AI is Not Simply Repricing SaaS—It Is Redefining the Value of Business Software

In SaaS, the realizable value delivered by software is what the AI dilemma is really about.

This article explores several aspects of SaaS products where AI is impacting that value. We address those factors first through the Investor Lens- "What Makes SaaS Durable Now", then as an Executive Playbook- "What to Do Now".

What is the software actually worth?

If AI can generate code, automate workflows, and increasingly act on behalf of users, then the definition of “software value” fundamentally changes. We are moving from a world of software that enables work to systems that perform work.

The value of SaaS is no longer anchored in access to features or interfaces. It is shifting toward:

• the quality and uniqueness of data

• the intelligence applied to that data

• and the ability to execute work and drive outcomes

This is not a simple repricing of SaaS based on cost or efficiency. It is a reweighting of where value accrues across the stack.

From an investor standpoint, the opportunity is to identify which companies successfully evolve into systems of intelligence and action—and which remain systems of record or engagement that gradually lose relevance.

From an executive standpoint, the mandate is even clearer:
you are no longer building software tools.
You are building systems that must think, act, and improve over time.

The Investor Lens: What Makes SaaS Durable Now

1. System of Record Still Matters—But It’s Not Enough

At the foundation of durable SaaS remains the concept of a system of record: a place where ground truth about a business is stored, audited, and relied upon.

Companies like Workday, ServiceNow, and Salesforce persist because they capture data that businesses cannot afford to lose or misrepresent.

But the bar has moved.

Owning data is no longer sufficient. The emerging question is:

What do you do with that data that others cannot?

2. From Systems of Record to Systems of Intelligence

The most interesting SaaS companies today are not just storing data—they are transforming it into decision-making infrastructure.

Consider Anaplan. It does not own the original financial transactions. Instead, it aggregates, cleans, and models data across systems to enable forward-looking planning.

This is a critical shift:

• Systems of record store truth

• Systems of intelligence create advantage from that truth

As AI lowers the cost of building software, the value increasingly migrates to how effectively a platform enhances and operationalizes data.

3. Workflow Depth Is the New Switching Cost

AI is exceptionally good at replicating features. It is far less effective at replacing deeply embedded workflows.

There is a meaningful distinction between:

• A tool users interact with occasionally

• A system that runs core business processes

Platforms like ServiceNow are difficult to displace not because of their UI, but because they are woven into operational processes. Removing them creates disruption, not inconvenience.

For investors, this becomes a key diagnostic:
If the product disappeared tomorrow, how quickly would operations break?

4. Data Compounding Is More Valuable Than Data Ownership

A growing misconception is that “having data” creates defensibility. In reality, defensibility comes from data that improves as scale increases.

Platforms like Snowflake illustrate this dynamic. As more organizations participate, the potential for benchmarking, collaboration, and insight expands.

This creates a reinforcing loop:

• More customers → more data → better insights → more value → more customers

AI amplifies this effect. Models trained on richer, more diverse datasets produce better outputs, widening the gap between leaders and laggards.

5. Decision Proximity Drives Durability

The most resilient SaaS companies increasingly share two characteristics:

• They influence high-value economic decisions

• They are used by senior stakeholders

Software that informs pricing, capital allocation, supply chain planning, or revenue forecasting becomes difficult to remove—not because of switching costs alone, but because of its role in decision-making.

Palantir is a clear example. Its value lies not in data storage, but in enabling high-consequence decisions. AI enhances its capabilities but does not displace its role.

6. AI Leverage: Tailwind or Headwind

Every SaaS company now sits somewhere on a spectrum:

AI as a Headwind

• Core value is organizing information or generating reports

• Functionality can be replicated by general-purpose models

AI as a Tailwind

• AI expands workflows

• Reduces cost to serve

• Moves product closer to outcomes

For example, Salesforce is attempting to evolve from a system of record into an active participant in revenue generation through AI-driven forecasting, automation, and engagement.

The key investor question becomes:
Does AI compress the company’s value—or expand it?

7. Pricing Is the Structural Fault Line

Perhaps the most immediate disruption is economic.

Traditional SaaS pricing has been built on a simple model:

• More employees → more seats → more revenue

AI breaks that relationship.

If software can perform the work of multiple employees, seat counts may stagnate or decline—even as value delivered increases.

This creates a mismatch:

• Value delivered ↑

• Revenue captured ↓

The industry is already shifting:

• From seat-based pricing

• To usage-based pricing

• Toward outcome-based pricing

The companies that adapt early will capture disproportionate value. Those that don’t will see margin pressure and slower growth.

What Investors Should Look For Now

If the above trends are directionally correct, then the profile of an attractive SaaS investment is becoming clearer.

Look for companies that:

• Own or sit close to ground truth data, especially data that is operationally or legally required

• Enhance that data into intelligence, not just store or display it

• Are deeply embedded in workflows, where removal would disrupt operations

• Have data that compounds with scale, creating increasing returns to adoption

• Operate close to high-value economic decisions, particularly those used by senior stakeholders

• Benefit from AI as a tailwind, expanding their scope rather than compressing it

• Are actively evolving pricing toward usage or outcomes, aligning revenue with value delivered

SaaS is evolving along a clear progression:

systems of record → systems of intelligence → systems of action → systems of outcomes.

As AI reduces the cost of building software, multiples will increasingly concentrate in companies that control data, workflows, and outcomes—not those that simply deliver features.

If a SaaS company becomes more valuable as AI advances—not less—it is worth serious attention.

The Executive Playbook: What to Do Now

1. Audit Your Source of Value—Honestly

Start with clarity on where your value actually comes from.

If your product is primarily:

• UI

• workflow convenience

• light automation

…it is more exposed than current metrics suggest.

Example: HubSpot
Historically a workflow and interface layer, HubSpot is now repositioning around customer data and AI-driven GTM execution—an implicit recognition that UI alone is not durable.

Implication:
If your value can be replicated by AI, it will be.
You must anchor your product in data, decisions, or execution.

2. Move Up the Value Chain

The strategic shift is from enabling insight to delivering outcomes.

From:

• Data → reporting

To:

• Insight → recommendation → action

Example: Gong
Gong evolved from call recording into a system that identifies deal risk and recommends next actions—moving closer to automated revenue execution.

Implication:
Dashboards are no longer enough.
The goal is to become a system of decision—and ultimately, a system of action.

3. Re-Architect Workflows—Don’t Just Add AI

Adding copilots is not transformation. Redesigning workflows is.

Most companies are layering:

• chat interfaces

• assistive AI

But the real opportunity is to shift from:

• software that helps users work
  To:

• systems that perform work on behalf of the business

Example: Zendesk
AI agents now manage ticket triage and resolution end-to-end, reducing the need for human intervention.

Implication:
If AI is only assisting your users, you are behind.
AI must become the operator within your workflows.

4. Build a Data Flywheel

Data advantage is not owned—it is constructed.

The goal is to create a system where:

• more customers → better data → better outcomes → more customers

Example: Snowflake
Through data sharing and its marketplace, Snowflake enables cross-company insights that improve with scale.

Implication:
Defensibility comes from data that compounds, not data that exists.
Your product should get better with every customer.

5. Redesign Pricing Before You’re Forced To

Pricing is the clearest signal of where your value actually resides.

The traditional model:

• more users → more seats → more revenue

AI breaks this relationship.

The shift is toward:

• usage

• workflows

• outcomes

Example: OpenAI
Pricing is tied directly to consumption (tokens, compute), aligning revenue with actual usage.

Implication:
If your pricing does not reflect value creation, it will eventually compress.
Align pricing with what the system does—not how many people log in.

6. Strengthen Defensibility While Expanding Offense

This is not a tradeoff—you must do both.

Defensibility comes from:

• deeper integrations

• stronger data context

• embedded workflows

Offense comes from:

• expanding into adjacent workflows

• embedding AI agents

• moving closer to economic outcomes

Example: Salesforce
Defends its position through its data and ecosystem, while pushing into AI-driven revenue execution with embedded agents.

Implication:
Winning companies will not just protect their position.
They will use AI to expand their scope and relevance.

7. Rethink Your Competitive Set

Your competitors are no longer just other SaaS companies.

They now include:

• AI-native startups

• internal tools built with LLMs

• general-purpose AI platforms

Competition is becoming:

• more fragmented

• faster moving

• less predictable

Example: Retool
Increasingly competes not just with SaaS vendors, but with developers building internal tools directly using AI.

Implication:
You are no longer competing feature-to-feature.
You are competing on structural advantage: data, workflows, and outcomes.

The Bottom Line

The SaaS model is not disappearing. But it is evolving into something fundamentally more powerful—and more demanding.

The last era of SaaS was defined by:

• digitizing workflows

• improving productivity

• scaling access to software

The next era will be defined by:

• embedding intelligence into every workflow

• turning data into continuous decision-making systems

• and automating execution, not just enabling it

The companies that win will not simply be better software providers. They will be:

• systems of intelligence

• systems of action

• and ultimately, systems of outcomes

This is a structural shift.

Just as value moved from hardware to software, it is now moving from:

• software → intelligent, agent-driven systems built on data

In that world:

• Interfaces matter less

• Seats matter less

• Features matter less

What matters is:

• what the system knows

• what the system learns

• and what the system does

Software is no longer the endpoint. It is the foundation—and intelligence is the new application layer.

And the companies that recognize this—those that evolve beyond software into intelligent systems of work—will define the next generation of market leaders.

By Duane Kotsen, Partner