Why Tomato AI Rejected Enterprise Sales Reps to Build a $100M Pipeline

Why Tomato AI Rejected Enterprise Sales Reps to Build a $100M Pipeline

Every enterprise software playbook says the same thing: to land Fortune 500 accounts, hire enterprise Account Executives. Experienced reps who know how to navigate procurement, speak CIO language, and close six-figure deals.

In a recent episode of Category Visionaries, Ofer Ronen, CEO and Co-founder of Tomato AI, explained why following that playbook would have destroyed his company. Instead of building a traditional enterprise sales team, Tomato AI built a self-service platform that now manages over 100,000 processes and has generated “over $100 million in pipeline.”

The decision to reject enterprise sales wasn’t philosophical. It was survival.

The Problem Enterprise Sales Couldn’t Solve

Tomato AI faces a sales challenge that makes traditional enterprise AE models impossible: infinite use case diversity.

“Orchestration is one of these things that can be applied in so many different ways across your organization,” Ofer explains. A procurement team uses Tomato AI to manage vendor onboarding. Legal uses it for contract approvals. Finance orchestrates quote-to-cash. IT routes support tickets. Sales ops manages deal approvals.

Try explaining that range to a prospect in a 30-minute discovery call. Even the best enterprise AE would struggle to identify which use case resonates, then articulate the value proposition, then demonstrate the solution—all while the prospect is thinking about three other meetings that day.

“The use cases are so diverse,” Ofer notes. This diversity makes traditional sales conversations nearly impossible. An AE who spent the morning pitching procurement workflows has to pivot completely when talking to a legal ops team in the afternoon. The discovery questions are different. The pain points are different. The demo flow is different. The competitive landscape is different.

Most companies solve this by forcing convergence—picking one use case and building the entire GTM motion around it. Tomato AI made the opposite bet.

Building for Self-Discovery Instead of Sales Pitches

Rather than training sales reps to explain what Tomato AI could do, Ofer’s team let users discover it themselves.

“We built the product to be completely self-service,” he explains. This wasn’t about reducing customer acquisition costs or moving downmarket. It was about solving the use case diversity problem at the product level instead of the sales level.

When someone can “connect it to all the different systems that they have, build a process, launch it into production” without talking to anyone, they discover the value through experience rather than explanation. A procurement manager doesn’t need an AE to explain how Tomato AI could help vendor onboarding—they just build the workflow and see it work.

This inverted the entire qualification process. Traditional enterprise sales starts with qualification calls, discovery meetings, technical evaluations, and proof of concepts orchestrated by sales reps. Tomato AI’s model lets users self-qualify by actually building and testing their own processes.

The result: prospects arrive at sales conversations already convinced of the value because they’ve experienced it firsthand.

The Enterprise Security Paradox

Self-service typically means SMB. Enterprise requires white-glove service. Tomato AI needed both simultaneously.

“We actually have more enterprise-grade security than Workato, than Zapier, than any of these other guys,” Ofer states. This wasn’t accidental. While competitors built self-service tools for small businesses or enterprise platforms requiring implementation teams, Tomato AI invested heavily in SOC 2 Type 2 certification, comprehensive data governance, and enterprise security features—all while maintaining complete self-service functionality.

This combination proved critical. Fortune 500 companies have security requirements that kill most self-service products. But they also have operations teams frustrated by six-month implementation cycles. Tomato AI gave them both: bank-level security with zero implementation time.

An operations manager at a Fortune 500 company could start using Tomato AI in the morning without IT approval, build a workflow by lunch, and have it running in production by end of day—all within their company’s existing security parameters.

This is the key insight most founders miss about bottoms-up enterprise: it’s not about avoiding enterprise requirements. It’s about meeting those requirements without requiring enterprise sales processes.

Why Business Teams Made Enterprise Sales Obsolete

The traditional enterprise sales model assumes IT owns software decisions. Ofer bet on a different future.

“These processes belong to the business teams. They’re not IT-owned processes,” he explains. This philosophical stance had massive GTM implications. If business teams own the processes, they should own the tools that manage those processes.

This created a natural bypass around traditional enterprise sales cycles. Instead of an AE convincing a CIO to mandate Tomato AI across the organization, individual business teams adopted it to solve their immediate problems. No RFP. No vendor evaluation committee. No lengthy procurement process.

The viral mechanism was built into the product design. Someone in procurement builds a vendor onboarding workflow. A colleague in legal sees it and asks how they did it. That colleague builds their own contract approval process. Someone in finance notices and builds an invoicing workflow. IT only gets involved when they notice multiple teams successfully using the same platform.

By the time sales conversations happen, they’re not convincing prospects to buy—they’re helping existing users expand and formalize what they’re already doing.

The Hybrid Model That Scales

As Tomato AI grew from 30 employees to over 100, they faced pressure to adopt traditional enterprise models. Board members suggested hiring experienced enterprise AEs. Investors pointed to competitors with large sales teams.

Ofer resisted, but adapted. “We actually believe that in order for us to be successful, we need to have a bottoms up approach,” he maintains. But he acknowledges evolution: “We’re definitely more high touch than we used to be.”

The key is understanding what changed and what stayed the same. Tomato AI added resources to support enterprise customers, but not traditional enterprise sales. “We still want to be much less high touch than these traditional vendors,” Ofer explains.

The distinction matters enormously. Traditional enterprise software assigns dedicated implementation teams that build solutions for customers. Tomato AI provides experts who guide customers building their own solutions. Traditional vendors own the customer’s processes. Tomato AI helps customers own their processes themselves.

This preserves the bottoms-up DNA while addressing enterprise needs. A Fortune 500 customer orchestrating 50 processes gets strategic support, but they’re still fundamentally self-service. They can build new workflows, modify existing ones, and expand to new teams without waiting for vendor resources.

When Bottoms-Up Actually Reaches the Top

The beautiful paradox of Tomato AI’s model: rejecting enterprise sales didn’t mean rejecting enterprise customers. It meant reaching them differently.

“We’re definitely going after larger organizations,” Ofer confirms. But the path isn’t through CIOs and enterprise AEs. It’s through operations teams who adopt Tomato AI, expand it across their department, share it with other departments, and eventually surface it to leadership as something already delivering value.

By that point, the enterprise sales conversation is easy. Leadership isn’t evaluating an unknown vendor proposing theoretical value. They’re formalizing support for a tool their teams already depend on.

The “over $100 million in pipeline” Tomato AI has built didn’t come from enterprise AEs cold-calling CIOs. It came from thousands of business users discovering that they could solve their own process problems—then telling their colleagues, who told their colleagues, who eventually told their executives.

The Deeper Principle

Tomato AI’s success reveals something important about modern enterprise software: the fastest path to enterprise adoption isn’t always through the enterprise sales motion.

When your product has high use case diversity, when business teams increasingly own their own tools, when buyers are sophisticated enough to evaluate solutions themselves—traditional enterprise sales can actually slow you down.

The bottoms-up enterprise model works when three conditions align: your product solves immediate, tangible problems that users can recognize themselves; you can meet enterprise requirements without requiring enterprise implementation processes; and the value compounds as usage spreads within organizations.

Tomato AI had all three. Most importantly, they had the discipline to resist conventional wisdom even as investors, board members, and conventional wisdom pushed them toward hiring enterprise AEs.

For B2B founders facing similar choices, Ofer’s path offers validation: sometimes the enterprise playbook is wrong. Sometimes the fastest way to build a nine-figure pipeline is to let users discover the value themselves.