If you’re building AI agents, you need to rethink your TAM
👋 Hi, it’s Bocar and I’m here with a 🔥 edition of The Midnight Text, Forum Ventures’ bi-weekly newsletter that provides honest answers to the unspoken questions that keep founders awake at night. I’m a Partner and General Manager at Forum Ventures, guiding portfolio founders from 0 to $1M ARR. As part of the founding team of the Enterprise product at Hootsuite, I helped the business grow to well over $150M+ and 2,000+ employees in under seven years. Now, I advise, coach, and angel invest in SaaS and AI startups from pre-seed to Series A.
Up Today
A founder in our portfolio, building an AI agent solution, had her first proactive conversation with a potential investor. When they got to her TAM slide, the investor commented that her Bottom-Up approach didn’t reflect the real opportunity of AI agents. She called me after, embarrassed she had gotten this wrong and admitted she had no idea where to start. She knew her platform had the potential to unlock massive value, but she wasn’t sure how to quantify it. Here is what I told her.
Why Traditional Market Sizing Methods Fall Short
Most founders rely on one of two approaches for market sizing:
Top-Down Approach
Start with the total market size for an industry (e.g., $100B for customer service software).
Estimate your penetration rate (e.g., 20%).
Calculate TAM: $100B x 20% = $20B.
Bottom-Up Approach
Identify total number of potential customers (e.g., 1M customers).
Determine the average annual revenue per customer. This could be based on your subscription fees, take rate percentage or other revenue models. (e.g., $5K each).
Calculate TAM: 1M x $5K = $5B.
Investors typically prefer the Bottom-Up method because it’s grounded in real-world assumptions that can be substantiated through other aspects of your pitch, such as your customer definition, revenue model or GTM. But for AI, these approaches dramatically undervalue the opportunity.
Why? Two reasons:
1. Most companies dedicate far more budget to salaries than software. Traditional SaaS competes within the limited “software” budget. But Service-as-Software—AI agents that complete tasks traditionally performed by employees—can also tap into the much larger “workforce” budget.
Take 11x, for example. Their AI sales agent charges per task—finding leads, sending emails, booking meetings—at a fraction of the cost of a human SDR. Their TAM isn’t limited to software budgets but stretches into the $1.1T companies spend on sales salaries globally.
2. Value delivery changes how AI agents are priced. Unlike traditional software, which charges for access (seats or usage), AI agents deliver measurable outcomes. Value-based pricing ties costs directly to ROI. For example, instead of a flat fee, an AI agent might charge based on time saved, tasks completed, or revenue generated. This creates a much larger TAM because customers will pay more when they see a direct link between cost and outcomes.
How to Calculate TAM for AI Agents
When an investor asks you to rethink your TAM, they’re looking for a model that reflects the unique value AI agents provide. Here’s how to break it down:
Define the Value Your AI Delivers
Is your agent saving time, cutting costs, or driving revenue? For example, an AI sales agent might boost close rates (drive revenue) or shorten sales cycles (saves time).
Quantify the Value
How much money or time are you saving your customers?
Example: If your AI replaces an SDR at $100K/year but costs 5x less, you’re saving $80K annually per agent deployed.
Set Pricing Based on Value Delivered
Most AI agents charge 10-30% of the ROI they create.
Multiply by Potential Customers
Combine this pricing model with the number of businesses or individuals who could use your solution.
Framing the Future Opportunity
The shift from “subscribing” to software to “hiring” is just beginning. AI agents unlock productivity gains that traditional market sizing misses. Beyond saving money, they solve problems humans can’t—like providing 24/7 patient care or running factories nonstop.
McKinsey recently estimated that generative AI could add trillions to the global economy. Investors know this, so they’ll expect you to factor these transformative shifts into your TAM.
Think Big, But Stay Credible
Investors aren’t just looking for numbers—they’re looking for vision. Here’s how to impress them:
Start with a realistic foundation. Use a Bottom-Up approach to define your initial market. Then layer in your AI agent’s ability to unlock new budgets, like workforce spending.
Be specific. Highlight measurable value like increased efficiency or cost savings, and back it with examples.
Show you’ve done your homework. Reference case studies or industry reports to validate your assumptions.
You’ve Got This
If you’re building an AI agent and struggling to quantify your market opportunity, you’re not alone. Focus on the value your product delivers, and don’t be afraid to dream big. AI agents are rewriting the rules—and your TAM should reflect that.
If you want to dive (much) deeper into market sizing in the age of AI agents, check out Bocar’s latest post on Substack. Don’t forget to subscribe!
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