Tomato AI’s Series B: Perfect Timing or Perfect Planning? How AI Transformed Their Category

Tomato AI’s Series B: Perfect Timing or Perfect Planning? How AI Transformed Their Category

Timing in venture capital is usually about quarters and market conditions. Sometimes it’s about cultural inflection points that reshape entire categories overnight.

In a recent episode of Category Visionaries, Ofer Ronen, CEO and Co-founder of Tomato AI, revealed how his company raised their Series B “right around when ChatGPT launched”—a coincidence that transformed their positioning, accelerated their roadmap, and validated their long-term vision in ways no amount of planning could have predicted.

Today, Tomato AI manages over 100,000 processes with “over $100 million in pipeline.” But the path from process orchestration platform to AI agent coordinator required recognizing how AI would fundamentally reshape their category.

The Pre-AI Challenge

Before November 2022, Tomato AI faced a consistent adoption barrier: technical complexity. Building orchestrated processes required understanding workflow logic, system integrations, and business rules. “We built the product to be completely self-service,” Ofer explains, but self-service still meant learning a new paradigm.

Operations teams could connect systems and build workflows, but the learning curve was real. Each process required mapping out logic, configuring integrations, and testing edge cases. Non-technical users could do it, but it took time and training.

This complexity limited viral adoption. Someone would build a workflow, and colleagues would admire it—but rebuilding something similar for their own use case felt daunting. The self-service model worked, but it didn’t spread as organically as Ofer wanted.

Then ChatGPT launched, and everything changed.

The AI Inflection Point

“AI actually makes everything that we’re doing much, much easier and much better,” Ofer explains. But the impact wasn’t just about making existing features easier to use. AI fundamentally transformed what process orchestration could be.

Previously, building a workflow meant explicitly defining each step, condition, and action. With AI integration, users could describe what they wanted in natural language. “Someone can just describe what they want to do” and Tomato AI’s AI could build the process, configure the integrations, and handle the logic.

This eliminated the primary adoption barrier. The same operations manager who previously needed hours of training could now say “I need a vendor onboarding workflow that routes high-risk vendors to legal and low-risk vendors straight to finance” and watch Tomato AI build it.

The timing of their Series B meant they had capital to invest in AI capabilities exactly when the technology became viable. While competitors scrambled to add AI features with existing resources, Tomato AI had fresh funding to rebuild their product architecture around AI-native experiences.

From Workflow Tool to AI Coordinator

But Ofer saw something bigger than just easier workflow building. AI didn’t just improve Tomato AI’s existing value proposition—it validated their entire category thesis.

“The way that people work is going to completely change,” Ofer predicts. The future isn’t humans using software to complete tasks. It’s AI agents completing tasks while humans provide oversight and governance.

This reframing transformed how Tomato AI positioned itself. They weren’t just orchestrating workflows across systems anymore. They were building the infrastructure that would coordinate AI agents across enterprises.

Consider a procurement process in this AI-driven future. An AI agent handles vendor communication. Another agent reviews contracts. A third agent manages approvals. A fourth updates the ERP. These agents need coordination—someone ensuring they work together coherently, follow company policies, and operate within proper governance boundaries.

That’s process orchestration. Tomato AI’s existing platform, built to coordinate human activities across systems, became the foundation for coordinating AI agents across enterprises.

The Strategic Pivot Nobody Noticed

The genius of Tomato AI’s AI positioning was that it didn’t require rebuilding the product from scratch. The orchestration engine they’d spent years developing already did what AI agents would need: coordinate actions across multiple systems based on complex business logic.

“These processes belong to the business teams. They’re not IT-owned processes,” Ofer explains. This principle became even more important with AI. As enterprises deploy specialized AI agents across different departments, business teams need to orchestrate how those agents interact—without waiting for IT to manage everything centrally.

Tomato AI’s self-service model, which previously enabled business teams to build their own workflows, now enabled them to coordinate their own AI agents. The product architecture remained fundamentally the same. The use case expanded dramatically.

This is the difference between adding AI features and capitalizing on AI trends. Most companies added chatbots or copilots—helpful improvements to existing products. Tomato AI repositioned their entire platform as infrastructure for the AI-driven enterprise.

The Adoption Acceleration

The AI wave didn’t just improve Tomato AI’s product—it accelerated customer adoption patterns. Previously, prospects needed to be convinced they had complex orchestration problems. Post-ChatGPT, everyone understood they’d soon have AI agents that needed coordination.

This transformed sales conversations. Instead of explaining why process orchestration was different from workflow automation, Ofer’s team could ask: “When you deploy AI agents across procurement, legal, and finance, how will you ensure they work together coherently?” Prospects immediately understood the problem.

The bottoms-up adoption model accelerated too. When building a workflow required technical expertise, viral spread was limited. When users could describe processes in natural language and watch AI build them, colleagues started trying it immediately.

“Orchestration is one of these things that can be applied in so many different ways across your organization,” Ofer notes. AI made each of those applications more accessible, accelerating the within-organization viral growth that drove Tomato AI’s expansion.

The Category Transformation

Perhaps most importantly, AI transformed the process orchestration category itself from a nice-to-have to a must-have.

Pre-AI, process orchestration was valuable but optional. Companies could muddle through with manual processes, point solutions, and disconnected systems. It wasn’t efficient, but it worked.

Post-AI, orchestration becomes infrastructure. As enterprises deploy dozens or hundreds of AI agents, coordination isn’t optional—it’s essential. Without orchestration, AI agents work at cross purposes, duplicate efforts, and violate governance policies.

“We’re actually creating a new category,” Ofer explains. That category creation became dramatically easier post-ChatGPT. Instead of educating buyers on why they needed process orchestration, Tomato AI could position as the infrastructure layer for AI deployment.

Analysts started asking about AI agent orchestration. Enterprises started budgeting for it. The category Tomato AI had been building suddenly had industry-wide validation.

The Long-Term Vision

The Series B timing gave Tomato AI resources to build for this AI-driven future before competitors recognized it was coming. While other companies added AI features to existing products, Tomato AI rebuilt their entire platform around the assumption that AI agents would soon be everywhere.

This meant investing in governance frameworks that could manage AI agent behavior, security models that could control what agents could access, orchestration engines that could coordinate dozens of agents simultaneously, and natural language interfaces that made configuration accessible to non-technical users.

The result is a platform positioned not just for today’s workflow orchestration needs, but for tomorrow’s AI agent coordination requirements. As Ofer sees it, every enterprise will eventually have hundreds of AI agents handling routine tasks. Those agents need orchestration.

Tomato AI’s moat isn’t just their current product—it’s the architectural decisions they made with Series B capital while competitors were still figuring out their AI strategy.

Timing and Vision

Was Tomato AI’s AI transformation perfect timing or perfect planning? Both, but not in the way most people think.

The timing was fortunate—raising capital exactly when ChatGPT made AI capabilities viable. But the planning was deliberate—recognizing that AI didn’t just improve their product, it validated their entire category thesis.

For B2B founders navigating similar inflection points, Ofer’s approach offers a framework: when a technology wave hits your market, don’t just add features. Ask how that technology transforms the problem you’re solving and the category you’re building.

Tomato AI didn’t add AI to make workflows easier. They repositioned their entire platform as infrastructure for AI-coordinated enterprises. That’s the difference between riding a wave and redirecting it toward your vision.