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Human-AI Collaboration Best Practices: A 2026 Guide for Business Leaders
In 2026, organizations are no longer separated solely by their access to advanced AI models or their technical prowess with prompts. The defining gap has shifted: it’s now about management, process orchestration, and intentional unlearning of outdated workflows. The shift from “operator” to “orchestrator” is unlocking tangible gains in productivity, accuracy, and revenue—but only for teams that restructure both roles and processes around AI’s new capabilities. Below are pinpointed, actionable insights based strictly on the latest thinking in applied business AI, illustrated in practice and not in broad strokes.
Rethinking AI Implementation: From “Operator” to AI Orchestrator
A central obstacle in today’s workplace is the tendency to insert AI into existing, often ineffective, business processes—akin to putting a patch on a leaky funnel. This retrofit approach limits business value and does little to address the evolving complexity of AI systems. Instead, leaders must move beyond “just upskilling” and instead orchestrate and redesign workflows with AI as the centerpiece.
In effective teams, managers are no longer the ones "pushing buttons" or verifying every output. Their role advances to setting clear parameters, defining success criteria, and embedding contextual direction—making them orchestrators rather than passive supervisors. This critical mindset shift means onboarding, supervising, and auditing AI agents the same way one would manage a team of high-producing specialists.
Discarding Outdated Paradigms: The End of "Human in the Loop"
The notion of “human in the loop” as the primary safety net for AI outputs is defunct. With the rise of agentic AI—automated systems capable of reasoning, planning, creating iterations, and spawning sub-agents—overseeing every outcome in real-time is impossible for humans. Recent studies have traced surges in AI-generated hallucinations across professional fields (such as 700+ court cases worldwide involving fabricated citations) and revealed that overreliance on passive human oversight introduces organizational risk, not mitigation.
Instead, the strategic focus must be on identifying where human expertise distinctly outpaces AI and doubling down on those intersections. It becomes essential to compare human and machine outcomes not to theoretical perfection, but grounded in speed, scale, and domain-specific accuracy.
Expert-Driven Loops: Replacing Generic Oversight with Senior Expertise
Transitioning from generic junior oversight to embedding genuine expertise creates measurable business impact. A legal technology case study illustrates this: reviewing contracts with senior partners in the AI oversight loop delivered an 86% faster review process and 65% improvement in issue detection compared with the use of less experienced reviewers.
Generic button-clicking oversight—often relegated to the most junior staff—undercuts both quality and efficiency. Compounding ROI is demonstrably tied to placing multi-disciplinary experts at decisive points in the AI-driven workflow and aligning these participants with core business objectives.
Process Redesign: Stop "Slapping AI On" and Start Building AI-Native Workflows
Poorly implemented AI does more than slow teams down; it creates “augmentation debt”—the organizational equivalent of technical debt—where workflows accumulate hidden errors and demand costly corrections as automation scales. Upgrading legacy practices or simply layering AI atop broken processes accelerates the number of tasks done incorrectly, multiplying risk and management overhead.
Complete workflow redesign—making processes AI-native from the ground up—streamlines communication, oversight, and delivery. Task context, process documentation, and team expertise must all be captured in persistent, updatable “context vaults,” which are reused across similar AI tasks for accuracy and efficiency.
Context Engineering: The New Differentiator for Superior AI Results
The technical differentiator in 2026 is context engineering. Outcomes depend less on the base model and more on the quality and structure of the data, task instructions, internal company knowledge, and ongoing feedback provided to each AI system. Business leaders are encouraged to stop “starting from zero” and instead build modular “skills” or repositories containing reusable process knowledge, domain context, performance benchmarks, and competitive intelligence.
These context repositories (often formatted in markdown and used for retrieval-augmented generation workflows) continuously evolve and are shared across teams for compound gains in productivity. The shift from a completely prompt-driven approach to a context-driven ecosystem amplifies project outcomes and protects against drift or quality regression when teams scale or pivot.
Championing AI-First Roles: Elevating Internal Champions and Domain Experts
Sustainable competitive edge depends on developing internal AI expertise. Successful organizations maintain a cohort whose day-to-day is dedicated to staying current on AI changes, scoping and validating new opportunities, measuring impact, building modular fallbacks, and educating others.
These “AI champions” are not just technical leads—they are cross-functional experts who automate their own domains and propagate their expertise across the organization, enabling scalable, company-wide adoption of best practices. The result is not just improved technical implementation—but the creation of new profit lines, business units, and job roles that simply did not exist three years prior.
Practical Takeaways
Abandon attempts to simply upskill outdated processes; orchestrate fully AI-native workflows.
Replace generic oversight with expert-driven loops that feature senior or domain-specific expertise.
Standardize context management through shared, continuously updated context files and skills vaults.
Prioritize automating repetitive, low-value tasks and free human expertise for high-empathy, ambiguous, or novel judgment tasks.
Institutionalize ongoing AI education by embedding AI champions responsible for continuous team-wide upskilling and process optimization.
The organizations that evolve from AI “operators” to “orchestrators” are not just keeping pace—they are dictating the tempo and scope of change in their industries. As agentic AI systems expand, the capacity to design, supervise, and continuously improve both the human and machine elements of work will define market leadership.
To accelerate this transition, review all organizational workflows for AI-native potential, identify high-context opportunities for human value-add, and invest in persistent, modular context and expertise repositories accessible to every team. The productivity, risk management, and financial gains are immediate for those willing to unlearn legacy mindsets and adopt these best practices.
Topics Covered in This Episode:
- Human-AI Collaboration Best Practices 2026
- Shift from Operator to Orchestrator Roles
- Human-in-the-Loop Limitations Explained
- Expert-Driven AI Review Loops vs. Generic Oversight
- Orchestrating AI Agents for Business Productivity
- Building Reusable AI Context and Skills
- Elevating AI Champions on Team
- Human Strengths vs. AI Strengths in Workflows
- Avoiding Augmentation Debt and Workflow Pitfalls
- Mindset Shifts for Effective AI Management
Episode Transcript
Midroll [00:00:00]:
This is the Everyday AI Show, the everyday podcast where we simplify AI and bring its power to your fingertips. Listen daily for practical advice to boost your career, business, and everyday life.
Jordan Wilson [00:00:18]:
Be honest. How has your working relationship with AI changed in the past few years? Are you still copying, pasting into ChatChippity 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 learned to prompt. In 2024, hopefully you learned to iterate. And in 2025, you started wondering why this still feels so manual.
Jordan Wilson [00:00:59]:
And now in 2026, the skill gap isn't technical anymore. It's managerial. It's human. 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.
Jordan Wilson [00:01:31]:
Does that get you a little fired up to re examine your working relationship with AI? I hope it is, and welcome to volume four of the start here series with everyday AI. We're gonna 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 four. This is essential beginner and advanced AI 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, 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 gonna take you straight to our start here series page.
Jordan Wilson [00:02:29]:
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. It's a daily livestream 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 gonna do about a dozen or so of these episodes in the first part of 2026, that I hope is gonna 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 missed 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.
Jordan Wilson [00:03:23]:
We're gonna be updating the show notes of all the start here series, so you can very easily if you're listening to the podcast, just flip around that way as well. Alright. 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 chat g b t moment of November 2022, 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 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.
Jordan Wilson [00:04:21]:
You set the parameters, the constraint, and the success criterias. And the AI is probably gonna 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 this big shift. And I think the winners of this era aren't those that who can effectively, you know, onboard, supervise, and audit digital agents. 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.
Jordan Wilson [00:05:05]:
You are the manager. You're not out on the field doing all of the work. So here's what we're gonna cover in today's, iteration of the start here series. We're just 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 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? You guys remember the, the the infomercial.
Jordan Wilson [00:05:44]:
Right? You slap some flax 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. That's the wrong way to do it. I think that it's about unlearning. Right? I'm gonna be griping on a couple of these terms that I absolutely hate.
Jordan Wilson [00:06:03]:
One is human in the loop. I'll get to that later. One is upskilling or reskilling. That alone, I think, has set so many companies back years. Because when, you you know, the c suite and the boardroom, you you know, go around HR and they're looking at, you know, AI investment. They're like, who needs upskilled? Who needs reskilled? They're looking at it almost as a, proactive or or sorry, a reactive measure. Right? It's almost like a course correction. No.
Jordan Wilson [00:06:35]:
Who needs who needs upskilled this year? Who needs reskilled in AI? No. You need to unlearn. Right? I've been, chirping out, this unlearn word for a very long time because it's how I really started getting the most out of AI myself 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. So if you're still holding on to those skillsets, right? Whether you've been using them for two years or thirty, 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 gonna 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.
Jordan Wilson [00:07:46]:
Right? So we think of this concept of human in the loop, and that's our job. Right? And that's a job that we thought was gonna last into the, you you know, into the late twenty twenties. 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 AI is doing. No.
Jordan Wilson [00:08:12]:
It is dead. Right? Because agentic workflows really create miles long action traces that humans cannot realistically interpret. We can't keep up. The complexity has out far outpaced what a human can review. Right? When you see a a large language model or if you're working, with agents that have sub agents and they can work around the clock, what good is a human gonna do at that point? Right? A human in the loop. Right? Like, I mean, let's say what it is. It's it's 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.
Jordan Wilson [00:08:54]:
Right? It's your last hope that, you don't make a tragic mistake from, what a bunch of agents did. Because if you're human in the loop, can't push back, pause, or ask hard questions, that's not even oversight. That's a fail safe, probably in 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. My gosh. So this isn't hype, this is measurable. And the problem is, well, models are smarter than us.
Jordan Wilson [00:09:34]:
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? And that's what you should be focusing on and double down, doubling down on and seeing where the overlap is between that one thing and what ultimately makes your company or your team more 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 I'm I'm not gonna say every single use case. Right? But I'll say the overwhelming majority of use cases. If you have an average skilled human, right? Pick pick your pick your niche, pick your vertical, right? Financial analysis, marketing, PR, whatever.
Jordan Wilson [00:10:39]:
Pick your job. Find an average person. Someone that's, you 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. The smartest person in that vertical cannot compete with the person who knows enough. Right? They speak the language, but is a whiz at AI. It's not it's it's not close. Assign that a competition. Right? That's like having 20 Michael Jordans on a team versus, you you you know, a pee wee team.
Jordan Wilson [00:11:15]:
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 ahead. They can 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 you know, what I like to call mixture models, that that judge all of those inputs. Right? You humans can't compete anymore. So why? Why are we still thinking that the human AI collaboration in 2026 when it comes to AI is human in the loop? It's not. Right. There was a, a recent study that looked at 700 plus court cases worldwide now involve AI hallucinations and fabricated citations.
Jordan Wilson [00:12:15]:
And that rate is accelerating to to a handful of new k, 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 gonna have it do most of my work. It's gonna be right because my company is paying for it, and they're not training me on it. And this is a huge risk.
Jordan Wilson [00:12:45]:
You know, JPMorgan, they acknowledge this risk openly. They recently talked about that when systems perform correctly, most of the time, human attention drifts. Right? But if AI is right 85 to 95% of the time, well, your human in the loop falls asleep. Right? Or your 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? They just let everyone go in. They don't care.
Jordan Wilson [00:13:21]:
They're like, alright. 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 a a concepts, a concept here that I wanna talk about.
Jordan Wilson [00:13:36]:
It's Amdahl's law. And that's essentially, you can speed up one part of the process or many parts of the process, but the whole system is bottlenecked by whoever can't keep up. Right? It's the, the old sports, you know, cliche or, you know, teamwork cliche that you're only, you know, strong as your weakest link. And this is true for the human AI collaboration. Right? And, 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 working on on front end AI strategy, back end AI, implementation, go through and ask these questions.
Jordan Wilson [00:14:26]:
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 and how do we start to change that human role? Because, yes, humans are still needed in all this. Right? I never said that, and I I'm not implying that. Human roles are changing, and I'm gonna 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? Like, people, like, people are like, okay.
Jordan Wilson [00:15:11]:
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 its dive. And that's why the fix is expert driven loops. That's what I've been advocating for, you know, EDL, for, for a long time now, not just generic oversight, because the difference is when you embed experts in building, right, whether it's you know, we don't have we don't even 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 in your chat GBT teams account. Right? Or how you're gonna tackle work and clog cowork. A lot of times you have one person, sometimes it's IT, sometimes it's, you know, your your your AI champion team, which is important. And they kind of do it for everyone, and they set it up for everyone.
Jordan Wilson [00:16:24]:
And that's, again, the wrong way. You know, one thing that was interesting, 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, you know, not in a bad way, but, you know, kind of the human in the loop overseeing AI. A lot of times they put younger people or more inexperienced people, 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 gonna put my senior people on this? Why am I gonna put my smartest people on this? So in this case study from Legalon Technologies, they found 86% faster contract review and 65% better issue detection when they had senior partners instead of junior reviewers.
Jordan Wilson [00:17:23]:
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. 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 it's it's again, it's not putting one expert, you know, on one AI powered workflow or one, 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.
Jordan Wilson [00:18:10]:
And other studies show this other organizations are seeing the ROI triple when they move from generic oversight to expert driven collaboration. So how does this get to this point? Right. And it's almost like the better the 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 nineteen minutes. You could have set hundreds of agents up in nineteen minutes. I kid you not. Right? 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.
Jordan Wilson [00:19:12]:
So I like to say this. If you had a bad workflow with AI and you upskilled or reskilled that workflow, right, you you can't just throw makeup on an ugly process and think it's gonna be pretty. 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, and 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 in the right processes. Right? You're putting anyone in old processes.
Jordan Wilson [00:20:09]:
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. 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. You know, I actually don't have agents running right now.
Jordan Wilson [00:20:55]:
When 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 gonna be one of those buzzwords of 2026. Right? In 2024, I called it, first company data. Right? 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.
Jordan Wilson [00:21:35]:
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 do its, does its thing. Because, you know, before when the models were just, you know, non reasoning models, non thinking models, they would just spit something back pretty quickly. Right? Now they might go work for five minutes. Right? Ten minutes longer. I have one run the other day on the front end, not on the back end. Right? On the back end, if you're using it via the API, it's pretty easy to get these things working for hours.
Jordan Wilson [00:22:11]:
On the front end, you know, yeah, you might have a model work for five, ten, fifteen, twenty minutes. I had a an actual model, not a deep research run, run for, I think, ninety two minutes, the other day. Right? You have to give it the context. And then at that point, you're orchestrating. Right? I'm 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.
Jordan Wilson [00:22:40]:
Right? If I'm in Claude, I'm let's I'm saying let's update this skill. Right? 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. And that's why we're talking about these things like agent supervisors, app 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 twenty twenty two 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.
Jordan Wilson [00:23:34]:
Right? Treating AI like an operating system. This part, volume four here, while 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.
Jordan Wilson [00:23:59]:
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 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, ten, fifteen, thirty 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.
Jordan Wilson [00:24:52]:
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 wanna 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. 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 aren't 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.
Jordan Wilson [00:25:48]:
Then there's things that they kind of fail at. 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. Right now, AI wins at pretty much everything else. Right? Data synthesis, first draft, pattern recognition, repetitive cognition. Right? All of those things.
Jordan Wilson [00:26:30]:
Those are the danger zones for if those are your competitive notes or you think that's your competitive advantage right now. Right? If you think, one of those things, data synthesis, first draft, pattern recognition, if that's what your department, your career, your company is built on right now, you you you've gotta find your 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 worse outcome and a worse 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, you know, your, 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. 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.
Jordan Wilson [00:27:46]:
Stop starting at zero, every single prompt. You have to start building, context vaults, right? You can think of those as skills, right? You can have a markdown file with all your skills, with your company knowledge. Right? 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 GPT, you can do it there as well. Right? But you need to be building and reusing these, you know, custom GPTs inside of, inside of chat GPT, the clogged projects and skills, Google gems, whatever. You need to have that rag for your personal use.
Jordan Wilson [00:28:36]:
Right? We think of when we think about retrieval augmented generation, we we used to think about these 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. What are those things? I 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 facts 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.
Jordan Wilson [00:29:17]:
You have to have these things that are reusable because if you're just starting at zero, 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. And then last but not least, you need to elevate your champions. Okay? Here's what I mean by that. Funny.
Jordan Wilson [00:29:49]:
I had, Chris Caldwell, the CEO from concern, Concentrix on, a couple weeks ago. And he kind of said, hey. You don't want a 100 Jordans running around, do 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? Every large organization needs dozens of people who are in my position. All they do, they, they read about AI every day.
Jordan Wilson [00:30:29]:
They're scoping 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 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, 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.
Jordan Wilson [00:31:19]:
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 gonna exist in three years. And you need those people, 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, AI podcasts and other, you know, YouTube people on AI reading newsletters, and you need to be scoping, breaking, and and training people every single day. But the largest organizations aren't even doing that. So stop looking for cool AI tricks, focus on automating the dull stuff first. Right? Get rid of shiny AI object syndrome.
Jordan Wilson [00:32:18]:
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 gonna become your company's 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 that we just laid out in reexamining the human AI collaboration, going through the best practices of shifting away from being the operator, from being the button, 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. Alright. I hope this start here series was helpful.
Jordan Wilson [00:33:17]:
Volume four done in the books. So if this was helpful, remember, please go to starthereseries.com. That's gonna give you access for free to our inner circle community, and you're gonna see all of the start here, episodes right there. We're gonna 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 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.
Jordan Wilson [00:33:55]:
Thanks, y'all.
Midroll [00:33:58]:
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. It helps keep us going. For a little more AI magic, visit your everydayai.com and sign up to our daily newsletter so you don't get left behind. Go break some barriers, and we'll see you next time.