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Executive Summary:
Rather than flattening vertical software, AI is driving a bifurcation between surface-layer productivity tools and core operational platforms that function as systems of record and action. As AI becomes further embedded, tools that primarily surface insights or guide workflows are increasingly vulnerable to compression. In contrast, platforms that own authoritative data, enforce governance, and coordinate real business activity grow more valuable as AI shifts from assistance to execution. Over time, these systems move from storing work to orchestrating it: consolidating integrations, payments, compliance, and accountability. The result is a market where value concentrates around platforms that own workflows, state, and trust.
Introduction
A recurring narrative, and one recently amplified by volatility in the public software markets, is that AI will “kill SaaS,” ushering in a so-called “SaaS-pocalypse.” This overstates the risk. AI is changing where value accrues within the tech stack. Some products will be displaced, while others will become more embedded in core workflows, entrenched, and difficult to replace. The result is not uniform disruption, but the vertical software landscape is getting re-sorted. The gap between mission-critical platforms and peripheral tools and point solutions is widening.
Historically, vertical SaaS functioned as a productivity layer, software that was helping businesses and their employees work faster and more efficiently. Up until recently, the employee was the operator while the software streamlined and automated segments of the workflow. Now, AI is shifting that dynamic. In certain categories, the software is not just supporting the execution, but it will perform the entire workflow autonomously.
That shift is creating a structural divide between:
- Applications that enhance productivity (point solutions).
- Platforms that serve as the operational backbone of the business (systems of record).
The first category is more exposed while the second becomes more differentiated and central to business operations. Over time, platforms will evolve beyond serving as the system of record to a system of action (system of record + AI agents) by combining their housed operational data with embedded AI capabilities.
What Gets Replaced vs. Becomes More Valuable
For more than a decade, vertical software has seen a steady rise in point solutions (applications designed to solve a specific business problem). These solutions are typically specialized, easy to implement, and generally cost effective. In many cases, the primary function of these solutions is to surface information or guide human action. This positioning also makes them the most vulnerable to the growing use of AI. This category includes standalone analytics layers, dashboarding tools, monitoring systems, workflow guidance products, and reconciliation and data-prep tools sitting between systems.
This is what AI compresses. Capabilities that were once justified as standalone increasingly become features. The more a solution lives “inside someone else’s workflow,” the more likely it is to be absorbed by the platform that owns the data and the user relationship.
In contrast, systems of record become more important. They are the layer where state, governance, accountability, and trust live, and where work is ultimately coordinated. AI models have recall, but it is probabilistic and context-dependent. AI will generate responses, not a persistent, versioned, and auditable state. Businesses, however, run on persistent state: the records, contracts, assets, and reconciliations that give operations continuity and legitimacy.
Over time, these platforms evolve from systems of record into systems of action. They no longer just store the history of work--they orchestrate and execute.
Integrations converge there. Embedded payments and financial services attach there. Institutional accountability lives there.
Software Insulation Among SMB and Mid-Market Customers
Paradoxically, the software vendors many expect to be most disrupted by AI—those serving SMBs and the mid-market—may prove to be among the most insulated. AI unquestionably lowers the cost to build software. But it raises the cost of owning software.
While AI reduces upfront development efforts, it shifts ongoing responsibility onto the operator: maintaining workflows, managing model behavior, adapting to regulatory change, and ensuring auditability and compliance. These costs compound overtime. Vendors can amortize that responsibility across thousands of customers. Individual businesses cannot.
This dynamic is especially true for systems of record and systems of action. AI may lower the cost of building tools and automations at the edges, but maintaining a platform that stores authoritative data, enforces contracts, reconciles finances, and governs workflows is not a one-time build, it is an ongoing operational commitment.
What Else Is Poised to Change
As AI bifurcates vertical SaaS, we expect implications around pricing, product structure, and market structure.
Seat Based Pricing Will Come Under Pressure: As AI increases operator leverage, fewer users generate the same output. When one employee, augmented by agents, can do the work of three, seat-based pricing compresses, even if the product remains essential. The risk is not churn but pricing pressure. Pricing therefore shifts toward outcomes and consumption, and hybrid models will emerge, with value, not seats, as the anchor.
Consolidation Accelerates: Point solutions that once justified standalone SKUs become features inside broader platforms. As AI reduces the marginal cost of adding functionality, depth concentrates within fewer systems.
Systems of Action Become More Embedded: As systems of record evolve into systems of action, switching costs increase. These platforms become the natural convergence point for integrations, embedded payments, financing, compliance tooling, and other adjacent services. The more operational responsibility they absorb, the harder they are to replace.
Implications for Vertical SaaS Diligence
A central diligence question is whether AI creates existential risk or additive opportunity for a given platform. The task is not to assess current AI features, but to understand how the industry’s tech stack will be reshaped over the next two to three years as AI matures. The key question is: Does AI make the platform more central or more optional? This reframes how assets should be evaluated and underwritten.
Key Dimensions to Assess:
1. Workflow breadth
Ownership matters more than feature depth. Is this an end-to-end system of record or a narrow point solution living inside someone else’s workflow?
2. Output replaceability
Does the product execute transactions and coordinate work, or primarily generate text, insights, recommendations, or analysis that AI can replicate?
3. Proprietary data
Control of longitudinal, auditable, compounding data drives defensibility. Does the platform own meaningful state, or operate on interchangeable inputs?
4. Competitive landscape
AI expands the universe of substitutes. Risk may come less from direct competitors and more from adjacent platforms, horizontal incumbents, or embedded AI features.
5. Pricing durability
Is revenue anchored to outcomes and value flow, or exposed to seat compression? Can pricing migrate as operator leverage increases?
6. TAM elasticity
If AI doubles productivity, does demand expand with throughput or shrink with fewer required users?
Ultimately, AI is a structural reset. It will compress shallow layers and concentrate value in platforms that own workflows, state, and trust. Vertical SaaS that evolves into a system of action becomes more defensible, not less. The question is no longer who has AI, but who still matters when AI is everywhere.
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