Transcription
Today, I'm going to talk about how to find winning AI SaaS startup ideas. They're going to be great businesses; they're going to create a lot of Enterprise Value, a lot of cash flow—the ones that are worth 10 to 30 to 50 to $70,000 a month. And I'm going to be sharing my practical framework for identifying these hidden opportunities in Enterprise software. So we're going to cover how to spot repetitive pain points, add intelligence to manual processes, and build solutions that customers will pay for immediately. So hope you enjoy this [Music] episode.
So today, we're going to talk about how do you come up with winning AI SaaS startup ideas? There's just a lot of opportunity right now to be building SaaS because, uh, SaaS is kind of imploding, and there's, uh, just a lot of, uh, opportunity to go and use platforms like Bolt, Lovable, Cursor, Repet, um, and start building audiences, marrying that software together, um, and start building businesses. But the question is, how do you come up with great ideas? And today's episode is all about how do you come up with great AI SaaS startup ideas? If, if you don't listen to the show a lot, it's like, how do I trust this guy? Well, we have a—I'm a co-founder of an agency called LCA, and we're literally working with the biggest SaaS and AI companies to help them build and design, uh, SaaS products, um, software products, um, so I know a thing or two about how to do it, and I developed this framework, um, that I wanted to share with everyone.
Um, and originally, I was like, this was just for myself; it was just a framework for myself thinking about like, okay, how do I know if this is going to be a good startup idea in SaaS? But then I was like, I really do want to share it with you all. Um, I think that it'll be really cool to see what you build with this. So I'm gonna, in this episode, I'm going to go through my exact framework for how to come up with these ideas, um, and a plan for you basically, and H and and how you can build an AI SaaS business, uh, in 30 days; how I would do it if I were you, um, and at the very, at the, you know, at the, at the worst case, this is just going to get your creative juices flowing around SaaS ideas. So let's get into it. I call it, uh, the export button theory of AI opportunity.
So the way I think about it is this: every export button in software represents a business opportunity. So think about this: when a user clicks export, what are they saying to us? Uh, they're basically saying this software doesn't need, doesn't do what I need to, so I'm taking my data elsewhere to do manual work. So each export represents a workflow breakdown, a pain point, manual labor that could be automated, and a potential 10 to 30k a month feature. So this was a huge aha moment for me is, okay, if there's an export button, there's basically a workflow here; there's basically a pain point; there's basically manual labor that could be automated; and there's generally at least a $10,000 a month feature that could be created. Now you might end up not wanting to go and create that business for, uh, a variety of reasons, but that, that opportunity does exist. So let's talk about the five-step framework for finding these opportunities.
So the first is, uh, identifying repetitive pain points. So, um, this sounds obvious, but you know, a lot of people don't do this. Watch how people actually use enterprise software in their daily work. So look for these patterns. So I've broken it out into pain pattern, an example, and an AI opportunity. So let's, let's go through this. A pain pattern is exporting data to reformat it. So, for example, you're going from Salesforce to Excel to PowerPoint. So there's obviously an AI opportunity here to do automatic report generation. Go through another example: pain pattern, copying and pasting between tools; for example, Jira tickets, which is a bug tracking software owned by Atlassian, uh, to Slack updates. Where's the AI opportunity? Automated status syncing. Let's do another one: pain pattern, building the same report weekly. I'm sure you've all, you know, I'm sure a lot of people listening to this are the people creating the reports. I've created reports in the past; I've got reports in the past, um, for example, a Monday dashboard export. The AI opportunity is self-updating reports. Um, let's go through one more: um, pain pattern, maintaining spreadsheets by hand; example, manual, manual inventory tracking. AI opportunity: intelligent inventory system.
So when you look at how people actually are using enterprise software, and by the way, how do you do that? Well, one is you can just ask people, right? Um, you can just be like, how do you use this software? Um, and the second, the second way is you can just, in your, in your everyday job, you might be listening to this; you work a 9-to-5, and you might be realizing, wow, like this is such a, I could, this could be automated; um, there's an export button here; this could be automated; um, sure, I'm getting paid to do this, and um, I want to keep my job, but maybe there's a bigger opportunity in creating that startup.
Quick break in the Pod to tell you a little bit about Startup Empire. So Startup Empire is my private membership where it's a bunch of people like me, like you, who want to build out their startup ideas. Now they're looking for content to help accelerate that; they're looking for potential co-founders; they're looking for, uh, tutorials from people like me to come in and tell them how do you do email marketing, how do you build an audience, how do you go viral on Twitter—all these different things. That's exactly what Startup Empire is, and it's for people who want to start a startup but are looking for ideas, or it's for people who have a startup but just, they're not seeing the traction, uh, that they need. So you can check out the link to StartupEmpire.co in the description.
So, uh, this is a real-world example of an anonymized, uh, just because, you know, I don't want to share the revenue number of this particular, their company, but it's a $130,000 a month AI SaaS company that built their entire business by noticing Financial analysts were exporting QuickBooks data to Excel just to reorganize it for management. So they built an AI, uh, layer that generates executive-ready reports directly—super simple. And we're going to talk more about QuickBooks, uh, towards the end and some opportunities there. Uh, so that's number one, you know, identifying the repetitive pain points. Um, I've talked in the past about other tools that you can, uh, use, um, to figure out pain points. I talked about a tool called, um, Gumroad Search, um, which goes through Reddit and and figures out pain points. Um, you can go on X and just start searching, um, so you don't necessarily have to ask people or be one of those people to come up with, uh, one of these patterns. Um, you can also just go online, start searching and seeing what, on platforms like Reddit, X, Instagram, what are people complaining about, um, and and figuring out what are the different pain pattern, example, and opportunities. Sounds simple; it's, it's simple enough, I think. Okay, so this is where it gets, uh, really fun.
So number two, you're going to want to add intelligence to manual processes. So every manual task is an LLM opportunity, waai to happen. So, for example, you turn exports into instant insights; Stripe export goes into an AI-powered Revenue analysis; opportunity size, 50 to 100,000 MRR all day. Let's go through another one: converting messy data into clean reports, uh, example: so CRM data into AI-formatted, uh, presentations; opportunity size, 80 to 120,000 MRR. Uh, generate analysis automatically; example: when you take customer support tickets and then you make them into sentiment trends; uh, that opportunity size is $3 to $70,000 MRR. So these are literally million-dollar businesses, um, by just adding intelligence to manual processes. You'd be surprised how many manual processes, uh, happen in workflows, um, that if you just added some intelligence to it, you're looking at, you know, million-dollar businesses. Do one more: um, so, uh, if you, uh, surface patterns humans miss; so the example here is sales calls recordings and then into closing pattern detection; that opportunity size, at least $100,000 MRR. So, um, you know, what the case study I put here that, that came top of mind was, uh, just Notion AI, right? Notion AI started as a simple tool to help users write better content, but their real growth came when they focused on automating specific, specific document types that users were manually creating over and over. So they saw what people were creating over and over again, and then they're like, oh, how do I automate that? Um, that's, you know, seeing, uh, how that blooms and adding intelligence to those manual, ual processes, uh, is something that's, uh, going to help basically create winning startup ideas. So that's, that's number two on, on, uh, on the framework. So let's talk about number three, and this is actually missed; a lot of people missed this point; they actually, a lot of people understand one and two, but they missed, uh, three.
So three is identifying data silos that need bridging. So every organization has valuable data trapped in silos. So you got to find places where reports need manually updating, dashboards go unexamined because they're outdated, analytics are trapped within one Department, key metrics exist in different systems that should talk to each other, critical insights are buried in spreadsheets. So this is the way I think it'll, it'll be easier, uh, to think about it like this: look for phrases like this, quote-unquote, "I need to pull this data every week," "I wish I could see this alongside that," "I have to manually check if these match," "we keep this in a separate spreadsheet." So these are just a few examples. Um, when you see examples like that, "I need to pull this data every week," there's definitely some data silo that's going to need bridging. So these are aha moments. Um, another example of this, a real-world example: a B2B SaaS company built a, at Le, it's, it's at least 250k MRR business by creating an AI layer, AI layer that connects customer success data with sales data, automatically identifying upsell opportunities that were previously missed in handoffs between teams. In B2B, in Enterprise, there's so many handoffs that are happening between data, uh, and teams, and that's where, you know, this number three, that's where the opportunity exists, and people will pay for that. So that's number three, uh, on the, on the framework.
Number four is finding missing connections between tools. So check this out: watch for places where people say, "I wish these two things work together." So, uh, think about it like this: system A is an HR system, and system B is payroll. The manual work is reconciling employee data. So the AI opportunity is to automatically sync with anomaly detection, um, and that's going to create a lot of value. To give another example: system A over here is a sales CRM, and system B over there is a marketing automation. The manual work, well, is lead status updates. So the AI opportunity here is bidirectional sync with AI prioritization. I'm going to give one more example: uh, system A, project management system; uh, system B, time tracking. The manual work is the manual time allocation. AI opportunity here is automatic work categorization. So you get, you get the idea, basically, where, where, where there's places where people say, "I wish these two, uh, things work together," um, that's, you know, an aha moment for, uh, an opportunity.
So we're going to go to number five, and, uh, it's, it's start small, grow naturally, right? So the most successful AI SaaS businesses pick a specific niche that big players are ignoring; example: industry-specific document processing. You don't do document processing, processing; you do a specific industry. I always think about it like this: think about, uh, you know, horizontal, a product that's horizontal, then you think about a niche, and then you think about a sub-niche. So, for example, um, you know, the legal category is pretty horizontal; the divorce category is a sub-niche of legal; the, um, prenup category is a, is the sub-niche, right? So you have legal, divorce sub, uh, and prenup. So, uh, thinking about how you can build an AI SaaS startup, think about a very, very small problem, you know, for example, how do you, how do you create a, uh, AI ver, you know, AI version of prenuptial agreements? Easy for people, right? That's, that's an example, um, and, uh, I think that, yeah, the biggest ones, just because you're competing against—if you just look at the recent YC batches, right? Like if, if a YC batch is 250 companies, and at least 200 of them are, are AI SaaS businesses, um, you're competing against very highly funded businesses. So you want to start a very niche and then make your way expanding from there. So focus on one painful workflow, um, make it, you know, I'm just going through, you know, the most successful businesses; the most successful AI SaaS businesses are niche; they focus on one painful workflow; they make it 10 times better with AI; they let the AI suggest next actions; and they charge immediately, and they charge immediately. If you're solving real pain, people will pay from day one. A mistake a lot of people make is they make it free; um, you don't, you don't need to, you definitely don't need to, and and it gives you the optionality for the people who do want to bootstrap; they can, they could bootstrap, um, and it lets users pull you into adjacent problems; you want that to happen. When, when, when someone asks you, "Can your tool also handle this related issue?" That's amazing. I know it might not feel good because you're like, oh, no, you know, I, I started this business; it's this, this SaaS idea; it's too small. No, no, no; you're onto something; it means that you can expand, you know, land and expand from there.
I wanted to give some other ideas beyond exporting, the export button, right? So now whenever you see an export button, you're going to be like, wow, Greg told me that there's, you know, a, a good idea here, um, and now you have a framework to go and actually go create these ideas, but there's more buttons that you're going to see, uh, in software now, um, that can also give you ideas on what to build. So one, one button, and and these are all manual buttons, by the way; these are manual buttons that you're going to see on software that are going to give you an idea for how to come up with winning startup ideas. So generating report: okay, you've seen that button before, right? What's the opportunity? Well, it's automatic Insight generation; market size is $25 billion. Another one: manual button, schedule meeting; AI opportunity: context-aware scheduling; market size is $1.8 billion. Manual button: upload CSV; I know you've seen that before; intelligent data processing is the AI opportunity; you've got, you know, $3 billion, $3.2 billion market size. Um, manual button: reconcile data; real-time data harmonization; um, another manual button: create template; well, what if you had Dynamic template generation with AI? Totally doable, depending on what it is. Manual button: formatting document; I know you've seen that; AI opportunity: one-click formatting with brand rules. All these manual buttons: compile data, as a manual button that you can do automatic data aggregation as the AI opportunity. Review changes; what's the AI opportunity? Well, it's AI-powered change significance detection.
So all that to say is when I first came up with this concept around the export button, I started seeing it; then I started seeing all these other manual buttons all over, and they're also going to lead you down the rabbit hole of really, really good AI startup ideas. Um, also, I've never, I've actually never used QuickBooks, but, um, we were looking at buying a business, and they were using, uh, QuickBooks, and I started going through it, and I, I realized that, you know, QuickBooks, QuickBooks users export 250 million Financial reports annually; it's actually massive. Each export is about 45 to 90 minutes of manual formatting and Analysis, and the average value of that to that accountant and bookkeeper time, think about it, is about $75 to $150. So the total addressable Market is 12 to 18 billion annually. So the QuickBooks, there's a, just, all, you know, just putting it out there for people, there is a ton of startup ideas in the QuickBooks export gold mine. So, um, focus on, here's how to think about it, right? So focus on specific Financial reporting use cases: cash flow forecasting, tax preparation. We talked about going niche; you know, this is where you can go niche; build AI that automatically generates management-ready Financial Insights; create dashboards that eliminate the need for exports altogether; charge 15 to 25% of the Professional Service time they replace. So, you know, that's what you got to do; you know, that's the format; that's the sort of framework for the QuickBooks export gold mine; that's a specific, um, you know, platform that there's just a ton of exports that are happening, and there, and there's a lot of opportunity there. So, um, okay, so, you know, you might be listening to this, and you're like, okay, I've got a few ideas, or this is getting me to think in a different way, but how do you actually get started?
So if you were going to go and create, um, an AI startup, uh, SaaS startup, uh, what, how should you think about your first 30 days? So this is just one way to think about it. So your first few days, day 1 to 5: select a specific Enterprise software with high export volume. So research, you know, communities, forums, Xs, social media with pain points; join these groups to understand the workflows, and I honestly would actually create a, create an, an audience on one of these social platforms, like probably X, honestly. So actually go and create, uh, like if you're targeting, um, the divorce community or or the divorce people interested in divorce or people interested in, um, Financial Planning and stuff like that, go and, go and create an audience for people interested in that. Days 6 to 10: interview power users about their export habits. So, you know, what do they do with the exported data? How long does, uh, post-export processing take? What value would automating this create? Um, and, you know, just reach out to them; try, try to get them, you know, some time; you might have to pay them realistically or, or, or something; you might have to give them something, but, you know, you're trying to learn about who they are. Obviously, if you are the power user, right? Like if, you know, that's ideal; that's obviously ideal, but if you, if you don't, you can interview people, uh, to get that Insight. Uh, days 11 to 20, you can build that minimal viable prototype. Obviously, I would suggest using something like Vercel, uh, something like Lovable, something like Bolt, something like Repet, something like Cursor. So using some of these AI coding platforms to get, get it out there, um, and then you connect it to the original data source; you perform the top one to two post-export functions; you deliver the results in a usable format. And then the last nine days, Days 21 to 30, it's getting three to five beta users and charging them immediately. So ideally, you know, while you're, while you're going through these days, you're still Tweeting and posting, and you're trying to get audience, audience people, uh, into what you're doing, right? So you reach out to these people; you try to get them on the, on the product; you price based on time saved, 20 to 30% of manual labor costs; you focus on quantifiable ROI, time saved, accuracy improved; and then collect testimonials; focus on business outcome. So you give them a deal, you know, give them a deal; say, hey, we're, I'm going to give it to you for 80% off, but need a testimonial. And think about video testimonials; those work, work the best, not just text, because people are just glancing over at testimonials these days, and they're like, is this even real? Like, who is Johnny, right? But if you do a video and put it on, on your website, that's going to help with conversion, um, and, uh, actually helps, you know, deepen your relationship with that customer.
So what is the point of this whole podcast episode? My, my final thoughts on this is the best AI opportunities aren't where everyone is, is looking; they're hiding in these mundane, repetitive tasks that knowledge workers are doing every single day. Every export button, every manual update, every data reconciliation task represents a potential $1 million ARR business waiting to be built. Um, I think that the winners won't be the companies that create the most impressive AI demos; they're going to be the ones that understand these boring, painful workflows of specific user groups and transform them with AI. So I hope this got the creative juices flowing, you know, I mean, even me just like going through this again, I'm just like, there's so much opportunity right now, and, uh, you just, you just got to go and find these painful, boring…
Workflows and go niche. Start building your audience. Build very simple prototypes, and uh, and keep iterating. So um, hope this was helpful. Um, I will, if people comment on the YouTube and they want access to this uh document, I could uh package it up for people so that they can download it. Um, just let me know in the comment section. I read every single YouTube comment, um, and uh, and read every single Spotify and Apple review, the Startup Ideas podcast. Hope this has been helpful. Uh, people charge for this, but not me. This is free for you. Um, I live to serve, and I love to see you out there winning, creating businesses. Um, so thank you for your time, and uh, I'll see you on the internet. [Music]