# 333 | Stop Prompting. Start Playbooking: How to Automate Your Business with AI with Rachel Woods

An episode of [Leveraging AI](https://invisiblenetwork.ai/podcast/1676634678), 6 Oct 2026, 41 min.

Audio: https://www.buzzsprout.com/2149574/episodes/19913374-333-stop-prompting-start-playbooking-how-to-automate-your-business-with-ai-with-rachel-woods.mp3

Learn more about Multi-agent orchestration Course and use LEVERAGINGAI100 to get $100 off! https://multiplai.ai/multi-agent-orchestration-course/ What if the reason your AI automations keep falling apart isn’t the AI, it’s the way you’re defining the work? Most businesses are still using AI as a smarter search engine or an occasional assistant. But if you want AI to reliably take work off your plate, you need something much more structured. In this episode of Leveraging AI, Isar Meitis sits down with AI entrepreneur and former Facebook AI team member Rachel Woods to break down the concept of AI playbooks — repeatable, detailed processes that allow AI to execute business tasks consistently and reliably. Rachel shares the framework she uses to turn everyday business processes into AI-powered systems. The idea is surprisingly simple: define the trigger, inputs, steps, and expected output. Then break complex workflows into smaller pieces that AI can execute extremely well. In this session, you'll discover: • Why most AI automations fail when you give AI too much freedom • How to turn existing business processes into AI playbooks • The four components every AI playbook needs • Why breaking complex workflows into smaller tasks dramatically improves AI performance • How Rachel uses more than 100 playbooks to help run a 10-person company • How AI can manage email triage, content creation, project planning, and other operational workflows • Why you should “own the playbook and rent the tech” • How to build reusable AI processes that aren't dependent on ChatGPT, Claude, or any specific platform • Why your first AI playbook probably shouldn't be built by AI • How individual playbooks can eventually work together like an entire AI team Rachel also walks through real examples, including her LinkedIn content workflow and a project-planning system that helped her team dramatically increase the number of projects they can execute. Connect with Rachel Woods on LinkedIn: https://www.linkedin.com/in/woodsrach/ About Leveraging AI • Multi-Agent Orchestration Course: https://multiplai.ai/multi-agent-orchestration-course/ • YouTube Full Episodes: https://www.youtube.com/@Multiplai_AI/ • Connect with Isar Meitis: https://www.linkedin.com/in/isarmeitis/ • Join our Live Sessions, AI Hangouts and newsletter: https://services.multiplai.ai/events If you’ve enjoyed or benefited from some of the insights of this episode, leave us a five-star review on your favorite podcast platform, and let us know what you learned, found helpful, or liked most about this show!

## Transcript

[0:00] Isar Meitis: Hello, and welcome to the Leveraging AI Podcast, the podcast that shares practical, ethical ways to leverage AI to improve efficiency, grow your business, and advance your career.
[0:09] Isar Meitis: And I have a really awesome, amazing, incredibly useful episode for you today.
[0:15] Isar Meitis: Everyone, literally everyone who's using AI, which is everyone, wants to automate things in their business with AI.
[0:21] Isar Meitis: Now, some people actually have one-off solutions of different aspects of automations that they were able to build, but the vast majority of people don't even know where to start when it comes to automating things with AI.
[0:33] Isar Meitis: Most people use it as a glorified, Google search or maybe a little more, but never really dove into how to actually make AI do things in your business.
[0:42] Isar Meitis: Now, the biggest gap I believe that people have between,"I know what the problem that I'm trying to solve," to,"How do I actually implement AI so it actually solves that problem for me?" is that people don't know how to accurately and effectively define the actual requirements.
[0:57] Isar Meitis: What is it exactly that the AI needs to do in order to help me do this thing that I'm trying to do in my business in a more effective way?
[1:05] Isar Meitis: Now, since AI is incredibly good at implementing whatever you tell it to implement, defining the requirements is the biggest issue.
[1:16] Isar Meitis: If you know how to effectively define the requirements, then you're already 80% on your way to success because the AI will know how to implement these requirements, probably by asking you some follow-up questions.
[1:28] Isar Meitis: Now, this is exactly what we're going to teach you today, how to create playbooks that are basically a, like the name suggests, it's a recipe on exactly what the AI needs to do in order to solve a specific part of the problem, and then you can take what you're gonna learn today and implement this in your business.
[1:45] Isar Meitis: Now, our guest today, Rachel Woods, is first of all, an AI ninja.
[1:49] Isar Meitis: She's been very, very active in the space, more or less since I was, right?
[1:53] Isar Meitis: So the end of 2022 when ChatGPT came out.
[1:55] Isar Meitis: she's a serial entrepreneur.
[1:56] Isar Meitis: She started multiple businesses.
[1:58] Isar Meitis: Before starting all these businesses, she spent time at Facebook AI, so she also has the back end, more detailed technical knowledge and experience in how to implement AI, and that's pre, ChatGPT.
[2:10] Isar Meitis: So she's been in that space for a very long time.
[2:12] Isar Meitis: But she's been extremely active in teaching people and individuals on how to implement AI in their businesses in an effective way.
[2:21] Isar Meitis: She has three different companies that are all involved in doing this in different, ways, and she's going to show us step by step exactly what is a playbook and how you can create your own, and how you can implement one in order to automate things in your business.
[2:33] Isar Meitis: Now, Rachel and I have known each other for a while now because we're swimming in the same lakes and we started doing very similar things, a long time ago, again, roughly at the same time.
[2:42] Isar Meitis: So we're coming up on four years.
[2:44] Isar Meitis: That's crazy.
[2:44] Isar Meitis: So about four years, and we talked several times before, but we never actually had a chance to collaborate, and then we ran into each other speaking at a conference a couple of months ago, like,"Oh, we gotta do this," and like,"Yes!" And so we're finally here, so I'm really humbled and excited, to have you on the show.
[2:59] Isar Meitis: I know that what you're gonna share, a lot of people will find extremely attractive, and so welcome to Leveraging AI, Rachel.
[3:08] Rachel Woods: Thank you.
[3:09] Rachel Woods: I do feel like this is, quite overdue, isn't it?
[3:11] Isar Meitis: It is.
[3:12] Isar Meitis: It is.
[3:12] Isar Meitis: But, but again, we're here.
[3:13] Isar Meitis: That's what matters, right?
[3:14] Rachel Woods: We're here.
[3:14] Rachel Woods: We're here.
[3:15] Rachel Woods: The, the important things always find a way to, come about, right?
[3:19] Rachel Woods: So-
[3:19] Isar Meitis: Yeah
[3:20] Rachel Woods: I'm, I'm excited for this.
[3:20] Rachel Woods: This is gonna be really fun today.
[3:22] Isar Meitis: Yeah, me too.
[3:22] Isar Meitis: I, again, you I think you and I think in a very similar way.
[3:27] Isar Meitis: I think we approach these things in a very structured, process-oriented way, and I think that's what most people miss.
[3:34] Isar Meitis: Most people are trying to wing it, and you can wing one or two, and it's gonna be very painful, but if you wanna automate your business, you can't wing it.
[3:44] Isar Meitis: It has to be very much process driven and very structured, and in that, you and I approach it in a very similar way conceptually.
[3:52] Isar Meitis: probably very different ways on the tactical level, which will make this really fun.
[3:56] Rachel Woods: Yeah.
[3:57] Rachel Woods: that's a nice way of saying that we're a little bit of, like, ops nerds.
[4:00] Rachel Woods: Aren't we?
[4:00] Rachel Woods: Yeah.
[4:01] Isar Meitis: 100%, 100%.
[4:03] Rachel Woods: But, I think, it's about time that operations is, is considered sexy, so there we go.
[4:08] Isar Meitis: I'm with you.
[4:09] Isar Meitis: Revenge of the nerds.
[4:11] Rachel Woods: Yeah.
[4:12] Rachel Woods: yeah, where should we jump in?
[4:13] Isar Meitis: so let's start I, I know you've kind of like, y- your main thing is those playbooks, and I think I, I used it in the intro, and I've saw you speak about this many times.
[4:23] Isar Meitis: So let's talk about what the hell is a playbook, and why should people care so people understand why they should stick with us for the next 35, 40 minutes, and then we can dive into the actual tactical details.
[4:32] Rachel Woods: Cool.
[4:33] Rachel Woods: That sounds good.
[4:33] Rachel Woods: Yeah.
[4:34] Rachel Woods: as you alluded to, you know, there's a, a ton of different ways to use AI, and, the last thing I want people to feel is that, like, um, the way you're using it is, quote-unquote,"wrong." there's just so many levels to it, and what I hope today is that we can introduce you to a new level that you might already, not already be using or thinking about, which is, really trying to think about AI not just as something that you're chatting with or using to get, answers or searching or researching, but really how to use it as an operational tool, right?
[5:00] Rachel Woods: And, again, when I think about operations, it's really about being organized in your work so that then you get things done the same way or the way that you want every single time.
[5:13] Rachel Woods: And bringing that type of mindset to AI is actually, to me, is really the pathway to unlocking AI as something that you can trust and you can actually start to hand off a lot more work to it.
[5:24] Rachel Woods: Not too dissimilar from normal operations, right?
[5:26] Rachel Woods: Of, like, building out a team and training people and, you s- you build operations so you can trust it.
[5:29] Rachel Woods: So at a high level, that's really what playbooking is it's a methodology that we have, but in the goal of creating, AI operations.
[5:37] Isar Meitis: Awesome.
[5:37] Isar Meitis: Yeah, I think you touched on the key point, right?
[5:39] Isar Meitis: Consistency.
[5:40] Isar Meitis: Like, you want consistent results regardless if it's a human operation or a AI opera- whatever other operation.
[5:45] Isar Meitis: If you wanna run a business, the results have to be consistent, meaning they have to deliver the same kind of output, whether it is a product, a service, a something, and it has to deliver it the same way at the required quality- Right every single time, regardless on who's the user, who's the operator, whether it's AI or human or whatever.
[6:05] Isar Meitis: And if you can build your environment to do that, then you can scale it.
[6:10] Rachel Woods: And I think it's also, something that helps you move up the spectrum, right?
[6:13] Rachel Woods: Of'Cause we all ideally want this AI employee, agent, whatever you wanna call it, in the background that's just doing all of this work for us, we never have to check on it, and we trust it's doing a good job.
[6:23] Rachel Woods: you know, but if anybody's tried to set some of those up and, been disappointed, where it's like, man, the longer the leash that I give it, the more chaos it creates, right?
[6:32] Rachel Woods: I don't know if you've faced that, but, I certainly have whenever I get a little bit, a little bit too, lazy or fast or am- ambitious about, like, oh, I'll just have the agent go off and do all this stuff.
[6:40] Rachel Woods: It's like, man, you really, it really bites you in the butt pretty quickly, right?
[6:44] Rachel Woods: instead, when you give it a longer leash, if you can give it that, but also give it the structure so that it's, either doing what you told it to do or it's helping to create a standard way of doing something, that's what's gonna help you actually, again, hand off more work to it, and then it be able to end up with a deliverable that you actually want.
[7:03] Rachel Woods: and I think that's, a lot of us, again, are kind of looking to reach that next level, and that's really, the shift that me and my team have made of not just, playbooking or creating these processes and structures for, small tasks, but actually then, stacking them all together into a system, and then you have the playbook checking other playbooks, and then them all working together, and that's then I feel like you have, this whole department or whole team of AIs working for you.
[7:25] Rachel Woods: And this, kind of method we use is really that path to get there.
[7:29] Speaker: This episode is brought to you by the Multi-Agent Orchestration Course.
[7:32] Speaker: It's a course that I have been teaching personally since April of this year, and every single one of the cohorts that we have opened have sold out.
[7:41] Speaker: And the people who have been through these courses made incredible transformations for themselves, switching jobs, starting their own companies and so on, and definitely for their businesses by being able to build really complex processes by following our methodology and our infrastructure settings that we teach during the course.
[7:57] Speaker: The course is four sessions of two to two and a half hours each over four consecutive weeks, and through it, you can go from knowing a little bit about how to use AI and using it in ways that most people are using it today to learning how to build really complex, sophisticated systems that can automate more or less everything in your business.
[8:13] Speaker: The current cohort that is open starts on November 2nd, and it's gonna go on November 2nd, November 8th, November 16th, and November 23rd, and we are almost sold out for that cohort as well.
[8:24] Speaker: It is very likely that we will not open another cohort in December, meaning the next one after that is gonna be in January.
[8:29] Speaker: So if you still wanna learn how to build really incredible automations and learn everything that I've learned by doing this every single day for the last few years, don't miss this opportunity to come and join us on November 2nd.
[8:41] Speaker: There's a link in the show notes, And if you're going to use the promo code LEVERAGINGAI100, you're going to get$100 off of the price that is on the website.
[8:50] Speaker: Like I said, there's a few seats open, but they are selling out fast.
[8:53] Speaker: So if you're interested in doing this still this year, go and click on the link right now on your phones and complete the registration and the payment so you can guarantee your seat.
[9:02] Speaker: I'm looking forward to seeing you there.
[9:04] Speaker: And now back to the episode
[9:07] Isar Meitis: Awesome.
[9:07] Isar Meitis: I will add two cents and then I think we'll do two things.
[9:10] Isar Meitis: One, you'll give a few examples of what kind of playbooks you have so people can make it more tangible.
[9:14] Isar Meitis: Yep.
[9:14] Isar Meitis: And then we'll actually dive into what it is and how to use one and how to build one.
[9:17] Isar Meitis: But the two cents I wanna add is really have to do with what you said, and I think that's another critical aspect that you and I think very much the same.
[9:25] Isar Meitis: You wanna think on a very granular level when you build these things.
[9:28] Isar Meitis: And the reason you wanna think on a granular level, they become Legos that then can be built together into bigger things.
[9:34] Isar Meitis: And it-- you wanna do this for two different purposes.
[9:36] Isar Meitis: Purpose number one, AI just works better that way.
[9:39] Isar Meitis: So when AI has a very simple, specific, well-defined task, it will do it amazingly well.
[9:44] Isar Meitis: When you give it a very complex, long, ambiguous task, it may or may not do it great.
[9:49] Isar Meitis: Now, the reality is the big, more complex task is built out of a lot of smaller tasks.
[9:54] Isar Meitis: So if you can build these things to know how to deal with a smaller task, you can put them together in a way that will be able to do the entire process, or like Rachel said, have an entire team doing something that will also check the work and verify and will transfer the results to wherever it needs to be transferred and so on.
[10:09] Isar Meitis: And so thinking about your process not as the final outcome, but what are the things that have to happen in between, is very helpful because you'll be able to use these in more than one way.
[10:19] Isar Meitis: The best example that I always like to give people, when people see my slides when I speak on stage, they're like,"Oh my God, how did you create these?" I'm like,"I-- first of all, I don't create anything anymore." but there's multiple skills, not one that creates these slides.
[10:30] Isar Meitis: And the best example I like to give is my brand guidelines.
[10:33] Isar Meitis: everything has my colors and my fonts and blah, blah, blah, and whatever.
[10:35] Isar Meitis: I could have created a branded presentation skill or agent or whatever you wanna call it.
[10:42] Isar Meitis: I didn't.
[10:42] Isar Meitis: There's separate ones.
[10:44] Isar Meitis: Each one does something different, so I can reuse the one that creates the graphics in other things that require graphics, like my website or pamphlets that I create, and one that creates my color and branding on wherever I wanna use this, and one that create presentation.
[10:58] Isar Meitis: I can create presentations for my clients because it's gonna use the same presentation creation skill with other branding capabilities and so on.
[11:06] Isar Meitis: So being able to mix and match is very helpful once you wanna scale it beyond the simple thing that it's doing.
[11:11] Isar Meitis: Now back to you.
[11:12] Isar Meitis: A few examples, and then we'll take it from there.
[11:14] Rachel Woods: Yeah.
[11:14] Rachel Woods: I mean, I think even just building on what you're talking about, because, what you're really describing is the idea that you can have individual processes inside a business, individual things that get done, that then also add up to a bigger, outcome, right?
[11:30] Rachel Woods: Which again, my- one of my favorite things to do with AI is just, forget AI.
[11:33] Rachel Woods: Just think about normal operations, normal ways of building business, and if you were to think about, let's say you were to, I'll give an example.
[11:40] Rachel Woods: Hire and train an assistant for yourself, right?
[11:42] Rachel Woods: A human assistant.
[11:45] Rachel Woods: To help them be successful, you probably wouldn't, day one, just be like,"I want you to, do my entire morning routine, and I want you to, Here's, my inbox and my calendar and my, all my messages and all this stuff.
[11:54] Rachel Woods: go." you wouldn't just kinda, dump it all.
[11:56] Rachel Woods: If you were trying to set that person up for success, you would probably think through,"Okay, what's, the first thing that I should teach this person?
[12:03] Rachel Woods: First small process I could get off my plate.
[12:06] Rachel Woods: Okay, the first thing I wanna teach them is how to give me a daily briefing of my calendar.
[12:10] Rachel Woods: Great.
[12:11] Rachel Woods: Let me teach them that.
[12:12] Rachel Woods: That's, day one or two on the job Then day three you're like,"Okay, now let me teach the next thing," right?
[12:16] Rachel Woods: Which is"Here's how I want my email inbox managed." And then you would, teach them that.
[12:20] Rachel Woods: And you would keep going until eventually you're like, great, now in the mornings my assistant runs an entire morning routine for me because my assistant knows how to do all these different things.
[12:31] Rachel Woods: Oh, and by the way, if for whatever reason we get to, like, you know, 1:00 PM on a Tuesday and my whole day has just been chaotic and I need a new daily brief'cause my calendar completely changed, great, the assistant knows how to do a calendar brief in itself.
[12:45] Rachel Woods: It doesn't have to be constrained into the morning routine.
[12:49] Rachel Woods: And so, like, that's just, normal building a team, training people kind of mindset.
[12:54] Rachel Woods: And what I love is, like, that's just exactly how you can approach breaking up your work and designing these systems for AI to do.
[13:01] Rachel Woods: AI loves the exact same things that training a human does.
[13:04] Rachel Woods: And even just to give you examples, those are literally examples of playbooks I have that power my day, and then my morning routine, I can start the whole thing, with just one word, which is todaying, and that goes through and triggers all of these different playbooks, just like I hired that assistant.
[13:19] Rachel Woods: And so you can do this across anything.
[13:20] Rachel Woods: You can do it in your, your personal productivity.
[13:22] Rachel Woods: You can do it in your sales process, in the way you deliver your service.
[13:26] Rachel Woods: I'll give you another example, again, back to the, breaking apart work.
[13:29] Rachel Woods: if you think about any, service that you might have in your business or, let's even take something like customer onboarding, right?
[13:35] Rachel Woods: If you were to systematize that, what do you do?
[13:38] Rachel Woods: You probably chunk it up into the different smaller pieces that are required for customer onboarding.
[13:43] Rachel Woods: If you playbook each of those things, then you have a playbook running the whole process together that then becomes now a very easy way.
[13:51] Rachel Woods: Again, same thing to get it to do bigger things.
[13:53] Rachel Woods: I just really like thinking, again, about, if you were to teach a team of humans to do this, how would you break apart the work ideally?
[13:58] Rachel Woods: And then that's a great, jumping off point for AI as well.
[14:02] Isar Meitis: Awesome.
[14:03] Isar Meitis: So through this, you gave us two small examples, like a morning routine.
[14:06] Isar Meitis: What other kind of playbooks?
[14:08] Isar Meitis: what would you consider a playbook, at the simple level, and then what would you consider a playbook at the slightly more advanced level?
[14:14] Rachel Woods: Yeah.
[14:14] Rachel Woods: So actually to clarify, so even in the morning routine, there's actually a bunch of sub-playbooks, right?
[14:18] Rachel Woods: So there's like email triage is a playbook that I have.
[14:22] Rachel Woods: and the reason that it itself is a playbook is because, and I don't know if anybody probably can relate, like if you just give AI, like, damn, if you just give a person,"Triage my email inbox," you don't give it a lot of like clarity.
[14:34] Rachel Woods: you're never gonna trust that thing.
[14:35] Rachel Woods: It's gonna go crazy.
[14:36] Rachel Woods: It's gonna try to do all this different stuff.
[14:38] Rachel Woods: So instead, my email triage playbook actually has, like a decision-making guide of like what are the different types of emails that I expect?
[14:46] Rachel Woods: What do you do if you need to escalate an email to me?
[14:48] Rachel Woods: And so this playbook itself, I think, right now it's 11 steps, and it's because it's actually going through, pretty, pretty detailed.
[14:56] Rachel Woods: but that's how I can trust it, right?
[14:57] Rachel Woods: Because it's actually really following a real process.
[14:59] Rachel Woods: So that's one playbook.
[15:01] Rachel Woods: and then again, I have other playbooks, inside my morning routine.
[15:04] Rachel Woods: But then, and I like to think of this almost as like the manager playbook, right?
[15:08] Rachel Woods: Which would be like the morning routine playbook is the one that delegates the work to the other playbooks, kinda a project manager in a team of people would do, or a chief of staff with-- if you had a bunch of different assistants, right?
[15:20] Rachel Woods: and so that's an ex- another type of example of a playbook.
[15:23] Rachel Woods: But That's just in, yeah, personal productivity.
[15:26] Rachel Woods: the way that we run our business is everything is playbooked.
[15:28] Rachel Woods: So we actually have a system of playbooks for marketing, system of playbooks for sales, system of playbooks for, the delivery of our services, system of playbooks for even the leadership side, so how we manage strategy and reporting and everything.
[15:43] Rachel Woods: And inside each of those systems is probably on average, I'd say, probably a dozen to three dozen different playbooks.
[15:50] Rachel Woods: and that really is, like, how we run everything in the business.
[15:52] Rachel Woods: We're 10 people, 10 humans, and, I think the last time I counted we were well over 100 playbooks powering in the back end.
[15:58] Rachel Woods: So- Yeah I'm curious.
[15:59] Rachel Woods: W- out of those, what do you think...
[16:00] Rachel Woods: I can give some more examples, but what of those systems do you think would be, like, most interesting?
[16:04] Rachel Woods: Or what are you curious about?
[16:04] Isar Meitis: No, I think this is very helpful.
[16:06] Isar Meitis: I think if we...
[16:07] Isar Meitis: Y- you already gave a lot of examples for people to understand what it is.
[16:11] Isar Meitis: I think we can now dive into the how.
[16:14] Isar Meitis: Like, I think now people are curious enough.
[16:16] Isar Meitis: They're like,"Okay, so I understand this whole playbook thing, but how do I actually...
[16:19] Isar Meitis: Like, show me one.
[16:20] Isar Meitis: How do I actually create a playbook?
[16:22] Isar Meitis: What's in it?" And the other thing that I know the answer, I'm gonna ask you for the audience, is the creation of a playbook by itself, meaning is there a structured process on how to create a playbook that is the same every single time but creates different outcomes?
[16:34] Rachel Woods: Yep.
[16:35] Rachel Woods: Yeah, great questions.
[16:36] Rachel Woods: And, of course you know the answer.
[16:37] Rachel Woods: Yes, of course there's a, a playbook for creating, playbooks.
[16:40] Rachel Woods: and so I really, I think what's helpful maybe is to bring back what you even just introduced at the beginning of this conversation of the hard part and the valuable part is in deciding what needs to be done, right?
[16:53] Rachel Woods: The judgment, the structuring of the work.
[16:55] Rachel Woods: And that's really, the, the skill to hone, in my opinion, of people who want to use AI this way, is you need to build the skill of being able to break down any work.
[17:05] Rachel Woods: my team, makes me say this more often than I feel comfortable, but it is true, that I have not met a process I couldn't playbook because I have developed the skill of being able to break down literally any type of work.
[17:16] Rachel Woods: And that really is the thing to lean into.
[17:18] Rachel Woods: So how do you do that?
[17:19] Rachel Woods: the way that we do it, again, this is like our method of creating these playbooks, is if you start by defining th- these same four components for any type of work, then you're going to have the hard part done, and then from there it becomes a lot easier to get AI to help you finish the rest.
[17:34] Rachel Woods: But the first thing you need to figure out is what is gonna be the trigger Of this work.
[17:40] Rachel Woods: So what is gonna be the thing that starts the work?
[17:43] Rachel Woods: and that could be something like on a schedule, or it could be something like an event, like, when a meeting finishes, then start the playbook.
[17:49] Rachel Woods: But you need to figure out, like, when the heck are we even gonna, run this thing?
[17:52] Rachel Woods: and that actually helps you really, orient around, the next step, which is that you need to figure out the inputs.
[17:57] Rachel Woods: So I think of, playbooks again, really, as, you're wanting AI to do something for you.
[18:03] Rachel Woods: sometimes it can just do that without you needing to give it anything, right?
[18:06] Rachel Woods: it might be able to go get what it needs itself.
[18:09] Rachel Woods: But sometimes you actually need to give it something.
[18:11] Rachel Woods: So an example of that would be, like our newsletter writing playbook.
[18:15] Rachel Woods: that one, I actually give it a brain dump of what I wanna talk about because I want the topic and the substance to come from me, right?
[18:21] Rachel Woods: So that would be an example of an input.
[18:23] Rachel Woods: Once you have the trigger and the inputs, the third component you need to define is the steps.
[18:28] Rachel Woods: So this is really, if you were to sit here and talk to me about,"Hey, here's how I want you to do this work, Rachel," then that is, exactly what you need to outline for, deciding, how you want AI to do something, right?
[18:40] Rachel Woods: Of just writing out those steps.
[18:41] Rachel Woods: And then the fourth piece is the output.
[18:43] Rachel Woods: So what do you expect to get out of the playbook and what's the deliverable?
[18:47] Rachel Woods: And again, that helps be, like, a bookend, but, these things also help when you try to start stacking them together like we're talking about into bigger systems.
[18:53] Rachel Woods: But yeah, y- for any work, you just need to define those four things.
[18:57] Rachel Woods: Agreed and then once you do that, then you, depending on the type of work, you'll wanna, write out more in-depth instructions for some of these steps, right?
[19:06] Rachel Woods: Sometimes it's really easy.
[19:07] Rachel Woods: a step might be, let's see, Get a full list of all of the tasks assigned to me in my task manager tool, right?
[19:15] Rachel Woods: That would be a pretty straightforward step.
[19:16] Rachel Woods: You don't really need to overly explain to AI.
[19:19] Rachel Woods: But some other things you're gonna want it to really follow, especially the more unique it is to you, follow the way you want it done.
[19:25] Rachel Woods: back to the example of, a newsletter.
[19:27] Rachel Woods: for, let's say one of those steps is, create a subject line.
[19:30] Rachel Woods: the subject line, probably the way that you wanna write subject lines for your business is different than how I wanna write subject lines for my business, and some other people are out there putting, emojis and all caps and all that crazy stuff, right?
[19:40] Rachel Woods: And so what I wanna do in my playbook is I wanna make sure that I am writing out some more specific instructions of how I want the AI to do that step and, there's a whole art, we can go into that.
[19:49] Rachel Woods: But, but really once you've done that, defined those four components and filled out some more instructions for the key steps, then that's it.
[19:56] Rachel Woods: You've defined the work, and that's all AI needs to do to run it.
[20:00] Isar Meitis: Awesome.
[20:00] Isar Meitis: So yeah.
[20:00] Isar Meitis: I will add two small cents, and they're both related to the same thing.
[20:04] Isar Meitis: One is examples.
[20:05] Isar Meitis: AI is really good at following examples, both of the inputs and the outputs and the process, so you can actually give it...
[20:12] Isar Meitis: and I'll say something that I really love doing recently because the, the last six months, the, the models became so good, they just figure it out.
[20:18] Isar Meitis: So let's say you're doing...
[20:19] Isar Meitis: I just did a few workshops for, finance people, so it's a lot of, Excel stuff and going from this inputs to that output.
[20:25] Isar Meitis: And historically what I did is exactly what you're saying.
[20:28] Isar Meitis: Okay, we run this when, blah, blah, blah, blah, blah.
[20:30] Isar Meitis: here is how the input files look like, here's how the output files look like, and I would explain the process step by step.
[20:35] Isar Meitis: I go to column I and I look at what's in column I in this sheet, and that is then compared to what's in column V in that sheet, and then we manage this based on whatever, and then we...
[20:44] Isar Meitis: And now I just give it to AI, I said,"Okay, here's the input, here's the output.
[20:47] Isar Meitis: Tell me if you can figure this out." And it will sit there for five, 10, 20 minutes.
[20:50] Isar Meitis: It says,"Okay, here's what I think that's happening." And usually it gets you 85% there and you can just tell it what the gaps are and fill out the details.
[20:59] Isar Meitis: But examples just work very well, and AI is, A, very good at following these examples, and B, very good at- deducting based on these examples, I will say that.
[21:08] Isar Meitis: And the second thing that I wanna say that I'm sure is a part of your process, you're never done with version one.
[21:13] Isar Meitis: So you build this once, and a lot of my processes, and I don't call them playbooks, but they're identical to what you're just saying, some of them are probably version 87.
[21:22] Isar Meitis: A, because I'm learning and I'm...
[21:24] Isar Meitis: and I just wanna make it better.
[21:25] Isar Meitis: B is because things are changing in the environment.
[21:28] Isar Meitis: Microsoft comes up with a new version, and now Outlook looks differently, and it has another field that you can use or, stuff like that.
[21:33] Isar Meitis: So the environment is changing, the requirements are changing, and you, now that you have this, you always have new ideas, and I'm sure you're just like me.
[21:40] Isar Meitis: You're like,"Ooh, what if this can also do that?" and then you go and add either a new playbook or call it whatever you wanna call it, or you fine-tune ones that you already have, to do this one extra thing that you want because now your morning routine includes one more thing.
[21:54] Isar Meitis: your yoga skill or whatever it is that you wanna do that you can add to that.
[21:57] Isar Meitis: So Let's look at an example, like an actual one, and see how it's built and how it's done, and what is the inputs and outputs.
[22:03] Isar Meitis: I think that will make it very clear to people.
[22:06] Rachel Woods: Awesome.
[22:06] Rachel Woods: So why don't I do this first?
[22:09] Rachel Woods: I am going to share my screen, and we're gonna go through, So as we're talking about these playbooks, you might be wondering, okay, are we, like, making these in Claude?
[22:19] Rachel Woods: Are we making these in ChatGPT?
[22:21] Rachel Woods: Like, what tool are we using?
[22:23] Rachel Woods: what we actually do is we have a philosophy we call own the playbook, rent the tech, which is, you should be creating your playbooks in a document that is outside of these tools.
[22:33] Rachel Woods: That could be Notion, it could be Google Docs, it could be, like a lot of my team, we use GitHub.
[22:37] Rachel Woods: it-- But the reason is then you actually, focus on, the thinking and the operational process itself versus getting sucked too deeply into the tool.
[22:48] Rachel Woods: 'Cause these tools, like you're saying, like they s- they change all of the time, and if you're constantly having to change your process'cause the tool changed, that's gonna suck, for lack of a better word.
[22:57] Rachel Woods: but what's better is if you, again, create, the playbook to be a little bit more tool agnostic so that then...
[23:04] Rachel Woods: And I like to say a, a good playbook, literally a human could go through this and read it, and do it.
[23:09] Rachel Woods: That's really the sign of a, a good playbook.
[23:10] Rachel Woods: So let me just show you guys what a couple of those look like.
[23:13] Isar Meitis: I'll say one thing while you're opening your screen.
[23:15] Isar Meitis: The- Yeah if you're looking more to know about what Rachel just said, a few episodes ago, because I'm not sure when it is actually going live.
[23:22] Isar Meitis: So when you're listening to this, look a few episodes back.
[23:25] Isar Meitis: There's an episode where I'm sharing how I'm working with Claude and ChatGPT at the same time completely seamlessly, and I can connect whatever other tool I wanted.
[23:32] Isar Meitis: These are just two of my main tools, and how the whole document-based AI solution makes the tool completely irrelevant, and it gives you a lot of benefits beyond what Rachel said, that they're changing.
[23:44] Isar Meitis: You can manage your tokens across more than one tool, and then instead of maximizing and paying more, you can go,"Oh, I...
[23:49] Isar Meitis: For the last week I'll switch to this," or just ChatGPT is better at these tasks and Claude is better at these tasks.
[23:54] Isar Meitis: I can assign different tasks to different, tools and so on.
[23:57] Isar Meitis: So yes, I agree with you 100%.
[23:59] Isar Meitis: The tool is completely irrelevant to how the whole thing should work.
[24:04] Rachel Woods: Yeah.
[24:04] Rachel Woods: I love it.
[24:05] Rachel Woods: and that sounds like a valuable episode.
[24:06] Rachel Woods: I'll just plus one.
[24:07] Rachel Woods: Whenever that episode drops- everybody should go and listen to that.
[24:10] Rachel Woods: Yeah.
[24:10] Rachel Woods: I love it.
[24:11] Rachel Woods: okay.
[24:11] Rachel Woods: So let me, let me go through, let's see.
[24:14] Rachel Woods: I'll do a LinkedIn post writer playbook first,'cause I think, this is one that people really love, and this is literally how I, part of my process for creating content which is, again, so zooming out, to get AI to do any process or any work, you just need to find those same four components, right?
[24:31] Rachel Woods: The trigger, the inputs, the steps, and the outputs.
[24:33] Rachel Woods: And and this is like a little pretty template that we use just to be some extra scaffolding.
[24:37] Rachel Woods: Again, you could do this in Notion or anything.
[24:38] Rachel Woods: But if you look here for writing a LinkedIn post, what do I have?
[24:41] Rachel Woods: I have my trigger, which is that it, starts when you send a message, my, inputs, which is what are my raw thoughts or what I wanna talk about?
[24:49] Rachel Woods: Then I have my steps, and so for this, playbook that's gonna help me draft LinkedIn posts, I want it to actually match the process that I use when I'm writing.
[24:58] Rachel Woods: And so what I do when, whenever I was thinking about this, it's not just like I just write it out of the blue.
[25:02] Rachel Woods: I don't know if people do that, but, I'm not that good.
[25:05] Rachel Woods: I have to actually think about it, right?
[25:06] Rachel Woods: So what do I do?
[25:07] Rachel Woods: I take my brain dump, and I wanna, extract what am I actually trying to talk about?
[25:11] Rachel Woods: And that's, my one main point Then, what I, learned is if I can kinda, think about the audience who's, reading the post, and I can almost kinda predict what follow-up questions they may have, and I can actually try to answer those proactively in that content, then that post is gonna be better.
[25:30] Rachel Woods: And so that's the second step in my process.
[25:33] Rachel Woods: Then whenever I'm-
[25:34] Isar Meitis: Yeah, before, before you scroll down- Yeah just to, for those of you who are not seeing the screen, every one of these steps that Rachel is describing has a step input, which is in many cases probably the output of the previous steps or previous steps, depending on how far we go down that rabbit hole.
[25:47] Isar Meitis: Then there's step instructions on exactly what to do in that step.
[25:50] Isar Meitis: Then there's a success criteria.
[25:52] Isar Meitis: then there's the response format, so again, what the output actually looks like.
[25:56] Isar Meitis: And then the actual outputs themselves.
[25:58] Isar Meitis: So th- these exist in every one of these steps as a very detailed definition on how to do that particular step.
[26:05] Isar Meitis: So if you can see, it is very similar to the bigger process.
[26:09] Isar Meitis: Instead of a trigger, there's an input, but then there's the instructions, and then there's the formatting and stuff like that.
[26:14] Isar Meitis: So it's very similar.
[26:15] Isar Meitis: And something else that I have, two things that I have in my process, one that you mentioned in theory, but you address it when you give it the inputs yourself, and the other that you already have here, is who's the target audience and what's the success criteria?
[26:28] Isar Meitis: So in my process that is very similar to your playbooking process, I have these built into the process.
[26:32] Isar Meitis: So it's always asking me, who's the target audience?
[26:34] Isar Meitis: Is it just me?
[26:34] Isar Meitis: Is it other people?
[26:35] Isar Meitis: Is it people that are not in my company?
[26:37] Isar Meitis: who are going to be using this?
[26:38] Isar Meitis: what's their preferences, level of knowledge, and stuff like that.
[26:41] Isar Meitis: And then what's the success criteria?
[26:42] Isar Meitis: if this works, what happens?
[26:45] Rachel Woods: Yeah.
[26:45] Rachel Woods: And, yeah, great call-out because back to what I was sharing, like again, I think when you're first getting into breaking down your work, especially if you are l- watching the screen share right now, it might be like,"Oh my God, that's a lot" to, that's a lot to define.
[26:59] Rachel Woods: Especially like we're usually not used to being this specific with what we want done, right?
[27:04] Rachel Woods: oftentimes it's all this like knowledge that's just kinda"I don't know, it's just the way I do it." And you will, again like I shared, you'll eventually wanna get to the place where your instructions for each step really do look pretty detailed when those steps are unique to you.
[27:19] Rachel Woods: And if I were to just zoom out and say the easiest way to start, doing this, it's really just even, even just doing the, the steps themselves, right?
[27:27] Rachel Woods: Of, thinking about when I do a LinkedIn post, I don't just write it.
[27:31] Rachel Woods: I extract the point, I ask myself follow-up questions, and then I, keep going from there.
[27:34] Rachel Woods: So there's layers to this, of course, right?
[27:37] Rachel Woods: and so yeah, I think that's probably, helpful.
[27:39] Rachel Woods: The other thing, maybe I'll just throw this in here, too,'cause you might be thinking,"Wow, man, that's like...
[27:43] Rachel Woods: Can I use AI to make these playbooks?""Can I get the AI to, write it itself?" So in our work, what we do is we actually force people to write their first playbooks by hand, because if you haven't yet thought in this depth of level, it's gonna be really hard for you to what?
[28:00] Rachel Woods: To be the reviewer and actually understand what the AI did when AI writes its own playbooks.
[28:05] Rachel Woods: Once you have this kind of way of work down, though, of course, you can use AI to speed up the process, right?
[28:12] Rachel Woods: We have a playbook that writes playbooks that we use, in our business.
[28:15] Rachel Woods: But, Okay yeah, but anyway, so to keep going down this, LinkedIn post, one, the, third step here is I have the AI help me reframe it.
[28:23] Rachel Woods: the fourth step is I then have it draft the post.
[28:26] Rachel Woods: I can also, the way we do it, we, pull out things like examples or reference material or brand voice guides and stuff so that then also you can share them between playbooks.
[28:35] Rachel Woods: and then, edit it based on my style guide, and then the output is just LinkedIn post.
[28:40] Rachel Woods: And so that's just one example.
[28:42] Rachel Woods: what I wanted to do is just show you, again, if there's, one takeaway I want you to really see when you look at this playbook structure, it's if I can just learn to do triggers, input steps, outputs, I can playbook anything Because that is truly the same structure for every single type of work.
[28:58] Rachel Woods: And so wildly different use case away from content creation is actually one of my favorite playbooks, my project planning playbook.
[29:05] Rachel Woods: which basically,'cause I, I love this, people oftentimes are like, I don't really do p- repetitive work and processes.
[29:10] Rachel Woods: All my stuff is, one-off." And I'm like,"Oh, so you have, a lot of projects, right?" And people are like,"Yeah." And I'm like,"Great.
[29:16] Rachel Woods: Project planning is a process." And you know what?
[29:19] Rachel Woods: When I playbooked my project planning, I measured before and after playbooking this, and we now as a team do three times the number of projects at the velocity that we've accelerated because we've playbooked it, and so now AI's helping us get this work done.
[29:32] Rachel Woods: But to playbook it, same things.
[29:34] Rachel Woods: So the, this one, the trigger is we just start it whenever we're ready to plan a new project.
[29:38] Rachel Woods: the inputs are project description.
[29:39] Rachel Woods: And then I just have the same thing of, the steps.
[29:42] Rachel Woods: We have the AI ask questions to clarify the project.
[29:45] Rachel Woods: We have the AI then brainstorm approaches, recommend one, and make sure that sounds good with us.
[29:50] Rachel Woods: We have it then clarify the timeline and tasks, draft the project plan.
[29:55] Rachel Woods: And then my favorite thing, and it's hidden a little bit in here.
[29:59] Rachel Woods: I'm just gonna You can see.
[30:00] Rachel Woods: The instructions can get really detailed as you start playbooking this stuff.
[30:03] Rachel Woods: But is we have the AI, go through the project plan and say, I can actually do this task.
[30:09] Rachel Woods: I can do this task.
[30:10] Rachel Woods: I can do this task," as it's, assigning work out, right?
[30:12] Rachel Woods: So it'll be like,"Okay, Christie should do this.
[30:14] Rachel Woods: Rachel should do this, but, I, as AI, can do this." So that then whenever the AI finishes helping us plan the project, it can basically be like,"Okay, I'm ready to start.
[30:23] Rachel Woods: Do you want me to start on my tasks?" And then that way, oftentimes that means that a third or two-thirds or more of the project can be done before we even are, like, exiting out of the, chat with the AI.
[30:33] Rachel Woods: that's just an example again of a very different type of work, but same playbook structure,
[30:37] Isar Meitis: I love it.
[30:37] Isar Meitis: what do
[30:37] Rachel Woods: you say?
[30:38] Rachel Woods: should we run this?
[30:38] Rachel Woods: Yeah, go ahead.
[30:39] Isar Meitis: Yeah.
[30:39] Isar Meitis: So you go start running this, and I will do...
[30:42] Isar Meitis: I wanna add my two cents to this that is very relevant to what you just, said, because again, we're very similar in our concepts and the way we approach things.
[30:49] Isar Meitis: So I have, a, a whole system that is connected to every meeting I finish.
[30:54] Isar Meitis: So every meeting I finish gets transcribed, it gets pulled immediately, and Claude reviews the whole thing with a very set, very specific set of requirements, and then it opens tasks for me in ClickUp, which is the same like Monday or Jira or Asana or whatever, Notion, whatever task management tool you're using.
[31:08] Isar Meitis: So it's opened the task there, but then it runs through the tasks that it's opened and say,"Okay, which tasks do I know how to do?" And then it does all these tasks.
[31:16] Isar Meitis: so at the end of each, whenever I wanna jump into that board, I have the tasks that I need to do, tasks that other people need to do, they just get assigned, but then there's tasks that needs reviewed, and they're assigned to the right people to review because AI already did them.
[31:30] Isar Meitis: And I have another board in which the AI collects the tasks that it doesn't know how to do but gets assigned a lot.
[31:36] Isar Meitis: So that's basically the next thing you wanna build, right?
[31:39] Isar Meitis: So if these tasks, it happens every single day and the AI doesn't know how to do them, that's probably the next thing you want AI to know how to do.
[31:46] Isar Meitis: So then you will assign that to somebody to develop in your language, the playbooks for that.
[31:50] Isar Meitis: You develop the playbooks for that, and that nobody ever has to do this again, just to review the work, in the end.
[31:55] Isar Meitis: So very similar, mindset and concepts, and it becomes really fun to see how you need to do less tasks in everything that you're doing because AI learns how to do more and more things.
[32:06] Isar Meitis: And then, like you're saying, just the velocity becomes bigger.
[32:08] Isar Meitis: It's not that you're doing less work.
[32:10] Isar Meitis: You're just doing more work with the same amount of people
[32:12] Rachel Woods: Yeah, totally.
[32:13] Rachel Woods: I love that.
[32:14] Rachel Woods: We have a s- the same setup in ClickUp and everything, Awesome it's really the- Great
[32:17] Isar Meitis: minds, right?
[32:18] Rachel Woods: Yeah, it's really And back to like that human analogy, like that's why it works is because that's how, people have always managed delegation and tasks.
[32:26] Rachel Woods: And so again, if you're ever stuck with"How do I get AI to do this thing?" Just think about if you had people, like extra manpower, how would you set up the operations, and that's usually the path.
[32:36] Rachel Woods: cool.
[32:36] Rachel Woods: Okay, so I opened up, Claude, and this is just a, little, I like to have my disclaimers, a little demo account so I don't, reveal all the, I've learned my lesson of all the, the secrets and things, Yeah but, but okay, once you've written the playbook, again, you have it, in your, in your document, and if you wanted a really easy way to save these inside Claude, for example, and the same thing works in ChatGPT, is you can go to customize, and you can go to skills, and, you can add, upload the skill or even chat with Claude and be like,"Here's my playbook," like download it as a document, and then have it turn that into a skill.
[33:10] Rachel Woods: Because skills are a great place to save these playbooks.
[33:13] Rachel Woods: now, we could get into that like there's a lot of ways to build skills and structure skills and, the way I really think about when you're putting a playbook in a skill, it's because you're, saving that process.
[33:24] Rachel Woods: And the, the planning project playbook, what that looks like in Claude, as you'll see, it's just the exact same thing, the triggers, the inputs, the steps, et cetera.
[33:33] Rachel Woods: And I've really just taken the, document that I wrote and put it in here.
[33:38] Rachel Woods: Now, once you've saved it, the nice thing is then, it can be accessed from, anywhere.
[33:44] Rachel Woods: You can have the AI kind of know when to pull it in, or you could even, do a slash command and have it, start that skill directly.
[33:52] Rachel Woods: And so we'll do, help me plan a, a wine night where we drink pinot noir and write playbooks That'll be my next- Am I
[34:07] Isar Meitis: invited?
[34:07] Isar Meitis: that sounds like my kind of- my kind of event.
[34:10] Rachel Woods: I've been thinking, you and I end up at a lot of the same, events and stuff.
[34:14] Rachel Woods: we should, do
[34:15] Isar Meitis: a-
[34:15] Rachel Woods: 100% a side thing.
[34:17] Isar Meitis: 100%.
[34:19] Rachel Woods: And so what it's doing here, and part of what, again,'cause,'cause we're operations n- nerds over here, right?
[34:25] Rachel Woods: Where we really wanna make sure it's following the process.
[34:27] Rachel Woods: So I actually have in the playbook itself, for it to tell me that it's following the steps so that I can, it improves the reliability and also improves that I can kinda audit it later, right?
[34:36] Rachel Woods: Go look to make sure it actually did what I wanted.
[34:39] Rachel Woods: So this is what it's doing.
[34:39] Rachel Woods: It's already starting with step one of clarifying, the pitch.
[34:42] Rachel Woods: And so I can go through and, I'm not, reading those, but,
[34:45] Isar Meitis: Again, those of you who are not watching the screen, it's asking Rachel a bunch of questions, and Rachel has to respond to these questions.
[34:51] Isar Meitis: those of you who haven't used Claude, or ChatGPT for that matter, it pops these, multiple choice questions kind of thing, and you can always type something else and tell it if it's not one of the choices, which I end up doing a lot.
[35:01] Isar Meitis: But, combine that with voice typing, which I assume you do as well, makes it relatively easy.
[35:05] Isar Meitis: So even if it's not in one of the choices, you can always just click on something else and then voice type your answer and continue from there
[35:11] Rachel Woods: Yes.
[35:12] Rachel Woods: And I, yeah, part of my, morning, walk is I even have, playbooks that I'm running in voice, so that's a great, another great call-out, yeah.
[35:18] Rachel Woods: so once it did that, then, it's now following step two of brainstorming approaches.
[35:22] Rachel Woods: So I can say confirm.
[35:24] Rachel Woods: Now, this is a playbook that's, The way it's written is it's designed to stop and ask me, so it's a little bit more of a collaborative playbook.
[35:31] Rachel Woods: But you can also have these playbooks just go through and run all of these steps without your input.
[35:36] Rachel Woods: again, the beauty of learning just this way of structuring things is it works across everything, but anyways, so that's how, I would keep going through it,
[35:42] Isar Meitis: and then- a- another thing here, by the way, that I saw that we're doing very similarly, when it's deciding what to do, it's giving options, it's defining the pros and cons of the options, and it's telling you its recommendation and why.
[35:53] Isar Meitis: And I tell a lot in my courses that I teach, I push back on at least 50%.
[35:57] Isar Meitis: And when I say I push back, it doesn't mean I don't agree with the AI, it means I push back.
[36:02] Isar Meitis: I'm like,"Okay, I see you picked this, but what about this and that?" Or,"Did you consider this option?""Ooh, this is an interesting one, and let me look at that." So I sometimes will go with the, what it selected, but if I have any doubts, I will push back and ask it for more options, or what about this, what about that?
[36:17] Isar Meitis: And then I will decide whether I agree with the AI's recommendation or not.
[36:20] Rachel Woods: Mm-hmm.
[36:21] Rachel Woods: Yeah, I love that.
[36:22] Rachel Woods: And then even, again, as you're, we kind of, like, zoom out and you're thinking about the way that you want to run your business, right?
[36:29] Rachel Woods: I think kind of an interesting level you'll reach once you start doing this is, at least in my experience, you realize there are some things that you always wanna have your input on, and there are other things where The risk or the cost of error is, like, pretty low.
[36:45] Rachel Woods: And so then I always recommend, like, starting out with your playbooks being super collaborative, and then you'll learn where you can trust, and then those can become the ones that you set up, like we're talking about, with, like, ClickUp running them instead of you having to be, for example, in the chat running them.
[37:02] Rachel Woods: So- Yeah I know we're kinda, coming up on this already being a long episode.
[37:05] Rachel Woods: There's one other thing, that I wanted to show,'cause I think Yeah, I think we went through this playbook-
[37:11] Isar Meitis: Yeah
[37:11] Rachel Woods: well enough.
[37:11] Rachel Woods: Yeah.
[37:11] Rachel Woods: I
[37:11] Isar Meitis: don't think we gotta- Okay we need to run it all the way to the end.
[37:13] Isar Meitis: People are probably really interested in the whole wine thing, but they can listen- to a different episode
[37:18] Rachel Woods: on that.
[37:19] Rachel Woods: we'll keep you posted, yeah.
[37:20] Rachel Woods: But, and this is something that's, I like to just, again, like, um, be, super open and even kinda just sharing stuff as it's kinda newly emerging.
[37:29] Rachel Woods: But that progression that I'm talking about, of really thinking operationally about your work, and how you can keep scaling yourself, I think that really ends up being, like, a, such a big unlock.
[37:39] Rachel Woods: And so this is a, little visualization of my current, kind of personal productivity system where I have, playbooks that are all running and I'm, I bucket them whenever they start stacking them together into almost like a subsystem or this becomes which, agent is running them and, I have, recently split out stuff more explicitly between what are the playbooks that I actually wanna collaborate with the AI on versus what are all the things that AI can just do in the background for me.
[38:10] Rachel Woods: And then each of these have, escalation, processes, that then move into my, checking decisions log, which is where I need to be the one to, decide stuff.
[38:20] Rachel Woods: And so the way I think about this is, again, if I were, if I originally hired an assistant and I got them to do my email and my calendar and all these different things, and that was going really well, but now the assistant, is gonna either need to hire other assistants for it to go...
[38:35] Rachel Woods: oftentimes, I remember when I was at Facebook, the, chief of staff, executive assistant, people, they had, their own assistants, right?
[38:42] Rachel Woods: 'Cause there's just so much work, that can get done.
[38:44] Rachel Woods: and so it's kinda that same thing with these playbooks, is thinking about how can you, codify the process so that then it doesn't have to be something that you're in the loop on at all.
[38:53] Rachel Woods: And so yeah, so right now it's I have my overall chief of staff, which is the one that I work with, but then, it's all of these are running in the background of email, calendar.
[39:00] Rachel Woods: I have an EA and a projects, one that are kinda doing different things that don't need my input.
[39:04] Rachel Woods: And so each of these playbooks, back to the Lego blocks, that's really how you get there, is by starting one thing at a time and then being able to keep iterating and keep growing from there, I
[39:14] Isar Meitis: love it.
[39:15] Isar Meitis: Again, very similar approaches to the stuff that I'm doing.
[39:18] Isar Meitis: and the interesting thing is it's not because we shared information, it's because that what makes sense when you run a business, right?
[39:25] Isar Meitis: it has nothing to do with AI.
[39:27] Isar Meitis: It has to do with being able to define operational processes, make sure they're running effectively and consistently, so both these things matter.
[39:37] Isar Meitis: And then once it's there, then you can delegate more and more of that work, and you need to initially monitor and then even monitor less as things progress, and that's the whole biggest unlock, right?
[39:50] Isar Meitis: Is that people ask me,"Do you work less now?" I'm like,"I actually work more because the temptation to keep building more of these things." So I work more hours than I did two years ago, but I do 10X the output.
[40:05] Isar Meitis: So I work 15% more hours, but I do 10X the work.
[40:10] Isar Meitis: And you multiply that by the amount of people in your organization, and you understand the implications of that on the revenue and the growth of the business.
[40:17] Isar Meitis: Rachel, this was absolutely fantastic.
[40:19] Isar Meitis: Everything I hoped for and more.
[40:21] Isar Meitis: if people wanna follow loo- follow you, learn from you, work with you, what are the best ways to do that?
[40:26] Rachel Woods: Yeah.
[40:27] Rachel Woods: This was so fun, and I love that, yeah, we're both, we're both, the ops nerdy type.
[40:31] Rachel Woods: I hope this was really helpful, for people.
[40:33] Rachel Woods: And if you wanna connect, I am really active on LinkedIn.
[40:36] Rachel Woods: That's probably the best, spot.
[40:37] Rachel Woods: But really on, on all social channels, I'm there hanging out.
[40:40] Rachel Woods: And then if you wanna learn more about our company, our URL is runonamp.com.
[40:46] Rachel Woods: So we can, we can nerd out about anything, and I hope that after today you guys have, been inspired to write some playbooks.
[40:52] Rachel Woods: 'Cause remember that anything you're doing, you can playbook that, truly.
[40:56] Rachel Woods: So...
[40:57] Isar Meitis: Amen.
[40:58] Isar Meitis: Thank you so much, Rachel.
[40:59] Isar Meitis: This was really, really fun.
[41:00] Isar Meitis: I'm sure people learned a lot.
[41:01] Isar Meitis: I learned a lot.
[41:02] Isar Meitis: Gave me a lot of small ideas like, ooh.
[41:03] Isar Meitis: and that's always why I love these conversations, like there's always these little tricky things like, ooh, I don't have this, or I should update that.
[41:08] Isar Meitis: So I appreciate you.
[41:10] Isar Meitis: I appreciate you taking the time.
[41:11] Isar Meitis: Thank you so much.

Shows are listed from Apple Podcasts' charts and directory, and Podcast Index where it is used. Every show and episode belongs to its publisher and plays from the publisher's own feed; this site hosts none of them.