# There’s ONLY 5 Ways to Use AI in SaaS (prove me wrong)

Channel: Rob Walling
Video: https://www.youtube.com/watch?v=ABnIUv7G9IA
Duration: 11 min
Language: English
Words: 1993
Transcript page: https://viewrankai.com/tools/youtube-transcript/ABnIUv7G9IA

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[0:00] Almost every founder I talk to is asking the same question. Am I falling behind on AI? They're worried that they aren't using AI enough. And if they don't keep up, their business could become irrelevant. Here's the thing. They're not wrong to worry, but they're asking the wrong question. Instead of am I using enough AI, they should be asking, am I using AI the right way? I've been building out a framework around how successful startups are using AI. And so far, I've found that there are only five distinct categories, and each one has

[0:29] completely different risks and rewards. Today, I'll walk you through all five. And by the end of this video, you'll know exactly which ones make sense for your startup and which ones to avoid. As you watch this video, I want you to think about all the ways you're using AI and think about which category each of those fall into. And if you think of an additional category that isn't covered in this video, please drop a note in the comments. Before we dive into the five categories, let me tell you about today's sponsor, G2I. Because implementing almost all of these AI strategies means finding qualified engineers to build them. G2I doesn't

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[1:22] candidate might work with your team. Companies like Meta, Microsoft, and ShopMonkey trust G2I, and so do firsttime founders who just need to get this hire right the first time. If you need a prevetted engineer to join your team quickly, head to g2i.co/microcom to start your 7-day free trial. And if you mention microcom, you'll get $1,500

[1:43] off your first invoice. That's g2i.co. co/microconf. All right. So, category number one is where AI is your core business. This is where your product is AI. Meaning, if you were to remove the AI, there's no business left. This includes both foundational models like Gemini 2.5, Claude Opus, GPT5, but it also includes companies like fiscal.ai, Jasper, MidJourney. These companies aren't just enhancing their product. The AI is effectively the product. Some additional examples are Rosie, founded by Jordan Gaul. It's an AI answering service. One Accord, it's a tiny seed company. They offer live translation for churches. And AI is a huge piece of this. And Pod Squeeze. This is a bootstrapped startup

[2:30] that helps you create and prepare summaries and show notes of podcast episodes. Does actually a lot more than that. And we've used it for a few years on startups with the rest of us and it saves us a tremendous amount of time. So with each of these AI categories, I'm going to talk about upside potential and primary risks. And so if AI is your core business, the upside potential is that you can potentially become the default solution in your category. And there are massive market opportunities in emerging spaces right now. The primary risks are commoditization, meaning today's moat can be tomorrow's API call. Market education burden. Customers don't know

[3:06] what's possible. So oftent times with stuff that's this new, you have to educate the market. platform dependency. Think about OpenAI and Anthropic and how they could make a single change that ends you. So if you have a PDF converter and OpenAI just bakes that into the next model release, your business will probably go to zero. And the last one only really applies if you're building a foundational model, but it's capital intensity. You need a huge amount of funding for that. Category number two is AI as a feature. So AI is not your core product here. The key difference here is that AI enhances your core product, but

[3:40] it isn't the product itself. So, for example, if Notion removed their AI writing assistant, they'd still be Notion. If Zoom killed their AI summaries, meetings would still work. And if Loom removed their AI titles and chapters, which are very helpful, they'd still be Loom. AI makes these products better, makes them faster, easier, but their core product value proposition still survives without it. Let's look at the upside potential for having AI as a feature. One is you might be able to charge more. So in conversations with chat GPT, it suggests that some companies are able to increase prices between 20 and 50% for premium AI features. Another potential upside is

[4:19] adding AI now can offer differentiation in competitive markets and it can improve retention through habit forming workflows. Finally, AI as a feature can be a natural upsell path for existing customers. Let's talk about the risks. The first is that this isn't really a competitive advantage because competitors can copy AI features honestly usually within a few weeks or a few months. So, it is not a longlasting advantage. Second is quality expectations. Bad AI is often worse than no AI and so you really have to vet your results and what you're delivering for your customers and that it is actually providing benefits and it's not just a checklist item that you build. The third

[5:00] is cost management. AI API costs can destroy your unit economics if your customers use them often. And the fourth one is overpromising, which really ties back into quality expectations. But the idea is in your marketing or your sales, are you writing checks that the product can't cash? The third is AI for building your product. So this is for the devs in the audience. This is where you're not building an AI product. You're using AI to build faster. GitHub Copilot, Cursor, Claude Code. These tools mean an engineer can now build more faster. The AI never touches your customers, but it's transforming how quickly you can ship. Wind surf, lovable, so many

[5:38] examples in this space. The upside potential here is faster velocity. I've heard someone quote, you can get a 3x or 10x development velocity improvement. So, anytime you can make developers more efficient and ship code faster, obviously that's a good thing. This allows small teams to potentially compete with larger competitors. Your iteration cycles can be cut down especially in the early days from months to weeks or weeks to days. In theory, you can have a dramatic reduction in technical debt and an expansion of test coverage cuz like what developer really loves writing tests and frankly AI is actually quite good at that. So now let's look at the primary risks. The

[6:13] first is code quality. you can introduce subtle bugs that are hard to spot. And if you're not a senior or a mid-level dev really combing through the code and really looking at the test, it's easy to build up technical debt. That's number two. If you move fast without understanding your code, you can introduce technical debt and it's easy to get kind of lazy or kind of sloppy and start building technical debt over

[6:35] time. The third one is over reliance. Your team can lose the ability to code without AI. Now, you're not going to lose all of it, but you're going to lose certain skills over time. And the fourth one is security vulnerabilities. Is AI introducing exploitable patterns? Today it is. There are many examples of specific tools that have introduced security vulnerabilities. And you know another one is that the development

[6:58] velocity improvements are up for debate. There was a recent study where folks were saying that developers are not actually faster with AI coding assistance. Now I think the best developers are actually better with it, but I think that will shake out over time. Before I get into category 4, I wanted to let you know that tickets for Microconf US 2026 are on sale. The event is April 12th through the 14th in Portland, Oregon. Microcom is where all of this started. We've been running this event for 15 years. It's a gathering of 275 to 300 of your favorite bootstrapped SAS founder friends. It's an incredible event. You're going to make connections

[7:37] that will last you years. You're going to build your network. You're going to see incredible talks and be in the best hallway track in the world for Bootstrap SAS. To get all the details, head to microcom.com/ us. And for subscribers of this channel, you can use promo code rob50 to get $50 off your ticket. So, category 4 is AI for growing your business. This is AI as your growth engine. It's not in your product, but it's in how you acquire customers. This is AI powered cold

[8:06] outreach that personalizes at scale. It's content generation that helps you dominate SEO, ad copy that tests hundreds of variations automatically. This is where you're using AI to grow faster and cheaper than your competitors. So, some examples of this are Clay for outbound sales, Jasper.ai, and Copy.AI for content, AI email personalization tools, AI powered ad optimization. And the upsides, I feel like, are obvious. 10 to 100x increase in outreach capacity, dramatically lower cost to acquire a customer through better targeting, content production at crazy scale, and true personalization that can convert better. But there are some risks, and you might not have thought of these. The first is

[8:43] authenticity. Are customers going to detect or reject AI outreach? Are you going to cause brand damage when AI goes off-brand in public? Are there compliance issues? AI can violate regulations if you're not paying attention. And finally, channel saturation. Everyone's using the same tactics. Are they going to work now and in the future? All right, my fifth and final category is AI for operating your business. This is where you use AI internally. It's the stuff that helps

[9:12] you run your company more efficiently. Examples include AI handling tier one support tickets, AI screening resumes, analyzing customer feedback for patterns. The key here is that your team is the user, not your customers. It's about operational leverage, not product differentiation. Tools you might use here include chatgpt claude or whatever chat tool you prefer, intercom's fin for customer support, any AI hiring or screening tool, automated data entry, processing, categorization, internal knowledge bases, and AI powered analytics. The upside potential is significant. You can see 30, 50, 80% operational cost reductions in certain departments. You can scale support without linear headcount. It offers 24 by7 availability. And of course, it can

[9:56] achieve consistency that humans can't match. But the risks are significant as well. The customer experience can suffer. What if AI mishandles sensitive issues or frustrates your customers? Employee morale might be an issue. What if your team fears being replaced? And finally, compliance and legal. We all know that without human oversight, AI can easily violate laws or internal company policies. So again, those five categories are AI as your core business, AI as a feature, AI for building your product, AI for growing your product, and AI for operating your business. As you look at your list of how you use AI, if everything generally falls into these categories, can you hit like on this

[10:36] video? And if you have a use case that doesn't fit into these categories or an edge case that I should be considering, I'd love it if you drop it in the comments below. If you're considering AI as your core business or as a feature, you really need to think through platform risk. In this next video, I go indepth on the risks associated with relying too heavily on an outside partner and what you can do to mitigate that risk. Thanks for watching. I'll see

[10:58] you next time.

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