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

Founders often worry about falling behind in AI adoption, but the critical question isn't if they're using AI, but how. Successful startups leverage AI in five distinct categories, each with unique risks and rewards. Understanding these categories is key to strategic AI implementation.

These five categories range from AI being the core product itself to its use as a feature, a development tool, a growth engine, or an internal operational efficiency driver. Each application presents specific opportunities for growth and innovation, alongside potential pitfalls like commoditization, quality issues, cost management, and compliance concerns.

By categorizing AI usage, businesses can make informed decisions about which strategies align with their goals and how to mitigate associated risks, ensuring AI adoption enhances, rather than hinders, their long-term success.

• There are five distinct categories of AI implementation for startups.
• AI as a feature can lead to price increases of 20% to 50% for premium features.
• Using AI for building products can lead to 3x or 10x development velocity improvements.
• AI for growing a business can result in a 10 to 100x increase in outreach capacity.
• AI for operating a business can yield 30%, 50%, or 80% operational cost reductions.

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AI as Core Business [01:50]

• AI constitutes the entire product; removing it leaves no business.
• Examples include foundational models and companies like fiscal.ai, Jasper, and MidJourney.
• Specific examples mentioned: Rosie (AI answering service), One Accord (live translation for churches), Pod Squeeze (podcast summary tool).
• Upside: Potential to become the default solution, massive market opportunities in emerging spaces.
• Risks: Commoditization (moats become API calls), market education burden, platform dependency, and capital intensity for foundational models.

The core business category is where AI is the product itself, offering significant upside but facing risks like commoditization and platform dependency. This foundational approach can lead to market dominance but requires careful navigation of external platform changes and substantial investment.

"Meaning, if you were to remove the AI, there's no business left."

AI as a Feature [03:32]

• AI enhances the core product but is not the product itself.
• Examples: Notion's AI writing assistant, Zoom's AI summaries, Loom's AI titles and chapters.
• Upside: Ability to charge more (20-50% price increase for premium AI features), market differentiation, improved customer retention, and natural upsell paths.
• Risks: Competitors can easily copy AI features, quality expectations must be met (bad AI is worse than no AI), cost management for API usage can impact unit economics, and overpromising in marketing can damage credibility.

AI as a feature integrates into existing products to make them better, offering opportunities for increased revenue and differentiation. However, its replicability means it's not a lasting competitive advantage, and managing quality and costs is crucial to avoid customer dissatisfaction.

"AI makes these products better, makes them faster, easier, but their core product value proposition still survives without it."

AI for Building Your Product [05:17]

• AI is used to accelerate product development, not directly by customers.
• Tools: GitHub Copilot, Cursor, Claude Code.
• Upside: Faster development velocity (potentially 3x or 10x), enabling smaller teams to compete with larger ones, reduced iteration cycles, potential reduction in technical debt, and expanded test coverage.
• Risks: Code quality issues leading to subtle bugs, introduction of technical debt due to rapid development, over-reliance on AI leading to skill degradation, and security vulnerabilities introduced by AI.

This category focuses on AI as a tool for developers, speeding up the creation of software. While it promises significant efficiency gains and allows smaller teams to punch above their weight, it also introduces risks related to code quality, technical debt, and potential over-dependence on AI tools.

"This is where you're not building an AI product. You're using AI to build faster."

AI for Growing Your Business [07:58]

• AI is used as a growth engine, not within the product itself, focusing on customer acquisition.
• Examples: AI-powered cold outreach, personalized at scale, SEO content generation, ad copy variation testing.
• Applications: Clay for outbound sales, Jasper.ai and Copy.AI for content, AI email personalization, AI ad optimization.
• Upside: 10x to 100x increase in outreach capacity, dramatically lower customer acquisition costs, high-scale content production, and improved conversion through personalization.
• Risks: Authenticity concerns (customers detecting or rejecting AI outreach), brand damage from off-brand AI actions, compliance issues with regulations, and channel saturation as more businesses adopt similar tactics.

AI for growth leverages artificial intelligence to streamline and amplify customer acquisition efforts. It can dramatically increase outreach and reduce costs, but businesses must be mindful of maintaining authenticity, ensuring compliance, and navigating the increasing saturation of AI-driven marketing tactics.

"This is where you're using AI to grow faster and cheaper than your competitors."

AI for Operating Your Business [09:05]

• AI is used internally to enhance operational efficiency, with internal teams as users.
• Examples: Handling tier one support tickets, screening resumes, analyzing customer feedback, automated data entry, internal knowledge bases.
• Tools: ChatGPT, Claude, Intercom's Fin, AI hiring tools, AI analytics.
• Upside: Significant operational cost reductions (30-80%), ability to scale support without linear headcount growth, 24/7 availability, and enhanced consistency.
• Risks: Negative impact on customer experience if AI mishandles issues, potential decrease in employee morale due to fears of replacement, and compliance/legal risks without human oversight.

This category centers on AI for internal operations, aiming to boost efficiency and reduce costs. While promising substantial savings and scalability, it carries risks related to customer satisfaction, employee morale, and the critical need for human oversight to ensure compliance and appropriate handling of sensitive matters.

"It's about operational leverage, not product differentiation."