The ULTIMATE AI Consulting Course For Beginners (2026)

48,000 layoffs at UPS, 14,000 at Amazon, 1,800 at Target. A slew of layoffs have raised fears that AI is already whacking corporate jobs.

Whether we like it or not, the world of AI is here to stay. And this new world is creating both victims and victors.

How much money do you make?

Make $1 million every month.

And how old are you?

I'm 17.

So, right now, everyone thinks the AI world belongs to the young tech geniuses who've been coding right from the womb. But that couldn't be further from the truth. So, this is the complete AI consulting course, and it's going to allow 39-year-old Bob to get ready for that wedding, 62-year-old Aisha have that lavish retirement, and 47-year-old Maggie send her kids to college. You don't need to be tech-savvy, you don't need to be grinding 12 hours a day. Just follow this course and change your life forever.

But before we go any further, let's break down this AI business model. The AA automation space is worth 22.16 billion. The creative agency space is worth 276 billion. The SAS space is worth $370 billion. But the consulting space is worth $1.06 trillion. We are going to be discussing what a huge opportunity the consulting industry is, what is an AI consulting firm exactly, why it's the best model for everybody, and the three AI business models compared. And then finally, how you can go ahead and start your own AI consulting business.

So right now, the $1.06 trillion market is consulting. AI automation, which is what the vast majority of people think about when they think about AI, is only worth 22.16 billion as of recording this. And then you can see just it is a Psized compared to almost every other market. Even the marketing agency market is about 750 million. So consulting dwarfs every single market out there and it is the biggest and most slept on opportunity in the AI space right now.

So the old world equivalents of consulting companies like Bay & Company, McKenzie, Deote, PWC and for context each of these companies does north of $20 billion a year in revenue. Between the big four consultancy practices, they do over $300 billion a year in revenue. That's not the value of the company. That is the money they make per year in total revenue. Then you got creative agencies like Oggov, Publix and Macan. These are the companies that are responsible for branding and marketing for some of the best known companies and brands that you know in the world like Coca-Cola etc. And they're like the old school marketers and brand builders. Think of Madison Avenue, right? Then you've got the newer guard which are these AI automation agencies. Companies like InfoZ Morningside and Accenture. These guys are doing what most people think about when it comes to AI which is they are taking um the current AI tools and getting developers as well and then basically building solutions for businesses to automate tasks within the companies. This is a very developer-heavy segment.

Now you can have a look at the market and just see how drastically it's growing. So this is for context that you probably have never even considered. Now AI consulting and AI consultant is already got more market or or more search volume than management consulting which just to be clear McKenzie that does over $20 billion a year is predominantly a management consulting firm and more people are now looking for AI consultants and AI consulting than management consultants. And you can see just how stable management consults been for over 5 years and then out of nowhere you can see the drastic growth of AI consulting and AI consultants that businesses want cuz to be clear they need to adopt AI. This is not a decision. This isn't like oh we can opt in opt out type thing right this is if they do not opt in they are going to get crushed by the competitors that do opt in.

So the reason this is the best model for everybody is you to be a consultant be an AI consultant you need absolutely no technical expertise right so to be an AI automation uh agency or company you need to be at least some degree technical whether you're using tools like nan or make or whatever or you're doing custom developed solutions you have to be technical to deliver them you got to know what you're doing right when within a consultant you just need to know how to identify the problems and the bottlenecks within a business that can be solved by AI AI, you don't actually need to do any of the technical implementation. The next thing is you probably have industry expertise, vertical expertise, sector expertise, skill expertise that ordinarily you've not been able to do anything with. So you might have been in healthcare, manufacturing, supply chain, whatever for the vast majority of your career. Now you have something you can use to actually give you a moat when you are an AI consultant. So if you've been in manufacturing or supply chain for 15 years, you are now someone who's been in those industries for 15 years and you are now an AI consultant and you know how these sectors talk. You know the inefficiencies within their businesses and then you can go in, speak the language and basically become the AI consultant for that sector. And this also is a model where you could do it part-time or you can do it full-time and grow your a billion dollar business. Essentially, there will be 100% AI consultant firms that are multiple billions of dollars. We already have a track record like I said of major consulting firms doing tens of billions of dollars a year. I think Deote last year did $80 billion in revenue. Okay.

So AI consulting companies we're in the business of identification. Okay. So we're using things like AI audits, AI training and workshops. We then transition into implementation which to be clear we do not have to do and then maintenance of those implementations. You've got AI creative agencies. So these are things like Ogleby where you've now got the creative element on top and these are doing things like branded videos, user generated content, social media ads, social media content and they're in the business of creation. So previously when you had a lot of assets you needed created, you needed influence and stuff that a lot of that candidly is now going to be gone because you're going to be able to use AI to make much better content faster and at drastically uh larger scales. And then you got the AI automation agencies which are in the business of reduction. This is like emails, autoresponders, voice agents, um, knowledge based, chat bots, anything like that. Basically, they're building uh the systems to replace workflows that humans are currently doing.

So, for example, like this is a message I got just the other day where Raman was saying, "Hey, do I do AI training for people?" Josh and Bones also did AI training that turned into a 10K audit and then turned into a 12K implementation. Elliot here was a a ex SEO who managed to close a 2K audit on his first day by going back to one of his previous clients. Alexandra here is a bit more technical. She signed a 40k contract from a free audit where she did the the audit for the company and then they actually wanted all the implementation. And then did 7k audit into the implementation into the maintenance being 100k total contract. So you can see with AI consultancies you can basically get paid for all those different elements. Okay.

Some benefits with AI consulting is you're selling clarity, right? You're untangling this web of confusion around AI. So you don't need to be technical. You want industry expertise. Okay? So that's like us doing company. You got AI creative agencies where they sell output. So you got companies like Arcads where they specifically focus on user generated content for social ads. You got Poppy.ai that focuses more on written content. So like scripts and um written ads and written social posts. And they're really good at that. And then for AI automations, they sell the system which like Accenture and that's what they do. They build the systems to replace staff within the business because of their workflows currently being done manually things like that. Right? So this is we're selling the clarity. We then do specific implementations based on the clarity uh of the audit and then we get result certainty.

Now the reason result certainty is so important is right now 95% of AI projects actually fail to deliver on the promise and this is for a few reasons that I want to discuss. So there's a ton of AI hype. Everyone right now is like, "Oh, AI can do absolutely everything." And it can't. It currently has a like a a world in which it operates very well. And then outside of that, it starts to get pretty ropey. Okay. So there's the the big hype train, but then these two are the most important. So one is they do edge cased implementation. And what I mean by that is they can technically do something with AI, but it's it's a complicated workflow and there's a lot of break points because AI maybe isn't quite ready to get there. So they might implement this solution and it kind of works and it does the thing and then what happens which is the third and actually the biggest reason is they don't maintain the implementation. So a big reason AI implementations fail is there is no maintenance post implementation and this is what I tell everybody is once you are implementing you should always have a maintenance contract in place whether they are you know they all work on Microsoft office or all these different tools they will have a managed an MSP a managed service provider who will maintain all of their IT solutions. So if anything breaks they call up this MSP and they fix the problem they maintain it so that the client actually gets the solution that they paid for aka that implementation and we do those implementations through a partner network. We don't actually have to do them ourselves. Okay.

So this is the four stage client transformation blueprint. So first we actually diagnose their specific problems. There's a great saying which is people don't care how much you know until they know how much you care. Now the AI audit process is the diagnosis. Okay. When we have diagnosed their specific problems, we will prescribe a solution to that specific problem. We aren't putting them in a bucket of a product that we want to sell, which is what a lot of people do in the AI automation space. They go, "Hey, we know how to build AI chatbots. Now, we just need to find someone to buy our chatbot." That is exactly the wrong way around and not what you should be doing. You should be auditing these businesses, getting paid for that, then prescribing solutions that you know are going to work, and then you are going to be maintaining those solutions. So the implementation and the result they wanted is essentially a guarantee and then after that you also have the option to do additional consulting because in your in their eyes you will be a figurehead of the like essentially cutting edge AI consultant in their sector. This is why having that sector expertise is a real massive moat because you know how they speak you know the things that they want to implement. know their process and their workflows they're currently going through and when new AI comes out and new stuff that AI can do, you can actually give them continuous recommendations. So what ends up happening is you go back from this consulting back to prescription and then maintenance and it kind of once you've done the initial diagnosis, you know how the company runs you it's basically going to be using prescription, maintenance and consulting to get more and more recurring revenue coming your way.

So, let's break down a traditional consultancy versus an AI consulty versus a productized service. And I'm actually going to use my last consulting practice that I sold in February of 25 because it's a great example. So, we were a sales consultancy for VC back startups. And it was a traditional consulting practice where it was all human input. So, we were generating the leads. We were doing all the fulfillment of making the sales script, making the market material, all that stuff and then actually delivering all the consulting calls. when uh I acquired my stake in that business, we actually became an AI first consulting practice where all the acquisition and all the fulfillment was then handled by AI and then the only thing we actually had to do was turn up to consulting calls and we ended up between a couple of us having like north of an 80% profit margin on that business. So even higher than this and basically the only thing you have to do is duplicate the workflows and build the templates and stuff like that when a client comes in and the acquisition we also hired handled with the air and then we just turned up to the consulting calls and you can easily we were we were north we were nearly a million we're like 850k per head in that business and the final uh variation would be a productized version of that where a big part of that process also was sales call reviews. So now we're getting to a point where AI can actually analyze sales calls and give feedback at scale. And that is how you turn this business, my my previous consultancy into a productized service where actually all the call reviews are then actually being handled by AI too and you'd have 80 90% plus margins and then you just have a few consultant calls and you'd be well north of a million dollars ahead and the only thing you worry about is um is acquisition of more clients to scale up.

Okay. So these this is kind of the flowchart of what an AI consultancy really looks like uh when you're wanting to uh to get involved. So your your industry expertise becomes specific identification of industry bottlenecks. You've got automated workflows means when you are bringing in clients for your business but also delivering for them you have speed on your side. So you have the ability to execute very very very quickly and that also gives you a massive edge. You're not taking months to deliver projects. You are doing them much much faster than that. And then because you are maintaining any implementations you do do, you have better outcomes because the implementations that are done are maintained. Therefore, they get the result they want. So you are part of the 5% that are successful. And I can tell you from experience, every single one of my implementations that I have done with my team has been successful because we require maintenance post implementation. It is not an option we we give. We say this is a requirement for two reasons. Good Mr. Client. We say one we want to make sure that if anything breaks that we are there to fix it as soon as humanly possible and two AI is evolving so fast exponentially faster than IT solutions. This thing then gives you a unique value prop. So again you're in supply chain healthcare manufacturing you're in administration this is different for every single person because you have designed it properly. You have these two you have maintenance and you have workflows right and this puts you in an absolute blue ocean with traditional companies. There is absolutely no competition. No one right now is doing AI consulting which is exactly what these traditional companies want. So there is just untapped demand. There is genuinely worldwide hundreds of millions of companies. I have a client of mine who is in Egypt who closed a 5k audit for a company in Egypt because they need to adopt AI. I've got people in Spain, France, Canada, America, England all doing this because there is so much demand for this because people do not understand how AI fits within their specific uh company and that is why we start with an audit process first.

So let's actually go through this. So the audit again is the identification of the issues. We then automate the processes we we can. So this is what we're selling. So this is our business. We are then automating the processes we can which is um the acquisition side. So with cold outbound or or media like ads or content and then we are delegating the additional tasks. So in AI consulting the biggest uh bottleneck of providing the solution is actually the interview process which you don't want to be handled by AI because that is actually where the uh personal relationships and rapport is built and then we can take all this information and actually start developing products around it. So you start developing your own products that you could sell and you get more scale. Now, there will always be an element of consulting that has a human touch point within it because it is a relationship game, but you can get to build some pretty damn sick products which basically take this this automation and then just juice it up to another level.

Okay. So this is this is really what it looks like from a revenue model for consulting practices in this day and age. So, the audits anywhere from 5K to 50K. I've got people who I've worked with and trained as well who've charged deep into the six figures just for an audit because the companies are so big. had clients do audits on IPOed companies, three and a half thousand staff and they're hundreds of thousands of dollars for an audit. Then we've got the implementation anywhere from 5K to 500K be a million plus like this is such a broad range because it really depends on the company. So that's like Accenture and obviously done in company us this is where we do the implementation from the discovery during the audit process and then the big and most important one really is actually the maintenance because we ensure that they're happy with this uh the implementation and that's company like brick as well. Now, Brick, for example, does $20 million a year, and all they do is their average um client pays them $3,000 a month. They do $20 million a year, and they just work with traditional companies. Uh and and this is all they focus on, right? Is maintaining solutions that have been implemented. And yeah, like I said, they do 20 million.

So, here's a flowchart for you for your decision-making on how you could go ahead and set up an AI consulting practice right now. So if you've only got industry expertise, you can just go ahead and start one because you don't need to be technical. So it's absolutely fine. That is your go to market strategy. You're going to be reaching out to your networks, people in your industry, vendors, things like that. And we'll touch on that shortly in a second like give you the actual road map. If you are only technical, you are going to need either partner up with somebody who does have industry expertise because they will have that mo, they will have that understanding, they will have those relationships. You can hire that person instead of partnering with them or you will need to learn that sector. Now, this is obviously going to be the slowest. Um, so I would potentially, if you're wanting a middle ground, you could hire, you're obviously going to have to pay someone a bunch of money, but then you are going to learn industry very, very quickly and some of the use cases, and then you potentially become that industry expert very fast, but you're paying for it. You are going to want to target traditional industries. For my myself, it's consulting, it is engineering, and manufacturing. They're like the the ones that I focus on cuz I come from the engineering background. And that is your go-to market strategy. And again, if you are both, it is just a fast path to cash cuz you can make money from the consultant side and you make money from the technical side. Okay, so it's just it's just an absolute home run.

Now we know how the entire model works and how profitable it can be. But now we need to understand the blueprint to being a successful AI consultant or as I like to call it a vibe consultant. So this blueprint is a direct result of stuff that I've done, expensive lessons I've had, businesses that I've worked with. You know, we did took risk, made mistakes, and we essentially have refined the processes uh along this uh journey over the last couple of years of being deep in this AI wormhold. Um so this document and this training is definitely going to be your shortcut. So you are going to be building an a trusted AI consulting firm and you are going to be a Vibe consultant and this guide is going to make it totally obvious why there is immense value here for not only you but the companies you serve.

So the three-step Vibe audit system. So this blueprint breaks down the entire highv value audit process into three clear actionable steps. I've stripped away all the complexities and give you just the framework you need to get immediate results and give immense value to businesses. So you are going to be running a twoweek opportunity assessment. This is your audit. You're going to be charging 5 to 10k minimum for smaller clients and this can be upwards of 50k for bigger clients. Week one is going to be the interviews you are providing and the process analysis. And then week two is going to be the opportunity identification and the validation of the road map. And then also the upsell into actually the implementation of this uh of these solutions. You want to be charging 5 to 10k is a bare minimum. And as clients get bigger. So if you're a 20 person firm, you can easily be charging 5 grand. If it's a 200 person consulting firm or 200 person, you know, accounting practices, you can charge 50 grand no problem uh to do that because you're going to spend a lot more time in the interview process.

So step one, discovery interviews. Uh we're going to go through the templates below, but you're going to have two types of interviews, which is the stakeholders and the employees. And I'm going to teach you how to conduct these interviews correctly. Step two is going to be map, identify, and validate the opportunity. So you need to learn how to translate the interviews that you have into an opportunity canvas. And then you're going to use an AI opportunity matrix to pinpoint and prioritize the most impactful AI solutions uh on an simple and easy to use single diagram that you're going to present in your uh consulting uh deck. And then step three is presenting your findings and the money slides. This is the slide templates and the ROI calculator. And I'm going to just show you exactly how to build like a very nice um slide deck that presents your findings well and makes it obvious to use you moving forward.

So step one is the discovery interviews. So to uh the foundation of any successful AI audit is understanding the business from the inside out. And this is why I said if you have come from an industry like warehouse or distribution or supply chain or anything like that, you've got years of industry experience. You are at an advantage because there is no one in those industries doing this yet. So your primary goal is to understand and not sell, right? You need to be an inefficiency detective and you are looking for the hidden friction, the repetitive tasks, the manual processes that drain time and resources. Here's what most consultants get wrong. They ask about visions and goals. Wrong move. You need to hunt for the broken stuff. Okay, these are the processes that make the smart people quit and leave these companies and get everyone stuck kind of within the organizations. So the best way of doing this is talking at people at different levels of the organization, stakeholders, managers and the people doing the tasks and you get understanding from each level. So I'm going to break down the interview game plan for you. So excuse me. Um, you should who interview? You got two people. You got the leadership and these are the people who understand the goals uh and the end users. Um, and then you've got the employees um as well right this is um this is why sorry let me start again so you need to understand two groups you need leadership and to understand goals and the end users the employees doing the work to understand the reality this is why you have two separate interview templates which I'm giving you below the gap between leadership of what thinks is happening and what is actually happening is where you'll actually make your money is where you understand the AI inefficiencies are actually going on.

How many calls? So if you've got a small business, 10 to 15 uh 10 to 15 employees, aim for 3 to five interviews with key team members to start. For slightly larger organizations that are more complex, you need to do 10 to 15 interviews to get a a full picture. Do not scrimp on the interviews. This is actually the most important part because this is when you are going to get the understanding of what's going on here. So you want to keep each interview between 30 and 45 minutes, right? And this respects their time and it keeps conversation on track. Any lose any longer you'll lose their attention and any shorter you'll miss the good stuff. So um these can be conducted remotely or in person. Remote works fine just you need to record them. So this is just an example of my calendar when I'm doing some interviews. So pro tips, listen more, talk less. Aim for an 80/20 split. Constantly ask why repeatedly why and how questions. you know, these are the ones that work best and these get people to really open up uh on on root causes and things of that nature. Record the calls. Obviously, you need permission, but this is a key element of doing this. You can use free note takers uh like Fathom if you really want to. But again, having a good robust AI notetaker um is is going to enable you to really streamline this process and this is a core tenant of being a vibe consultant. you are going to use AI and leverage in every aspect of your firm to make more money and spend less time in it. So, focus on problems, not solutions. Avoid the temptation. I see this so so often. Oh, oh my god, I'm so excited. Yeah, yeah, I can do that. Yeah, yeah, I can do that. Yeah, yeah, I can do that. Shut up. Right, you are looking for inefficiencies. You can talk about solutions later. right now you need to be digging in and twisting the knife to figure out where all the problems, all the inefficiencies are in their businesses.

So, here's a template for your stakeholder interviews. This is like the 30,000 foot view. This is where you get an understanding of like business goals, team structures, major challenges, uh anything that might be affecting the business in its entirety. Okay, here's a one tip. the leaders are usually wrong about the retail the details but they are right about the impact it has on the business uh and it's usually you know with the employees we'll dive into that in a second but they're usually the ones who have a much better understanding of the inefficiencies on the day-to-day basis so key areas so role and overview can you describe your role and your team's primary responsibilities what are the main goals and KPIs quarter per year can you walk me through team structure structure. Uh how long have you been in this role? Um so then core processes and workflow from a high level. What are critical processes your team manages? Uh which team uh tasks seem to take the most hours of resources. I'm not going to go through each each one of these. There's literally a bunch of them. Um so uh we'll we'll highlight the ones. Um so if you could make a wave a magic wand, what's the one workflow issue you could fix? Like what would that be? uh technology is usually where there's a lot of friction and this is where AI can like really help. These are usually the uh low-effort high output kind of opportunities. So what's the main software systems your team rely on? What are the biggest frustrations? Where does your team spend the most time? What do the what are the most complaints from your team on on from a top uh software uh standpoint? Pain points and strategic challenges. Um, now you're going to get to the good stuff, the problems worth solving. Well, so what's the biggest challenges your team are facing right now? If you had a magic wand, what is the one problem you'd solve for your team overnight? What do you feel is preventing your team from being the most effect efficient and effective? What makes your job harder than it should be? And where do you see money walking out the door that it shouldn't be? Then you want to get into the future vision. So where do you see the biggest opportunities for improvement in your department? Uh what does success life look like if we could solve all these problems? Um, and then template two is all for the end user. So these are the employees. These are the like the on the ground reality people doing the tasks. So to understand the on the ground reality, this is where you'll uncover specific time consuming and often frustrating details that managers don't see on a day-to-day basis. These people know exactly what's broken because they live with it every day. So daily roles and responsibilities. So can you walk me through a typical day in your role? Um, what are the, you know, 13 most common tasks or 27 most common tasks you're doing every day? How much time is spent on core responsibilities versus administrative and repetitive tasks? What time the day are you most productive? A step-by-step deep dive is a must. Can you walk me through and get them to walk you through exact step by steps of doing the processes and record this? So, all the tasks they're doing in their main ones. So, making invoices, on boarding, you know, um ch taking a document from one place to another could even be just an email into the system, all that stuff. you want to get all uh recorded and documented. Um, what information do you need to reference to complete a task? How often do things go wrong or you need to redo stuff? Uh, what workarounds are you using? So employees are usually the best at finding these workarounds. I'm not sure if you I know Bill Gates said um if you want to get the job done the fastest, find the laziest person to do it because they'll find the workarounds to get get through it. Employees are great at finding workarounds to get through painful software issues. Um, tools and frustrations. What software do you spend the most time in your day in? What do you find the most frustrating? Do systems crash freeze? You know, what uh what would you change about your current tools if you could? Like what tests wouldn't you, you know, should they do that they don't? How much time do you spend waiting for system to load? Sync? What are the pain points and the weight list? So, what is the most boring or repetitive part of your job? If you had an assistant, what task would you give them immediately? This will tell you the kind of mind-numbing stuff that is just repetitive that they just hate doing um over and over. How do you currently track your work? What part of your job makes you want to bang your head against the wall? If you could automate one thing, what would it be? What would make your job 10 times easier? And then communication and collaboration. How do you typically communicate with your team throughout the day? What information do you need from others? And how do you get it? um, how often do you have to chase people down? What meetings do you feel like are a waste of time that you have to attend? Um, and and and things of this nature. So these are a great sample. There's obviously a lot of questions here, but um, you will really unpack a lot of key insights from this. So by conducting these two types of interviews, you will get a complete picture, the strategic overview from leadership and the practical day-to-day reality from the team doing the work. The gap between these two perspective is often where the biggest AI implementation opportunities lie. And this is where you're going to be able to deliver the most value for these companies.

So step two is mapping the process and finding the opportunities. So after your interviews, you'll have tons of notes, insights. This is why it's important to record them with AI. So you don't have to be writing like a maniac on these calls. Um, now you can do uh a process mapping and an opportunity canvas. So opportunity campuses tend to break down predominantly for most businesses unless it's a very complicated one into like acquisition, delivery, and support. So you could say how do they how do they get people coming into the door knocking to buy. Uh so it could be lead and sales basically. Delivery is like how do they fulfill on the promise? And then the support is you know when things go wrong who is kind of uh dealing with that those issues. So I've got a template here for mind map. You can use something like Fig Jam Miro. There's loads of AI ones. I've got a template for you here. And they'll end up looking something like this where you just are mapping out all the different processes and how they link uh together. Okay.

So, you're going to basically map out and there's going to be two core um things that you map which are time syncs and quality risks. Right? So, time syncs are tasks that are manual, repetitive, and consume a ton of employee time. Aka you can save them hours of employee time which could be redistributed to revenue generating activities. And then quality risk is uh steps in the process that are prone to error or inconsistencies which will then mean that those jobs have to be redone again right which is wasting more time of of employees to do. So all of these present opportunities. So these are examples of time syncs manual data entry uh email responses documentation creation and templates or even sending them scheduling tasks updates research quality risks manual calculations data validation approval processes customer communication file organization compliance checks. This is what you should have in the back of your mind when you're running these audits. These are things that you're kind of subconsciously looking out for when you're like yeah I can identify that as something AI can solve.

So part B is going to be actually building the opportunity matrix. So now you want to visually map out uh all of these things for the business, the problem areas and how you see essentially the opportunity for AI to fix these. So take each of the time syncs and quality risks you've identified into the opportunity canvas and brainstorm a potential AI solution for it. Then plot each of these on the matrix with two simple questions. How much impact to the business will it have and how much effort will it take to implement? So this is what an opportunity matrix will look like and these are very common solutions for AI and this is how you can break out the engines as well. So uh AI coaching, client success dashboard, um onboarding workflows, CRM call recordings and updates, nudges, contract creation. Um, these are very common implementations of AI that businesses can um see improvements from candidly, right? And also it's going to be different for every business. So if you are running let's say a mortgage company and you're sending out a ton of contracts this this say low business impact but low effort on all main contracts but like on a company that's very contract heavy and sending out a lot all the time could be real estate mortgage would be an example. This would be really high impact right but low effort which would be worth doing.

So let's go through um quick wins low effort high impact. This is your number one priority and this is actually where you're going to focus on when you are presenting these to the business. So these are like the no-brainer projects, easy to implement, huge value to the company. They they you're going to do this and they're going to just feel this huge uplift straight away. So the reason this matters is people want quick wins when they work with you. You want to reinforce the fact that they picked the right person as fast as possible. So when you are identifying these in the audit and then from the implementation standpoint when they get these done they'll be like oh yeah I chose the right person like this this was identified and it was executed correctly this is awesome. So these are examples of quick wins. Automated uh email categorization, routin, chat bots, um, it could be lead qualification as well, scheduling um automated report generation, so like dashboards of data, stuff like that. Big swings. So these are high for eye impact. These are much more like very custom um solutions that over time can be put together that will have huge impact on the business, could give them a very big moat in their industry, but they will take a lot of time and money to do. So custom, you know, machine learning models, complete CRM automation and like adding AI into it, advanced document processing and analysis, stuff like that. They're nice to have. So low effort, low impact. So yeah, they're easy to do, but they're also not going to have big impact on the business. Uh, so these are just like minor improvements, minor efficiency gains, uh, that could be done. I would see these more like a bonus you could almost throw on because they're not going to take you a lot of time to do either. So notifications, visualization, simple workflows, stuff like that. And then these are the ones you depp prioritize are high effort, low impact. These are the ones that are going to be very difficult to do and have marginal impact on the business and we just don't even bother. These are like, yeah, they're cool, but they're not really worth doing. So overengineering solutions, uh, like bleeding edge AI, like, oh, we could build this thing. It's cutting edge. Don't do any of that. It's not worth it. Uh, complex integrations, which just have very marginal payoff. There is tons of tasks that humans are just better at doing. and they're faster at it than AI is right now. Don't even worry about them.

Part C is validate your solutions. And this is kind of the reality check. So you're like, "Hey, this is what I know we can do. This is the reality check." And this is so so so so key within the engagement. This is what separates amateurs from pros. Your matrix is a powerful hypothesis, but it's not battle tested against the client's deep internal knowledge. They have way more context than you'll ever have about company culture, team dynamics, and hidden complexities. This is where most consultants get wrong. They present their findings like their gospel truth. Hey, you've got to do this. Totally the wrong move. The best consultants will co-create the solution with their clients. By the end of the validation session, they're not buying your recommendations. They're buying the plan that they helped you build. This is why it's so powerful. And this also transpires in why it's so easy to close them after a successful consulting audit because they literally have helped build this entire road map with you. So you want to your goal of the of this call with the stakeholders to cocreate the prioritization of projects. So the key questions are something like this. Looking at the quick wins which resonate most likely um with your challenges your team described. We've identified you know X problem. So automate CRM updates as a high impact opportunity. Are there any team dynamics or hidden steps we might have missed that would complicate this? From your perspective, which of these solutions would the team be the most excited about? Which may they resist the most? Uh does this road map align with your strategic priorities over the next 6 to 12 months? What am I missing about company culture that could make or break these implementations? Which of these would give the biggest win to your boss or board? So if this is a part of a bigger organization that has a very corporate structure, they might be part of a bigger group of businesses like this is what they want to present the findings and and show that you know they're going to be able to increase margins by 5% which in um industrial companies for example is huge. It's like it would be a 50% bump in profit if they could do that. Um, are there any compliance or security concerns we need to factor into the timeline? And what is your team's track record with adopting new technology? what's made stuff successful or unsuccessful in the past. All right? So, you need to get the team on board with doing this. So, this is a collaborative process. It's not a dictatorship. You're not telling them what to do. You are collaborating with them to get the end result that they ultimately want. But you will have to navigate waters and complexities of each company uh based on culture and dynamics. Okay? You by the end of this meeting, you are not going to be pitching them solutions anymore. You'll be presenting a plan and you you have already agreed on together.

So from matrix to road map. The opportunity matrix isn't just a static image. It's a blueprint for the client's AI journey. A logical road map often starts with quick wins to build momentum into then strategic processes towards big swings. Your final presentation uses this matrix to tell a clear story. Right? We'll start here with a high impact loweffort projects to get the uh immediate ROI. Once uh these are in place, we'll have the foundation to tackle these larger, more transformative initiatives. So this is what a typical road map is going to look like for a client. And this also bakes into the fact that how you can build a very small micro vibe consulting firm um as AI uh advisers to an industry and make a lot of money because of how you can build out these workflows and work with people for a very long time. Okay. So you could be 23, 13, 9, whatever it is, high impact, loweffort solutions. These are going to be the immediate ROI, get team buy in, quick wins. They're going to love it. And this is going to present the foundation. So this is going to be roughly your first 30 to 90 days depending on how big the organization is. The bigger it is, the closer to 3 months. The smaller it is, the close to a month. Months 4 to8 medium swings. These are more complex solutions. These going to require more usually custom development, deeper integrations and automations. and there will be a much more measurable long longerterm impact to a business. So, uh like you're getting more ingrained within a a business. A lot of these quick wins are usually a lot of the AI implementations that are quote unquote offtheshelf. There's nothing that's truly off the shelf, but there is a lot of products now that can be slightly tweaked and work in basically any industry. And that's usually what is used here. What is used in these next two sections are usually much more custom stuff. um which is where you make the huge dollars on the back end by either doing the implementations yourself or like I promised at the start of this if you are not techsavv of it all you will be partnering with an organization that could deliver on your behalf for you. So months 9 to 18 are transformational projects. These are the very big swing projects. So each uh this phased approach does two things. It brings immediate client value to justify the investment and it creates a natural upsell path of working together. So it's hey they've had a great result with this one thing. They want to do more. They want to do more. They want to do more. People would much rather work with you when you're a known quantity. So just keep doing good work. uh keep fulfilling on the promises and then always just keep an open eye of communication and people will want to keep working with you. It is much much easier than constantly going out and finding new people to work with. Trust me.

So step two, presenting your findings and the money side. So we've done the interviews, we've mapped out the opportunities, now it's time to bring it all together into a powerful executive presentation. This is what companies like Mckenzie, Ben, Deote do for their clients, which is how they end up having deca million dollar contracts with huge companies. Um, they have these professional consulting slides, presentation, executive agreements, and that's what I'm going to show you how to do. Now, this is going to uh position you as a trusted adviser, and you're going to back everything up with data uh to help present the client's AI transformation. Most consultants present ideas. Smart consultants present math. You are going to be dealing with savvy business owners here. These are going to be very logical individuals for the most part where they're going to be driven by the numbers and that is exactly what you're going to present to them. When you show an a CEO exactly how much money they're wasting and how much they'll save, the decision becomes obvious. So your presentation is going to sell uh present a very simple story. So one, here's what we learned about your business. Two, here's the biggest opportunities for AI we found within your company. Here's our recommendation and starting out with the quickest

wins, and here is the potential ROI you can expect. So, the five key, these are the five key slides that you must include. Below, um, we're going to walk through, and I've got all the templates right here in Canva that you can just download and manipulate for yourself, so you can get going on this right away.

So, number one, scope and objectives. This is where you've understand understood the client's, uh, challenges that they are currently going through and the goals they want to achieve. This basically aligns everyone with the before and after. So, original problem statement, uh, what you've been asked to investigate, the scope of the analysis, and the key stakeholders you've interviewed.

Number two is the opportunity matrix. This is what we showed before. This is the high impact, and then this is high and high. So, low, low. So, these are, like I said, keep stale strategy plan of the AI assistant, automated share of wallet percentage calculation, stuff like this. And this is going to, this is going to be like a real, it's not the technical money slide, but they love this. This is where they really get to see how well you've understood and compartmentalized the issues they've got within their business. And it also gets them bought into the idea of like, "Oh, like that thing right there has got high, is like high impact." But they're really from their perspective, they're looking at the low-effort stuff as well. They're like, "Okay, high impact, low effort, sales, technical support, training chatbot, like automated CRM to update so the sales team isn't worrying about that, automated email routing for the customer support so they're not worrying about that." These are all like, "Oh, this is amazing right now. We don't have to waste, you know, X number of hours on that, uh, those tasks."

So, then number three is the roadmap summary. So, this, uh, slide translates the matrix into a timeline. So, that's here. So, these are the like, the easy wins. So, the low effort, high impact. And then it's just basically going up to the longer-term projects here, which are maybe higher effort, but higher impact.

And then the opportunity deep dive. So, these are your quick win recommendations. Um, and you basically have a dead the Kate slide for this and kind of the math behind it. So, you've got the current situation. These are the staff and the hours. And then with AI, this is what it looks like, a co-pilot. And they're basically saying, "Hey, you're going to save 130 grand. If these numbers are correct, what we understood from our interviews, um, AI can do this in this time. It costs this amount. So, your savings on this or your hours saved would, um, basically account to $130,000." Uh, in this case, automating CRM management offers these wins. So, this is their current workflow, and this is what their new workflow would look like. Is the hours difference because now staff aren't having to do these really irritating tasks. So, again, you are just presenting this in a manner that is easy for them to understand.

Then we're going to go on to the money slide, which is the ROI summary, where you are presenting exactly each of these recommendations, the current things you understood, and then the things on the, uh, basically they care about the cost savings they've got. So, here you've got AI sales assistant, assistant time saved. Um, FTS is full-time employees. Um, and then you've got estimated implementation costs. You've got additional annual benefits. So, how much they're saving, and then the estimated ROI just in year one. So, this includes like the implementation costs, but it doesn't include, obviously, the recurring. So, if, sorry, it includes both on year one. So, year one, let's just round number it and say you got it, cost 30k a year to run, and your implementation cost is 30k. So, in year one, it's 60k. So, it's 60k versus their current savings. But in year two, that 30k implementation now no longer is net. So, you've got the disparity between 30k and the cost. So, the ROI jumps up, um, even more so. So, this is where you're going to close the deal because this is just pure numbers, and this is what owners, CEOs just love to see. So, it's pure numbers, no fluff. And you're going to have an ROI calculator like this that I've given you that is going to enable you to build that deck out simply and easily. And you can get that literally here. And you just plug in the numbers from each of the departments. It will spit out the savings, the percentages, etc. And then you literally just put that into here. Makes a slide creation very easy.

So, this is just an example of calculating the direct savings for like the obvious wins. So, calculate hours saved, right? Get total out. So, you do this time spent on task per week. Number of employees is the total hours saved per week, right? And then you got, uh, total hours per week times estimates saved by AI is a total hours saved by AI. And this can only be an estimation when you're starting out. Obviously, as you get more industry expertise and knowledge, let's say you go and do five, I don't know, food and retail distributors or something, and you understand that when you implement this automation AI agent in a certain sub-sector of, you know, maybe it's, um, assigning and labeling or tagging certain orders or something like that, and your estimated time saving might have been at at start like you thought, "Oh, it's like 50%." And then over time, you realize it's actually like 56.3. And the more odd the numbers are, the more realistic people assume they are because they're backed by data. So, when you're like, "Oh, actually, it's 47.2%," um, is estimated percentage based on the last 10 companies we've done this for, just all of a sudden, again, just further reinforces that you're the person to to work with on this. Then convert these, um, hours into savings using this calculation, and then basically just use the savings divided by your implementation cost is the ROI.

Now, the revenue uplift is like, this is a little bit more complicated to work out because there's some more assumptions here. The, the big one is, you're the assumption here is if those people who you're saving the time from, um, were then to go and do revenue-generating activities, how much more money that company could potentially make if that was the case. Now, you're going to present this, it makes it look great. The reality is, is if you are saving, you know, a hundred hours from customer support desk, the customer support desk isn't then going to go and do sales, right? So, you know, they might not have the opportunity to do a revenue-generating activity, and therefore the reality is that, you know, they might cut headcount to basically increase the margin that way because they realize these people are no longer needed, and there is not a revenue-generating activity that they could do. Okay. So, total hours saved times 50%, um, this is, you know, the estimate of of 50% on the revenue-generating activities, and then calculate the additional revenue using this calculation. So, if it takes a sales rep two hours to close a deal worth 5,000, each hour is worth two and a half thousand. So, revenue, uh, generating hours, um, unlock times value per hour, additional weekly revenue, right? Additional weekly revenue times 52 is the annual revenue. So, this would be a real-life example, uh, if we were to do this. So, five, five employees, four hours wasted is 20 hours. The hourly rate is based on their annual, uh, payment of 60,000. So, it's 28.85. So, they're going to save 577 bucks a week. So, it's 30 grand a year. Um, and so if the AI implementation cost 5 grand to do to save 30% 30,000 in year one, so the saving, um, annual savings, right, is 24,000 anyway. The ROI is divided by the 5,000 would be 480%. And this implementation cost is usually a one-time fee. There might be a recurring cost as well on top of this. Um, there might not be, and they might literally just realize this 5 grand one-time at 480% ROI, and then moving forward, um, it's, you know, essentially infinite ROI for them.

So, uh, now add the revenue upside. So, if these 16 hours were to be reallocated to, you know, revenue-generating activities, like I said, this be equates to 83,000. Again, this is going to be, this is like we're going to present this again candidly because it looks good, but, um, there is a high chance that there isn't revenue-generating activities they can do. The only thing that they could potentially do is either cut headcount, or they now have the ability to scale their business and keep those staff on, and the essentially their business grows, and then the savings, the hours saved, so to speak, um, aren't saved anymore because the company's, let's say, you know, 30% bigger, and now those hours are need to be used, even with the AI efficiencies in in place.

So, um, presentation tips. So, keep it visual. The reason in this presentation I've actually just been reading these slides to you, um, for the most part word for word, is because humans, when they see something and when they hear something, they take in like 60% more of the information as opposed to just doing one or the other. So, lead with impact when you are speaking, especially you're going to be doing this with a lot of bigger companies. You're not going to be targeting like solo consultants for the most part. You're going to be targeting predominantly speaking, I would say 10, 20, I'd say is usually pretty much the minimum, but 20-person orgs, and then you'll realize quite quickly, uh, like one of the companies that I've done this for was a manufacturer, a manufacturing company in England, and they have 54 people. And from the outside, I didn't have a clue. I thought there was maybe 20, 25 people in it. And then you realize all the floor staff, all the people out on jobs, like doing different stuff, and the impact it had was insane. But you just realize, like, companies are actually, for the most part, like, I don't want to say, but like, real businesses are actually much bigger than most people think. So, the idea of like, "Oh, it's a 20-person business," they think, "Oh, it's huge." It's actually not that big. Um, still class as micro. I think under like 200 is still classed as micro. You want to use that language. This is why it's important to understand their industry. And when you come from that industry, this is why you're going to have much more leverage than other people. Be conservative in your estimations. Always address objections they may have, and end with the next steps. At the end of everything, you always want to say, "This is where we go next, right? You want to walk them across each finish line."

So, congratulations. You now have the exact three-step framework to use and deliver high-value AI audits. You can now conduct discovery interviews, map business processes, identify high-impact opportunities, and present a data-backed case for investment. This blueprint is your starting line. It gives you the tools to move beyond, uh, simple development and be begin positioning yourself as a strategic AI advisor, a Vibe consultant, right? You are going to be able to build your own Vibe consulting firm off the back of this. But here's the thing, the blueprint is only as good as the builder. AI landscape is constantly changing and evolving, literally on a daily basis, as I'm sure you are aware. Uh, what works today might be outdated tomorrow. And the real challenge isn't just knowing the process. It's mastering its execution and staying ahead of the curve. It's understanding how, as this world is constantly evolving, when you're going, uh, into these interviews, even if you are not doing the actual implementation for these companies, and you're helping outsource that to, uh, a preferred partner of yours, it's understanding the new technology that can be used to get a better result and a shorter timeline, make more money, all that stuff. Uh, that's what you need to be doing. You need to kind of have, uh, have your feet on the ground. We now understand how to be a successful AI consultant. But how do we recommend the right solutions to companies? Well, that starts with getting familiar with slightly technical concepts like AI agents. The difference between these two is knowing and doing.

So, most people think AI and AI agents are the same thing. But that is like thinking a smart person is the same as a really helpful assistant, right? It's like having the difference between Albert Einstein on my shoulder, who's just like some absolute genius telling me everything to do, but he just sits there, which would be equivalent of AI. And an AI agent is much more like saying, having Elon Musk on my shoulder, who goes and builds a rocket and actually sends me to space. So, an AI can go, "2 plus 2 is 4," like I'm sure you can. All right. And AI agents, in theory, can actually calculate all your taxes for you and also file them correctly. The AI knows how to write an email, but an AI agent could write an email in your tone of voice, send the email, and then continuously follow up with that person until you get a response, right? So, like, what good really is having all this intelligence, having the Albert Einstein, if it just sits there and does nothing? It doesn't actually benefit your life, right?

So, how do these AI agents actually turn intelligence into action? Cuz that's like, very key distinction, as we've already discussed. So, let me show you that in three simple steps. There is a magic formula, or a formula that we use in the AI agent space, which is called listen, think, and then act. And this is how all AI agents work. You see, every AI agent follows this basic pattern. And it's a pattern even a child could understand, right? It's like having a conversation with the world's most helpful friend, like we've already discussed. So, let's say, for example, I would say to an agent, "Hey, I want you to schedule me a dentist appointment." It will listen to that, right? It will then think about it. So, the agent will then go ahead and figure out how to do it. So, it could check for the dentist office, check your calendar, check how to do it. It could find the dentist's office open hours, right? Then it's going to act. So, the agent is actually then going to call the dentist or book the appointment, put it on your calendar, and do that cross-referencing. That is that framework. So, it's listening, then thinking, and then acting. And this all happens in a few seconds. It doesn't take hours like having a personal assistant or executive assistant do this for you. It's like having someone on your team that never gets tired, that you don't have to pay, that can handle all of your daily boring tasks with a simple command from you.

But here's where it starts to get really interesting. So, how do these agents know exactly what to do, right? So, you probably been thinking about this, and this is what I like to call the babysitter training method. So, I want you to think about this way. If you're a parent, or with your parents, how did they hire a babysitter? So, they don't just hire a random person, leave, and hope for the best, right? They're going to give detailed instructions to that person specific to their needs and requirements. So, an example could be the parent would say to the babysitter, "Hey, the bedtime is at 8:00 p.m. The snacks are in this cupboard. If there is an emergency, I want you to call this specific number on the fridge, and this is where we are currently located. This where we're going out for a meal, right?" A agents get the exact same treatment, right? So, whether that's you or a developer, you are going to be giving them detailed sets of instructions for every single situation that might actually rear its head. See, there's some agents out there that come pre-trained, like a babysitter. They already know the basics of the tasks you're actually wanting them to fulfill, when others can be fully customized to your specific requirements and needs. So, think about it this way. What if you could train every digital assistant to handle your life exactly how you wanted it, and you essentially never had to pay them to do so?

Now, you might be wondering, what does it actually look like when I'm using it? And this is what I like to call the invisible workflow magic. See, when you use an AI agent, you only see the simple part. So, imagine like the chatbot or a voice command or a text message, but behind the scenes, there's a whole factory of code just working away for you. Imagine like a load of monkeys in a in a facility just trying to figure out exactly how to to to achieve what you're looking to achieve. So, you see this like super simple chat window, right? You might try, "Type book me a flight to Miami." Now, the agent sees like 50 million variations going on its head and decision points and things that it's got to check and look for, right? So, it might check flight prices, compare airlines, compare time, look at your preferences, things it knows about you, look at your calendar, look at maybe other people you're wanting to invite, and it's trying to figure all of this out to complete that command. Then, it will give you options based all that. You might get a flight Friday, 2 p.m., right? It's 357 bucks. And you might go, "Done. Yeah, that's perfect for me." And it will then just automatically go and book that for you.

Ultimately, up till this point, like, why should you care about the complexity to get the result you want? Because we're just outcome-based people, right? But here's the part that's really going to blow your mind. And this is actually where we're heading now. And this is the impending AI agent explosion. You see, right now, there is a small number of AI agents that are doing a lot of basic tasks for people and businesses. But what we're about to see over the coming years is an absolute explosion where we will have numerous specialized agents for every single part of our life. See, today we might have one assistant that will handle like all the different tasks, and it might not be amazing, but it can help. Tomorrow, we'll have specialized agents that will handle every single task in our life. You could have a grocery agent that knows your dietary needs, your budgets, your meal preferences, right? Could have a fitness agent that tracks your workouts, your meal plans. It motivates you daily when you're not doing this stuff, right? Like imagine like every single task in your life that is now got a dedicated digital expert just helping you achieve those goals that you set out for yourself so you don't wear off the path. And this is why timing matters way more than you probably think right now.

There is this business transformation tsunami that's happening right now today. See, it's not just about making your personal life better. AI agents are going to completely reshape how every business operates and how you interact with all of those businesses also. A common use case that's literally working right now is a customer service agent where you interact with a business, and this agent goes and helps solve your problem pretty much instantly because it understands the business. It understands what you're asking. It has all the required frameworks, and it can deal with your problem. That helps a business because now they don't need customer service agents, and it helps you because you actually get the answer to your question instantly. There's also like sales agents that follow up with every lead perfectly. It will never forget. It will personalize every conversation to that individual. So their experience is 10 times better than just being put through some type of mill. There's also administrative assistants which could handle scheduling, calendar, booking, invoices, management, meetings, all for you. The businesses that are adopting these agents are going to get superhuman capabilities. They are going to have advantages over every other business in their specific market. And it's going to allow them to make much more money right now with these 24/7 perfect AI employees. And this is going to give them a huge edge in every known market and with every known business that adopts this. But here's what most people are missing about the transformation. It's not happening someday. It is happening right now. Everyone's talking about AI agents like they're some future distant mythical technology, but they are already here. People are already using them. Businesses are already using them, and they are working behind the scenes in a vast majority of apps that I guarantee you are using, and maybe even some businesses you're interacting with. Like, for example, you talk to Amazon, that is an AI agent in the customer support, right? Netflix recommendations, they're AI agents deciding what you should watch. Your bank's fraud detection, that is also an AI agent. Your GPS navigation trying to figure out the best path for you, also an AI agent that's working on your behalf. The only difference between then and now is these agents are becoming more visible. They're becoming more controllable to you. You are now able to command these digital employees to do the things that you want them to do in your specific use case. The AI agent revolution isn't coming. It is literally already here. The question is whether you are going to be the one to use these tools or get left behind by the people who actually are.

Now that you understand how AI agents work and why they're set to transform every part of both life and business, what if you could actually deploy a team of specialized agents right now that drives revenue without hiring a single extra employee? I'm about to break down the six exact AI agents that can take your business to a million a year. And it all starts with an AI agent I like to call the hustler. See, most sales teams spend 80% of their time or so doing like the research and the admin side of the sales role. They only actually spend around 20% maybe 30% of the time on sales calls actually closing deals. Now, the hustler agent will work alongside your sales team. It will do stuff like scrape, uh, LinkedIn, Twitter, industry databases to find those ideal prospects for your team to reach out to. It can also then research the leads, do detailed briefings for the salespeople. So when they are talking to these prospects, they're like fully informed. If anything, they're over-informed. They know tons about these individuals, especially the individuals that are really active on social media, which is like crazy powerful. Imagine walking into a meeting and being like, "Hey, did you enjoy the Bills game the other day?" Because they posted on one of their socials that they just went there. Like, this is the type of power we're giving them. And it allows them to build rapport massively, right? It can also handle things like the follow-up sequences, the appointment booking, freeing up your team to focus on that rapport building and closing the deals, which is all you want them to be doing as well, right? I know one sales team, no joke, that went from a 20% close rate to a 45% by just implementing a sales agent within their business because it allowed their team to just focus on closing deals and building great rapport and connection with those prospects, which just gave them more time to close deals. Trust me, I have hired hundreds of sales people. The thing they like least is updating goddamn CRM. They want to be closing deals. Right? But the best prepared sales team need support during the whole sales process. Right? Which is where the second agent comes in, which is one I like to call the rainmaker.

So, your best salespeople have instincts. They have relationship skills. They know how to close. And AI actually can't replace that. This is human-to-human, hand-to-hand combat, right? But what if AI could handle all of the prep work and all of the follow-up for all of these deals, right? Giving them all of their time back and leverage. So, this agent will actually analyze the prospect's digital footprint and create detailed buyer profiles for your sales team. It can draft custom proposals. It can handle initial objections through emails that they might have based on the information it's gathered, leaving your team just to focus on rapport, connection, to close deals, right? It can also suggest the optimal timing for follow-up and provide conversation starters for recent prospect activity. Like I just gave you that example of like they've just been to a football game. Like it can tell your sales team so they can walk into a meeting and ask them a question about it straight away, or where they're from. This is literally how humans connect. Now we're able to do this at scale. It can also handle stuff like post-meeting summaries, CRM updates. Your sales people can just go from sales call to sales call to sales call to sales call, closing deals, deal after deal after deal, and they don't have to do the stuff that they hate. And you just get more money in your back pocket. Imagine now your sales team is closing 50% more deals than they were previously because they're just spending their time on nurturing these relationships and closing deals and not paperwork and admin rubbish. They don't want to be doing it. You don't want them doing it. Great. So now your sales team is operating at the peak efficiency.

All right. So, now who's going to deliver all these results for all these new clients that you are closing? And this is what brings me on to the agent number three, which is one that I like to call the operator. Your delivery team has expertise and creativity that your clients love, right? But project management and documentation shouldn't be eating up all of their valuable time. So, this agent is going to focus on taking your client's requirements and automatically creating detailed project plans, freeing up your team's time to focus on execution and client happiness. It will handle things like progress tracking, timeline management, client updates while your team works on the important tasks. It manages things like checklists, test protocols, ensuring your team is meeting the deadlines and the quality standards that it should be. It can also draft communications with your clients, progress reports. This is allow going to essentially allow your team to focus on solving the actual problems that you were hired to solve. Right? Your delivery team is going to become three times more productive because you are creating value and not managing spreadsheets. Also, your team satisfaction is going to go through the roof because now they are doing this skilled work that they love rather than the admin that is just a byproduct of their role before these AI agents could help them. Now your delivery team is operating at full capacity.

But what about content that keeps your pipeline full? Which brings me on to agent number four, the storyteller. Your marketing team understands your brand voice and your audience better than AI currently keyword. But right now, content production bottleneck shouldn't limit creative potential of your marketing team. So, this agent is going to help handle all of the research to draft initial marketing documents. So your team has time to focus on strategy and creative direction. So they aren't going to get sucked into the button-clicking tasks, right? It's going to create multiple content variations for your team to then refine and improve upon, right? It can do stuff like handle all the optimization for your website. You can do publishing schedules, publish all the content on all the different social media platforms, which is like crazy. So you're now omni-present. I literally know a marketing manager where previously they would produce 10 bits of content marketing materials for platforms. They can now produce thousands in minutes. Their output is now 100x what it previously was. This now frees up your marketing team to spend time on the big picture of like, "How do we blow up this brand and make it global?" rather than like grinding on producing, you know, the next bit of content that needs to go out there. AI can now handle all of that for you. And as we know, like content ultimately, the point of it is to drive leads, and leads do become customers. That is why we are doing it. But customers need support.

Okay. So, what happens when your support team is snowed under? We want them to shine, right? And this is where the next agent comes in and gives them a little bit of help. And I like to call agent number five the guardian. So, your support team builds relationships and solves complex customer problems which require human empathy. They shouldn't be required to answer really basic mundane tasks that just absorb a ton of time. And this is why this agent is so powerful. You see, this agent can handle over 90% of routine inquiries instantly, escalating complex issues to the correct team. You, I guarantee, have interacted with an AI agent like this because this is one of the very robust solutions that already exists. If you go to Amazon and you have a question, you got a little chat thing that is an AI agent. Amazon uses an AI agent in their chat functionality to help you solve quick and easy problems, and that gives you a better experience, and then it will also escalate those problems to someone if it is more of a complex issue. So, it can handle simple transactions like password resets, account updates super easily and very quickly. It can also track things like customer sentiment. Can flag opportunities for your team to turn upset customers into advocates, right? Like increasing the trust scores and the happiness scores for your customers if they're not engaging or if they're not doing certain tasks depending on your business, right? The support team then literally becomes the customer success specialists instead of just mundane ticket processes. So customers get faster resolutions, you get cheaper operations, plus the people on both sides are happier. You're happier because you're spending less money, and the people are happier because they're getting better results. Right?

So, now you have every department operating at its peak efficiency, but there is one agent that I haven't told you about yet that coordinates everything and empowers your entire management team. And this one I left for last 'cause it's top secret. All right. And this one I genuinely like to call the COO. This is the leadership agent. This empowers your leadership team to make strategic decisions and drive growth without getting buried in operational details and reports. So, this agent is going to liaise with all of these other agents. It's going to monitor them. It can provide things like insight. It can provide things like reports. It can provide things like recommendations. So, you get to be given all this information and make all those strategic decisions that are going to drive growth and improvement not only for your business but for the customers, right? It can handle things like routine accounting, handle things like invoicing, financial reporting. It can give your team clean data and allow you to actually talk to the data to make really precise decision-making. Imagine all of the data you've had in your business over this time. If you were to able to talk to it and try and pull out insights, that was a pipe dream. That is now a reality. It can identify the performance trends, the bottlenecks where problems might be rearing their head in your business that you might not see. It is a proactive management agent. It is finding the issues and finding the opportunities before you can necessarily even see them. It can manage stuff like freelancer coordination, HR, like I said, all the other agents, all the other people within your organization. Think of this as your AI operations manager handling all of the details so you just focus on leadership and growth.

Now, we've covered six types of AI agents that are absolute gems for any business. But let me show you some real-world agents that we've built that you can deploy for businesses today. We've built them using a no-code tool called NAN that allows you to implement AI without being super technical. So, it's perfect for people like us. Anyway, let's get to these agents. So, we're going to be using Vappy and N. So, as you can see here, the workflow isn't actually that crazy, which is good because it means there's very little that can go wrong, but I'm telling you now, this has a huge amount of value. So, why don't we actually talk to Vappy and see what this demo restaurant has to sell.

All right, so we're going to, um, connect it up here.

>> Hi there. Welcome to Voice AI demo restaurant. How can I help you today?

Hey, I'm wondering how, um, how good the parking is.

>> Thanks for asking. We have plenty of parking available right next to the restaurant, so finding a spot is usually easy. If you have any specific needs or questions about accessibility, just let me know.

>> Yeah. So, uh, no, that's great. And what time is the restaurant open till?

>> I'm glad to hear the parking works for you. Our restaurant is typically open until 10:00 p.m. If you need details about specific days or have plans for a late visit, just let me know.

>> Yes. So, I'm thinking of coming for my partner's engagement party. So, she or proposal. I'm actually going to be proposing to her. So, um, I was just wondering if it was possible to book out a corner of the restaurant so it's a bit more private.

>> That sounds so exciting. Congratulations in advance. We'd love to help make your proposal special. While we can't book out an entire section, we can definitely reserve a more private corner table for you and your guests. How many people will be in your party? And do you have a date in mind?

>> Yes, I'm thinking, um, March 10th, um, and about 7:00 p.m. And we're all wanting to eat pepperoni pizza. Does that work for you?

>> That sounds perfect. I can reserve a private corner table for your engagement party on March 10th at 7:00 p.m. How many people will be in your group? And would you like me to also pre-order pepperoni pizzas for everyone so they're ready when you arrive?

>> So there'll be 10 people in the group. And, um, uh, no, we'll, we'll order them when we get there.

>> Great. I'll reserve a private corner table for 10 people on March.

So, as you can see, that's how the Vappy Agent works. And this is great. Right now, it's coming along and it's booking a table for us if the front of house is actually busy. Now, there's a few things I want to touch on here that you might have pulled out. You might not like the voice or the delay and things like that. So, I actually want to show you how you can actually change these and some of the quirks of voice AI agents. So, first and foremost, we've got the model. Now, I personally think 4.1 is the best model because it's got one of the lowest latencies and the best cost structures. So, you can see here, GPT5 might is 8 cents, but it's also got a 1,550 millisecond latency, which means how long it's going to take to respond. So, that's quite long. When, as you can see here, there's other ones which have slightly less, like 510, and they're cheaper, 0.1, but they're also not as good at responding. So, in my opinion, I would stick with 4.1, but you can change the model here. All right. And as for the voice itself, 11 Labs is still absolutely the king. So, 11 Labs, we just got Kira here, but you can go ahead, change this to any voice you want. Uh, and they're all down here. Pretty, pretty standard stuff. Now, the under the model, we're going to come down here. We've obviously got the system prompt. And all these prompts, every part of this workflow is going to be downloadable. So, don't worry about having to copy and paste this right now. We want to scroll down though, because there's a couple of pretty important, uh, elements here. So, as we keep scrolling down, scrolling down, scrolling down, we are going to see a couple, a start speaking plan and a stop speaking plan. And these are really important to note. Okay. So, start speaking plan is obviously how long until the AI will actually start talking. So, this is set at 0.4. Now, you can reduce this, and what's going to happen is it's basically going to try and stop, uh, so much overlap between me speaking and them speaking. And that goes for also the stop speaking section. So, this will be how long it takes to stop speaking when you've started speaking. So, if you find that the AI has too much crossover, these are the, uh, functions that you would actually want to change within Vappy. Then the final one is also the call timeout settings. So, let's say someone is still on the phone, or they're just not talking anymore. How long will it be until the AI agent actually just hangs up the call and says, "Okay, they're now gone." So, these are like the important, uh, settings that you can change within Vappy, which is basically going to change and alter the quality of the voice, how realistic it sounds, the delay between it speaking. Um, so this is basically all the things that make it sound good versus not good. Okay. And obviously the voice you want to use also.

So, what I'm going to do is now we're just going to jump into the NAM workflow. This is super, super simple. So, we are, we just have a very simple webhook here, and it is just grabbing the information from Vappy. Nothing too crazy here. We're then doing a routing system. So, what's going to happen here is if I had actually called in rather than using the Vappy talk to assistant, uh, we would have had a phone number, and what would have happened is when I booked that, uh, time at the restaurant, it actually would have sent me a confirmation text saying that, "Basically, you, you've got here. We go. I'll show you what it says. Thanks for trying our demo restaurant. We're glad. We'll follow up with you shortly." And then it would also have the time. We could have the time booking in there too. And then we could also then, this this webhook here is for adding it to a CRM. Now, we don't have that in this workflow, but you, if you use GoHighLevel or HubSpot, Close, whatever, you just add that node in. But what this is essentially going to do is it texts the customer confirming that their appointment has been booked, or whatever they had booked has happened. The next node here is actually going to be booking the calendar event. So, sending a calendar event to obviously the business saying, "Hey, this one is booked," and to the guest also. So, basically reserving it, and then also updating the CRM. And then the final one would be if it was a takeout order in this case, then it would actually just say, "Hey, it's a takeout order. Update the CRM." So, the NAM side of this is actually super basic and simple, but this exact workflow I have sold for over $10,000, uh, using the AI, the voice agent like Vappy, purely because the value to certain businesses with answering the calls on time and getting stuff booked is insane, right? And a good example of where I use voice agents and selling them is service businesses. These guys absolutely lap this stuff up because they're out on jobs, they're up roofs, you know, shingling a roof or whatever, and they're getting calls all the time, and there's just no one there to answer them. And previously, we, you know, we've tried doing workflows, uh, inside of, you know, a CRM and whatnot, but actually having a voice agent that can answer and booking jobs or potentially even give quotes, which you can absolutely bake in with like another NAM workflow, is huge to these people. It is like absolutely massive. And you think like one roofing job could be worth 20, $30,000 to them. So them having this fully handled and taken care of where they really don't need to rely on someone who's front of house, or we just empower them, and especially during out-of-hours times, they can use this and basically just fill their calendars with work.

This next one is an agent that significantly saves you time and you can easily sell this one to companies. An AI agent that reads all your incoming emails. It will decide if they're customer support or not. It will then write a reply based on your company's policies. And the best part is it will reply in the same thread and alert you on Slack. Let's dive right in. So, there's two main elements to this workflow. We have the main AI agent, which is this top part. And then we have this bottom part, which is actually where we're going to be inserting the customer support policy documentation. So, we have to start here. So, what we're basically saying here is we want to upload all of the customer support policy documents to Google Drive. And I'll show you examples of ones that I just threw together from old ChatGPT here. And you can see this basically is the policy documentation that we're going to be referencing for this workflow. All of your documentation, all the customer support documentation would just need to be added here. And then essentially what it's going to do is pass it through to a Pine Cone vector database. Now, to make this really simple to understand, essentially all it's doing is it's going to read and extract all the information from the documents you give it. And then the vector store is essentially allowing AI to just pull out the relevant bits. So, it doesn't need to read the full policies every single time a query is asked. It's essentially downloaded and extracted all the information. It understands what your policies are. So, when an email comes in, it goes, "This is the, this is our policy on that," and it can then craft that email around it. So, this only needs to be done every time you want to insert or update your customer support documentation. That's why it's actually a separate part of this flow. And essentially, once you've uploaded the customer support information, you don't need to use this anymore.

The next part of the workflow, which is obviously the main part, which is the AI agent itself, starts out with obviously a Gmail trigger. So, this is somebody emailing the company asking a question. We are then going to be evaluating that email. We're then going to be splitting it out. Is it customer support? Which it, if it is, is going to the AI agent. And if it's not, we are basically going to be sending a WhatsApp notification. Okay. So, if I hit, uh, play on the WhatsApp notification, it's just going to allow you to see the test data that I've previously pulled through. Okay. So, we can see this working through here. It's saying, "Oh, this was a customer support question. It's going to the agent. It's asking the model. It's going to the vector database, which has this information in down here. And then it's drafting an email here, and then basically it will then send me a WhatsApp message saying, 'Hey, like, you need to respond to this email.'" So, let's look at each step in its singularity. So, we're going to come over to here and see what the email itself was. So, here it is. "Hey, I was wondering how I can track my order." Fair question. Definitely customer support. So, it's come to the right area. So, it's come over to the agent, and we can just pull up basically, uh, the expression within the agent. So, it's saying, "This is a system role. Look at the support documentation. Here's examples of like crafting emails." All of this is obviously included within the free template that I'm giving away. And it said here is the output of this. So, "Dear James, thank you for reaching out to us to track your order and start as you open up smart support ticket, blah, blah, blah." And basically, it's created this draft email that can go out. Okay. So, that that's just done through this module here, and you can see the output here. Yeah. Then what it's done is it's come and it was actually said, "Yeah, we have sent you a message right, telling you that there is a customer support email that needs answering," and this was what the context of that email was. Okay. So, imagine this at scale, if you've got hundreds or thousands of people coming in every day, and you need to answer support queries. You might have 10, 20, 30 support crews a day. It obviously gets a lot. This agent is so powerful because it essentially gives you all that time back. It gives you ultimate leverage. You can use this agent to draft all of the responses based on all of your documentation and policies. So, you don't need to respond to these people.

Now, you've gotten a glimpse of some implementations you can recommend to clients and companies. How do you really find clients who want your AI clarity? A no-code plug-and-play AI outreach system that got me 153 clients from only 14,100 emails sent. This is the exact same system that Mike used to close a 24K textiles manufacturer, that Eric used to close a 100K software deal, and Brandon used to also close a 12K deal with an automotive manufacturer, too. So, understand the typical outreach system converts at about a 1 to 3% reply rate. And candidly, if you're 3%, you

are doing very well. The vast majority of people aren't even at that.

As you can see here, our system converts at 9.8% on these 1.6K. And over the 14.1K, we convert at 8.7% gaining those 153 opportunities.

You see, most people think you got to do one or two things. Either hundreds of hours of research and hyper tailored Loom video style outreach where you do very small scale, hyperpersonalized. The truth is that people just don't watch those videos. or you go the other like end of the market where you're going, "Hey, we're going to spam everyone with a million emails and it's going to have no personalization and we're just going to play the numbers game." And both of these strategies have elements that are right, but separated they are wrong. You want to basically come up with a system that merges them, which is exactly what I've done. And these are the exact systems that Mike used to get his 24K deal, that Zach used to get his first responses, that text down in a very efficient manner.

Um, but before I carry on, if you love these no code plug-and-play AI um, strategies, drop a subscribe down below. Trying to reach 10K before the end of the year. I'm going to help you build a vibe consulting firm totally for free on this channel.

All right, so let's dive into the tech stack. So, we're going to use GoDaddy. You can use namecheep, pork bun, whatever you want. Just buy your domains. Your domains want to be something relevant to your current business. So, let's say you were known as Andrewsacounty.com. You could get like try Andrew accountancy useandrewac accountancy.com and we want to set them up on Google inboxes. This is important from a deliverability perspective. Uh you used to be able to use Google and Outlook but Outlook just absolutely tanked recently. So just trust me on this. Go with Google. Set up independent workspaces for each of your domains. This is going to increase your deliverability and the chances of success doing this.

Then from a personalization standpoint, this is where AI really comes into its own. You want to use a platform called clay.com. Now, clear.com, I'm going to give you an example in a second. Imagine basically a Google sheet where you can kind of do whatever you want with it. You can implement AI within the cells. It can do your research for you. You can do your personalization for you. It can do all of the outreach like it links with all the tools. It's amazing. And that's what we're going to use to actually get this mass personalization at scale, which delivered those those results of, you know, 153 opportunities from the 14,000. Right? So, and then the next sending tools instantly.ai, Smartly.ai, uh Alec instantly and then if you are going to be mass sending this is like 100k million plus emails a month I know for the vast majority that won't be you but if you are mail reef is the one I'd recommend so let's just look quickly at a clay table not going to go like super in depth but again just looks like any excel Google sheet you've got all your columns in here but the beautiful thing about it is you can actually implement AI and implement workflows within these columns so let's say for example you found this company so worth.co and you got AI to go to a website, say, "Hey, what do they do? Give us an understanding of the company and the information, scan the website. You can then use that information and then ask AI to say, "Hey, I want you to use the information that you found on the website to fill in the blanks."

Now, rather than just talking about this, I'm actually going to show you exactly how that works with our email that delivered us these insane results and actually got us a 20.6x uh return on investment on this. So, this is a good framework for your emails. You want to start with first name. You never want to send outbound if you don't know the first name. You want to do personalization, which is where clay.com and using AI comes in. How you help them specifically. So, this is your dialedin offer. And then your social proof on how who you've helped just like them. And then a simple call to action. So, this is legit the email we used in our consulting firm that got us a 20.6x return on our spend. Hey, first name. And the ICP snippet was, "I was on your website and noticed you are doing, you know, AI implementations for healthcare companies would just be an example. Um, but I noticed you don't have a sales team yet and you're still doing founder sales. I'm reaching out cuz over the last six years, we've helped early stage founding teams build, train, and scale their first inside sales playbook. We work with 300 plus clients essentially just like them is building out the sales playbook on your radar." and this framework again. So it's name, personalization, offer, social proof, call to action. Very, very, very simple. Right?

So now you can see our full email breakdown. If you want to take your outreach a step further, you're going to want to implement my AI outreach engine that will literally 10x your marketing speed whilst also increasing conversions by at least 30%. I'm going to break it up into three core variations, saving the very best to last. So, let's dive in.

So, this is version one of the outreach engine. And this is like really nice and simple. And you might think, hey, this doesn't look that good. But believe it or not, this actually has one of the main core components of a highly effective cold outbound strategy. So, right here, we've got a Google Drive, which looks like this, which has our essentially our lead list in. We then attach the sheet in it with the lead list in it obviously. And then essentially we have the model. Now let's open up the model and see what we're actually doing here. So it says here, you're an assistant that generates short personalized outreach emails. Objectify run only the following structure without um anything else. So this is a structure that I found worked incredibly well for my business. This is one of the core components which was I was on your website and noticed and this is where we talk about something specific to their business which is why I want to reach out. So it needs to be something specific about the business. It's also relevant to obviously why you're reaching out. So this little prompt here is literally an absolute gamecher and this is what will just 10x the output of your cold outbound. Then basically what we're doing is we're just going to be writing that blur and then we're going to be looping it for as many rows in the sheet. Okay.

So, let's um let's execute this and we'll see what it's going to look like. So what it's going to do as you can see here it's going to loop. It's going to write it. So it's just going through the sheet. Now what I'm going to do is I'm going to pull the sheet up here. So this is what a sheet would look like. So if you scrape from Apollo or anything like that, this is basically what the lead list is going to look like. And what you're going to see over here on the right hand side is the messages that it is writing. So we should keep seeing it basically populating in here. But let me just pull up one of these messages. So, I was on your website and noticed you provide executive search, consulting, and leadership development services, which is why I wanted to reach out. Okay, this is great. I was on your website and noticed that you provide uh printing technology and business uh process services worldwide, which is why I wanted to reach out. And again, we just want to make this specific for your business. So, your second line in your cold outbound template would reinforce this, right? So, like this obviously isn't your entire email. This is just one part of it. And all this whole workflow is doing is using their website. That's it. So like this is like a really simple basically can't go wrong mass personalization which costs absolutely penny fractions of a penny to run.

All right. Now this is version one. So version one is more than good enough for you to go and get results with. I guarantee you that. Okay. And you can obviously expand on this and add more personalization in on different parts of the spreadsheet. So I'll just give you another example. Like previously we actually used to then look them up on uh LinkedIn and find out if they had salespeople and if they didn't we would add a different personalization and if they did we would we would basically customize it which is kind of the next part of this workflow. So inside this workflow that you're going to be able to download uh right below we actually have a step two here. Right now the step two, you basically are going to plug in where this loop is here. Okay. And what this is doing is we're now actually using scraping B to get more data for and basically be more accurate. And then another thing we're also adding in is we're adding in scraping their LinkedIn profile also. So now we're not only just looking at the website to gather information, we're also now looking at their LinkedIn profile to gather information to further increase our opportunity to personalize, which is like I just said that we did in our last business. That's actually what we did. So, we had one scraper uh going to the website and saying, "Hey, this is what the company's all about. This is what they're doing." And then we actually had another scraper that would go and look at their LinkedIn profile and see if the employees were there that uh made it more beneficial or less beneficial for us to work with them as a client. So, basically, if they were a bigger company, we didn't want to work with them. And then essentially, we got the personalization here again. And then rather than putting it into just the sheet here, which is what this is doing. So, it's just kept filling this out here, right? So it ran this number of rows. It will also then if you wanted to send it over to instantly now instantly.ai or smartly.ai, AI, whichever you prefer to use, but this will essentially enable that uh connection. And you've now got something where all you have to do is upload a lead list into a Google Drive folder and then press run and it will literally fully autonomously go and write personalization using OpenAI, using the web, and using LinkedIn and actually start mass outreaching.

All right, so as I'm sure you agree at this point, this is already sick. This is so simple. As if you've watched any of the videos on this channel, I love simple stuff that doesn't break that's effective, right? A lot of people have really complex NAM workflows. The problem is they break all the time. They have very little commercial value. All right? Mine are not like that.

So, the next thing if you want to take this up a notch is the actual scraping part of this this workflow here. And if you're not in the no as of like right now, there used to be a tool called Apify that allowed you to scrape Apollo which made the leads very very cheap. That has now been deactivated. So it doesn't work. So this workflow here actually enables us to scrape Apollo still in this case and still gather that data and then run the campaign. Okay. So we're still getting like really cheap leads. So like this lead file here was scraped using ampify and it was pennies right for I think it's something like 2,000 contacts in here and this is what this is basically allowing us to do do that at scale because if you look at Apollo how expensive it is to export contacts it is crazy.

All right so this is like the the third part. So if you want to have like a really robust scraping engine this is that. Then the final one and this is like the creme de la creme of cold outbound engines. And when I say creme creme, I've sold this exact engine for multiple times for over $10,000. No joke. This is this good. So this entire engine will do a scrape request. Then it will put the results in a spreadsheet. Then it will enrich the data automatically. Then it's going to write the personalization automatically also. Then it's going to look at adding it to not only instantly but also hay reach. And hey reach is what I like to use for LinkedIn automation. So when you look at this, we are literally going to put in a request. So the request would be the Apollo URL. That's basically all we add in this parameter here. And then it will basically do the rest. Right? A long story short, this is a commercialgrade outreach engine. So what I used to do and what you can still do using clay.com, but clay.com's $350 a month. This is the same thing using NAN's $24 a month plan and a few API integrations. All right, so it's like crazy what we can now do inside of NAT and how quickly it is advancing.

While reaching out to clients is important, there is one way you can skyrocket your AI consulting business and that is by getting a partner. And choosing the right partner for your AI consulting business is one of the biggest growth levers that you can pull. It's also one of the most important decisions, which is why it's super hard and can take a very long time. But luckily, I've come up with an AI system that will save you hours and significantly increase your chances of finding the right partner for you. For those that are uninitiated, this is what we like to call the dream 100. Now, I first heard this from Russell Brunson, whether it was him that coined it, but the idea is is you want to find a hundred people who are your either ideal clients or ideal people to partner with, right? So a good example of this is Alex Becker who some of some not of you may know. He runs a company called Hyros and he wanted a load of big faces so he can put them all over his website. So he did this dream 100 strategy. So he got people like Alex Hamozi, Tony Robinson, Dean Gratziosi all on his website using his product and that's what the dream 100 strategy is about. It's about finding those ideal people. So for us it's about finding the people with the contacts that we could leverage to scale our AI consulting business even faster.

As you can see here, it's actually not that complicated. Like there is there is elements to it that we're going to walk through, but you can see it's not like this crazy long and crazy complex workflow, which is great because it means it won't go wrong for you. So, let's just go into the start. So, we do have uh the functionality to add Slack. It's the same. Could be Slack, SMS, WhatsApp, Telegram. It makes no real difference. Okay. Then, we're going into the chat message. Obviously, for us using N. And then we're going to be going into the AI agent. And I'll I'll just pull this up here to so you can see. So we're basically saying you're a helpful research assistant designed to help business owners identify their dream 100. This is 100 people who are their most relevant customers, most likely to pay lots of money and the least hassle/service drain on the ambassadors and the company wants loyal. We want to get some clarity. So we want to know, hey, what job title are we looking for? What company size, industry, vertical, geography, location, any exclusions? And we give a bunch of examples here. This is basically what the AI agent is going to be asking us and this is what's going to be defining the list that gets pulled. Then what we're going to be doing is we're using open router because it's just easy way of accessing a bunch of different models and we're using 4.1 to then do uh part basically come up with obviously the um response and then we're going to be inserting the data into a Google sheet. So this is the Google sheet I've got here. So we've got the prospect name and the summary and then we've even got a reason here. So this is one I previous around earlier and this is basically what it's going to be filling out and then we're using perplexity for research. Um, so essentially what we're going to be doing is agent's going to keep doing it for 100 rows and it's going to check the row go row last insert the data and then just keep looping.

So now we're going to actually run this workflow and we're going to see how it functions. Okay, so we're going to say open chat here and then I'm just going to start by saying hey. So now we can see the workflow is now running. We got the agent and the open router modules and it's checking the sheet. So, as we can see here on the left, it says, "Hey, we want to know what the business sells." This is about you. Details about ideal customer. Again, title, company size, vertical, industry, geography, what's your ideal customer audience, uh markers that are a good fit, outcomes for your ideal client. So, what you want to do is fill this out obviously with as much information as possible, but I want to show you what happens when you don't. I want to work with engineering companies in the UK who are more than 100 employees. Right? So I answered three of its questions there. Right? I entered the demographics. So UK, I entered the sector engineering and I entered the employee headcount. Right? So let's see if it comes back to us and say hey great to refine more. So wants even more information. We need to know what exactly does a business sell to or offer these engineering companies. One, we sell AI audits. This is to help them understand well AI can cut costs in their business. And number two, I want CEOs. Three, no, four doesn't. So, I'm just going to answer four of them. Okay. So, I'm not even going to answer them all. So, now it's going to basically work away and find all these people, right? Unless it's got more questions. Before start prospering, could you share any markers of you know basically people are a good fit. So I'm gonna say James Dyson would be ideal but now he is too big. Let's focus on companies smaller than that. So this is now just saying hey do you have any people in mind? Right. So if you're going after a certain industry or vertical that you really like, you probably do have some people in mind. So again, you know, a lot of people might go, "Hey, I'd love to have Alex Mos in my dream 100 or Tony Robinson or Alex Becka or all these other influencers because they're really good at promoting product." So that's basically what we're saying here.

So now what the AI is doing is it is going to basically go away and start doing the research. So we can actually see now is actually doing research using perplexity. So, this is going to just take a little bit of time before uh we basically have any data.

All right, so that took a few minutes. Now, let's look at what it's got. So, we've got 12 results here that we can just quickly run through and basically see the output of this workflow. So, Jerome Frost, OBBE, he's the newly appointed CEO of ARUP. It's a UK based in engineering consultancy with over 18,000 members. So, massive, right? So, and then it's going to give us the reason, which is another thing that I baked into this I absolutely love. So, large UK engineering consulting suitable for AI audits. Now, not only is that company obviously suitable, but more importantly and what we're really leveraging it for is, hey, if we got Jerome on our team, we reach out to him, we have him as like this figure head as part of our basically AI consultancy and we're focusing on say engineering for example, we have obviously got to prove to Jerome that we can deliver and get him on board, which is the the relationship building and that can obviously take time, but just finding these super niche individuals that you probably don't know about who would be a massive growth lead for your business is insane, right? So like let's just look at one more. James Harris, CEO of M McDonald. Okay, so again another engineering consultancy with sustainability focus, a large workforce, ideal client profile. So that would be maybe a great one for an order also, but also to leverage. So you can just see imagine this is just going to rip through 100. You can obviously do more if you want, but the idea here is is like if you don't have a huge network or you are just wanting to go insanely quickly by partnering with somebody, then this AI agent and this model is going to enable you to do that.

Now you've gotten the partner and you're reeling in the customers. How do you ensure they have a quality experience with you? Well, let's start with dialing in your onboarding process. I used to lose trust with clients during onboarding, even when I thought I was being super careful. You see, emails got delayed, forms would break, and everything just felt clunky. But I found a way to fix it. So, this system that I built doesn't just automate onboarding. It actually makes it better. Every client gets a warm personal email and their details are collected perfectly in our system and everything runs whilst I sleep. I will show you step by step exactly how the system is built. Let's dive right in.

So, as you can see here, it's a super simple workflow, and that's awesome because it doesn't break. Now, you can expand on this as much as you want. Let's say you want more emails or you want different data sending to different places, etc. But this is the core foundation that you will need to run this. And I want to dive into both of the agents and we'll start with this top one. So, this top agent is obviously centered around sending that warm personalized email. So, let's look at the expression that I built out. So, can you please create a warm welcome email for a new client based on their information? We want the email to be concise and friendly, but professional. It should be personalized to them based on their industry and what they're hoping to do during the partnership. Here's their information. Make sure to always sign off with Bob Bob and ABC cob and internal relations. Now, obviously, you need to update for your name, your business, um your position, etc. And also these fields here, these are from the form that the client will be submitting that we'll be running through in a minute and you will update those accordingly also. So like with my last company, we would have quite an extensive list that we wanted people to fill out during onboarding. So we would just basically transfer that over to the N form and have them fill it out there also.

Now we're going to come into here and we're just going to look at what the Open AI model does. And to put this in layman's term, it's going to take the AI agent gibberish and convert it into normal human speak that we're going to be using in the email. Now, let's come down to the second agent here. And this is the one that we're using for data capture. Now, if we open this up here and let's have a look at the expression. So, much simpler this time. Take the client information provided and create a summary of their client profile. Here's the client information, name, company, industry, etc. We want you to output it like this. And then again the open AI model is converting that from AI gibberish into human speak. And then we are updating the row of the sheet which is our new client sheet.

So let's actually run through this. I'm going to hit execute workflow here. And what you can see it's doing is pulling up the client on boarding form. So I'm just going to fill this out with some dummy information. So I'm going to put engineering. Um, I'm going to put make more money. I love dogs. Okay. Then hit submit. So again, this form you would expand based on what your service you're providing and obviously what information you need from the end client. So now we're coming back here and we can see the model working. So the model is gone through the agent. It's now turned the AI jargon into email format and then it sent the email. Then again AI jargon, normal human speak and then updated the sheet. So let's have a look. So we'll come here. Andrew, welcome to ABC Cop. Andrew, excited to partner with you. Now obviously the formatting of this email you would just update to how you want it to look. And you can see obviously here Bob Bob VP of internal relations. This can all get updated. But you can see here it is perfectly crafted this email based on that parameter we set. And that AI parameter you can obviously update if you want it to be more specific or longer. Obviously that's just totally up to you and your business. So, um, we we're excited to work with Monik in the engineering field. Our team is committed to helping you achieve your goal of making more money in an effective time tail to your industry. Any questions or support, please don't hesitate to reach out. We're excited for a great partnership. That's awesome. It's a really nice email. The more specific it is, the better it is. Now, we come back to the sheet and we can see here, this is where we've actually captured their information. So, Andrew, Andrew's email, and then Andrew is an engineering professional seeking to collaborate on his company's profitability. His primary goal is to make more money and focus on financial growth and improve in the engineering sector. That is exactly what we wanted. We can expand upon this as much as we want by just adding additional nodes here. But this is the foundational part of starting to nail client on boarding and giving a really nice personalized experience.

Look, handling customers is only half the battle. The other half is not breaking down yourself, which is why you need these seven AI tools that will free up more of your time. so you can focus on the bigger picture. And the last one on this list is a real hidden gem.

Now, the first tool is something that allowed me to personalize our outreach at scale without spending hundreds of hours doing it, and it's a tool called clay.com. Now, clay.com is essentially a very fancy spreadsheet. So, imagine like a Google sheet or an Excel that you can kind of do anything with. So you can plug in scraping, you can plug in AI, you can plug in automations and you've got this big table where it essentially enables personalization at scale. So the way we utilize clay.com is specifically for our outbound marketing. So we were able to scrape companies like Apollo, bring in their data, so the contacts we wanted to reach out to. We would then be able to do all of the filtering within the T clay table using AI and then we're able to actually personalize all of the outreach to every single contact and every single company at scale before then pushing it over to the outbound platforms.

Now the tool we use which is the second one which is kind of the goat of this AI revolution is actually chat GPT. Now, when it comes to chat GPT, what a lot of people get wrong is exactly how to use it and where it's best utilized. So, like I just touched on before, we actually heavily used chat GPT to actually masspersonalize all of our outbound marketing efforts. And the way you do this most effectively is by giving AI guardrails. So rather than saying to it, hey chat GBT, can you go find out some unique information about this company and write a blur that I can put into an email, you will get anything from a single sentence all the way through to a paragraph. That is not the best way to do it. The way you actually want to do it is to give it fixed guardrail. So an example of this would be using the company information that you found on the website, I want you to fill in the blanks. Hey, I was on your website and noticed you do XY Z. I was really impressed with ABC. And then you could go into a templatized email outreach. This is useful for a couple of reasons. One, the AI personalization obviously lands in the inbox because each email is unique. And the next thing is it actually helps connect you with the end user.

Now once you've done all the scraping and the personalization, the next thing which is actually the next two tools is how do we actually use AI and use technology to get out there at scales. So from a cold outbound perspective, we used instantly.ai and this enables us to put all these leads in and all the AI personalization again at scale and automate all of the outbound process. This whole system that I'm talking about I literally had to work on 15 minutes a week to actually respond to everybody and book meetings. And then the next tool is heyreach.io. Now hey is very similar to instantly. What instantly is for cold email hey is for LinkedIn automation. And once again, you can build automated outbound campaigns. You can natively ingest AI personalization for all of the contacts to do again mass personalization at scale.

The next tool which is an absolute peach for anyone who has a lot of documents that need building for their clients or their customers that they come on and it is relevance.ai AI and this tool is an AI agent platform and what you can do is you can build agents to basically go ahead and do certain tasks. Now the way we used relevance.ai is we had a lot of documents that needed processing whether it was proposals, contracts, but when they actually came on board we would build all their sales scripts, all their outbound and all this other stuff. All of that was done within Google Docs and documents. And what relevance allowed us to do was take all of our templates, all of our knowledge base and then we insert this AI agent and said, "Hey, this is who we are. This is what we're doing. These are the outputs we need using these documents and this persona, which is the persona of the client, obviously. We need you to go away and build all of these documents." What literally used to take us weeks would now be done instantaneously as soon as they submitted what we had was a buyer persona worksheet. and then all of their sales scripts, all their objection handling, all of their outbound, all their subject lines, all their marketing materials would have been instantly created for them.

Now, we're talking about actually copyrightiting. So, we had a newsletter and this was a big proponent of us actually getting clients as well as just direct outbound. And to write newsletters, to actually write anything, including our social posting, we used Claude. Now, Claude 4.0 Sonnet at the time of recording was just amazing. And it enabled us to produce very very very high quality writing. And this really enables you to extract your voice out of your previous content and then write in a way that seems very much like you are doing it candidly to the point now where 95% of my writing is done by Claude 4.0 Sonnet and it just gives me a ton of time back. So, what used to genuinely take me 10 hours plus of figuring out ideas, initial drafts, editing is now done in a matter of minutes. The one beautiful thing about Claude 4.0 or using any AI in a writing scenario, for me specifically at least, is I really want help with zero to one and when I've got something to work off, I can work a lot faster. And that's what Claude enabled me to do.

When it comes to research, the GOAT is perplexity. Now, Perplexity just has an incredible research engine. So, whether you are talking about specific topics again for content and posts, this is where Perplexity and Claude really were a match made in heaven because Perplexity's deep research tool really allows you to go deep on a specific topic to really find unique information about an industry we may be working in or helping a client in. And this again shortened our time arising from weeks and using contractors and freelancers to maybe do research for us to about half an hour depending on the search you're doing with perplexity and it brings up all the research from writing articles to publications to uh YouTube videos to podcasts and has saved us thousands of hours over the course of the year. Um, not only that, we were then able to basically just pass this information through to Claude to dissect it and then basically start writing based on all of the research that Perplexi had done.

Now, the final tool that I want to discuss, and this really is the hidden gem in the market, is a tool called Poppy. So, it's get poppy.ai. And what Poppy allows you to do is it allows you to give context to stuff at scale. So, you can give it all the information about you and it's just there. You could then go ahead and find someone else's like high performing VSSL for example. You could plug it in. It will then transcribe the full VSSL. You can then say to the AI, hey, based on my offer, who I am, I want you to make a VSSL using this high performing, high converting VSSL script that I've also given you. And it will write it end to end. I've also used it for ad scripts, ad copy, like I said, VSSL, sales pages, email marketing flows. The list is genuinely endless. And it plugs in with all of the major AI solutions that is you know Grock, Claude, uh ChatgPT, Gemini, they are all a part of Poppy.

Just like before, if you want to get even more leverage on your time, then you need my AI personal assistant. I built it with zero code, all from one central AI agent using simple tools like N, Telegram, Gmail, and Google Sheets. Now I don't have to spend hours stuck in inboxes or managing annoying admin errors. It frees me up to actually do work, grow my business, and honestly give me back a life again. I'll show you exactly how to set up your own AI assistant that runs your life automatically. So let's dive right into the workflow.

So as you can see here, this is the personal assistant agent. And you can see here we've got access to different sub agents. And the reason we do this is because if you give one agent a task of many things, so in this case it would be managing your email, managing calendar, sending emails, doing research, finding information for you, it can start to get very messy and very confusing and the agent basically will start making mistakes. So the correct way to actually build agents is in like teams. So you'll have one host like manager agent which in this case is the personal assistant agent and then we have sub agents here which are email, calendar and research. And each of these has one specific task. So the email agent obviously manages my email. The calendar agent manages my calendar and the research agent will do research on the internet for me. That's how this works and that's how it is so effective.

So what I want to do is show you exactly how it works. So can you uh tell me the latest um AI news? So here we go. Here are some of the latest articles. Air foil has done this. Open source has done this. AI skeptic friends are all nuts. So let's have a look at actually what went on behind closed doors. So we see here we had an execution and this is what it looks like when we open it up. So it got the trigger went sent to the personal assistant. It then went to the research agent. Then if we come over to the research agent and have a look at its executions, we will see here it got that execution there and it came in research agent. It went over to hacker news and to the open AAI model. We've found the latest news and then it sent it back through the success. We'll have come back to the personal assistant here and then it sent us this message in NA10. That's how it works. Now we can also say uh what meetings uh do I have next week? I don't have any. So it should come back and say none. But these were the uh meetings I had in the last month. Oh, there we go. I have my live feedback call on Wednesday. Okay. So there you go. So again, if we just come back in here, we can see come back to the editor and then open up the executions again. We can see here's the latest execution and let's just open it up and you can see came in went to the calendar agent and the same thing happened.

So let's actually look at what each of these uh components are doing. So the personal assistant here, if we just open up system prompt, super simple. You're a personal assistant AI agent designed to handle various tasks. Your prime role is to manage contacts, emails, calendar, events for this user. you have access to these tools and we're basically just telling it what it's meant to do and what it has access to. This is how it knows it is the manager, right? Then we obviously have our different subflows here. So this is our just an open AI model, right? Nothing too special in here. We're using 4.1 mini cuz it's the best cost. We have a contact. So uploading say all the phones, emails, names of everybody in my database here. So it has access to it. So let's start by just checking out the email agent. So you can actually right click and click go to subflow. So I've got to open it up here. Now the email agent is so nice and simple and all of these that's the point. They're all actually really simple but they're all very very specific. So the point is that they don't break because they are just designed to do each individual task. Right? So there is very little margin for error here. So when we open it up, you're a helpful assistant. Uh if a number of emails isn't specified, let's just assume it's five. We're sending an email or sign off with Andrew. you'll never include something like your name, right? Super simple, like nothing crazy at all. And then we're sending emails. So, this is just the sending emails node with the uh with the queries in there and then the get emails also. Same same thing, just once you've connect it. Nice and simple.

Now, if we come over to the research agent, this has got a little bit more going on. So, basically, this agent is able to go to these sites like Wikipedia hacking news and the SER API. So can actually scrape the internet and find out the information you want. Again, very simple prompt, works seamlessly. So then we've got the different modules for Wikipedia hack and news and the SER API. Then it will push back again to the personal assistant agent or it will try again if it fails.

So now we're in the calendar event and this is a really simple AI agent, but it has a few different layers of functionality. So, not only can it create events, it can create events with attendees. It can also check for the events and the meetings that you have. So, this is how you're able to message your personal assistant like I am and say, "Hey, can you set up a dinner date with, you know, whoever Elon Musk uh at 2 p.m. on October 29th." And it can then go ahead and actually schedule that event and invite the person as long as you tell them what the name is and what the email is and date, time, etc. So you can see how powerful this personal assistant is. It's got access to all these different subflows and is essentially able to take care of your dayto-day life through managing your emails, obviously managing all your calendars, rescheduling stuff when needed and also doing the research. Also can do the research on people as well, which is also a really nice thing when you are going into new meetings.

Okay, let's be honest. At some point you can't do it alone. You're going to have way more clients than you can handle, and you're going to need to hire an actual team to save you from unnecessary stress. But hiring people can either make or break your business, which is why I've created a bulletproof hiring workflow.

Now, as you can see here, super simple workflow, just the way I like it because it doesn't break. This is a live active workflow that I use. And it all starts with a form submission. So, this form is what the applicant would submit. So this would be attached to our job postings. We then have the setup info. Now this is the information about obviously the job and where the information gets sent on our side. So we have the Google sheet that we are going to be filling out with the applicant details. We've got obviously the string applicants, the drive folder where we're hosting it. We've got the job description itself here. So what we're looking for, so in this case an automation specialist, 3 years of employment, full-time history, etc., etc. We then have the position name AI automation expert. Then we have the Calendarly link that they are going to be uh sent if we want to interview them and obviously if they want to. And then this is their email that they will be getting the response to. Then we go through to the Google Drive. So this is where we're taking the information they submit and uploading it. We're then adding it to a sheet. Then we're extracting. Then we're evaluating the prospect based on our metrics here. So here you read uh the job description, resume application and and give two output items, a score and a summary. The score is a simple one, zero. Um if they're a good fit for as an applicant, zero would be uh absolutely not a fit. You know, do they have experience? Do they have the amount of years we're looking for? Do they have the specific skills? Do they have the education? Etc. And then in summary, output a string of two to five bullet points. Okay, really good highle overview. This is exactly what someone would be doing during the hiring process.

Okay, now when we come over here, we're going to be updating the applicant list. Now the applicant list is obviously the people that we are shortlisting versus the people that we are not shortlisting. So the ones we want versus the one we don't. We are then going to send them an email where we're going to ask them for an interview. And this is really nice. I'm going to show you exactly how this looks. So we'll just open it up here, but we say name, summary, full CFA. would you like to send an interview invitation? This is for us so we can respond yes or no. And then we have send the email invitation. And then it says, hey, you know, thank you for your application. Basically, we would like to interview you. And we give them the opportunity to book on the calendar. And then obviously we update the sheet if they have accepted and would like an interview.

Okay, so let's see this in real life. So I'm going to hit execute workflow here and it's going to pull up the job application. So, this is what it would look like. And then we're going to say, okay, first name, last name. I'm just going to put in some of my details here. Going to just change this to lots of zeros. I'm going to put seven years of experience. And I'm going to upload a fake CV that I made earlier. So, see CV here. I literally just got chat GPT to make something saying I'm an AI automation specialist and just filled out some stuff for us. Okay. So then I'm going to hit submit and we're going to come back to the workflow.

So as we can now see it's uploading that CV that I've put in to the drive folder. It's appending the sheet. It's downloading the CSV. It's now evaluating me based on the criteria that we have set. Then it's going to see if it wants to shortlist us based on that criteria or not. Then it's going to ask if we want to interview and then it will send the email. So you can see now it's actually stopped, right? So this is where it's basically sent the email. And if you just heard the little ping, then it's actually sent it. And here we can see it. So I'm just going to hit refresh here. And we can see here says, "Hey, Andrew Dum, you have been shortlisted. Here's, you know, all the information. Here's your full CV that you submitted, etc., etc. Here's what's really nice. The applicant can just hit approve right there and then saying yeah I would like an interview right we come back here we can now see it is going to send me another email which we will see here we go and boom so thank you for your application we've reviewed and would like to interview here is the calendar link to get interviewed look forward to discussing the opportunity now we can obviously just change how this is edited but this is exactly how it works now let's have a look at the hiring sheet. So, as we can see here, my name, the email, the phone number that I put in, the exact link to my CV, should I say, all downloaded into Google Drive. Here we also have a score shortlisted summary. So, this is the summary we spoke about here. So, 8 years deeper expertise, cloud ops, BSC in computer science and they have requested an interview. So you can see right now just from this highlevel overview that we've gone through how insanely powerful this would be to just streamline the entire thing because the most important part of interviewing is that is like culling those first batches like you will get especially when hiring say development automation guys hundreds and hundreds of applications to any given job. So, if you get them to fill out that job form that we just showed with all their information with a CV, with work experience, etc., and you push them through this and then it'll add them to the sheet, it makes it much faster for you to review everything because a bunch of them will get kicked out instantaneously.

So, this is your launch plan. So, you have your niche. So, it needs to be a traditional niche. You ideally have experience in it, okay? or you are finding a partner that has these two. You're then going to select a channel. I want everybody to start with their network. That is the best place to start. You'll get the fastest deals there. You then from a cold outbound perspective, we'll go to LinkedIn and email next. And then if you want a cold call goal, that's obviously a very fast way you can do it. Just a lot of people hate it. And then for organic, I focus on YouTube. And then for paid, I would focus on meta ads or Facebook ads. The offer is always going to be an AA audit. You do not offer the audit, implementation, maintenance all at once. It starts to get very confusing. Just focus on the audit process and the outcome for these businesses which ultimately is the only thing they care about is they reduce uh costs and they make more money.

I hope you enjoyed this video as a special treat for you going through this full course, you can download all of the workflows that I have shown you down below and you can also check out this video next.