Data management guru Dave McCrory coined the term data gravity in 2010 to refer to how data attracts applications, services, and other data to it, a black hole-like gravitational force. That force exerts more of a gravitational pull in the age of AI and autonomous AI agents where, needing a heavy volume of high-quality data, the question of where to run AI workloads becomes important. Should AI move to the data, or vice versa?
For autonomous AI agents to deliver on their promise and deliver real-time, hyper-personalized experiences such as flawlessly negotiating the best deal on a car, or proactively managing a customer’s healthcare journey, agents require data readiness at scale and speed. This requirement precludes bringing data to the AI, with the latency concern just one of several reasons why moving data is not in the best interest of the enterprise.
What is Data Gravity?
Data gravity operates on a simple, Newtonian principle: mass attracts mass. This is because when your data is too large, too dense, and too interwoven with other systems, it is economically and technologically difficult to move.
Your massive, constantly growing customer data (behaviors, transactions, affinities, events, predictions, campaigns, social posts) is the greatest mass, attracting associated workloads, applications, and services (including your AI agents and real-time CX decision engines) toward it.
This “irresistible force” is what leads to the critical strategic decision of bringing AI and the compute to the data, not the data to AI. For any enterprise aiming to thrive in the age of autonomous agents, designing an architecture that respects data gravity is non-negotiable.
The Triple Threat: Why Ignoring Data Gravity Fails AI and CX
Attempting to move or copy massive datasets for every business process leads to what we call the “triple threat” to data readiness:
- The Latency Trap (Performance)
AI agents do not act in a single step. To accomplish a goal – say, rescheduling a complex service appointment – an agent must execute an iterative, continuous loop of planning, acting, and reflecting. A single, seemingly simple autonomous request can trigger over 10,000 inference cycles in the background.
If an agent has to wait for data to be copied or transported across networks for each of those 10,000 steps, the resulting latency makes a real-time customer interaction impossible. The agent slows down, becomes unreliable, and ultimately fails to resolve the customer’s request. Low latency access to data is paramount for agent success.
- The Cost Killer (Economics)
The traditional method of solving data access problems has been to copy data from its core repository and push it out to operational systems (like a cloud-based LLM platform). This model is economically devastating.
Although recent changes make it easier to migrate data away from cloud storage, cloud providers still charge punishing egress fees for day-to-day movement. For companies with petabytes of customer data, these costs spiral out of control. Gartner and IDC research indicate that egress charges often account for 10 to 15 percent of an organization’s total cloud bill, with some enterprises spending even more.
This reality is driving major enterprise decisions. A significant portion of organizations (55 percent in some surveys) plan to move workloads off the cloud or into hybrid models once these data-hosting and computing costs hit a critical threshold. Ignoring data gravity is literally draining the budget needed for innovation.
- The Sovereignty Constraint (Governance)
In heavily regulated industries, moving customer data is not just expensive; it is illegal. Regulations like GDPR and strict data sovereignty rules require that certain categories of customer data remain within specific geographic boundaries or private data centers.
By bringing the AI and the operational logic to the data and processing it within the secure environment, companies ensure continuous compliance. The agent is only permitted to export small, final, aggregated outputs (“result tokens”), keeping the raw, sensitive mass of data safe and compliant.
Moving the Decision Engine to the Data for Real-Time CX
The principle of data gravity is what separates the old-school marketing cloud from the future of customer data: a composable data readiness hub.
The old model required copying, duplicating, and consolidating data from disparate silos into a single, separate marketing database. The new reality of AI agents and hyper-personalized CX requires a model that leverages the gravitational pull of your core data store.
A truly data-ready architecture must place the core decisioning capabilities (the personalization engine, the next-best-action logic, and the agent’s brain) at the center of your data gravity well, which is where data readiness must be centered as well.
This strategy allows you to:
- Exploit the Edge: With the majority of enterprise data expected to be generated and processed at the edge (in stores, factories, and devices), CX logic must be able to distribute and execute decisions closer to the customer interaction.
- Unify and Activate: A data readiness hub running directly in a “data-in-place” environment allows the AI agent to access the unified customer profile instantly, without moving the underlying data mass. This low-latency access is the only way to ensure the agent’s actions are relevant, personalized, and executed in real time.
- Future-Proof Investment: By architecting systems to accept and operate with data gravity, the enterprise creates a foundation that can scale to meet the demands of truly autonomous AI, turning the cost and complexity of enterprise data into a powerful competitive advantage.
The shift to autonomous, customer-controlled AI agents is here. The winners will be the organizations that respect the gravitational pull of their customer data, embracing an architecture where the intelligence is always ready, and where the compute chases the data – not the other way around.
For information on Redpoint’s composable Data Readiness Hub, click here.
