Market Sizing in the Age of AI Agents: Why Founders Need to Rethink Their TAM

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If you’re building an AI agent platform, it’s time to throw out the old playbook on market sizing.

Why?

Because, you're not just creating software—you're creating workers. And that changes everything.

Let me explain...

We’re witnessing a seismic shift in software value creation—from Software-as-a-Service (SaaS), where users perform tasks using software, to Service-as-Software, where AI agents autonomously execute tasks.

The shift from Software-as-a-Service (SaaS) to Service-as-Software is not just a buzzword; it’s a fundamental transformation in how value is created and delivered. SaaS relied on users to do the work, while Service-as-Software hands the reins to AI agents that work autonomously, 24/7, completing tasks that were previously unachievable or too labor-intensive.

And this shift is fundamentally reshaping the Total Addressable Market (TAM) for startups, opening up revenue opportunities far beyond traditional software budgets.

This shift isn’t just about efficiency—it’s about unlocking a massive new market opportunity!

The Problem: Traditional Market Sizing Is Outdated

Most founders still rely on conventional TAM calculations, which drastically undervalue the potential of AI-native solutions. The two primary methods—Top-Down and Bottom-Up—have served SaaS companies well, but they fail to account for the way AI agents unlock entirely new market opportunities.

Let’s dive into why these methods fall short and how you can rethink your TAM to reflect the true potential of AI agents...

Why Traditional Market Sizing Methods Fail for AI Agents

1. Top-Down Approach: The Oversimplification Trap

This method starts with the overall market size for a relevant industry and then estimates the portion that could be captured by your product.

• Identify the total market size: For example, if you’re building a product for customer service, you might start with the total market size for customer service software.

• Estimate the penetration rate: This is the percentage of the total market that could realistically be served by your product.

• Calculate TAM: Multiply the total market size by the penetration rate.

Example:

Let's say the total market size for customer service software is $100 billion, and you estimate that your product could penetrate 20% of this market. Your TAM would be $100 billion x 0.20 = $20 billion.

The Problem: This method assumes AI agents compete only within the software budget. But AI agents don’t just replace software—they replace human labor. The global spend on salaries is orders of magnitude larger than software budgets.

For instance, while Salesforce generates $35bn in annual revenue, the global spend on sales and marketing salaries is $1.1 trillion. By limiting your TAM to software budgets, you’re missing out on a much larger pie.

2. Bottom-Up Approach: The Narrow Beachhead

This approach focuses on the potential number of customers and their average spending on software.

• Identify the total number of potential customers: This could be the number of businesses in a specific industry or the number of individuals who could benefit from your product.
• Determine the average annual revenue per customer: This could be based on your subscription fees, take rate percentage or other revenue models.
• Calculate TAM: Multiply the total number of potential customers by the average annual revenue per customer.

Example:

If you estimate that there are 1 million businesses that could use your product, and the average annual revenue per customer is $5,000, your TAM would be 1 million x $5,000 = $5 billion.

The Problem: While this method is more grounded, it still assumes AI agents are competing within the software category. AI agents, however, can perform tasks traditionally done by humans, meaning they can tap into the personnel expense budget rather than just the software budget. This opens up a TAM that’s potentially 10-20x larger.

VCs prefer the Bottom-Up approach because it uses assumptions that can be substantiated through other aspects of your pitch, such as your customer definition, revenue model or GTM. Bottom-Up market sizing also shows you’ve done your research, you know your customer, you understand your value to them, and you have an idea of how much they would pay for your product.

The AI Agent Advantage: Unlocking the Workforce Budget

AI agents don’t just compete with software—they compete with humans. This means they can access the trillions of dollars spent globally on salaries, benefits, and other personnel expenses. For example:

• 11x, an AI sales rep platform, charges based on the tasks its AI completes (e.g., finding leads, sending emails, booking meetings). Their pitch? You get the output of a top-performing SDR at 5x lower cost.
• Doseform, an AI-native pharmacy platform, estimates a $40 billion TAM by automating 55% of tasks in administrative and support roles—20x larger than their bottom-up TAM calculation.

This reclassification of AI agents from software to workers fundamentally changes the game.

How AI Agents Deliver Value: Beyond Cost Reduction

AI agents don’t just save money—they create value in four key ways:

1. Making Money: Accelerating revenue growth by automating high-impact tasks (e.g., AI sales reps closing deals faster).
2. Saving Money: Reducing costs by automating repetitive tasks (e.g., AI customer service agents handling inquiries).
3. Mitigating Risk: Improving accuracy and compliance (e.g., AI agents in healthcare ensuring patient data is handled correctly).
4. Unlocking Productivity: Completing tasks humans can’t due to complexity or time constraints (e.g., AI analyzing massive datasets in seconds).

The way you charge for software should reflect the value it delivers. AI agents enable value-based pricing, where customers pay based on outcomes rather than seat licenses or usage.

A New Framework: Value-Based Market Sizing

To accurately estimate the TAM for AI agents, founders need to adopt a value-based market sizing approach. Here’s how:

Step 1: Identify the key value propositions of AI agents

For example, increased efficiency, cost savings, lower sales cycles, higher close rates etc.

Step 2: Estimate the potential value for customers

This could involve quantifying the benefits in terms of time saved, money saved, or increased revenue.

Step 3: Determine a price point based on the perceived value

Apply a percentage based on the ROI delivered. This is typically a sliding scale from 10-30% based on how the product is priced and how value is captured.

For example, if your AI agent saves $100,000, you might charge $20,000-$30,000 annually.

Step 4: Calculate TAM

Multiply the number of potential customers by the estimated price point.

Let's understand the framework in detail...

Example: AI Agent Startup in Sales Automation

Imagine SalesAI, an AI agent startup that automates lead qualification, follow-ups, and deal closure processes. Sales teams often spend 40-60% of their time on repetitive tasks like CRM updates, email follow-ups, and manual data entry. SalesAI streamlines these processes, reducing labor costs and boosting revenue.

Step 1: Identify Key Value Propositions

SalesAI provides value through:

• Cost Savings: Reducing the need for sales development representatives (SDRs) to handle repetitive tasks.
• Time Savings: Shortening sales cycles by automating follow-ups and lead qualification.
• Revenue Growth: Increasing close rates and pipeline efficiency.

Step 2: Estimate the Potential Value for Customers

Let’s consider a mid-sized B2B SaaS company with a 10-person sales team:

• An SDR costs $70,000 per year (salary + benefits).
• SalesAI automates 50% of SDR tasks, allowing companies to reduce their SDR headcount or reallocate efforts to higher-value activities.
• Annual savings per company: $35,000 per SDR → $350,000 for a 10-person team.

Step 3: Determine a Price Point Based on ROI

• Companies typically pay 10-30% of the value delivered.
• If SalesAI saves a company $350,000 per year, it can charge $50,000 - $100,000 annually.
• Let’s assume an average price of $75,000 per year.

Step 4: Calculate TAM

Bottom-Up Approach

• Target Market: 100,000 mid-sized and enterprise sales teams globally.
• Price Point: $75,000 per year.
• TAM = 100,000 × $75,000 = $7.5B.

Value-Based Approach

• The global B2B sales industry is worth $2 trillion.
• 30% of costs are spent on payroll ($600B).
• 75% of sales employees are not closing deals but handling admin tasks (~$450B in costs).
• 50% of these tasks can be automated → $225B opportunity.

This means the value-based TAM is 30x the bottom-up estimate, showing the true potential of AI-driven sales automation.

The True Upper Bound: Productivity and Growth

The TAM for AI agents isn’t just about replacing human labor—it’s about unlocking new levels of productivity and growth. For example:

• Manufacturing: AI-enabled robots running factory operations 24/7 could dramatically increase output.
• Healthcare: AI agents providing around-the-clock patient monitoring could improve outcomes and reduce costs.
• Sales: AI agents closing deals faster could accelerate revenue growth.

As McKinsey’s report on generative AI highlights, the economic potential of AI extends far beyond cost reduction. Founders should factor in how AI agents will disrupt entire industries and drive long-term growth.

Key Takeaways for Founders and Investors

1. AI Agents Compete with Humans, Not Just Software: Your TAM should include workforce budgets, not just software budgets.
2. Adopt a Value-Based Pricing Model: Charge based on the outcomes your AI agent delivers.
3. Think Beyond Cost Reduction: Factor in how AI agents will drive productivity and revenue growth.
4. Use a Value-Based Market Sizing Framework: This approach provides a more accurate and compelling estimate of your TAM.

If AI agents can access trillions of dollars in global workforce spending, what’s stopping your startup from capturing a slice of that pie? Are you still thinking in terms of software budgets, or are you ready to redefine your TAM?

By rethinking market sizing in the age of AI agents, founders and investors can unlock the true potential of this transformative technology. The future belongs to those who think beyond traditional frameworks and embrace the immense value AI agents can deliver.

What’s the biggest challenge you’ve faced when sizing the market for an AI agentic platform? Share your thoughts in the comments—I’d love to hear your perspective! ??

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