Strategy

Gartner Declares 2026 the Year of Context — But Which Context?

📅 March 14, 2026 ⏱ 12 min read

Seven vendors launched “context” products in one week. All of them solve the same problem. None of them solve the one that matters most.

Something remarkable happened at the Gartner Data & Analytics Summit in Orlando last week.

“Context is king” became the unofficial mantra of the entire conference. Analysts said it. Vendors pitched it. Attendees repeated it in every hallway conversation. One conference attendee wrote that context was “a buzz word on a lot of booths” — and then observed something crucial:

“Everyone had a slightly different definition of context.”

Within the same week:

• IBM published “Context Engineering: The Foundation for Trusted Agentic AI” — positioning context as federated knowledge architecture with runtime governance
• Glean published “Context Engineering AI: The Foundation of Reliable, High-Performing Models” — defining context as what goes into the model’s context window
• Stack Overflow published “The Context Problem: Why Enterprise AI Needs More Than Foundation Models” — framing context as institutional developer knowledge
• K2view launched the “AI Context Optimizer” — reducing LLM token costs through precise context delivery
• ArangoDB announced a “Contextual Data Layer” showcase at GTC 2026
• Sema4.ai launched semantic layer capabilities for “context-aware” AI agents

Six vendors. One week. All using the word “context.” All meaning something different. And all, critically, solving the same layer of the problem.

The Context Gold Rush Has Begun

The satirist Joe Reis predicted it perfectly: within 90 days of Gartner declaring “The Year of Context,” we’ll see at least 15 vendors adding “Context” to their product names, a Context Summit (probably in Las Vegas), three new Magic Quadrant submissions, and 200+ LinkedIn posts starting with “Context is the new data.”

He’s being generous on the timeline. It took five days.

But underneath the buzzword frenzy, something genuinely important is happening. The entire enterprise AI industry is converging on a truth that was invisible twelve months ago: models are not the constraint. Context is.

This is real. This matters. And the vendors mobilizing around it are solving a genuine problem.

The question is: which context problem?

Three Layers of Context — Only One Getting Built

When you map every vendor announcement from the Gartner summit against what they actually deliver, a clear pattern emerges. We’ve tracked this across 63 blog posts and 10 consecutive weeks of vendor analysis. The pattern has never broken.

Layer 1: Data Context — What information does the AI have access to? This is metadata, semantic layers, knowledge graphs, RAG pipelines, vector databases, data catalogs, and retrieval systems. It answers: Can the AI find the right data?

Layer 2: Action Context — What can the AI do with that information? This is governance frameworks, API orchestration, tool-calling capabilities, guardrails, policy enforcement, and workflow automation. It answers: Can the AI act safely?

Layer 3: Organizational Context — How does this company actually work? This is decision-making patterns, cross-team dependencies, unwritten rules, institutional memory, strategic priorities, cultural norms, and the accumulated wisdom of how a business operates. It answers: Does the AI understand the business well enough to make good decisions?

Here’s the scorecard from this week:

Six for six. Every vendor announced at or around the Gartner summit is building Layer 1 and Layer 2 infrastructure. None are building Layer 3.

This isn’t a coincidence. It’s a structural blind spot.

Why IBM Got Closest — And Still Missed

IBM’s piece deserves special attention because it came closest to identifying the real problem. They wrote:

“In an enterprise setting, ‘context’ includes institutional workflows, regulatory constraints and the operational state that determines whether information is final and approved.”

That’s exactly right. And then they defined the solution as “a federated knowledge architecture” with “governed APIs and connectors” where “governance policies are enforced at runtime.”

In other words: they correctly diagnosed that context is more than data retrieval. And then they built… a better data retrieval system. One with governance. One with federation. One with policy enforcement. But still a system that answers “Can the AI find and use the right information?” rather than “Does the AI understand why this company makes the decisions it makes?”

IBM even cited their own CEO study: only 25% of AI initiatives deliver expected ROI. Just 16% have scaled enterprise-wide. The primary barriers? “Governance, data integration, and trust.”

But governance of what? Data access governance is Layer 2. The governance that’s actually missing — the governance of organizational knowledge, decision context, and institutional wisdom — is Layer 3.

The Glean Paradox

Glean’s piece illustrates the gap most clearly. They define context engineering as “the practice of designing and managing the information an AI system relies on to understand and execute a task.”

They then list what context includes: “instructions, user input, conversation history, long-term memory, retrieved documents, external data, and available tools.”

Notice what’s missing from that list:

• How this team makes decisions
• What happened last time someone tried this approach
• Why the CEO rejected a similar proposal in Q3
• Which stakeholders need to sign off before this moves forward
• What the unwritten rules are about cross-team resource sharing

These aren’t edge cases. These are the reasons AI pilots succeed in demos and fail in production. The AI has access to every document in the company. It still doesn’t know how the company works.

Stack Overflow’s Honest Admission

Stack Overflow’s piece was refreshingly honest about the gap. They described it precisely:

“Ask an AI about your company’s internal API for user authentication. Ask it to integrate with your legacy billing system. Ask it why your team deprecated a particular approach last quarter. Watch it hallucinate with confidence.”

The “why your team deprecated a particular approach last quarter” is the tell. That’s not a data retrieval problem. The deprecation decision might be documented somewhere. But the reasoning behind it — the failed experiments, the performance issues, the team dynamics, the strategic pivot that made it obsolete — that’s organizational context. And it’s rarely captured in any system.

Stack Overflow’s solution? Build internal Q&A repositories where engineers capture institutional knowledge. That’s a genuine step toward Layer 3 — but only for one department, and only when people take the time to write things down.

The 93/7 Problem Gets Worse

We’ve been tracking the Deloitte finding that enterprises spend 93% of their AI budgets on technology and 7% on organizational readiness. The Gartner summit just made that ratio worse.

Here’s why: when Gartner declares “The Year of Context,” every CIO takes it to their board and says “We need to invest in context.” The board approves budget. That budget flows to IBM, Glean, K2view, and the other vendors building data context platforms.

The investment goes to Layer 1. Again.

The organizational readiness — the training, the process redesign, the cross-team knowledge capture, the decision documentation, the cultural changes that actually make AI useful — stays at 7%. Or drops to 5%. Because now there’s a shiny new “context platform” absorbing the budget.

This is the most dangerous version of the context problem: the illusion that you’re solving it.

What Organizational Context Actually Looks Like

Let’s make this concrete.

Scenario: Your AI agent needs to prepare a quarterly business review for the VP of Sales.

Layer 1 (Data Context) gives the agent access to CRM data, revenue figures, pipeline metrics, customer churn rates, and market data. Every vendor at the Gartner summit can deliver this.

Layer 2 (Action Context) lets the agent pull data from Salesforce, create slides in Google Slides, schedule a meeting, and send a Slack summary. Several vendors can orchestrate this.

Layer 3 (Organizational Context) would tell the agent:

• The VP of Sales cares more about deal velocity than total pipeline because she was burned by inflated pipeline numbers last year
• The CFO will challenge any revenue projection that doesn’t account for the seasonal dip in Q2 that only the finance team tracks
• The last QBR included a competitive win-loss analysis that the CEO specifically asked to continue
• The EMEA numbers need to be flagged separately because the new regional lead wants visibility into their team’s contribution
• Marketing’s MQL numbers use a different attribution model than what Sales uses, and presenting both without explanation caused a 30-minute argument last quarter

Without this context, the AI produces a technically accurate, strategically useless QBR. With it, the AI produces something an executive would actually present.

No vendor announced this week builds this.

The Real Year of Context Hasn’t Started

Gartner is right that context is the constraint. The vendors mobilizing around it are solving real problems. Better data retrieval, better governance, better semantic layers — these all matter.

But the Year of Context won’t actually arrive until enterprises start investing in the context layer that determines whether all the other layers matter: organizational context.

That means:

1. Capturing decision context — not just what was decided, but why, by whom, and what alternatives were rejected
2. Mapping operational knowledge — the unwritten rules, cross-team dependencies, and institutional memory that live in people’s heads
3. Building organizational models — machine-readable representations of how the company actually works, not just what data it has
4. Measuring context quality — tracking whether AI agents have the organizational understanding to make good decisions, not just accurate ones

This is harder than building a semantic layer. It requires organizational change, not just technology deployment. That’s precisely why nobody’s doing it.

And that’s precisely why it matters.

What to Do Monday Morning

If you’re evaluating “context” platforms after the Gartner summit, here’s a framework:

1. Ask every vendor: “What does your platform know about how our company makes decisions?” If the answer involves data retrieval, you’re evaluating Layer 1.

2. Audit your own context gaps. Pick three AI failures from the last quarter. For each one, ask: Did the AI fail because it couldn’t access the right data? Or because it didn’t understand how the business works? The answer tells you which layer you’re missing.

3. Start with one team. Pick a department. Document their top 10 unwritten rules — the things every experienced employee knows but no system captures. That’s your organizational context backlog.

4. Measure the 93/7 ratio. Calculate what percentage of your AI budget goes to technology versus organizational readiness. If it’s worse than 93/7, the Gartner summit just made your real problem worse.

5. Read the vendor fine print. When a vendor says “context,” ask: “Context of what?” The answer reveals everything.

The enterprise AI industry just discovered that context matters. It hasn’t yet discovered which context matters most. The organizations that figure that out first won’t just deploy better AI agents — they’ll build competitive advantages that no technology platform can replicate.

Because you can buy a data platform. You can’t buy organizational wisdom.

Related Reading:

• The Context Gradient: A Framework for Evaluating Any Enterprise AI Tool
• Governance Certified, Context Absent: Enterprise AI in 2026
• Context Graphs: The Missing Layer Between Your Data and Your AI Agents
• The Readiness Deception: What Deloitte’s State of AI 2026 Reveals
• What Is AI Enablement? The Complete Guide