The Compounding Loop Revolution: Transforming Enterprise Software with AI

In recent years, the enterprise software landscape has been on the cusp of a transformation, driven by the integration of artificial intelligence (AI). This shift can be likened to the compounding loops that have defined consumer platforms like Netflix, Amazon, and Google. These consumer giants have mastered the art of capturing user interactions, learning from them, and iteratively improving their systems. The enterprise world, however, has traditionally been devoid of such dynamic loops. But that is beginning to change.

The Old Model is Breaking

The traditional model of enterprise software is showing its age. The value proposition of software that once relied heavily on feature-rich interfaces is being eroded by the rise of AI. Large language models (LLMs) can now automate and streamline many of the processes that previously required human intervention. This shift is compressing the value of software features, pushing companies to seek new ways to sustain enterprise value.

The answer lies in the compounding loop—a system that does not just capture the end state of decisions but also the reasoning behind them. This is where enterprise software has historically fallen short.

What Enterprise Software Actually Captured, and What it Missed

Enterprise systems have been adept at recording outcomes but have largely ignored the decision-making process that precedes these outcomes. For instance, a discount field might show the final price, but it won’t capture the negotiation process that led to that figure. Similarly, a resolved ticket indicates closure but lacks the context of the decision paths taken.

The missing piece is decision traces—the rationale and context behind every decision. These traces are often scattered across meetings, emails, and informal conversations, making them difficult to capture and learn from. Until now, there was little incentive to store this information as no system could effectively utilize it.

What's Changed

The landscape of enterprise work has evolved significantly. With the rise of remote work and digital collaboration tools, decision-making leaves a rich digital trail. Language models can now turn unstructured data, like chat logs and document comments, into valuable insights. This transformation enables systems to learn from decision traces and refine processes over time.

One of the most significant advancements is the deployment of AI agents that create decision checkpoints within workflows. These agents propose actions, which humans can approve, modify, or escalate, thus generating valuable decision traces. Each modification or approval provides a learning opportunity for the system.

Why Incumbents Won’t Build This

Capturing decision traces requires being present at the moment decisions are made—not after. This presents a challenge for traditional systems of record (SORs) like Salesforce or ServiceNow, which focus on storing the current state rather than the decision process. These incumbents often lack the cross-system visibility needed to capture the full context of decisions.

Warehouse players, on the other hand, operate in the read path, receiving data post-decision. They can provide insights but are not positioned to capture the real-time decision-making process.

Startups developing systems-of-agents have a natural advantage as they sit in the write path. These systems execute workflows and capture decision rationales as they occur, enabling real-time learning and adaptation.

The Platform Opportunity

The shift towards capturing decision traces presents a dual opportunity for platform and application companies. Platform companies can develop infrastructure to build and manage context graphs, which are essential for structuring and querying decision traces. Application companies, on the other hand, can create domain-specific context graphs that enhance decision-making in areas like operations, customer service, and strategic planning.

As these context graphs mature, they open the door to predictive capabilities. Instead of simply recalling past decisions, organizations can predict future outcomes based on historical decision data, significantly enhancing strategic planning and operational efficiency.

The Trillion-Dollar Rewrite

The potential for enterprise software to harness decision traces is immense. Every industry, from legal to healthcare, holds vast amounts of tacit knowledge that has never been fully utilized. The ability to capture, structure, and learn from this institutional judgment could redefine enterprise value.

Like consumer giants that built empires on compounding behavioral data, enterprises now have the opportunity to build a similar foundation with decision data. The companies that succeed in this transformation will not only innovate but will also secure a competitive edge in the evolving business landscape.

The era of the compounding loop in enterprise software is just beginning, and the potential rewards are staggering. As this revolution unfolds, staying ahead of the curve will be crucial for any organization aiming to thrive in the AI-driven future.