I spend my weeks sitting with CIOs and enterprise leaders. Across industries, the pattern is identical: they’ve spent 18 months and millions of dollars on AI, yet their pilots are stuck in purgatory. Their copilots hallucinate, their fraud engines misfire, and their personalization feels generic.
When I ask why, their instinct is almost always the same: “We need more data.”
But in the age of Agentic AI, more is the enemy.
While massive volumes of data are essential for training a model, they are toxic for inference—the moment the AI agent actually makes a decision. Every extra byte you feed into a context window obscures the signal and forces your expensive LLM to behave like a glorified data integration engineer instead of a reasoning engine.
AI doesn’t fail because of bad models. It fails because enterprises feed it the wrong shape of data. The assumption that AI will “figure it out” on its own has become the most expensive misconception in enterprise technology.
To fix this, we need a hard pivot. We need to stop worshipping volume and start optimizing for Minimum Viable Data (MVD).
The Concept: Precision over Volume
MVD is the smallest, freshest, most contextual slice of data required for an LLM to make a specific decision right now.
Think about a bank fraud engine deciding whether to block a credit card swipe in Paris. That engine doesn’t need 10 years of transaction history (Big Data). It needs the last five minutes of real-time behavioral signals: location drift, velocity, and device reputation (MVD).
We see the same dynamic in travel. Consider an airline system trying to rebook a passenger during a blizzard. The AI Agent doesn’t need the customer’s entire lifetime CRM logs. It needs three specific things: current seat inventory, the passenger’s loyalty tier, and the cascading delay status across the network.
Feed it a haystack and it hesitates; feed it the needle and it acts.
This isn’t just an architectural preference; it’s a cost model. When you dump raw data into an LLM, you are paying by the token for the model to search for a needle you should have already handed it. Every millisecond the model spends stitching tables is wasted spend and increased latency.
The Trap: Faster SQL Isn’t the Answer
The reason most companies can’t deliver MVD is that their data architecture is stuck in the past. Leaders are trapped between two extremes:
- The Data Warehouse/Lake: Great for analytics, but fundamentally designed for storage, not serving.
- Raw APIs: Real-time, but too messy and fragmented for an AI to trust.
We see the data platform vendors racing to patch this. They are rolling out “hybrid tables” and high-concurrency layers to speed up retrieval. But slapping a caching layer on a warehouse doesn’t turn it into a reasoning engine.
It doesn’t matter how fast your query runs if the logic is wrong.
We are trying to run reasoning engines on storage architecture. Even if the warehouse can return a row in milliseconds, it is still returning a row: a rigid, schema-bound artifact. Agents don’t need rows; they need context and relevance. And because no one owns the end-to-end truth of that context, accountability fragments just as quickly as the data itself.
The Fix: Don’t Ask the AI to Do the Data Integration Job
The organizations that are winning are reorganizing their data not by system (Salesforce vs. SAP), but by entity (Customer, Order, Device).
They are building Data Products: live, secure snapshots that pre-calculate the MVD and deliver just the right data to the Data Agent, exactly when the AI needs it.
That means moving away from simply hoarding data to actively curating it. Instead of asking the AI to join tables, clean timestamps, and resolve identity conflicts in the prompt window, you do that work upstream.
When you do this, you stop asking the AI to “figure out” the data. You hand the AI a trusted fact, in a relevant context.
From Chatting to Acting
This is where the architecture must evolve from simple retrieval to actual execution.
LLMs can reason, but they shouldn’t navigate integration protocols, permissions, or complex data pipelines. They need a link between the reasoning engine and the enterprise systems’ data. An execution layer that gives the AI the power to act, not just reason.
This is why MVD is the prerequisite for Agentic AI automation. MVD provides the precise vision required to let the AI safely touch your enterprise systems.
If you give an AI access to your APIs but cloud its vision with bad data, you aren’t automating success, you’re scaling chaos. Precision is the only safety mechanism that scales.
The New Standard
The era of hoarding data is over. The winners of the next cycle won’t be the companies with the biggest data lakes or the fastest queries. They will be the companies that can deliver the precise slice of truth to a reasoning engine in under 200 milliseconds.
The risk is existential, that you’ll never go to production and stay in pilot forever. If you don’t redesign your data for MVD, your competitors will respond faster, operate cheaper, and outperform you. And they’ll do it long before you get a chance to course-correct.
