What Changed and Why It Matters

AI has moved the battleground in SaaS from systems of record to systems of action. Investors and buyers now underwrite higher value for products that automate full workflows, not just surface-level copilots.

Software Equity Group’s latest view: buyers are explicitly valuing AI-native and credibly AI-enabled companies higher, and penalizing thin wrappers. Beacon VC, Forum Ventures, and Bessemer agree: vertical AI is expanding TAM and reshaping how work gets done across industries.

Here’s the shift most people miss: the win is less about who has the best model and more about who owns the workflow and distribution in a niche.

The moat isn’t the model — it’s the workflow plus distribution.

The Actual Move

This isn’t a single product launch. It’s a synchronized market turn across startups, incumbents, and buyers:

• Startups are going vertical and agentic. SoftwareSeni highlights Harvey (legal), Cursor (developer productivity), and early ERP challengers like Rillet — evidence that AI-native challengers are attacking the application layer with deep workflow automation.
• Investors are publishing playbooks and raising the bar. Bessemer’s early-stage guide for Vertical AI emphasizes data leverage and workflow redesign. Forum Ventures and Beacon VC argue vertical AI can unlock larger-than-expected TAMs.
• Operators are reframing product goals. Tidemark’s “System of Action” thesis centers on building workflow software that owners and managers use to run the business — consistently, efficiently, profitably.
• Buyers are repricing risk and potential. SEG reports acquirers prefer AI-native or truly AI-enabled products that show measurable impact on outcomes and margins — not just feature parity.
• Incumbents are defending distribution. Bruce Maxwell’s take (via PitchBook analysis) notes a binary narrative — AI startups win or SaaS incumbents lose — but the reality is hybrid: enterprises want agentic workflows integrated into existing systems.
• The ecosystem is wrestling with productization. Hg Capital’s conversation with Tidemark surfaces a consistent obstacle: teams need the resources and discipline to translate models into reliable, compliant, unit-economics-friendly products.
• Ambitions are escalating. Some argue vertical AI agents could be far larger than traditional SaaS categories — a directional signal of where builders think value is heading.

What enterprises are buying isn’t “AI.” They’re buying outcomes: speed, accuracy, margin.

The Why Behind the Move

Founders and incumbents are optimizing for the same end state: own the highest-value workflows end-to-end, prove ROI fast, and compound via proprietary data loops.

• Model

• Vertical AI works when models are fused with domain data, structured workflows, and guardrails.
• “System of action” beats “system of record” because it influences decisions and output, not just storage.

• Traction

• AI-native challengers earn wedge traction by eliminating steps, not just assisting them. Harvey and Cursor exemplify this pattern. ERP challengers like Rillet signal the next wave: ambitious full-stack automation in complex domains.

• Valuation / Funding

• Per SEG, buyers reward AI-native or credibly AI-enabled products with higher enterprise value. Thin AI layers or add-ons don’t move multiples; workflow depth and measurable impact do.

• Distribution

• Incumbents still have the advantage: data access, integrations, and a captive base. Startups must wedge into a daily critical workflow and expand from there.
• Industry channels, partner ecosystems, and cloud marketplaces remain decisive.

• Partnerships & Ecosystem Fit

• Successful vertical AI stacks blend foundation models, domain ontologies, private data connectors, and compliance tooling.
• Partner with data owners early; secure rights and pipelines that improve over time.

• Timing

• Falling inference costs and better tooling make agentic workflows feasible. Enterprise readiness (security, auditability) is catching up, widening the deployment window.

• Competitive Dynamics

• Incumbents will adapt; “SaaS is dead” is a myth. The race is about who can ship reliable agents with strong controls inside real workflows. Distribution plus trust is a formidable moat.

• Strategic Risks

• Reliability and safety: hallucinations, edge cases, and drift can erode trust.
• Unit economics: inference costs and human-in-the-loop ops can compress margins without careful design.
• Vendor dependence: overreliance on a single model or proprietary API is fragile.
• Change management: AI that doesn’t fit existing processes stalls adoption.

The durable control point is embedded workflow + proprietary data exhaust, not the LLM itself.

What Builders Should Notice

• Build a system of action, not a feature. Own the decision and the output.
• Start narrow. A single high-value workflow wedge beats a broad assistant.
• Prove ROI in weeks. Time-to-value is your strongest sales enablement.
• Design for distribution early. Integrations, channels, and compliance are strategy.
• Make data rights a product feature. Secure, structured, and compounding.

Buildloop reflection

Clarity compounds. In vertical AI, so do workflows.

Sources

Software Equity Group — How SaaS Founders Can Reinvent, Defend, or Exit Stronger
SoftwareSeni — AI-Native Startups vs SaaS Incumbents – SoftwareSeni
Tidemark Capital — System of Action — Part 1: Hero Users
LinkedIn — Enterprise SaaS: Incumbents vs AI-Native Challengers
Beacon VC — The Rise of Vertical AI SaaS: Unlocking Unprecedented Value in Specialized Industries
Bessemer Venture Partners — Building Vertical AI: An early stage playbook for founders
Euclid VC — Software Is Dead — Long Live Software
Forum Ventures — Verticalized AI — The Drivers Behind the Next Wave of Vertical SaaS
Hg Capital — AI, Control Points, and the Next Wave of Vertical SaaS (with Tidemark Capital)
YouTube — Vertical AI Agents Could Be 10X Bigger Than SaaS