right? but I'll say the overwhelming majority of use cases.

Starting point is 00:11:24 If you have an average skilled human, right, pick your, pick your niche, pick your vertical, right? Financial analysis, marketing, PR, whatever. Pick your job. Find an average person. Someone that's, you know, worked there for one of your colleagues, but that colleague that's amazing at AI and they know. everything and then pick the smartest person in that same vertical.

Starting point is 00:11:55 The smartest person in that vertical cannot compete with the person who knows enough, right? They speak the language, but it's a whiz at AI. It's not close. It's not in a competition, right? That's like having 20 Michael Jordans on a team versus, you know, a peewee team. It's not fair. So I think that's what we have to get to is we have to understand. that these models now that are agentic by default, that can reason, that can think, they can plan

Starting point is 00:12:25 ahead, they can iterate, loop back, take different paths, use tools, all faster than humans, and all spin-off agents that do the same thing and, you know, can do it hundreds at a time, you know, spin off hundreds of sub-agents, and then come back and, you know, have multiple, you know, mixture of experts or, you know, what I like to call mixture of models that judge all of those inputs, right? Humans can't compete anymore. So, why? Why are we still thinking that the human AI collaboration in 2026, when it comes to

Starting point is 00:13:04 AI, is human in the loop, it's not, right? There was a recent study that looked at 700 plus court cases worldwide. Now involve AI hallucinations and fabricated citations. And that rate is accelerating to a handful of new cases. daily. And that's because the AI is too fast. Humans are too lazy. And for the most part, organizations aren't training their people. A lot of people assume, oh, if my company pays for a good AI, okay, well, I'm just going to have it do most of my work. It's going to be right because my company's paying for it and they're not training me on it. And this is a huge risk. You know,

Starting point is 00:13:47 JP Morgan, they acknowledge this risk openly. They recently talked about that when systems perform correctly most of the time, human attention drifts. But if AI is right, 85 to 95% of the time, well, your human in the loop falls asleep, right? Or you're a human in the loop. Maybe if they're a good one, they double down and just produce twice as much. But they just wave them Wendell, right? Chicago reference right there, right?

Starting point is 00:14:19 They just let everyone go in. They don't care. They're like, all right, everyone's safe. You're in, you're in, you're in, you're in. It's a passive approval of just automating things that need more oversight. And there's a concept here that I want to talk about. It's Endal's law. And that's essentially you can speed up one part of the process or many parts of the process.

Starting point is 00:14:45 But the whole system is bottlenecked by whoever can't keep up. right is the uh the old sports you know cliche or you know teamwork cliche that you're only you know strong is your weakest link and this is true for the human AI collaboration right and this is why this is so important so few business leaders zoom out and think about this so few people think of the human AI relationship so few people people working on front-end AI strategy, backend AI implementation, go through and ask these questions,

Starting point is 00:15:27 right? What happens when multi-agentic systems are way smarter than humans? And the humans don't know how to run them, right? And the human that two or three years ago was integral is now a liability, right? And how do we start to change that human role? because, yes, humans are still needed in all this, right?

Starting point is 00:15:51 I never said that, and I'm not implying that. Human roles are changing, and I'm going to get to what that looks like. But that's why we need human reviews everything. That's why we need that. Right? That's why you have to, I'm a huge advocate, right? If you're using front-end large language models, what do you do?

Starting point is 00:16:11 Like, people are like, okay, what do you do with all this time savings? Or what do you do if you're using a thinking model while you wait? Well, you read the chain of thought, right? And you rerun it and you correct it and you bring in more of your company's data, right, at the right points before the model goes too deep into its dive. And that's why the fix is expert-driven loops. That's what I've been advocating for, you know, EDL for a long time now, not just generic oversight.

Starting point is 00:16:43 Because the difference is when you embed experts in building, right, Whether it's, you know, we don't have to have to talk about, you know, get too technical and talk about, you know, multi-agentic loops. Let's just talk about embedding the right experts and setting up your team's AI processes and your chatGBT teams account, right? Or how you're going to tackle work and clawed co-work. A lot of times you have one person, sometimes it's IT, sometimes it's, you know, your, your AI champion team, which is important. and they kind of do it for everyone and they set it up for everyone. And that's, again, the wrong way. You know, one thing that was interesting,

Starting point is 00:17:30 there was a legal on technology study, kind of looking at this concept of the, you know, human in the loop versus an expert loop. And they saw that one law firm that they looked at put senior partners in the loop, right? Where normally this is a, you know, not in a bad way, but, you know, kind of the human in the loop, we're seeing AI, a lot of times they put younger people or more inexperienced people,

Starting point is 00:17:55 people who cost less because that's what companies think, right? They're like, okay, well, this is just someone clicking a button, clicking approve, why am I going to put my senior people on this? Why am I going to put my smartest people on this? So in this case study from legal on technologies, they found 86% faster contract review and 65% better issue detection when they had senior partners instead of junior reviewers. 86% faster and 65% better. And that's not like versus the human only baseline. That is better than the AI, the augmented, right, junior reviewers plus AI.

Starting point is 00:18:40 So, I mean, you're getting compoundingly better and better results. The smarter and the more expert. people that you put in the right places. And I think it's, it's again, it's not putting one expert, you know, on one AI powered workflow or one,

Starting point is 00:18:59 you know, agent run. It's putting multiple people in there at the right place. Right. It's experts driving the loop, not a single human overseeing. And other studies show this other organizations are seeing the ROI, triple when they move from generic oversight to expert-driven collaboration.

Starting point is 00:19:24 So how does this get to this point? Right. And it's almost like the better that technology gets and the more advanced it gets and the less tech know-how it requires, right, that anyone can go in there and click a button and, you know, set up AI agents that are connected to your data, literally, right? This show's been going for 19 minutes. You could have set hundreds of agents up in 19 minutes. I kid you not, right?

Starting point is 00:19:55 One click, very easy. But poorly implemented AI can crush productivity because you just end up spending more time, correcting your errors and managing expectations that maybe went awry and then running multiple parallel backups. So I like to say this. if you had a bad workflow with AI and you upskilled or re-skilled that workflow, right? You can't just throw makeup on an ugly process and think it's going to be pretty.

Starting point is 00:20:32 It's still ugly. Now it's just, you know, got some makeup on it. Got a little shine that it doesn't deserve. All this does is it recreates workflows that weren't working. And if anything, it just creates this augmentation debt that ultimately tanks productivity. Because now you just have to go back. You know, instead of getting more things done in a better way, now you're just getting more things that need fixed faster, but potentially more errors. Because you're not putting the right people and the right processes, right?

Starting point is 00:21:06 You're putting anyone in old processes. you have to rebuild them to be AI native. So how do you do this? It's a mindset shift. Like I said, you're not the one if your team wants to excel and outrun the competition in 2026 and beyond. You can't just use AI.

Starting point is 00:21:40 You can't leverage AI. You have to orchestrate it. right what's funny is is most most times this is one of the times i you know i'm looking at my other screen here um you know i actually don't have agents running right now uh where normally i would right for the most part we have to start thinking of ourselves as orchestrators or i like to say tastemakers sometimes right you need to provide that context right context is going to be one of those You know, context engineering is going to be one of those buzzwords of 2026, right? In 2024, I called it first company data, right?

Starting point is 00:22:24 But I still think that that's more realistic in what you need. Context can be anything, right? Context needs more context to be defined. But, you know, context engineering, to put simply, you know, when I said, hey, in 2023, you were prompting and then you were iterating and then you were providing more context, right? That's just more data. more data, more direction to a model before it goes off and does its thing. Because before, when the models were just non-reasoning models, non-thinking models,

Starting point is 00:22:54 they would just spit something back pretty quickly. Now they might go work for five minutes, right? 10 minutes longer. I have one run the other day on the front end, not on the back end. On the back end, if you're using it via the API, it's pretty easy to get these things working for hours. on the front end, you know, yeah, you might have a model work for 5, 10, 15, 20 minutes. I had an actual model, not a deep research run, run for, I think, 92 minutes the other day, right? You have to give it the context.

Starting point is 00:23:28 And then at that point, you're orchestrating, right? I'm looking at different models, what they bring to me, different agents, what they bring to me. And I'm saying, this is good, this is good, this isn't. Let's rebuild that skill, right? If I'm in Claude, I'm saying, let's update this skill. If I'm in Chad GPT, I'm going and updating that GPT, right? You always have to be improving the processes and not just trying to, you know, sprinkle some AI on an old process that's broken.

Starting point is 00:23:58 And that's why we're talking about these things like Asian supervisors, op orchestrators. This is a fundamental shift in how work is getting done. This is the difference in the human AI collaboration and why I think you need to rethink. your working relationship with AI. Because if you're still using it like you were in, you know, late 2022 or 2023, right? If you're going out of your way, we tackled this earlier in our start here series about treating AI like an operating system. So one, that part covers the tech, right?

Starting point is 00:24:36 Treating AI like an operating system. This part, volume four here, what we're tackling, the human AI collaboration. That's the mindset shift. That's going from an operator. I'm the one pushing the buttons to, nope, I'm orchestrating an agent that's technically going out there and pushing the buttons and coming back. And then I'm telling it how to improve, right? I am building that expertise, even if you don't know, hey, what is that one thing that, you know, you're definitely smarter than these AI models that are genius level on offline IQ tests, right? you might be saying, okay, what could I be smarter than an AI model that knows everything if you give

Starting point is 00:25:15 it the right context? Well, you don't know until you look at its chain of thought. You don't know until, you know, you've put in, you know, five, 10, 15, 30 hours on a project having multiple AI models go and do something that you know how to do front to back. That's how you carve out your expertise. And then you really have to then deploy that and duplicate it across your team or your organization, that is how you go about shifting your mindset to go from an operator to an orchestrator. Because then that output compounds, right? Your ability to generate revenue compounds.

Starting point is 00:25:53 Your ability to do things that you didn't have time to do last year. All of a sudden, that frees up, right? And this is where the most advanced businesses are shifting toward right now. So the last thing I want to talk about, last two things, is where our expertise actually belongs, right? Where should we be spending our time? Well, you have to go do what I just said. You have to make that transition successfully from operator to orchestrator.

Starting point is 00:26:30 But then I want you to think about kind of this jagged frontier of capabilities, right? Because there's things that humans are good at. And then there's things that we're terrible at. And those things are changing all the time. Right. And the same thing with AI models. There's things that AI models are amazing at that we never thought would be possible. Then there's things that they kind of fail at.

Starting point is 00:26:52 So right now, this is today. Right. So if you're listening to this in January, February, 2026, probably still safe. If you're listening to it, you know, July, September, this could be different. But right now, humans win at high context empathy, ambiguous decisions with incomplete data, accountability and novel judgment, right? Reading between the lines where there's not structured data or there's not company context to fill those cracks.

Starting point is 00:27:22 Right now, AI wins at pretty much everything else. Right. Data synthesis, first drafts, pattern recognition, repetitive cognition, right? All of those things. Those are the danger zones for if those are your competitive moats or you think that's your competitive advantage. right now, right? If you think one of those things, data synthesis, first drafts, pattern recognition, that's what your department, your career, your company is built on right now, you've got to find your

Starting point is 00:27:53 pivot. Because right now the number one predictor of human AI success isn't just the model you use. It's the quality of the context and kind of those procedures that you create and you provide. because if garbage context goes in, a worst outcome comes out. So here's some quick takeaway advice. Right. I can't sit here and give you advice for how to set up your agent orchestration. What I can tell you, your individual using different platforms, right? One of the biggest mistakes is skipping over repeatable and scalable context.

Starting point is 00:28:39 So stop starting from zero, right? And you need to take this, start this at the individual level, but then take this to your team. Stop starting at zero every single problem. You have to start building context faults, right? You can think of those as skills, right? You can have a marked down file with all your skills, with your company knowledge, right?

Starting point is 00:29:00 If you use Claude skills, you know, you're probably familiar with these markdown files. But start creating these skill files based on the task that you and your team repeatedly do. This is something I'm constantly updating mine, right? If you're using as an example, Claude, you can update them in, kind of in chat. If you're using the GPT builder in chat, GBT, you can do it there as well.

Starting point is 00:29:23 Right, but you need to be building and reusing these, you know, custom GPs inside of, inside of chat, GvT, the Claude projects and skills, Google gems, whatever. You need to have that rag for your personal use, right? We think of when we think about retrieval augmented generation, we usually think about these, you know, vector databases that would cost millions of dollars three, four, five years ago to build. And we think about these very complex things. I want you to think about your personal rag, your personal retrieval augmented generation.

Starting point is 00:29:54 What are those things? I update mine all the time, right? I have my, you know, as a small business owner, these are the things that I'm thinking about. These are, you know, my important fact, stats about my role about what I'm trying to drive. Here's my KPIs and I'm constantly, you know, updating these things. But you need to have your personal context, your team context, your company context, your competitive landscape context. You have to have these things that are reusable because if you're just starting at zero,

Starting point is 00:30:22 you're wasting time. That's being a button pusher, right? Instead of agents that don't need to have the button pushed. They're already doing it. They're already delivering it. They have all that. And they're able to use it in a repeatable. and scalable way.

Starting point is 00:30:40 And then last but not least, you need to elevate your champions. Okay? Here's what I mean by that. Funny, I had Chris Caldwell, the CEO from Concentrics on a couple weeks ago. And he kind of said, hey,

Starting point is 00:30:58 you don't want a hundred Jordan's running around to you and you don't. But I'll tell you this. And I don't want this to come off in the wrong way, but you need people like me. on your team and you need a lot of them. Here's what I mean. You need people who's mainly their only job is to keep up with AI every single day, right?

Starting point is 00:31:21 Every large organization needs dozens of people who are in my position. All they do, they read about AI every day. They're stoping out new projects, measuring, building modular backups, right? Because you don't want all of your, you know, systems in one model, the model, changes and your whole company comes to a halt. And then you need to constantly have those people training the non-champions. So you need to find where your time savings opportunities are. You need to scope those, measure those, and then deploy the ones that can easily gain back time the quickest. And then you apply those hour saves into creating those, you know, go create your dozen Jordans

Starting point is 00:32:03 on your team, right? Your domain experts, right? It might feel weird to start automating some of their work or automating some of their roles, but then you find those people that are actually able to automate parts of their job and then teach others, build scalable systems. There's still new lines of revenue to build in your company, right? People, when they look at AI, yes, I do ultimately think AI will cause a net negative in the job market. But there's millions, tens of millions of new jobs that we have no clue that are going to exist in three years. And you need those people,

Starting point is 00:32:46 those champions on your team. You need to elevate them, challenge them, and you need to deploy them, right? They need to be listening to this show every day and other, you know,

Starting point is 00:32:56 AI podcast and other, you know, YouTube people on AI reading newsletters and you need to be scoping, breaking, and training people every single day. But the largest organization, aren't even doing that.

Starting point is 00:33:10 So stop looking for cool AI tricks. Focus on automating the dull stuff first, right? Get rid of shiny AI object syndrome. Go, the invoices, the summarization, the filing, all those boring AI things beat the flashy AI every single time. Because as AI starts to handle more and more digital interaction, face-to-face and high empathy relationships are going to become your companies,

Starting point is 00:33:39 differentiators, right? But you can't have that if you're still the one pushing buttons. You can't. The human premium is rising. So you have to use it wisely. But you can only do that. If you go back and follow the steps

Starting point is 00:33:53 that we just laid out in re-examining the human AI collaboration, going through the best practices of shifting away from being the operator, from being the button pusher, the chat GPT prompter, into being the orchestrator, the tastemaker, and the champion that's pushing your organization to do the same top to bottom. I hope this Start Here series was helpful, volume four done in the

Starting point is 00:34:20 books. So if this was helpful, remember, please go to starthereseries.com. That's going to give you access for free to our inner circle community. And you're going to see all of the Start Here episodes right there. We're going to be throwing in and adding more and more additional resources. to help you with your journey. So whether you're just starting out, I hope this episode helped you better understand some things. If you're an expert doing this every day, I hope this challenge you to look at AI a little bit differently

Starting point is 00:34:48 and to push even your own human AI collaboration. So thank you for tuning in. I hope to see you back tomorrow and every day for more Everyday AI. Thanks y'all. And that's a wrap for today's edition of Everyday AI. Thanks for joining us. If you enjoyed this episode, please subscribe and leave us a rating. helps keep us going. For a little more AI magic, visit Your EverydayAI.com and sign up to

Starting point is 00:35:14 our daily newsletter so you don't get left behind. Go break some barriers and we'll see you next time.

Everyday AI Podcast – An AI and ChatGPT Podcast - Ep 845: Human-AI Collaboration: Best practices for working alongside AI (Start Here Series Vol 4)

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