Lori Schafer is CEO of Digital Wave Technology, an AI-native platform delivering AI, GenAI, and Agentic AI on governed master data.
Enterprises aren’t struggling with data. They are struggling to act on it quickly. Businesses rely on powerful systems that capture, store and report on data. These “systems of record” include ERP platforms, product information management (PIM) systems, customer databases and data warehouses, often governed and synchronized by a master data management (MDM) layer. They compose the digital backbone of an operation, standardizing operations and enabling visibility across the organization.
But in a real-time, outcome-driven environment, systems of record alone are no longer sufficient. Enterprises now need systems of action, which turn insight into immediate execution.
Traditional systems slow down business.
Traditional systems of record are designed to manage and standardize enterprise data and processes. In retail, it’s software and tools that support product management, such as aggregating historical data and generating reports.
Systems of action are all about product intelligence, using the data and insights that traditional systems produce and executing tasks to drive outcomes. According to a recent McKinsey report, 80% of executives say their companies are using AI to become more efficient and innovative, and 62% are testing AI agents.
The reason is simple: Companies are under pressure to deliver immediate results. Consider a global apparel retailer managing thousands of SKUs across regions. The company could see a surge in demand for yellow sweatshirts, all due to a sudden TikTok trend. While systems of record can capture the uptick in demand, by the time reports are generated and teams can react to increase supply and meet demand, the opportunity has passed. They’re left with stockouts and lost revenue.
Too often, traditional systems sit on legacy architectures with siloed data scattered across departments. The data flows to the company through long and slow batch-driven processing cycles, and the execution relies on human intervention or limited rules-based automation tools.
The limitations lead to friction and delayed insights, and teams spend more time managing data than putting it to work to improve the business.
Systems of action emerge and execute.
Powered by a unified platform with native AI, generative AI and agent-based capabilities, companies are building new engines of execution that fundamentally change how enterprises work. As opposed to traditional systems of record, systems of action connect data across domains and orchestrate workflows autonomously.
These systems move beyond static reporting and toward dynamic, real-time decisioning. Action models include connected data ecosystems that unify information across functions, AI-driven workflows that adapt to changing conditions and natural language interfaces that make advanced capabilities accessible to business users.
The global apparel retailer that missed out on the yellow sweatshirt trend can take immediate action on any items, such as repricing products based on regional demand shifts, filling in gaps in assortments and launching new promotions to move product overflows.
Central to the success of deploying systems of action is constant monitoring of the results and continuous education of the AI agents.
Closed-loop feedback supports systems of action.
As enterprises adopt systems of action, a new operating paradigm also emerges: the closed-loop feedback model.
A closed-loop enterprise is one where data flows continuously between systems, AI agents and human teams. So, while actions are taken autonomously by AI agents where appropriate, expert humans remain in a closed loop to guide, educate, validate and refine the AI-powered decisions and outcomes.
AI helps execute at scale and speed, but it requires closed-loop direction and domain experts who know what “good” looks like in terms of data quality and outcomes, understand how workflows should be structured and prioritized and possess keen oversight skills to override AI when necessary.
The apparel company will need merchandising teams that monitor how AI agents prepare a purchase order with a vendor, ensuring timelines and deliverables are accurate. It’s a continuous feedback loop of data, action, evaluation and refinement that improves the speed of work and enables companies to be more agile.
Enterprises prepare for a shift to action.
Going from systems of record to systems of action is not a singular technology upgrade; it’s an operational shift.
Enterprises must take immediate steps to prepare the company. CEOs and CTOs should begin by identifying one workflow to convert into a system of action, starting small, auditing where decisions are accelerating or still delayed and adjusting data accordingly. At the same time, this test system of action can establish a human-in-the-loop feedback system, so governance and expectations get measured.
Investment in technology is critical, but the bigger investment is in culture, process and workforce enablement. It’s going to take time for companies to fully move from systems of record to systems of action, so start with specific use cases and expand from there.
Ready or not, the shift is underway. This is the next era of digital transformation, and enterprises must put solid plans in place to transition from systems of record to systems of action. Technology is no longer just a system of record. It’s becoming a system of execution.
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