The argument in one line.
Service business owners can double or triple client capacity by building AI skill systems that handle 80-90% of repetitive work, allowing them to focus judgment and strategy instead of hiring additional staff.
Read if. Skip if.
READ IF YOU ARE…
- A service business owner with a repeatable client delivery process who spends 20+ hours monthly on the same task and wants to reclaim that time without hiring.
- A fractional service provider (consultant, accountant, designer) at 5-15 clients who's hit a hard capacity ceiling and needs to scale without adding headcount.
- Someone comfortable with Claude and basic AI workflows who wants to move beyond using AI as a faster tool into building systems that work semi-autonomously.
SKIP IF…
- You're a solo freelancer doing fully custom work for each client — this method works best for repeatable, templated deliverables.
- You've never used Claude or built any AI workflows before — this assumes you already know how to prompt and iterate, not starting from zero.
- Your business model scales through productization or passive revenue, not client labor optimization — this is specifically for time-trading service providers.
The full version, fast.
Service businesses hit a ceiling because revenue scales only with hours worked, but chained AI skill systems break that link by turning your workflows into an AI employee that handles 80-90% of delivery. The method splits a full workflow into sequential skills� load client config, pull data, calculate metrics, validate, generate charts and commentary, produce the deck, run QA� then chains them so one trigger executes the whole pipeline while you handle only the final judgment layer. The fractional CFO case study shows the payoff: 40 hours of monthly reporting compressed to 10 minutes, client capacity doubled from 7 to 15, revenue jumped from $24k to $52k monthly, no new hires, running on a $200 subscription. Treat AI as a trained employee, not a tool, keep strategic judgment with you, and invest the upfront 40 hours mapping delivery before lead gen.
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00:00 – 01:24
01 · The promise
Service owners are capped by calendar hours; AI as a skill system can lift the cap without more hours or hires.
01:24 – 03:16
02 · Revenue up, hours flat — the dream
Visualizes the 'two-curve' goal: revenue climbs while hours hold or fall. Sets up the diagram that the next chapter inverts.
03:16 – 04:22
03 · The trap — hours climb with revenue
Most owners using AI as a tool watch both curves rise together; they just work faster, not less.
04:22 – 05:22
04 · AI as employee, not tool
The perspective shift: treat AI like a hire you train over time, share IP and context with it, expect skill compounding.
05:22 – 06:36
05 · Skill systems defined
A skill system is a chain of skills AI runs as a workflow; improving any one skill is like coaching an employee.
06:36 – 08:34
06 · Meet Kieran
Fractional CFO for SaaS Series A/B founders, 7 clients capped at 6–8 hrs/client/month, $24K–30K/mo, turning away leads.
08:34 – 10:15
07 · Same ceiling you're in
Before/after card: 7 clients → 15 clients, 6–8 hrs → 10 min, $24K → $52K, hire zero. The result is shown before the method.
10:15 – 12:46
08 · This is what infrastructure looks like
The 8-step pipeline diagram: /run-monthly [client] → Load Config → Pull Data → Calc Metrics → Validate → Gen Charts → Gen Commentary → Render HTML → QA → human review.
12:46 – 14:26
09 · He knew. He just couldn't fix it.
Identifying the bottleneck is easy; carving the time to systematize it while still delivering is the actual blocker.
14:26 – 16:29
10 · Map first. Build second.
Used a business map + bowtie funnel to confirm the bottleneck was delivery, not lead gen — that determines build order.
16:29 – 18:05
11 · Build for one. Template the rest.
Get one client's pipeline to 90% quality (took ~40 hrs), then 70% of the work transfers to every new client.
18:05 – 20:13
12 · Recurring vs one-time cost
8–10 hrs/client/month forever flips to one ~10 hr onboarding, then zero hrs/month after.
20:13 – 23:15
13 · AI does the work around your judgment
AI builds the deck and flags anomalies; Kieran writes the commentary and decides what to say. Judgment stays human.
23:15 – 24:56
14 · Three paths
Stay capped, hire an analyst, or build a skill system. Only path 3 raises the ceiling — at $200/mo on Claude Code.
24:56 – 25:57
15 · CTA — Chief Leverage Officer community
Hard pitch: community launching May 18 for owners who want digital asset systems, skill systems, AI employees. Link in description.
25:57 – 16:11
16 · Recap calls → Strategy calls
Second-order win: Kieran sends a Loom of the numbers before the call, so the live call becomes strategy. Clients value it more.
Lines worth screenshotting.
- A skill system extracts a service provider's intellectual property into a chain of AI skills that replicates 80% of the delivery workflow without the provider's ongoing input.
- Using AI as a chatbot still requires manual guidance for every task; treating AI as an employee that is trained over time is the model that produces leverage.
- A fractional CFO going from 6-8 hours per client to 10 minutes per client is not a marginal efficiency gain — it is a structural business model change.
- Hitting client capacity and turning away revenue is the moment that signals a skill system build, not a hiring event.
- Improving an AI skill chain over time is functionally identical to improving a human employee's SOP — both require iterative feedback and quality correction.
- AI automation built in a single weekend rarely produces lasting leverage; the durable version is assembled incrementally across real client workflows.
- The goal of AI implementation in a service business is revenue up with hours flat, not revenue up with hours up proportionally.
- Skill systems differ from individual skills in that they chain together in sequence, allowing a full workflow to run with a single trigger command.
- Kieran's progression from 7 clients at capacity to 15 clients without additional headcount shows that AI employee models scale nonlinearly.
- A $200 monthly Claude Code subscription replacing an analyst's salary is not a frugality decision — it is a margin and capacity decision.
- Service businesses that use AI as a speed tool still exchange time for money; only those who use it to delegate workflows escape the billable hour ceiling.
- A skill system's quality improves the same way a human employee improves: by receiving examples of good output and being corrected when quality falls below the standard.
Steal the slide rig, not just the thesis.
Build with Rashid playbook
Rashid's slides are doing 80% of the teaching — the talking head is just a presence cue. That ratio is the unlock for technical content.
- Build 5–7 hand-drawn lesson cards (Excalidraw works fine) as the main canvas — drop yourself into a PiP bubble in the corner.
- Number every lesson top-left (`## · LESSON #`) so the viewer always knows where they are. Add a thin rail along the bottom that names each visual element on the slide.
- Color code religiously: green = the path you want them to take, red = the path they're on, white = neutral structure. Hand-circle the key number on every before/after table.
- Show the result before the method. Rashid drops the 7→15 clients / $24K→$52K table four minutes in, then spends the next twelve explaining how. Curiosity stays high the whole way.
- Every claim gets a 2D chart. 'Revenue up, hours flat' is literally drawn as two lines. Visual proof beats verbal proof.
- Pitch your offer (or product) as path 3 of 3 — make 'do nothing' and 'do the obvious thing' explicit, then show your option as the only one that breaks the ceiling.
- For Mod Boss / JoeFlow content: take any creator workflow (record → cut → caption → schedule → post) and draw it as an 8-step labeled pipeline. That single slide is the whole pitch.
Terms worth knowing.
- fractional CFO
- A part-time or contract chief financial officer who provides executive-level financial strategy and reporting to a business without being a full-time employee — common in small and mid-sized companies that need financial expertise but cannot justify a full-time hire.
- skill system (AI automation)
- A reusable, chained sequence of AI-powered steps — each performing a discrete task such as data extraction, analysis, or report generation — assembled into a repeatable workflow that can be triggered on demand without manual intervention.
- ARR (annual recurring revenue)
- The annualized value of all active subscription contracts — a core SaaS financial metric used to measure predictable, recurring income and company growth trajectory.
- MRR (monthly recurring revenue)
- The total predictable revenue a subscription business collects each month from active customers — used as the primary pulse metric for SaaS and recurring-revenue businesses.
- churn
- The rate at which customers cancel their subscriptions or stop doing business with a company — typically expressed as a percentage of customers or revenue lost within a given period, and a critical metric for any subscription business.
- YAML front matter
- A block of structured metadata placed at the top of a plain-text file, enclosed between triple dashes (---), that defines configuration or parameters in a human-readable key-value format — used here to configure and parameterize AI skill templates.
- Power Query
- A data transformation and query tool built into Microsoft Excel and Power BI — used to import, clean, reshape, and combine data from multiple sources without writing traditional formulas, making it a common tool for financial analysts and CFOs.
- bowtie funnel
- A revenue visualization model that tracks both the pre-sale acquisition funnel (narrowing to a closed deal) and the post-sale expansion funnel (widening through upsells, retention, and advocacy) — giving a complete view of the customer lifecycle, not just acquisition.
- anomaly detection
- The automated identification of data points that deviate significantly from expected patterns or historical norms — used in financial reporting to surface unusual spikes, drops, or outliers that warrant human review.
- Loom
- An async video messaging tool that lets users record their screen, camera, or both and share a link — widely used by distributed teams for asynchronous walkthroughs, client updates, and explanations that would otherwise require a live meeting.
- Claude Code (skill context)
- Anthropic's terminal-based AI coding agent — used here as the engine powering automated financial reporting skills, where it reads data, applies logic, and generates formatted outputs without manual coding by the end user.
Things they pointed at.
01:05personKieran (client, fractional CFO at Biyondata)
06:20channelPrevious video on skill systems
15:05toolBowtie funnel (business mapping)
24:10toolClaude Code ($200/mo subscription)
25:00productChief Leverage Officer community (launches May 18)
Lines you could clip.
01:05
“Kieran was spending forty hours a month on monthly reporting for his clients and got that down all the way down to ten minutes using a skill system.”
02:42
“If you use AI like a tool, you're just gonna work faster. Yes, you might be able to serve more clients, but because you're working faster, you're gonna work more hours.”
03:15
“The perspective shift we wanna make here is we want AI to work as an employee.”
08:34
“Within two weeks of implementing the skill system, he was able to serve 15 clients.”
09:15
“Now he can make around 52k per month, and he doesn't need to hire anyone.”
21:36
“AI should do most of the work, but you should do most of the judgment.”
24:34
“It works on a $200 a month AI subscription on Claude Code.”
Word for word.
Read-along
Don't just watch it. Burn it in.
See every word as it's spoken — crank it to 2× and still catch all of it. The same dual-channel trick behind Amazon's Kindle + Audible.
metaphoranalogystory
00:00If you're a service based business owner like me, your goal is increase the amount of revenue you make every month without needing to work more hours to generate that revenue. Now the problem with service based business owner is that we have a that we could serve based on the amount of time we have on our calendar.
00:15And traditionally the only way to get more clients is to either work more hours or hire more people to help with clients delivery. Now what if I told you you can increase the amount of clients you serve without needing to increase the amount of hours or hire anyone but with implementing AI into your business. More specifically, you'd implement a skill system that would extract all of your expertise, your intellectual property, and your workflows on how you deliver value to your clients.
00:41AI can do that for you. Now this is completely different from using AI like a chatbot where you need to guide it and do everything from scratch while a skill system actually does 80% of the work. All you have to do is review the work, sign it off, and send it to your client.
00:55Now if you don't know what a skill system is, in my previous video, I explained exactly how you can build a skill system and how it's different from just having normal skills that you would build on Claude. And in this video, I wanna walk through a case study of my client, Kieran, a fractional CFO, who was spending forty hours a month on monthly reporting for his clients and got that down all the way down to ten minutes using a skill system.
01:16And I'll break down exactly how Kieran used that skill system in order to scale his business without needing to work more hours. Alright. So the dream of running a business is, is ideally over time, we want our revenue to go up like so, but we want the hours that we work to remain the same or best yet go down.
01:33And that's the goal of AI. Right? Everyone talks about AI automation.
01:37They sell this dream that, hey. We're gonna implement this AI automation, and your revenue is gonna go up and the amount of hours you're gonna work is gonna go down.
01:46Now very few business owners actually experience this dream because in order to get AI automation, like I always talk about in all the videos on this channel, it's gonna require a lot of complexity to eat because AI automation doesn't happen in one weekend, and you get to live this dream of, oh, you start stacking unlimited amount of revenue.
02:06You don't need to work at all. That is something that is not true. In order to get AI automation to work, you need to spend time building a skill system, which we're gonna go over in a moment.
02:15And if you don't know what a skill system is, in my previous video, I went over that, so I highly recommend you to watch that. So what actually happens for a lot of business owners is because they don't know how to implement AI the right way, which is implementing skill systems, is the the amount increase amount of revenue they make, like so, but the amount of hours they need to work also increases with them because every client requires their input or they're just using AI like a tool.
02:39So if you use AI like a tool, you're just gonna work faster. Yes. You might be able to serve more clients, but because you're working more faster, you're gonna work more hours, more hours.
02:49Now the perspective shift we wanna make here is we want to have work have AI , AI employee. And when we work as an AI employee, that means AI is able to be smart enough or we treat AI like an employee.
03:02We're gonna be meeting it like a human employee. You'd share it with more context. You'd share with your intellectual property.
03:07You'd treat it like someone that learns over time and improves their skills. So as AI gets better and learns more about your business, ideally, the amount of hours required for you goes down.
03:18Right? Now in order to get AI to work as an employee, we need to build skill systems.
03:24Skill systems. I'm a do a quick recap over here. Skill systems is a series of skills that that allow AI to run workflows.
03:31And as you improve the skills in the chain, that is kinda like treating or improving the training of a human employee in your team. Let's say you you help them improve their their you improve an SOP.
03:43They can do an SOP better. They improve their skill. They actually know the difference between a good quality output and a bad quality output, and that takes time.
03:52No one gets that right at the first shot. So AI needs to learn that over time. So once we have skill system, that allows us to then have AI employees, and then that allows us to eventually generate more revenue in our business while working less hours.
04:06Now the big problem is a lot of business owners don't do this. They treat AI like a tool or like a product. They think about, hey.
04:12I'm just gonna work on AI this weekend. I'm gonna build this fancy thing or this elaborate sophisticated system, and it's gonna make me tons of money.
04:19Very rarely does that happen. So meet Kieran. Kieran works with SaaS companies as a fractional CFO, and he basically sends them monthly reporting and consulting to help them make smart decisions with their financing in order to grow the revenue or to potentially exit their companies.
04:34Now as you can imagine, as a fractional CFO, there's a lot of number crunching, a lot of documentation, reporting, putting things together.
04:42It does take a lot of time and effort. And what was stuck at before was he was spending a lot of hours trying to do all this documentation and stuff.
04:51He'd even have an analyst working for him on his team. So was a good case study because when Kieran came to me, he was working with around seven clients, and he was capped because each of his clients would take around six to eight hours a month to deliver.
05:06Now he was making a good amount of money. He was making around 24 k per month all the way up to 30 k or in some cases 50 k per month with his clients. So here is a point where he hit his client capacity.
05:17So he hit client capacity, and some clients actually wanted to work with him, and he had to say no. So can you imagine saying no to clients, saying no to money?
05:26Well, that's really hard for a lot of business owners. You feel really bad about that. So in this position, Kieran was actually thinking about hiring another analyst because he already has an analyst on his team.
05:35He was thinking about hiring another analyst in order for him to serve more clients so he can scale his business. Now Kieran Kieran is not a type of guy who wants to manage a big team. He likes to keep things small, leveraged, and lean.
05:47So that's where I came in and helped Kieran with building a skill system. So after building the skill system, what happened was within two weeks of implementing the skill system, he was able to serve 15 clients. What would take him around six, eight hours per client for monthly reporting would take him around ten minutes take him around ten minutes.
06:05Now he can make around 52 k per month, and he doesn't need to hire anyone because he was able to treat AI like an employee in his business that he would train over time on how he delivers work, uh, to his clients. So how did Kiran's skill system actually look like? So it looked like so.
06:20So Kiran would then split up a whole workflow into separate skills or separate phases. So step number one, for example, would be load the configuration for that specific client. So he'd he'd have a YAML front matter that would show Claude where to find the brand colors and the basically, the configuration to create that monthly report.
06:40Then AI would pull in the data. It would use Python and Power Query Query to find the data. Then it would calculate the metrics, ARR, MRR, churn moves, everything.
06:50Then it would validate the data before it moves on to anything. It would look for any anomaly detection or mistakes. Then it would generate the charts.
06:57Then it would generate the commentary. Then it would create an HTML or in a PDF after that, then it does a quality assurance review, and then Kieran comes into the picture. So all of this stuff is 80% of the work, and Kieran comes in and does 20% of the work, or this could be even, like, 90%, and he does, like, 10%.
07:17And then the output is a client's deck and then a briefing that, uh, Kieran can share with a client. And by the way, if this is your first time seeing, uh, or skill system look like this, I have another video in my channel.
07:29My previous video just posted. Check that out to explain to you how skill systems actually work. But this is actually what you wanna do.
07:36This is what you wanna do if you wanna get AI to actually generate revenue in your business. You wanna break down your workflow into smaller tasks, create skills, and then chain them together into a skill system so it can actually create an output that is 90% quality or more.
07:50Now this is what AI automation looks like. AI automation looks like so.
07:56It is this going through all these skills step by step and creating an output that is 90% quality. Now in order to get to this level, you have to treat this like an employee again. Right?
08:06Each of these skills can be improved over time. You're gonna add more edge cases. You're gonna add more guidance.
08:11You're gonna add more intellectual property, more, uh, mental frameworks to get AI to think more like that you do. Now potentially, Keyring can get to a point where it does a 100%, but that again, that will take a lot more time because then AI will need to get the context from these calls.
08:26So then Kieran can potentially feed the calls, uh, to AI to run the full thing, uh, a to z. But that's something that I wouldn't recommend to do right now. I think maintaining your judgment is really important, uh, with this stuff because you need to be on top of the strategy.
08:41So Kieran knew that the reporting was a bottleneck because he was spending sixty eight hours on reporting, sending monthly reports. As a CFO, you do have to send reports on the finances and give some insights to his clients so they can make better decisions, um, in order to grow the business or to egg, uh, or to exit the business.
08:58He was specifically working with SaaS companies. So he knew that for months, he said he would keep fixing it, but he just couldn't find the time. So so he was in a difficult position because he was already way too busy delivering the work, and he knew if he had to hire someone, has to do more handholding and wait for them to learn.
09:14And he didn't know how to break it apart. He actually need to figure out how to break up his workflow so that the skill system can execute the work for him. So the first thing we did was when we hopped in a call was we mapped out his business so we can understand how his business works, and we can find clarity on how we can get AI to do the work for him in his business and which skill system would be relevant for him.
09:33So we started by mapping out a business map where I like to call, and then we identified that what was his problem was delivery, not lead generation. Kieran thought his problem was was lead generation, but it was actually delivery. So we then focused on that.
09:48And that was evident to us because I like to use something called the bowtie funnel, which kinda maps out your business from the lead gen side all the way up to client delivery side. And we saw that a client delivery side was where we could find good AI opportunities to help him scale his business. So what we started was we started with developing a scale system for one specific client at first.
10:08So we started by going over reporting scripts, creating some Python scripts, creating some deck templates, creating the brand files for that client, and then, uh, putting it all into a trigger called run monthly that, um, Kieran can run every month for that specific client. So Kieran worked on that and he got it to around 90% quality to get it get it to work.
10:29So it got it it got to a point where it was he was satisfied with the result to 90%. All he had to do was and the last 10% and ship it off to clients and clients were impressed with that.
10:40So with this in place, Kieran was unable to then pretty much copy paste this process, tweak it a little bit for other clients because other clients were a little bit more custom. So he just needed to change maybe 20% to 30% of the process. So now he can extend this to all of his other clients.
10:55So he had seven clients. He was able to extend it to all of his seven clients like so. And that started by creating one full pipeline or skill system built.
11:04And this took him around forty hours of training AI where specifically he would do the report and have AI shadow him while doing it. This allowed him to not only deliver to his current clients without, uh, firefighting and letting things go while still getting AI to develop, uh, the skill system for him.
11:21And the best thing about a skill system is that for Kieran to onboard a new client to run through that monthly reporting process, it would take him around ten hours to onboard just to get the API key, set things up. So that in turn would save him ten hours every single month. So instead of needing to spend, like, eight to ten hours every month doing the monthly reporting himself like so, now he just spends ten hours one time to onboard them, and then AI does all the work from here.
11:46So this is a skill system doing the work for him. So you can imagine how much more time is he gonna get get over time. So this compounds.
11:53Right? He's avoiding spending ninety six hours a year on a specific client. So that's a lot of client capacity he can get back.
12:00Ninety six hours back per client is a lot. So it onboarding the client so spending around ten hours onboard the client pays itself over the months so on because he doesn't need to spend as much time. So next thing about the skill system is that Kieran didn't let AI do all the work or 100% of the work he actually let AI do 80 to 90% of the work which is most of the repetitive and execution but when it came to his judgment which is identifying the the diagnosis and giving advice to his clients, he would take care of that because that's what the clients appreciate.
12:36So AI would build out these reports like so, and then Kieran would come in and do the the small adjustments or the small commentary to guide the client because Kieran has all of the context.
12:48You know, AI doesn't have all the context. If you are the service delivery business owner, you you are you're the person on the calls.
12:56You're the one who understands the emotions. You're the one who understands what the client needs and everything. So AI can help you probably make some commentary, but at the end, you wanna put in the last 10% because it is an emotional connection or relationship with your clients.
13:09So the principle here is . So you should not let AI do all the judgment. So previously, Kieran would take three to four hours writing the commentary.
13:21AI would do some of it, and he would just come in and do the last thirty to sixty minutes to shift things off, uh, to the So with this in place, generally, most service business owners have, like, three paths in order to scale their business. Number one, they could just do nothing, keep keep the status quo.
13:36And for Kieran, in this case, he would just stick to serving some clients, he would just continue working, uh, normally. Number two, he could hire someone. So for Kieran would be like to add an analyst, but that would add training, review, payroll, and that would just add more stress and handholding that he will need to do.
13:52Or third path is you could build a skill system or an AI employee AI employee, and you can increase the client capacity you have like Kieran did.
14:01He increased it from seven to 15 clients, and you don't need to hire anyone, and it takes and it works on a $200 a month AI subscription on Cloud Code as an example. I don't know about you, but I would definitely pick the skill system option because the gains here are really high and the cost isn't that much. You know, you're paying $200 a month and you might spend a little bit time upfront to learn how to use Cloud Code and how to use how to create a skill system, but that skill will pay you really well over the long term because not only would you create one skill system for this, you can create skill system for other parts of your business.
14:35Lead generation, sales, delivery, everything, and skill systems stack up on each other. So that's a goal for me as a business owner, and that's what I do when I help with my clients. So if your goal is something similar to this where you wanna build skill systems in your business and you wanna have AI employees that do 80% of the work for you in your business, I am launching a community next week on May 18.
14:55It's called the chief leverage officer community. It's called that because as chief leverage officers, our goal is to grow our business based on leverage.
15:04That means we look to implement digital asset systems, skill systems, and AI employees so we can scale up this without working more hours. K?
15:11And if you want that, I'm gonna put more information in the description below. So next thing about Kieran is that his calls with his clients became more productive and became more valuable to the client. Before he would recap numbers, he would read from the deck and he would tell them, hey.
15:23This is what happened to numbers and so on. Now what Kieran does instead is because AI does most of the number work and everything, Kieran just records a Loom video a Loom video. He's he does what he would do before on the call.
15:36He sends a Loom video, and then on the call, he focuses on things that that is decision making stuff, things that require judgment. So the client watches a Loom. The call is more strategic conversation.
15:47You know? Kieran's more energized. He's not doing a boring call to explain numbers, but he can actually talk about decision making strategy.
15:54And clients appreciate that more because now they're spending their time on stuff that they actually value more. So they actually talk about more important discussions. And, again, if you wanna build skill systems in your business so you can get 80% of the work done done by AI, I do have a community.
16:08I'm launching on May 18. I'll see you there in the inside.
The Hook
The bait, then the rug-pull.
The pitch is the dream every solo operator has been sold a hundred times: more revenue, same hours. The proof is more interesting — one of Rashid's clients, a fractional CFO named Kieran, swapped 40 hours of monthly reporting for a 10-minute review pass and doubled his client list without hiring. The mechanic, and the slides Rashid uses to teach it, are what's worth studying.
Named ideas worth stealing.
02:45concept
AI as tool vs AI as employee
Treating AI like a tool means you work faster and hit the same hour ceiling. Treating AI like an employee means you train it on your IP over time and let it compound — hours drop while revenue rises.
Steal forany 'why your AI workflow isn't paying off' explainer — works as a 60s short or a long-form opener
03:16model
Skill System (chained skills)
- Load Config
- Pull Data
- Calc Metrics
- Validate
- Gen Charts
- Gen Commentary
- Render HTML
- QA Review
A skill system is a series of single-purpose AI skills chained into a workflow, triggered by one slash command, ending in a human review pass. Each link can be improved independently like coaching an employee.
Steal forMod Boss / JoeFlow tutorials — turn any creator workflow (recording, editing, posting) into an 8-step labeled pipeline diagram
16:29concept
Build for one, template the rest
Pick one client/use-case, build the full pipeline end-to-end to 90% quality, then 70% of the work copies to every new client with 20–30% customization.
Steal forany 'how to systematize your service' pitch — also applies directly to MCN onboarding templates
15:05model
Bowtie funnel for finding AI opportunities
- Traffic
- Converters
- Products
- Funnels
- Math
- Team
- Goals
Map the whole business (lead-gen left side, delivery right side) on one canvas before deciding where to apply AI. Most owners assume the bottleneck is leads; for service businesses it's usually delivery.
Steal forconsulting / coaching content — also a great Killing Excuses sketch: 'I need more leads' vs the bowtie showing it's actually delivery
20:13concept
AI does the work around your judgment
Let AI handle the 80–90% repetitive execution (build the deck, flag anomalies, draft commentary). The owner does the 10–20% that requires judgment (what the numbers mean, what to tell the client). Don't outsource judgment.
Steal fordirectly transferable to Joe's content workflow framing — and to any 'why you still matter in the loop' positioning piece
23:15list
Three paths to scale a service business
- Do nothing (stay capped)
- Hire an analyst (more management overhead, same ceiling)
- Build a skill system / AI employee (ceiling lifts)
The classic three-choice framing — only one option breaks the ceiling. Each option gets its own color-coded card (red/red/green).
Steal forevery offer page where you want to corner the reader into the third option — works for $6 Stack pitches
How they asked for the click.
VERBAL ASK
24:45product
“I am launching a community next week on May 18. It's called the Chief Leverage Officer community... if you want that, I'm gonna put more information in the description below.”
Soft, value-led — lands after a full lesson, framed as the next step for owners who already buy the 'skill system' thesis. Re-mentioned at the close with no price reveal, deliberately driving to the description link.
FROM THE DESCRIPTION
PRIMARY CTAWhere the creator wants you to go next.
OTHER LINKSAlso linked in the description.
Visual structure at a glance.
