71 | How Rachel Woods Uses AI Playbooks to Scale Operations

Rachel Woods believes the biggest AI skill isn't coding, it's systems thinking. In this episode, she joins Audrey Chia to explain how businesses can transform everyday workflows into AI-powered playbooks that scale execution across teams. Rachel shares her framework for identifying AI opportunities, training employees to work alongside AI, and creating repeatable processes that can run across tools like Claude, ChatGPT, and Copilot. She also demonstrates a project-planning playbook that helped her team execute more projects with greater consistency and efficiency. 

From AI adoption and workplace change to practical implementation strategies, this episode offers a clear blueprint for leaders looking to build AI-first operations.

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Transcript

Audrey Chia 0:01

Hello, and welcome back to The AI Marketer's Playbook, where we cover actionable frameworks to help you leverage AI and marketing strategies in your business. I'm Audrey Chia, your host, and today I have with me Rachel Woods, CEO and co-founder of AMP, which stands for AI Momentum Protocols, an AI operations education and implementation firm helping businesses turn AI into an actual teammate. Now, Rachel has been working in AI for over eight years and started her career as a research data scientist on Facebook's AI team, even before most companies were talking seriously about AI. Now, she's also the person who coined and developed what is now known as AI operations, and has trained more than 500 AI operators through her AI operator bootcamp. Rachel has been featured in Forbes, on Tony Robbins, and across major publications. But what I love most about her approach is that she's really technical, but also incredibly operational. So Rachel, welcome to the show.

Rachel Woods 0:57

Thank you so much for having me. It's so good to see you. I'm so excited for this conversation. It's gonna be a blast.

Audrey Chia 1:01

Awesome. Can you tell us a bit more about, you know, your backstory? Now, I know you were working in AI long before the current AI wave. What first pulled you into this space?

Rachel Woods 1:11

Yeah. Um, you know, it's so funny, I feel like I was just a nerd, and then I became a cool nerd because the world started caring about what I was nerdy about, which is always convenient when that happens, right? Um, and, and so when I first got into AI, um, it was a little bit, I wouldn't say by chance, but it was, I was mostly obsessed with how technology and business kinda came together and could solve problems, and I liked marketing a lot. And so whenever the job came up to be able to work at Facebook in their AI research team on the ads stack, you know, as someone who likes those three things, it was just like, "Oh, my gosh, dream job. Of course I'm gonna do this." Little did I know how much I would learn about, again, like, AI and its capabilities then, um, not to mention how much the tech has progressed since. Uh, one of the things that blows my mind is I was using language models back in the day, but they weren't anything nearly as large and impressive as they are now, um, and just how the world has, has advanced.

Audrey Chia 2:06

Wow. And what do you think is the biggest change you have seen, you know, besides the advancement of it? The way, has the way people use AI also changed along the way?

Rachel Woods 2:14

Oh, absolutely. I mean, whenever I started my career in AI, you would really have to have kind of a machine learning team, data scientists. It was a pretty technical build-out to even start playing with this stuff. You'd also have to have all your own data centers and warehouses and all this stuff to train the models. And then maybe once you set all that stuff up, then you could start experimenting and maybe get something kind of interesting and useful. And so you had to, there was so much of a startup cost to using AI. And then, I mean, now we can just be on our phones typing in to chat, asking AI to do completely new and novel things for us. So just a complete 180 of how accessible it is. Um, and whenever ChatGPT came out, that ended up being my, like, oh, my gosh, this changes everything moment from an accessibility standpoint.

Audrey Chia 3:01

Wow. Um, I'm curious to know, Rachel, so you coined the term AI operator, right? Tell us more. What exactly is it, for folks who may not know?

Rachel Woods 3:10

Yeah. So I think that one thing that's really helped me is looking at AI as really being a very big umbrella space, right? There's tons of different ways you can use AI. There's AI in the, you know, more creative space, especially with, like, image generation and video. Um, there's AI in the traditional sense with machine learning models and all the algorithms and stuff. And I, uh, from both the AI background and I've also been an entrepreneur since the age of 15, um, I kinda fell in love with this use of AI in business to help it, um, or help you operate better, right? To be able to think about what you want done, and then to organize it in a way that then AI can help take that stuff off your plate. Um, and so whenever we first started using AI in this way, you know, in the systems and processes in the business, there really wasn't a word for it. This was back in early 2023, and I remember sitting down with my co-founder, who's my sister, and, um, our team at the time, and we were throwing all sorts of words up on the wall- of, like, should we call this, like, AI workflow implementation? We're like, "No, that's, that's too specific." Um, or should we call it... You know, there's just so many different types of words, and we finally were like, "You know, this is just the new version of operations," right? Wow. Which is AI operations. Um, and so that's why we decided to start calling it that. And then, of course, the hat that you wear when you're someone who is using AI for AI operations naturally lends itself to being the AI operator. Um, and that's kinda how that all came to be, and it's been fascinating to see then, you know, in just the last few years, how widely that's been picked up, and I lo- I love it, so.

Audrey Chia 4:45

Yeah. I saw so many folks who call themselves operators, go-to-market operators for AI or, like, fractional AI operators. I mean, it's very cool to know that you ladies are the one who started it all- just from, you know, an idea in your head.

Rachel Woods 4:58

Yeah. At least we, uh, we sure did argue over what was gonna be the best term- that made sense for us. So this is what, you know, just what we started calling it, so.

Audrey Chia 5:07

Yeah. Awesome. Yeah. But can you tell us a little bit more? When you say operations, 'cause in a business, right, ev- so many things, you know, can be supported with AI. But what are some very tangible examples? So let's say you go into a new business now. What are some areas you look at?

Rachel Woods 5:22

Yeah. So when I think about AI operations, it's really just all of the systems and processes that power the business. So it is across every department, from marketing to sales to how you onboard clients or customers to the support you get, even to how you operationalize maybe your service so that you can efficiently deliver a service all the way to your, uh, finance and, you know, HR or their planning functions. Operations really does touch all of those pieces. Um, and so there are ways to look at the processes in each of those departments and then see, oh, okay, like, we could either have people do this process or how could we set it up so that AI can do it. Um, and that's what we spend our time on is, like, how do you take a process that was being done by hand and now set it up so that AI can do it reliably? Uh, and our method that we use, that we, we call it, is our playbooking method. So it's kind of funny 'cause I'm on the Playbook, uh, you know, podcast and, um, obvi- obviously it's a word that's, uh, near and dear to both of our hearts, but that's what we call, um, whenever we take a process and turn it into something that AI can do.

Audrey Chia 6:27

Wow, interesting. Given that there are so many functions where AI can support, right, how does a company even decide where do I begin? 'Cause technically they have so many things they can do, everything is possible, but at the same time they might have to figure out where to start and I'm sure there could be some hesitation, uh, especially initially when they don't know what's gonna happen and whether it's gonna work or not. So where do companies begin?

Rachel Woods 6:50

So there's two ways to think about where to start. One is really focusing on the word operations, right? Which is if we just took AI out of the equation, where would you invest in your operations? And you'd probably do it in the stuff that's gonna have the highest impact or, you know, if we solve this we would have a breakthrough in the business and be able to do more or grow more. Those are the same types of areas that usually have really high impact AI operations projects, um, because it's, it's really the same thing, just a new flavor. Um, but maybe my more counterintuitive advice and observation from now doing this, this specific work for three years has been, um, that actually if you try to pick the best use case as your very first use case, you are overcomplicating what you need to actually be focused on and you're gonna go slower than if you instead just prioritize what's gonna be the fastest way for us to get momentum and for us to learn. And so when we work with teams, we say, "Amazing. So glad that you have really high impact stuff that you wanna use AI for, and l- great, let's put that second on the roadmap." The first thing we should do, though, is this is a whole new capability and mindset and way to think for- Mm a team of how to use AI, how to be the one defining what the AI does, how to check the AI's work. And so let's actually just start with the easiest possible, you know, use case or s- you know, something that's still gonna be useful, but, uh, that gets the team momentum, and that way then everybody can learn, and then your second project goes so much better.

Audrey Chia 8:14

Oh, that's a very interesting perspective. I think people are always looking for, like, an ROI first kind of project, but like what you're saying- Totally starting from the foundations. so Rachel, you talked a bit about the team, right? I'm curious to know, for a team, right, a team member, how do you actually teach them or upskill them so that they are comfortable using AI and they know when to use it?

Rachel Woods 8:33

Yeah. So what we've found is it's best to think about, again, this, like, AI operations bucket more as a capability that's gonna be across all sorts of stuff that you're doing.

Audrey Chia 8:44

Mm-hmm.

Rachel Woods 8:45

And so it's kind of similar to the first time you learn to, like, write down a process or make a checklist. Yes. You wanna give people the opportunity to playbook or create a process AI could follow, um, for even just a small piece of their work. And once they have that first win, then it clicks, and then you can start using that momentum to do more and more things.

Audrey Chia 9:05

Hmm. Interesting. And do you feel like a lot of team members are also resistant, or are they pretty open to adopting AI?

Rachel Woods 9:13

It varies, and what we found is the biggest indicator is not actually the person, it's more about the culture of the company.

Audrey Chia 9:21

Oh, how

Rachel Woods 9:21

so? So I'll give you an example. Mm. So if you're, um, if you're somebody that is maybe hesitant about AI, but you work in a team where it's very clear that there's more than enough work, more than enough demand for what the company does, and so okay, like I could see how if AI did some of these things I'm doing, there's tons of stuff that's c- now I can do instead. It's a lot easier for you to then lean into and trust that process versus, you know, frankly, we do work with teams where I think it's very valid if you're sitting there and you're like, "Well, if AI's gonna do half of my job, but we don't have more work for me to do, then what?" You know? And so, um, I think it's, yeah, like those are bigger culture and business viability questions. Um, but if you have the demand and a really strong business, giving people and painting that path for them, uh, we've seen people really open up.

Audrey Chia 10:16

Interesting. I also wanted to talk about maybe a slightly more sensitive topic, but I think it's also important to discuss. So in Singapore recently, we had a wave of, uh, layoffs from tech companies without a lot of warning. Um, some people found that they had no jobs the next day, and there was also like a sweep, you know, a sweeping statement their companies made about, uh, p- them using AI, hence some functions were no longer, um, as necessary as, as before. So I was curious to know, so what are your thoughts on where, where does the pe- the person come in when AI can take over maybe not everything, but lots of functions that, like what you said, people used to do?

Rachel Woods 10:53

So with any thing you're gonna have AI take over, if you wanna trust that it's doing a good job, you need somebody that has the judgment to define what good looks like. And that really is what we've seen as like the role that has to be human, has to be based on real expertise, has to be based on, um, the background experience that someone has. Like, that's how you have the judgment to even know whether the AI is doing a good job or not. So I think what's really challenging right now is there are a lot of companies that, uh, they're having to shift around their workforce because they, um, you know, are bringing in AI to some areas where then, you know, one person can have a judgment and that actually covers 20 or 100 previous roles. Mm. Other places, though, it's You really have a closer, like, one-to-one relationship, right? Yeah. Where it's like if one person's doing something, and now AI comes in and helps with that, great. That one person can now do more.

Audrey Chia 11:54

Yeah.

Rachel Woods 11:54

Um, so I think if I You know, when I talk to anybody about how they can position themselves, I really ask, like, "What do you feel like you're really an expert in? Where do you have judgment?" Mm. "Where are you excited to build more judgment and more expertise over your career?" That should be where you're investing your time, and then learning how to be the AI operator that's running the AI that helps you, um, amplify what you do.

Audrey Chia 12:16

Definitely. I think also for me, um, having that mindset of, okay, what is Like what you've mentioned, the higher level skill set, uh, that can continuously value add to the team. Um, what are, what are the human skill sets that you definitely need that cannot be replaced? Perhaps even, like, relationship management, right? Uh, these are things that are one step above, uh, that you still need that human element. Then number three, learning how to adopt AI or be comfortable with it so that, um, when AI processes come into play, you don't feel too overwhelmed. Now, Rachel, going back to what you do, right? So you talked a bit about the playbook. Can you tell us just a snippet of what you cover in the playbook and, and how companies can go about their own processes?

Rachel Woods 12:57

Yeah. So in our world, playbooking is really a method for how to take a process and turn it into a process that AI can do. You can give these, um It's really just a document that outlines the step-by-step of that process, and you can give that document to an AI in a chat tool. You could give that document to an AI agent, and you can even have systems of playbooks that are run by systems of agents. Um, and so it's almost this, like, building block of defining what you want the AI to do and building your operations, uh, through writing these playbooks.

Audrey Chia 13:30

Mm. And when companies first came to you versus now, right, that has been, like, three to four years, do you think there has been any change? Are they more ready compared to before?

Rachel Woods 13:39

Oh, completely. Yeah. And also, the, the tech has changed a lot. So in the earlier days of even ChatGPT, when you started trying to have AI do more complex stuff, usually you had to go into automation tools, right? Like a Zapier or an n8n or, like, some of these more technical tools, and it just took longer to set all these things up. Um, you know, I'm, I'm curious if you're using, like, Claude Skills and, uh, some of these, uh, things that have come onto the scene in, in the last six months. I, I'm sure you're using it, uh, to the Nth degree. But, um, it's so much easier to set these things up now. The hard part is deciding what the process is in the first place. Um- Mm so I think not only the companies are more eager, but the tech also is so much easier now- Yeah which I think is really great.

Audrey Chia 14:27

Yeah. It has made it so much more accessible for non-technical folks, and I think one of the other, um, uh, uh, someone I was speaking to recently said, "You don't have to be technical at all, Audrey. You just gotta go for it and then see what you manage to build with AI."

Rachel Woods 14:41

Yeah, I think that the real hard part and the skill is the systems and process thinking, right? Um, and so you don't have to code anymore to do any of the stuff the AI does, just build itself if you can think in the systems and process, you know, so it stays organized.

Audrey Chia 14:56

Yeah. Let, let's unpack that a bit, right. So you mentioned systems and process. So even for me, like, if I were to prompt for a copy, I break it down into, like, seven to eight steps. So that's my own internal system of how I break things down. Can you give us an example of how does a business take one use case and break down their process? What does that look like?

Rachel Woods 15:14

So just like you're describing, um, I think we as people tend to skip a bunch of steps when we think about the work we do. And so usually, um, what we find is when you really sit down and force people to be like, "Okay, but then how do you do that? Okay, that actually sounds like three steps right there." And you, you kind of pull apart what is the unspoken logic that might exist in a process. Um, there are a lot of ways to do this. One of the, you know, most straightforward ways is to make a process map if something is more complicated. Um, the other way is to really just say, "Hey, Audrey, if you had to teach me how to write copy like you do, what would be your best effort and way of breaking that down in a way that I could follow?" Okay. Mm. That's probably the start of what the playbook could look like. Um, and then one of the best things I really like about getting to make these processes that AI runs is it's really fast to iterate, right? So- Yeah um, you know, you could think, okay, here are the different steps, and start running it, and then you're like, "Oh, actually, step four might need to switch to step five." And so you, uh, have a really fast feedback loop to get better about your process.

Audrey Chia 16:24

Oh, interesting. Um, I... Now, when you mentioned it, I also thought about, you know, my own kind of workflows. And sometimes I realize that as a copywriter in the past, I would used to come up with copy, just, you know, skip all the micro steps in my head, right? But then when I had to prompt for AI, uh, with AI, I had to teach it almost how I came to that landing page copy. So- Totally then figuring out what are all the steps before. And I realized that it actually took a very structured way of thinking, um, that I think not, that doesn't come as naturally to everyone. So it's almost like training yourself to view things in a different lens so that AI understands you from that lens.

Rachel Woods 17:02

I completely agree. It's, um, I think I've seen it really most similar to people's ability to delegate, right? Mm-hmm. Like delegating to somebody else is actually really hard. Yeah. Uh, you don't like wake up, you know, or you're not born with the ability to delegate. Usually you have to like learn from somebody that was showing you stuff, or like you try delegating, it doesn't quite work, and you kind of build your own way of how to, um, empower somebody else to do, you know, work the way that you want or need it done, um, or the outcome you need. And so it's, it's just like that, but for AI, you know? You have to learn it.

Audrey Chia 17:35

Yes. Yeah. And the more specific you are, um, with your micro steps or instructions, then I guess the more AI understands what you want. So I think also being able to build this ability to break down your own thought process, see your, your work in systems, um, is very important. I've learned now that I can no longer just be a creative. I need to be a creative person and a structured person at once. Um, that's where AI comes in. So Rachel, having said all of those things, would you be able to share what it actually looks like? Do you have a kind of demo or something that we can share with the audience so they know what it looks like in practice?

Rachel Woods 18:09

Yeah. Um, I have one that I pulled up. So if I were to, uh, zoom out for a second, so what, you know, we do is, uh, we take processes and we turn them into AI playbooks that AI will run. Um, what that looks like is usually we're writing that process out first in a Google document or like just completely outside of the tool we're using. And the reason is, that gives us more space to actually think about, like you're saying, how we wanna break up the work into the smaller tasks. Then once we create that process, and I'll show you the structure in a second, then we have this philosophy, we call it own the playbook, rent the tech. So you can actually run that playbook that you wrote in any tool. So you could run it in Claude Skills, you could run it in, uh, ChatGPT, custom GPTs. Um, you could run it in like Microsoft Copilot agents if you want to. Ooh. Like really any, any tool out there that can follow instructions, you could run a playbook in it. Um, and what's nice about that is then you're actually building like your operations as an asset that you own instead of these tools. So I pulled up, um, like I, you can playbook anything, so I could go through so many different examples, but I wanted to pull up, um, one of my unexpected favorites.

Audrey Chia 19:22

Ooh.

Rachel Woods 19:23

Which is my project planning playbook And the reason that it's unexpected is 'cause you might think project planning, that's not sexy. Like- Mm we're not gonna do, we're not gonna do like, you know, a marketing, some marketing thing or making an analysis or report or research or a contract review or something. But, but actually, um, once I created this playbook, I realized, wow, like I plan projects all the time. And you know what? If you actually turn project planning into a process, then, uh, not only... For us, we saw like our projects, we were planning them better, which means they go smoother. You don't have like, you know, down the line, oh, we didn't think through blah, blah, blah, or we didn't decide who's gonna own X, Y, Z. Uh, and then I'll show you one of the coolest things is this actually sets it up so that, uh, then the AI can even help you execute the project more easily. Wow. So-

Audrey Chia 20:13

Let, let's see it.

Rachel Woods 20:13

Yeah. So this is a fun one. Um, so let me see if I can screen share. All right. Can you see this in Claude? Yes,

Audrey Chia 20:23

perfect.

Rachel Woods 20:24

Okay, great. This is a little demo account I have. So, um, uh, this is not the full glory, I will say, of the project planning playbook that I used, uh, I guess I probably used it twice today. Um, but this will give you a good idea of, of what I'm talking about. So, so in Claude, in, uh, Skills, um, what I've done is I've uploaded my project planning playbook, and really again, a playbook is a process. And so for it to be a playbook, um, we say it needs four components. It needs to have a trigger, which is what starts the playbook. It then needs to have inputs, so what is the AI gonna ask for before it starts running the playbook? And then it needs to have steps. Um, and so in here I have step one, step two, et cetera. And then the very last thing is it needs, um, outputs And then inside here for each of the steps, so let's go to this one, uh, we have a lot of very specific instructions and I'm probably not on time to go into all this today, but, uh, basically kinda like you're saying, just the more specific you are, and we have some ways that you can get really specific to make sure that it follows these instructions.

Audrey Chia 21:35

Mm.

Rachel Woods 21:36

And so if you, um, I know, I mean, I'm sure you've seen tons of different skills, setups in your day, right? I would describe this as like, it's like the playbooking method helps you write a skill that's structured as a process versus there are a lot of other ways to build skills, right? Different, different structures.

Audrey Chia 21:51

Can you explain that? What do you mean by helping you to build skills as a, like, process?

Rachel Woods 21:56

Yeah. So, when you, um... I don't think I have any here of, like, non-playbook skills. But a skill really is just saving instructions in your Claude account that then the AI r- can use next time you're doing that type of task. Yes. And so it could be as easy, or, uh, an example of another type of skill would be if you wanted, like, a brand guideline skill that had all your brand guidelines. That doesn't need to be structured as a step-by-step process. It'd be a little more like a reference guide or doc, right? Versus this is a skill structured as, like, a playbook, where it's very specific of, like, that, you know, you want that step-by-step process. Does that make sense? I

Audrey Chia 22:38

see. So when you're structuring your playbook, basically it needs to wait for step one to be done before it goes to step two, so then it forces it down a very systematic kind of funnel.

Rachel Woods 22:47

Correct. Okay. So then it's more reliable, and you can actually trust it to do, uh, more in-depth work.

Audrey Chia 22:54

Got it. Yeah, I think also- Mm-hmm 'cause, uh, when you mentioned a brand guide, I also have a brand guide skill. So, uh, in that brand guide skill, for listeners who may be new to it, I put in information like my company colors, fonts, logos, um, X, Y, Z, right? So then it knows how to build a document that looks like Close with Copy's document. Um, in the same way, like, in that case, I'm giving it a ton of knowledge, but I don't have to necessarily get it to follow each step because it's just drawing from that pool of knowledge. Whereas I think what Rachel is also sharing is a very systematic way of thinking 'cause it's forcing it to go through each step and even you have things like wait till you confirm step one, right, before you move to step two. Um, I think- Yeah that helps the process as well.

Rachel Woods 23:33

Yeah, totally. It's like almost the skill you're describing is, like, a context skill, right? Um, versus this, yeah, is the process-oriented. So tons of use cases for skills, right? Um, and I think that's actually... All these AI tools have tons of different ways to use them, and so it's about, you know, learning the methods that work for you. But, um- Awesome. So

Audrey Chia 23:51

how does this playbook play out?

Rachel Woods 23:53

Yes. So this playbook, uh, so once I have it saved, and I'll, I'll just run it as our way to see what the steps are. Um, I can say, "Help me plan," and I can just p- uh... If you do slash, you can actually search for the skill. Um, and then I will say, uh, let's start there. So what it's gonna do is it's gonna go read that skill

Audrey Chia 24:24

So while we're waiting for the skill to load, I wanted to ask Rachel, how long does it take for you to personally develop a skill?

Rachel Woods 24:31

It's a good question. Um, we, and how I make playbooks is I outline them by hand, and then I use AI to fill in the instructions, but then I do still go through and, um, review and edit and, like, make sure that it works 'cause I want the process to be something that, like, I trust. So it's pretty fast, but I will say it's, it's probably a little bit slower than a lot of people that, you know, are like, "Oh, I just want AI to generate everything for me." Yeah.

Audrey Chia 24:58

Yeah.

Rachel Woods 24:59

So, um, so yeah. Okay, so load up the skill, and then what it's doing is, as you can see, it's already following step one. Um, so for this one, it's saying, "Okay, well, I don't really... You know, I don't have the project that you're trying to do at all, so you need to share all this context," um- Mm which is great. So I'll say, okay, so the context, um, we're working on, uh, let's see, recording an awesome podcast with Audrey, um, and want to, uh, make a cool demo. Goal: explain playbooking. Constraints... C- of course, you can't type when you're trying to, but that's okay. AI can understand-

Audrey Chia 25:47

Yeah

Rachel Woods 25:47

typos. Um, how it worked. Uh- So let's do that. And so what this is, and I wanna pause on this while that's running, um, is it's following my first step, which if I go in here, is I told it like, okay, a vague pitch, which is what I use to describe the start of a project, um, produces a vague plan. And so it's gonna ask me for, uh, any information across the context, goal, constraints, and succ-success criteria. Yeah. Um, ask with clarifying questions, and if still unclear, ask more focused batch. And so you can see now that it did that. Um, let me see, make assumptions

Audrey Chia 26:32

I like that Claude asks a lot of clarifying questions, and I realize that Claude asks better questions than ChatGPT. ChatGPT doesn't typically ask questions to you, just, "Okay, run." But Claude does ask you a little bit more.

Rachel Woods 26:43

You know, that's funny because, um, I've observed the same thing, and then whenever you're playbooking, you wanna think about, like, do I want it to ask questions?

Audrey Chia 26:52

Mm.

Rachel Woods 26:53

Um, and so actually for a playbook, so we say, like, if you write a good playbook, it should run the same in any tool. Um, but if you run that playbook in Claude, you don't specify, it's definitely gonna ask you follow-up questions, right? So, uh, if you don't want that, just putting that in your playbook, don't ask any follow-up questions, you know. Like, for this one, it, I think it would've asked more follow-up questions, but I had said in here, "Do not proceed past two rounds," right?

Audrey Chia 27:16

Mm.

Rachel Woods 27:17

Um, and so then now it's going to step two, so brainstorm approaches. Uh, I used an approach called the, I call it the, uh, the 1-3-1, which is come up with three options and recommend one, uh, end result, and so that's what it's done here. Um, sounds good. And so you can see it's following my playbook. So step three of this one is to determine the timeline and tasks.

Audrey Chia 27:42

Wow.

Rachel Woods 27:42

When does the pro- progress record? Today. What playbook are you generating? Um, I'm gonna actually say LinkedIn post playbook. And then, uh, nope And so that's doing step four And it's making the task list. Um, I can say it looks good And then step five. So in our project planning process- Mm-hmm before we were using AI, you know, just stop here, right? Okay, made a, made a plan.

Audrey Chia 28:18

Yeah.

Rachel Woods 28:18

But what's great is because AI's helping me plan the project, I could add another step, which is like, "Hey, AI, you know what you can do."

Audrey Chia 28:25

Yeah.

Rachel Woods 28:25

Can you tell me what tasks you could actually start on?" Yeah. Wow. Um, and then I can say like, "Yep, looks good." And then, um, I have it make a final plan, and then I believe I had this one also sending into ClickUp. Um, yeah, so I don't know if my demo board is connected, but-

Audrey Chia 28:47

Got it. So you also a- add additional connector so that then you can ship it to, like, a ClickUp or any other platform, um- Yeah and then you can, you can review it there.

Rachel Woods 28:55

Exactly. And then for playbooks, what's great is, like, it's just as easy as Let's find that step. Add to ClickUp. Um, it's just as easy as saying, like, you know, use ClickUp. Now, if you were using a custom tool, you'd wanna put the custom tool name in here. Yes. But, uh, yeah, AI's pretty smart, so.

Audrey Chia 29:12

Wow.

Rachel Woods 29:13

So, yeah. That's so

Audrey Chia 29:13

cool.

Rachel Woods 29:14

Um, so yeah, now it's Oh, it's cr- trying to create it in my actual board. So we'll pause there. Wow. But, um, but yeah, so that's a playbook. And again, this is a really simple one for something that every single person plans projects, right? Yeah. And so what we've seen is, like, this, once we started using this as a team, we were able to- Yes we measured it. We started doing three times the number of projects- Wow because they're just planned better, and then AI does, like, half of it before you even start. Yeah. Um, and this is the exact same approach you can do for, like, contract review for, uh, potential brand partnerships, for example. Mm. Um, or writing your newsletter, or, uh, running and pulling, like I just did a webinar this morning, um, for our community, and we have a playbook that pulls all of the webinar registration data, who showed up, what questions were asked in the chat, pulls it versus the transcript, produces this really lovely analysis of, like, what are some opportunities to improve the webinar for next time. Again, all a playbook, all in this exact same structure. So, um, it's very- Very

Audrey Chia 30:10

super cool

Rachel Woods 30:10

very powerful. Yeah.

Audrey Chia 30:12

Yeah. I think also the possibilities are endless, right? You just showed one use case, but I'm sure if you are listening, you probably have so many other amazing use cases, um, that you can already start thinking of, even as you watch Rachel's demo. Now, Rachel, you said this playbook can be applied across different, uh, tools, right? Do you have a personal favorite?

Rachel Woods 30:31

I, I'd say it's it's tough. The tools, especially ChatGPT and Claude, I kind of view them as, like, really competitive sisters. Mm. Like, they're just always gonna be kinda outdoing each other a little bit. So I actually also really like that I can move my playbooks across these tools and, um, that's a, it's a huge benefit. Uh, I will say our company does run off of Claude. Um, so it does help in a team to have one tool and, uh, and Claude's, Claude's quite good and they have a lot of, again, like the connectors and all those features and stuff. But yeah, I, I like them both.

Audrey Chia 31:06

I, I started with ChatGPT. Um, that was my first love. Then Claude came into the picture, then I was like, "Hmm, so should I try a new lover?" Then I moved on to Claude, but then I realized that I still wanted to, like yourself, keep ChatGPT 'cause you never know, right? There could be something new that comes out, um, and it's still great at certain functions. So having both tools, I think also knowing when to use each tool, uh, that also helps in the process. For me also, for writing, uh, especially, I would recommend using Claude, um, and also really testing out, uh, what Rachel already demoed, the skills function, 'cause it's so powerful when you start thinking in skills, not just in minor copy tweaks. So Rachel, what do you think the future of operations could look like for most companies?

Rachel Woods 31:52

I mean, I, I think it's gonna look like this. Like, I think we're gonna spend a lot more of our time thinking about how the work should be done rather than doing it. Um, I'll actually give you, like, a wild example from last week. So I was, I was pretty sick last week, and, um, you know, being out sick one day, o- okay. But the second day, I'm like, "Man, like, I, I have gotta get, uh, stuff done." Well, I went and I opened up my laptop and I clicked the button on my computer that I- lets me, like, talk to my computer, and I just told my AI, "Okay, run my todaying, uh, playbook," which is the playbook that runs my morning routine. And I have that playbook actually connected to, I think, like, seven other playbooks that do all my sales tasks, that will follow up on proposals, that will do, um, marketing reporting and that's scheduling meeting, like, all these different things. Um, and you know, my AI ran for quite some time, but then, uh, once it finished, I was like, "Wow, this is actually, like I was pretty productive today from bed of clicking one button and saying, 'Run my todaying routine.'" You know, and, um, I've worked to set up the system that then makes that possible, but it- all it is is playbooks and making sure it has access to the right data and tools to be able to do the work. And so I think, you know, you can imagine, like, you go to the beach, uh, and you are e- enjoying your day and it's like, okay, well let me just kick off this, this one thing that creates then now 20 things that the AI is doing for me, but following, you know, the process or playbook that I've already outlined. So, yeah. Wow. I think that's how companies are w- will operate, and it's already how we're operating as a team. Um, it's what we teach businesses that we work with and, uh, how to operate. So I, I just think that's the future, and the future's here.

Audrey Chia 33:28

Wow. And it's like, you know, um, Tim Ferriss' Four Hour Workweek, I think right now it's gonna be very, very possible for most people to, to really start building out systems to help them get, get things done. Um, and Rachel, for yourself, how, how do you think companies need to evolve and adapt? 'Cause I do know, at least in my region, although companies are saying they want to be, uh, open to AI, I, I sense a lot of resistance because it takes up so much time, manpower, bandwidth to, and resources to even think about operationalizing certain parts of your business. So what do you have to say to business owners who are still on the fence?

Rachel Woods 34:06

I think it's a tough place to be in because the reason you're on the fence is because you probably have a lot of other stuff on your plate, right? It's not like you have nothing to do, and so you're sitting there going, "No, we still don't wanna do the AI thing," right? You have other priorities. I think what's challenging is how long are you willing to wait on this becoming a priority? And that's just every business and every industry is kinda playing a gamble, right? Of, um, I d- I, I'm not really, I don't subscribe to this way of th- thinking in a, in a fear-based sense. But if I just really look at, like, the math of- Yeah you can have companies that run on this stuff, and you have companies that don't, it's really hard to imagine how the companies that don't will continue to stay competitive, right? And so, um, I think one thing that's hard is, like, you don't have the time right now, and this is gonna take time to learn how to do- Yes to set up. And so, uh, hopefully, you know, you have that on your priority list, and then, or it's, it's gotta be on there soon, in my opinion.

Audrey Chia 35:04

Yeah. I, I think it's also, it's not a matter of, like, no time, right? It's about whether you prioritize it. Um, and a lot of companies, if you feel like you are okay where you're at, you, you may be okay for now, but what happens next when everybody is already moving onto the next wave?

Rachel Woods 35:18

The bar is moving.

Audrey Chia 35:19

Are you prepared? Yes.

Rachel Woods 35:20

Yeah. The bar, it's a, it's a continuously moving bar, right? So even if you were, the, I talked, we actually talked to, um, teams like this pretty often that were like, they felt really good about their AI usage because they were using custom GPTs a couple years ago, but then they've stayed at that state, you know? Um, and the bar has moved well, well past that, so.

Audrey Chia 35:42

Yes.

Rachel Woods 35:43

Yeah.

Audrey Chia 35:43

And what advice would you give to, let's say, a, a founder first, so someone running their own company. What is one piece of advice you would give them?

Rachel Woods 35:53

Um, around AI adoption or prioritizing it, or-

Audrey Chia 36:00

Yes, around AI adoption. Like, what is one piece of advice you would tell them, like you wish all founders would, would know or think about?

Rachel Woods 36:06

Yeah. I mean, I would tell them so many things. Um, yeah, it's... I, I think the... You know, we're lucky to work with... You know, our clients really trust us. Yeah. And so they... I know it's really weird just going back to say that the first thing you work on, especially if you're adopting playbooking, like your first playbook should not be your highest profile, highest ROI playbook. You should really prioritize momentum, and our clients trust us and, and do that, and they are always like, "Wow, I'm so glad we did that because now we understand. Now we can actually make s- smart investments." Versus, you know, I just see people really, really get stuck on a really- Yeah hard first project, you know? It's like, um, it's like saying, "Well, I haven't really been hiking before, but like, let's go do, uh, that super, you know, uh, tall one over there. That looks like that's gonna be promising." It's like, well, or we could go for like a nice, you know, mild hike.

Audrey Chia 37:04

Yeah,

Rachel Woods 37:04

yeah. You know? Um, and so yeah, I think you'll set yourself up for success better if you prioritize momentum.

Audrey Chia 37:10

Mm. And what would you say to an employee who is in a company that is navigating AI?

Rachel Woods 37:18

So also controversial, um, I think it's a really tough place to be right now if you are waiting for your employer to inv- invest or decide to do this stuff, because you're choosing to move at the pace of your employer. Yeah. Um, this is a lot more of a personal skill set at the end of the day. And, you know, I, I know there's a lot of amazing companies out there, but at the end of the day, you know, we see companies are companies, and like you said, the, of the news around a lot of layoffs and stuff. And so I think, um, I just really encourage people, again, I know it's hard, but you have to prioritize finding the time to learn and experiment, um, and build the skill set. And I, I just really caution people of, like, waiting and moving at the pace that their employer chooses. I think that's, uh, yeah, not the best approach. Yes.

Audrey Chia 38:11

I think one thing I would add to that is, um, I, I was previously also an employee before I moved into my, my, you know, working on my own consultancy, right? And the biggest change that I made personally was learning how to learn fast. Um, in the past, as an employee, you just wait for the brief to come and then you work on it. Uh, and maybe you have mentors around, maybe you are growing, you know, together with your team, which is great. But one thing I've also learned coming out of it is there, there is so much to learn as a business owner, and you have to learn the skill of learning. Um, and I feel like a lot of employees, they may be in a comfortable place like I was, where I felt I didn't feel a need to learn as much. Um, but with AI, I, I really and I strongly feel that you need to want to start learning and feel that desire to learn so that, like what Rachel said, you can either value add or you can be that operator, right? Um, or you harness the skill sets for yourself, um, and open up new opportunities. So AI can be both very scary but also full of beauty if you know how to leverage it. Um, and I think in today's podcast, we covered so many great use cases. So Rachel, thank you so much again for joining us. Where can our listeners find you, and who should reach out to you?

Rachel Woods 39:19

Yeah. This was so fun. Um, I always love every time that we get to jam and talk about this stuff. So I hope this was helpful, uh, for anybody listening, and I would love if you want to connect with me on LinkedIn, I hang out there all the time. Um, our company is Amp. Uh, our website is Run On Amp if you're interested in learning more about playbooking. But, uh, shoot me a message on LinkedIn. I would love to connect.

Audrey Chia 39:39

So if you are still thinking about it, no more sitting on the fence. It's time to take action today. Reach out to Rachel, learn more about what her company and her amazing team is doing. Thank you so much for sharing your insights, Rachel, and thank you folks for tuning in. Don't forget to hit the bell for more actionable AI and marketing insights. We'll see you next week. Take care.