Why AI Agents Will Disrupt Systems of Record

We recently wrote about how AI agents are collapsing the enterprise software stack—dismantling the traditional layers of Systems of Intelligence, Systems of Engagement, and ultimately, Systems of Record. Today, we're expanding on why this moment presents a unique opportunity for disruption of even the most entrenched systems of record like Salesforce, SAP, and Oracle. For the first time in decades, we're witnessing a perfect storm that makes enterprise systems of record genuinely vulnerable to disruption. What makes this moment unique is that all three foundational elements—data models, delivery models, and interfaces—can be completely reimagined simultaneously. This trifecta of disruption simply wasn't possible before.

In previous technology waves, disruption was partial at best. Cloud computing changed delivery models but largely preserved the same data structures and interfaces. Mobile expanded access but still worked within existing data paradigms. Even the API revolution, while enabling better integration, didn't fundamentally challenge how business data was structured and presented.

What's different now is that AI agents don't just improve existing systems—they fundamentally reimagine them. They can understand unstructured information, process natural language, and take autonomous action in ways that were technically impossible until very recently. We recently explored how AI agents are collapsing the enterprise software stack—today, we're expanding on why this moment presents an unprecedented opportunity to reinvent even the most entrenched systems of record.

Dynamic Data Models vs. Rigid Schemas

Traditional enterprise software like Salesforce imposes strict opinions about how business processes should work. Their rigid data models force companies to conform—defining what a "customer" is, how an "opportunity" progresses, and how various entities relate to each other.

The revolutionary insight behind LLMs is their ability to transform unstructured data into structured data on demand. This is transformative because approximately 80% of human work is unstructured—conversations, emails, documents, meetings, and decisions that don't fit neatly into database fields. Traditional systems of record can only capture the 20% that humans manually translate into structured formats.

AI agents eliminate this constraint. Rather than forcing everything into predefined fields, an agent can process natural business communications—sales calls, emails, meetings—and extract precisely the information needed for any particular task without schema limitations. This creates a more accurate representation of business reality based on actual activities rather than manually entered CRM data. The agent understands the nuance of customer interactions and can materialize exactly the data views needed without predetermined structure.

When Interfaces Disappear, So Do Switching Costs

Enterprise software interfaces are universally disliked—complex navigation, endless custom fields, and specialized training requirements. Companies spend millions on Salesforce administrators and training programs just to make the software usable.

AI agents eliminate this friction entirely. Instead of learning Salesforce's complex UI, users simply tell their agent what they want in natural language. "Show me deals likely to close this quarter" replaces navigating through multiple screens and configuring specialized reports.

This represents perhaps the biggest opportunity for disruption—when interfaces disappear, so does one of the strongest incumbent moats. Knowledge workers won't even know what Salesforce's interface looks like—they'll just converse with their agents about their business needs.

Economic Realignment?

The economic model of enterprise software is also ripe for disruption. Traditional vendors charge per user regardless of value delivered—Salesforce gets paid the same whether their software helps close deals or sits unused.

AI enables a fundamental shift toward outcome-based pricing—paying only for successful results like closed deals or revenue generated. This model aligns vendor success directly with customer outcomes.

While startups can build these new pricing models from the ground up, incumbents face a classic innovator's dilemma. They can't pivot to outcome-based pricing without threatening their existing revenue streams and potentially triggering stock market backlash (or other types). This economic realignment creates a compelling advantage for new entrants who can tie their success directly to customer success from day one.

Consider how this might transform the monitoring space. Datadog currently charges primarily based on the volume of data ingested and stored—a resource-based pricing model. But what customers truly care about is reducing Mean Time To Resolution (MTTR) for incidents. An AI-first competitor could charge based on actual incident resolution speed or system uptime improvements. By focusing on the outcome (faster resolution, less downtime) rather than the input (data storage), they could create a fundamentally different value proposition that Datadog would struggle to match without cannibalizing their existing business model.

Conclusion

Seven years after Systems of Intelligence emerged, the fact that there is no billion-dollar-revenue company in the space suggests the approach is fundamentally flawed. Instead of adding more layers to a problematic foundation, the market is ready for a true paradigm shift.

The moats that have protected traditional systems of record are eroding rapidly. Data model rigidity, per-seat pricing, and complex interfaces—once formidable competitive advantages—are becoming liabilities in an AI-powered world.

The race to build this new paradigm is just beginning, and the potential rewards for success are enormous. The system of record is finally vulnerable, and entrepreneurs who recognize this shift early will be positioned to create the next generation of enterprise software giants.

The era of separated systems is ending. The age of Systems of Agents has begun. SAP, Oracle, and Salesforce, beware.