The Missing Line Item in Your 2026 AI Budget: Context Infrastructure

AI’s next cycle is about accountability #

Earlier this year, I wrote about the AI value chasm — the widening gap between AI investments and the actual value teams are seeing. Since then, MIT Sloan put a number on it: 95% of AI projects never make it out of pilot.

It’s a familiar story. Every technological wave begins with promise, but ambition often runs faster than the foundations beneath it.

AI budgets are still growing fast — projected to exceed $2 trillion by 2026 — but so is scrutiny. IDC predicts that 70% of the Forbes Global 2000 companies will soon require ROI analysis for any new AI infrastructure investment.

This year’s story was all about acceleration — experimenting with copilots and imagining what was possible. Going into 2026, the optimism hasn’t faded, but it’s become more measured. The questions have shifted from “What can it do?” to “Is it really working?”

We’ve entered AI’s accountability cycle — where success depends not on what’s being built, but on whether it’s working in production and delivering measurable business impact.

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Why the context gap can make or break AI success #

Over the past six months, my team and I have spoken with more than a hundred data and AI leaders to understand why so many initiatives stall. Their experiences point to the same set of challenges.

First: Data without context #

“We have a thousand AI use cases on the roadmap, but we don’t even know what data we have.” — Chief Data Officer, Fortune 500 Tech Company

The issue isn’t data scarcity — it’s that data without meaning isn’t useful.

Second: AI that doesn’t understand the business #

“When someone says TAM here, it means Total Addressable Market. But online, it might mean something else entirely.” — CIO, Investment Management Firm

Every company runs on thousands of unwritten rules: when to offer a discount, when to escalate an issue, when to make an exception — and when not to. These rules live in people’s heads, Slack threads, and hallway conversations — nowhere AI can learn from.

Third: Governance that can’t keep up #

“I’m fine with a chatbot using payroll data for HR, but I don’t want anything else touching it.” — Chief Data & AI Officer, Public Software Company

Our governance frameworks were built for static data, not adaptive systems that learn and act in real time.

Taken together, these problems form what I call the AI Context Gap — the space between what models can do and what they actually understand about the business.

Large language models understand language. But who’s teaching them meaning?

Some organizations are beginning to close this gap. Netflix, for example, built its Unified Data Architecture so that when any system refers to a “movie,” it means the same thing everywhere. That consistency gives AI the grounding it needs to reason reliably.

Most enterprises will need something similar — not just for data, but to codify context: a living layer that captures how the business actually thinks.

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The new standard for AI: Context as infrastructure #

Context, like business itself, is a living system. It shifts with every decision, reorganization, and new system that enters the stack. The only way to keep it trustworthy is to make its evolution visible — every update tracked, every change explainable.

This is the idea behind the Enterprise Context Layer — a new system of record for AI-first organizations. In a world where agents make millions of micro-decisions each day, static SOPs can’t keep up. The context layer provides a dynamic foundation — a single, evolving source of truth that captures how the company thinks and operates.

It’s built on four key components:

• Context extraction: continuously pulling structure and meaning from data, documents, and workflows.
• Context store: the persistent, versioned memory that keeps definitions, relationships, and rules organized and auditable.
• Context retrieval: the interface layer that allows AI agents (and humans) to access the right context instantly—millions of times per day, in real time.
• Context feedback loops: human-in-the-loop processes that refine, correct, and evolve the system as people make new decisions and the business changes.

When these pieces work together, context stops being documentation and becomes infrastructure — a living layer that encodes how a company thinks, decides, and acts.

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Closing the context gap: The next evolution for CDOs #

If context is the key to getting AI into production, then someone has to be accountable for it.

The CIO focuses on transformation.

The CFO focuses on capital.

The CISO focuses on risk.

But no one today is clearly responsible for the shared understanding that connects them all.

That responsibility should sit with the Chief Data Officer.

The CDO is uniquely positioned for this because they operate at the intersection of data, semantics, and trust. They understand not only where information lives, but what it represents — how it’s created, defined, and used to make decisions.

Building a context layer isn’t just an engineering problem. It’s a meaning problem. It requires someone who can connect the language of the business with the architecture of its systems — and ensure both evolve safely together.

Over the past decade, CDOs built the data infrastructure that made analytics possible. The next decade is about building the context infrastructure that will make AI accountable.

This isn’t a new mandate — it’s a natural extension of the CDO’s mission: to turn information into insight, and insight into shared understanding.

As AI becomes embedded in business operations, the CDO’s role shifts from enabling decisions to ensuring they make sense. They are the stewards of meaning inside the modern enterprise — the leaders who will ensure that as intelligence scales, understanding scales with it.

About the Author:

Prukalpa Sankar is the Founder and Co-CEO of Atlan, a data and AI governance platform trusted by visionary data teams at Fox, HubSpot, Medtronic, HelloFresh, and Autodesk. Named a Visionary in the Gartner Magic Quadrant and a Leader in the Forrester Wave, Atlan helps enterprises manage and govern their data and AI products. Previously, Prukalpa co-founded SocialCops, which was recognized by the World Economic Forum and The New York Times. She is a Forbes 30 Under 30, Fortune 40 Under 40, and a TED Speaker.

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This article was originally published in CDO Magazine. View the original article here.

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