Your AI Strategy Fails Without Data and Context
- Heidi Schwende
- 3 minutes ago
- 5 min read
Everyone wants AI agents. The boardroom conversations have shifted from "should we do AI" to "how fast can we deploy agents." And yet most of these projects are going to fail. Not because the technology isn't ready. Because the data isn't.
Gartner predicts that 60% of AI projects will be abandoned by 2026 due to a lack of AI-ready data. We're not talking about pilot programs that didn't quite hit their metrics. We're talking about complete abandonment.
The gap between AI ambition and AI execution has never been wider.
The LLM Sugar Rush Is Over
Throughout 2025, businesses raced to slap a ChatGPT veneer on their existing operations. Build a chatbot. Add a copilot. Call it innovation. The demos looked impressive. The production results? Not so much.
According to MIT's NANDA research, only 5% of AI pilot programs achieve rapid revenue acceleration. The vast majority stall out, delivering little to no measurable impact on the bottom line. RAND Corporation puts the overall AI project failure rate at over 80%, which is double the failure rate of non-AI technology projects.
This isn't a technology problem. It's a foundation problem.
Moving from LLMs to AI agents is a fundamentally different challenge. LLMs respond to prompts. Agents act autonomously. They need to understand context, make decisions, and execute across systems. That requires clean, connected, contextual data. Most organizations don't have it.
Context Is the New Currency
I recently came across an interview with Rahul Auradkar, President of Data Foundations at Salesforce, who put it bluntly: AI without context is just guessing or hallucinating.
That's exactly right.
When an AI agent fields a customer service inquiry, it needs to know who that customer is, what they've purchased, what issues they've had before, and what's happening in the supply chain right now. Without that context, you get a sophisticated-sounding chatbot that can't actually solve problems.
Auradkar describes context as the bridge connecting disparate data to specific actions. Without that bridge, data is just text floating around. And floating text doesn't drive business outcomes.
The challenge is that most enterprise data exists in fragments. Customer information lives in the CRM. Order history sits in the ERP. Support tickets are somewhere else entirely. AI agents see pieces of the puzzle but never the full picture.
By 2026, Gartner projects 40% of enterprise applications will include task-specific AI agents, up from less than 5% in early 2025. Organizations racing to deploy agents without unified data foundations are setting themselves up for expensive failures.
The Data Quality Crisis
Here's what the AI vendors won't tell you: 63% of organizations either don't have or aren't sure if they have the right data management practices for AI, according to Gartner research.
Informatica's CDO Insights 2025 survey found that data quality and readiness is the top obstacle to AI success, cited by 43% of respondents. It beats out lack of technical maturity and shortage of skills. The data itself is the problem.
This tracks with what I've seen working with mid-market businesses. They want AI capabilities. They've got budget approved. But when we start auditing their data, we find customer records duplicated across systems, inconsistent formatting, missing fields, and zero governance. You can't build autonomous agents on that foundation.
What Actually Works
I talk a lot about foundations. Every successful campaign, every scalable strategy, every sustainable growth engine starts with getting the fundamentals right. You don't build a house on swamp land. You don't launch paid media without conversion tracking. You don't redesign a website without defined user journeys. You don't run SEO without technical infrastructure. You don't build social presence without a content strategy. And you don't deploy AI agents without clean, connected data.
Organizations that succeed with AI agents share a few characteristics:
They start with the data, not the model.
Winning programs allocate 50-70% of their timeline and budget to data readiness before they touch any AI implementation. That means extraction, normalization, governance, quality dashboards, and retention controls.
They unify before they automate.
Fragmented data creates fragmented AI. Companies need a single view of customer, product, and operational data before agents can act intelligently across systems.
They add context through metadata.
Knowing that a customer ordered "SKU-123" is useless if the agent doesn't understand that SKU-123 is the same as "Part A" in another system. Master data management creates the shared vocabulary that makes AI agents actually functional.
They build real-time operational awareness.
A support agent needs to know about the shipment delay that happened this morning. Historical data alone isn't enough. Integration with live operational signals separates useful AI from impressive demos.
The Mid-Market Opportunity
Here's where I see real opportunity for businesses in the $5M-$50M revenue range.
Enterprise companies are spending millions building custom data platforms and hiring armies of data engineers. Mid-market businesses can't match that spend. But they can be smarter about it.
The data foundation requirements for AI agents aren't actually that complex. You need unified customer records. You need connected systems. You need governance around data quality. These are solvable problems at mid-market scale.
The companies that get their data house in order now will be positioned to deploy AI agents effectively when the tools mature. The ones that keep chasing the latest AI demo without addressing fundamentals will join the 60% abandonment rate.
The Bottom Line
By 2028, Gartner predicts 33% of enterprise software will include agentic AI, enabling 15% of daily business decisions to be made autonomously. That's not a distant future. That's three years away.
The question isn't whether AI agents are coming. They're already here. The question is whether your data foundation can support them.
Most organizations are not ready. But readiness isn't about having the biggest budget or the most sophisticated tech stack. It's about doing the unglamorous work of cleaning, connecting, and contextualizing your data.
AI agents are only as smart as the data behind them. Everything else is just expensive hallucination.
Is Your Data Foundation AI-Ready?
Before you invest in AI agents, find out if your data can support them. I offer a straightforward AI readiness audit that assesses your current data infrastructure, identifies gaps, and gives you a clear action plan. No sales pitch disguised as strategy. Just an honest assessment of where you stand.
Sources
Gartner, "Lack of AI-Ready Data Puts AI Projects at Risk," February 2025
MIT NANDA Initiative, "The GenAI Divide: State of AI in Business 2025," August 2025
RAND Corporation, AI Project Failure Analysis, 2025
Informatica, "CDO Insights 2025" Survey Report
Gartner, "40% of Enterprise Apps Will Feature Task-Specific AI Agents by 2026," August 2025
Salesforce, "Why Context Is King in the Agentic Era," January 2026
Gartner, "By 2028, 33% of Enterprise Software Applications Will Include Agentic AI," 2025e Applications Will Include Agentic AI," 2025