If It Wasn't Recorded, It Didn't Happen to Your AI

**[00:00:00]** Here's a sentence every business owner should take seriously. If it was not recorded, it did not happen to your AI. And that may sound a bit dramatic, but for a company trying to use AI in real workflows, it is becoming very true. Your business probably knows a lot more than your systems know. Your team knows how to handle a difficult client. Someone knows which leads are worth chasing and which leads are expensive. Someone knows which vendor to call when something breaks. Someone knows why certain customer always needs a different process. Someone else knows the real reason proposals get delayed. And someone knows which discount is acceptable and which discount ruins the margin. Someone might know how the founder explains the company in a sales call. Someone knows what a great support answer sounds like. Someone knows the small exceptions that make the whole business run. And that knowledge is very valuable, but if it is not captured, structured, and made available in the right way, your AI cannot use it. To the AI, that knowledge might as well not exist. And this is one of the mindset shifts for small businesses because for years, documentation has been treated as a nice-to-have. It was the one thing that everyone agreed was important, but nobody really wanted to do. It was the thing that operations people asked for, that new employees wished existed, the thing that leaders promised to fix after the next busy season, and then the next busy season comes, and that just happens again and again. So, the knowledge stayed in people's heads. Some people then leave, and that knowledge is gone. It's stored in old image email threads, chat messages, half-finished documents, in recordings nobody watched, and in the memory of the person everyone calls when the normal process stops working. This was all already a problem before **[00:02:00]** AI. It made training harder, it made handoffs slower, it made the business dependent on a few experienced people, and it made quality inconsistent. It made it harder than it needed to be. But now it creates a second problem. It now makes your company less usable by AI. I mean, AI is powerful, but it is not magic. It needs context. We've talked about this. It needs examples, it needs rules, it needs access to the right information. It needs to know what good looks like. It needs to know what bad looks like. It needs to know when it has to stop and ask a human. And if you don't give it that, it will fill the gap with a generic output or even a hallucination, which is worse. And generic output is where most of the AI projects go to die, to put it in a terrifying way. The AI writes an email, but it doesn't sound like your company. Or it summarizes a client situation, but it misses important context. Or it drafts a proposal, but it does not understand your pricing logic. It can answer a support question, but if it does not know the internal policy behind the answer, it might not be the correct or logical or just straightforward answer. It might give a relatively decent answer, but you look at it as a person who knows about it, and you're like, this makes no sense. It might recommend the next step, but it doesn't understand the business risk. And then people say, "Oh, AI is not good enough for us." And sometimes that is true, but often the real problem is simpler. The AI was never given the company's real knowledge. It was asked to act like the business without being taught how the business thinks. I want to let that sink in a little bit. If you would tell a human to do that, would you expect them, **[00:04:00]** without having any prior knowledge or experience within your company, let's say it's a new hire, would you expect them to do a good job? And if you wouldn't, then that's something to consider. That's why it's not necessarily an AI problem, it's an AI legibility problem. I've been leading up to this in the previous videos. AI legibility means your work is visible enough, clear enough, and structured enough for AI to assist with it. It does not mean every process has to be perfect, it does not mean any that every document has to be beautiful, it does not mean you need a giant knowledge base management project before you can use AI, it just means that the important parts of the work cannot be completely invisible. If the best answer to a customer question only exists in Sarah's head, AI cannot retrieve it. If the qualification rules for a good lead only exist in the founder's instinct, AI cannot apply it consistently. If the onboarding process only works because one coordinator remembers every step, AI cannot safely improve it. If the exception rules live in scattered Slack threads or Discord threads, AI might find some of them, miss others, and confidently produce the wrong result. Confidently. That's the worst thing. I saw a uh an article from Nate We Jones uh it was I think last night. And uh he was saying how some documents, now that we have all the um Microsoft Word and and other office products integrations with Claude and other tools, it it seems like the output is great or it's perfect or it's it's properly designed, and so you fall for it. And some people might even sign off on that document because everything sort of checks out. It looks visually correct. And so he was explaining how it's very important to create a grounding layer before so that first you make sure that the data is correct and then do the nice **[00:06:00]** output. Because if you start with the nice output, you might get something that looks and feels like something that is correct, but it just looks correct and may have completely wrong data. And so that's why it's this is all this is important. If it was not recorded, it did not happen to your AI. You have to give it the data. Now, let me be clear about something. I'm not saying that you should record every private conversation and dump it into a tool. Please don't do that. There are many concerns around that, okay? Especially in certain industries with HIPAA and many other things. You have to be careful with what you record and have the appropriate disclaimers when you do. The strategy is getting everything that needs to be read aligned in a way that your AI can process it. All right? So this is not creating a filing cabinet with a bunch of information that you can never use. You still need judgment. You still need privacy rules. You still need access control more than ever. You still need to decide what should be captured, what should be summarized, what should be deleted, and what should never enter an AI system at all. So the goal is not to capture everything, absolutely. The goal is to capture the operational knowledge that helps the company do better work. Now, if in your space it's okay to record everything you do based on what you're doing, that's okay. That's great cuz then you can have even content for social media and other things. It's not just operational knowledge. But there's many industries where you can't record everything and you shouldn't record everything. So, what should you record? Let's start with repeated decisions. What decision does a team make every week? Which leads should we accept? Which tickets need escalation? Which proposals **[00:08:00]** need review? Which invoices exceptions are acceptable? Which client requests are out of scope? Which deliverables are ready to send? Every repeated decision has hidden criteria and if those criteria stay hidden, the AI will struggle the same as a human would. And the problem is it might give you something that looks acceptable. Whereas a human might just say, "I'm confused." So, if you capture them, the AI can help draft, classify, route, check, or recommend, but it will have a reference, a context to be able to do it properly. Now, second, record repeated questions. What does the team ask over and over again? Where is that template? What do we say when a customer asks for this? How do we set up a new client? Who approves this kind of a discount? What is a process for a refund? What do we do when the client misses a deadline? Repeated questions are the signal. They tell you where knowledge is not easy to find. They also tell you where a company brain could create immediate value. A company brain is not a science fiction idea and if you have been watching any YouTube videos on AI at all recently, you probably have seen a lot of videos talking about like a second brain or a company brain or things like that. Now, it may sound very fancy, but it's just a way to organize information. So, it's not a super intelligent system that knows everything and runs the business. Well, you could sort of build something like that. Um but for small businesses, that's not necessarily the most practical approach. A company brain is just your workflows, rules, examples, decisions, and documents organized, so people and AI can use them. So, you don't want to make a system that only AI understands. You want to make a system that your people also use, and it's the same one, and you collaborate within it. It is the approved source of truth of **[00:10:00]** how work gets done. It includes playbooks, checklists. Uh it might include examples of good output, bad output, like we were saying yesterday. It includes policies, decisions, rules. It includes customer context where appropriate. It includes lessons learned from past projects. It includes the things people currently have to ask around to find. Um when that exists, AI gets much better, not because the model became smarter, but because your company became more legible. You don't have to be searching for the newest thing, the most powerful model. In many cases, we've already gotten months ago to the point where the model is at a space where it will solve everything you need. It's just that you haven't given it enough context to work properly. Now, let's talk about examples. So, examples are one of the most useful assets you can give AI. If you want the AI to write a better better sales follow-up, collect your best follow-up emails. Don't give it all of your follow-up emails, because some of them didn't work. So, give him good examples. And then, you can also give it bad examples, so it knows how to classify them. If you want AI to help with proposals, collect strong proposals and explain why they worked. If you want AI to assist with support, collect great support replies, difficult tickets, and escalation examples. If you want AI to summarize meetings, show examples of what a useful summary looks like. Because not all summaries are built the same. You might get a summary from one tool and it's completely irrelevant to how you run your business. You might want points, you might want checklist, you might want next steps, you might want paragraphs. It really depends on who is receiving this information. And so, just getting a meeting summarizer is not enough. You need to give it the output examples of what you specifically like and enjoy consuming. **[00:12:00]** If you want the AI to draft internal reports, save reports managers actually found helpful. Examples teach style, structure, judgment, and standards. Without examples, the AI tends to produce the average version of whatever you ask for. And average is usually not your goal. Fourth, let's talk about recording workflows. And this is where many businesses skip a step. They want AI to improve a process they've never mapped, and we talked about this yesterday. They say, "We want AI for onboarding." But what does onboarding actually mean? Where does it start? When does it end? Who owns it? What information is needed? Which tools are involved in the onboarding? What gets copied? What gets checked? What gets approved? When do delays happen? We talked about all this yesterday. What happens when the client doesn't respond? What information is missing? What does a successful onboarding look like? If you cannot answer those questions, AI will not fix onboarding. It may create more output around onboarding, but that is not the same thing. This is why workflow-first thinking and AI legibility belong together. And if you didn't watch yesterday's video, I invite you to watch it or listen to it because we talked about how to determine which workflows and how a workflow is much more important to predetermine before you think about AI and how it can fit into it. A workflow tells you where AI could help. Legibility tells you whether AI has enough context to help well. You need both. Let's make this practical with an example in sales. Imagine a small professional services firm. The founder has handled most of the sales for years. They know the buyers real concerns. They know which industries are a good fit. They know which requests to **[00:14:00]** reduce to avoid scope creep cuz they've done it before. They know which phrases in a lead form are warning signs. They know the difference between a prospect who is ready to buy and a prospect who is just collecting free advice. Again, because they've seen it. But now the company wants AI to help with sales follow-up. And the simplest version is easy. A lead comes in. AI drafts an email. But if the founder's judgment that we just talked about has not been captured, the AI will draft a generic email. And it may be polite. It may be grammatically correct and not have any spelling mistakes. It may even sound confident. But it will not qualify the lead the way the founder would. It will not protect the company from bad fit work. It will not know the right next step. And it will very likely not ask the appropriate questions that will nudge the prospect in the flow of the way the founder would do it. Now imagine a different version. The company records sales calls. It summarizes the common objections. It writes down the qualification criteria. It creates a list of good fit and bad fit signals. It saves examples of strong follow-up emails. It defines when a founder should review a lead. It defines when a lead can be routed to a salesperson. It defines when the answer should politely say, "We are not the best fit." Now, AI has something useful to work with. It can summarize the lead. It can check fit against criteria. It can now draft a response using approved examples. And it can create a CRM note. It can assign the next step. It can flag ones that need human review. That is a real AI workflow. The difference is the recorded knowledge. And let's do another example with customer support. If a company wants AI to answer support questions, and this is very common, it's one of the most AI applied scenarios. **[00:16:00]** But if the support knowledge is messy, it's not going to work as effectively. Policies live in a document that was last updated eight months ago. Real answers live in old tickets. Exceptions live in Slack or Discord or Google. And refund rules are different depending on the customer. Escalation rules are understood by the support manager, but they're not written down. And if you connect the AI to that mess, you should not be shocked when the answers are inconsistent. The system is not being difficult. It is just reflecting the state of your knowledge. A better first step is to make support legible. Collect the top 20 repeated questions. Write the approved answers. Define the escalation rules. List topics that AI can draft but not send. List topics that AI should never handle alone. Save example of excellent responses. And at the very beginning, you might want it to just draft everything and always have a human in the loop just so that you're sure that it's doing what you're expecting it to do. And then you can give a little more freedom. That's when AI can really help. It can draft replies. It can suggest knowledge base updates. It can route tickets. It can summarize customer history. It can flag risky cases, it can help the human agent move faster without pretending the human judgment is unnecessary. Capture the knowledge. Clarify the workflow. Use AI where it can safely help. Then measure the result and improve the system. This is why unofficial AI use inside company is risky. You see this and we recently spoke at the National Lease Managers meeting and some of the people we were talking with had that exact problem where a an employee or several employees might be using different LLMs and different tools, and that's great. We want people to start practicing and testing on their personal time and then maybe **[00:18:00]** company time and helping them with I don't know, supporting invoices or parsing documents or doing repeated tasks, but it gets to a point where you have to be careful that they are not injecting messy outputs or messy data into the system just because it looks okay. So, if you're in a company or corporation and you're planning AI initiatives, you need to make sure what your employees are using and give them access to the tools in a way that you can see and oversee how they're being used, not to pry, but to understand how it's being used so that you can allocate resources to allow them to use them better. And also, so that if you already have company guidelines and brand guidelines and voice and tone, then the AIs that you use and that your employees use within the company should follow those guidelines so that the output matches because if every salesperson has their own flavor and they're using it to draft their emails, and it's not aligned with everything that we've talked about, it's going to become a real mess because the speed at which you can generate content with AI is astounding. So, you have to prepare for that as well. Microsoft and LinkedIn reported that many users bring their own AI tools to work. I think the exact number they reported was 78% of AI users. And that tells us something valuable. People are not waiting for a perfect company AI strategy. They're just already using AI. That's good. I mean, they're using it to write emails, they're using it to summarize meetings, they're using it to make spreadsheets easier, they're using it to draft documents. I mean, the adoption itself is is working. People are interested. They're using it because it helps. But, **[00:20:00]** if the company does not provide guidance, each person creates their own little AI island, different tools, prompts, standards, data habits, different levels of caution, no shared memory, no shared improvement, no clear policy, no real company brain. And that's not how capability compounds. Company does not become AI-enabled because 10 employees found a clever prompt. It can help individuals, but the company becomes AI-enabled when the best patterns become shared systems. A useful prompt becomes a skill, an approved skill that people can use. A repeated question becomes a knowledge base article. A messy handoff becomes an automated workflow. A founder's judgment becomes a decision checklist. A successful project becomes a reusable playbook. And that is how AI adoption moves from random experimentation to operational improvement. So, what should you do this week? Do not start by building a giant company brain. It'll just That's too big. It'll take a bunch of time from you, and it might not give you the output and resource that you need right now. Start with one workflow. Talked about this yesterday. Pick a workflow where better knowledge would clearly improve the outcome. Sales follow-up, client onboarding, support triage, weekly reporting, internal knowledge search, just whichever. Pick one, and then answer these questions. What does the team need to know for this to work well? Where does that knowledge live today? What is written down? What is only in someone's head? What examples do we have? Which are good examples and bad examples? What rules do we follow but never document? What mistakes happen because people cannot find the right information? What could AI draft, summarize, classify, or check if it had better context? Which tools or things should AI simply not touch? Who should approve the **[00:22:00]** output, especially when you have that human in the loop? Those questions are all simple, but they're very powerful. They turn vague AI ambition into practical work. They also reveal your deployment gap. And as we talked about, the deployment gap is not only the gap between tools and workflows, it is also the gap between what your company knows and what your systems can use. That is where a lot of projects don't succeed. If the workflow is invisible, AI cannot help much. If the knowledge is scattered, AI cannot be consistent. And if the rules are unwritten, AI cannot apply them reliably. If nobody owns a process, AI output has nowhere to land. You're just going to have a bunch of files scattered everywhere that nobody's going to read. So, make your work legible. Record the important meetings. Transcribe the ones that matter. Summarize the decisions. Turn repeated answers into documents. Turn expert judgement into checklists. That's how pilots do it. If you see anyone flying a plane, you'll see they have this massive checklist that they have to do, even though they fly the same plane every single day multiple times a day, because the checklists help. Turn your successful examples into templates. Um, turn scattered messages into approved guidance. Turn ask Sarah or ask John into a process that that specific person, Sarah or John, helped design. And please involve Sarah or John or Mark before you automate their brain. This is both practical and polite. Just make sure they're part of the process. Companies that don't do this well won't get very far with AI and they might get into a point where they say AI is not for them. The ones that do do it well then will have an advantage or will move faster, but not because they bought more AI tools or they chased the new agent demo **[00:24:00]** that's coming up every two days, but because they made their company knowledge usable. They made the work visible. They made the decisions clearer. And they gave the AI the context that it needs to produce better results. So, the takeaway is simple. If you want AI to help your business, do not ask what AI can do, ask what your company knows that AI cannot see yet. Ask which workflows depend on memory. Ask which decisions repeat. Which examples would make outputs better. Which rules need to be written down and which parts of the business need to become more legible before automation makes sense. Because if it was not recorded, it did not happen to your AI. And if it did not happen to your AI, your AI cannot help you use it. And if you have want help finding where your business is ready for AI and where the knowledge is still too hidden, look at our website. There's a few quizzes there. One of them is the AI automation opportunity map and it will help you identify those three to five automations worth building, your readiness grade, and risks to watch for your first two-week action plan. This is very important. It doesn't matter how you do it when you do it. Just make sure you ask these questions. Start there. Find the work, capture the knowledge, and then give AI a real chance to help. Thanks, everyone, and we'll see you in the next one.