Enterprise AI efforts stall for many reasons: uncertain use cases, unclear ROI, and organizational inertia. But one blocker still dominates the conversation: “Our data isn’t ready.” It’s scattered across internal and external systems, inconsistent and difficult to extract, riddled with quality and compliance issues, and often too incomplete to support reliable automation.
It sounds responsible. Sensible, even. But in 2025, that assumption is no longer true.
Yet, according to Gartner’s 2025 Hype Cycle for Artificial Intelligence1, 57% of organizations believe their data isn’t AI-ready to deliver business objectives”. In a separate survey by Digitalisation World 2, over 60% of IT leaders named poor data quality and governance as the top barriers to AI adoption, above skills gaps or infrastructure concerns.
For decades, enterprise leaders were taught that you couldn’t modernize analytics until you modernized infrastructure. You couldn’t deploy AI until your data was fully structured and centralized. That mindset made sense when automation tools required consistent schemas and predictable inputs. But that’s changed. There’s now light at the end of the data readiness tunnel, especially when it comes to inconsistent formats and unstructured data.
Agentic AI thrives in messy, fragmented, and unstructured data environments, overcoming the need for harmonization and centralization before AI can unfold its power. You don’t need perfectly processed data to begin – it happens in real time and in the flow of work. Agents can replace data extraction and harmonization teams, make centralization efforts unnecessary, and eliminate time spent searching for and cleaning data.
So how do you break the “Our data isn’t ready” mindset? Let’s explore.
There is Light on the Data Readiness Horizon
In our experience, data readiness issues tend to fall into four main categories:
- Provisioning Complexity: Data is scattered across systems. It takes a long time to give data scientists access to the data their models need
- Format Friction: Data arrives in many formats and data models, making it hard to extract and harmonize before AI can consume it and progress work
- Quality Reluctance: Data is not complete, consistent, or accurate to assure reliable AI outputs and may pose compliance risks
- Missing Data: Valuable information is either uncaptured or only available from third parties, leading to blind spots in decision-making and reporting
Each of these pose real challenges, but progress is happening, particularly around format friction, where Agentic AI thrives. Powered by large language models (LLMs), Agents can extract data, validate it, harmonize it, and trigger actions across PDFs, emails, system exports, handwritten notes, and more – even when the data structure is inconsistent. What once required manual data extraction and key-in is now handled in-flow, as agents navigate across systems and data models with minimal friction. Emerging standards like Model Context Protocol (MCP) are also reducing integration overhead, allowing agents to act on data across sources without heavy engineering. They don’t just surface issues, they resolve them, flagging anomalies and improving quality in real time.
The old barriers around format are fading fast, and that means some long-held assumptions about data readiness deserve a closer look.
Three Signs Your Data is More Ready than You Think
Yet despite this progress, in many organizations format friction is still addressed by small armies that manually extract data from forms and put them into common data models. Customer service staff read pages of emails to find one useful detail. Workflows are being held up. In response, many organizations have large-scale centralization efforts underway to clean and standardize data before applying AI. Or AI is not being deployed because data harmonization and extraction is too hard. This causes cost and holds up progress on better user and customer experiences, and on unlocking the power of AI.
In our experience, there are three “data readiness” related efforts no longer required:
| Old Mindset | Example Use Case | New Mindset |
| 1. Lots of people deployed to extract, clean and harmonize data | Offshore teams at an Agri-coop manually entering handwritten invoices into systems |
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| Real estate analysts interpreting freeform lease agreements across regions | ||
| Business analysts manually pulling KPIs from reports and system exports | ||
| Dispatch operations slowed by manual ticket triage | ||
| 2. People spend a lot of time aggregating and cleaning data before interpreting and acting on it | Support agents at a global gaming company manually reviewing fragmented chat, email, and forums |
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| Field engineers reviewing handwritten maintenance logs before dispatch | ||
| Lending teams assessing borrower income via uploaded PDFs | ||
| Mortgage applications delayed by repetitive document review | ||
| 3. Many data centralization and data harmonization efforts underway | Inconsistent metadata delaying asset publication at a media company |
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| Finance analysts create their own data marts to aggregate and manipulate data | ||
| Data Lakes are being constructed and data model and architecture harmonization investments are underway |
Start Shifting the Mindset and Accelerate AI Progress
How do you get going? Systematically catalogue these signals and get a new, Agentic AI mindset about unstructured data readiness underway. The impact can be tremendous.
In sectors like media, entertainment, and consumer technology, agents are removing content bottlenecks by enriching metadata and automating validation as soon as assets are ingested, cutting delays both in production and time-to-publish. Fragmented inputs no longer stand in the way: gaming platforms use AI to unify customer support threads scattered across chat, email, and forums, while agricultural teams rely on agents to convert handwritten invoices into structured data on arrival, reducing both manual effort and turnaround time. And in energy and sustainability, where diverse utility formats once slowed compliance workflows, AI now harmonizes incoming data in real time, accelerating processing and improving audit accuracy. These aren’t edge cases; they’re tangible proof that the barriers once slowing AI adoption are already being cleared.
Format friction, once a blocker, is now solvable. If you’re still treating it like a gate, you’re missing one of the fastest, clearest ways to build AI momentum.
