Why “70% of SaaS Features Are Unused” Became a Market Signal

AI & ML 4 min read

Why AI Makes “Build the Slice” Viable Again

Executive overview

Across the software industry, a recurring idea keeps surfacing in market commentary: enterprises often pay for far more SaaS functionality than they actually use.

In many discussions, this imbalance is described bluntly — up to 70% of features in large, monolithic SaaS platforms may remain unused in day-to-day operations.

While the exact number varies by organization, the underlying pattern is consistent. Teams adopt broad platforms but rely on a small set of core workflows. Everything else becomes bundled overhead.

Until recently, this inefficiency was accepted as the cost of speed and convenience. Today, advances in AI-assisted development are changing that trade-off — making it realistic to build and own only the workflows that actually matter, instead of renting entire platforms indefinitely.

What the “70% unused” narrative reveals

The idea that most SaaS features go unused did not appear in a vacuum. It reflects a broader frustration inside enterprises:

SaaS platforms grow by adding features for many customer segments
Individual organizations operate with far narrower needs
Licensing models bundle functionality, regardless of usage
Over time, SaaS spend grows faster than perceived value

As a result, subscription cost becomes disconnected from real operational usage.

What this reveals is a structural mismatch between how platforms are sold and how they are used.

Why this conversation intensified in 2026

In early February 2026, financial markets provided a visible signal that this issue is no longer theoretical.

After Anthropic released a new AI automation tool, investors sold off shares of software and information-services companies perceived to be exposed to workflow automation risk — including legal and analytics platforms

In parallel, traders openly discussed fears of a potential “SaaSpocalypse,” questioning whether broad SaaS subscriptions justify their cost when AI can automate or replace specific workflows

At the same time, commentators cautioned against the idea that software is disappearing — but rather that growth expectations and pricing power are being reassessed

For enterprises, this market reaction reinforces an internal reality: SaaS spend is under pressure, and alternatives are now worth serious consideration.

Why enterprises tolerated unused features for so long

Historically, “buy” made sense because:

Custom software required large teams and long timelines
Maintenance and reliability risk were significant
SaaS offered faster time-to-value and predictable operations

Even if only part of the platform was used, the alternative often felt worse.

Recurring subscription spend became normalized — especially when compared to large upfront build costs.

What AI actually changes (and what it doesn’t)

AI does not make full SaaS replacement trivial.
Core platforms remain complex, deeply integrated, and risky to rip out.

What AI does change is the economics of building focused software:

Faster development of backend and frontend components
Quicker integration with existing systems
Easier iteration on workflow logic
Lower cost to maintain and evolve custom code

This makes one thing viable again: building a slice instead of rebuilding the whole.

The “Build the Slice” approach explained simply

Instead of replacing an entire SaaS platform, enterprises can:

Select one or two key workflows that teams use every day
Build only those workflows, tailored to actual needs
Integrate them with existing systems (identity, data, ERP, CRM)
Gradually disable unused modules and licenses

This approach minimizes disruption while directly addressing waste.

From recurring expense to owned asset

Under a traditional SaaS model:

Costs repeat annually
Prices increase over time
Roadmaps are vendor-controlled
No intellectual property is owned

Under a “build the slice” model:

Investment focuses on what is actually used
Ongoing costs are more predictable
Workflows become owned assets
Dependency on vendor pricing decreases

Break-even timelines vary by scope and complexity, but in many cases, the economics favor ownership once only a narrow slice is replaced.

Why production readiness matters more than speed

AI makes building faster — but speed alone is not enough for enterprise use.

Successful “build the slice” initiatives require:

Security and compliance readiness
Monitoring and observability
Cost control and governance
Clear ownership and support models

This is where many AI-first startups struggle.
Enterprises need systems that operate reliably over time, not a series of demos.

When this approach makes sense

“Build the slice” is most relevant when:

SaaS spend is significant
Only a small subset of features is used daily
Workflows are well understood
Data ownership and control matter
Long-term cost predictability is a priority

The idea is not to overpay for unused functionality.

When SaaS is still the right answer

SaaS remains the best option when:

The platform’s full functionality is actively used
Network effects or ecosystems are critical
Switching costs exceed potential savings

The goal is to use SaaS more intentionally.

Key takeaway

The “70% unused features” narrative reflects a real tension: enterprises operate on focused workflows, while SaaS platforms sell broad bundles.

AI does not eliminate SaaS.
It makes it practical to own what you actually use.

Instead of renting bloated platforms indefinitely, organizations can selectively build, integrate, and gradually exit unnecessary subscriptions — without taking on full replacement risk.

Talk to us about SaaS Exit Sprint

If you are evaluating whether this approach makes sense for your organization, we can help.

SaaS Exit Sprint (6–8 weeks) helps you:

analyze SaaS usage and spend,
identify high-impact workflow slices,
build a production-ready replacement,
and define a safe, phased exit from unused licenses.

Contact us to discuss your SaaS Exit Sprint and see whether “Build the Slice” is viable for your SaaS stack.

Author: First Line Software
Last updated: February 2026