The pressure to use AI to bring value to organizations continues. However, after wading into the pond with Generative AI (GenAI), most organizations realize that just generating human-like answers isn’t enough.
Systems are moving toward being agentic: intelligent and autonomous networks of tools and models that retrieve, reason, and execute entire portions of a workflow.
Deloitte predicted that 25% of companies using GenAI would launch agentic AI pilots or POC. In two years, that number will double.
Your data needs to be AI-ready for you to consider agentic AI. Yet, survey after survey says that data quality, lack of traceability, and disconnected governance consistently top the reasons why AI initiatives stall.
Even among organizations that have invested heavily, nearly half struggle to quantify ROI, often because the data powering their efforts is scattered, unstructured, or stripped of context.
While conventional wisdom holds that massive amounts of data are necessary to build successful AI, it’s more important that the data be normalized, complete, and accurate.
“The lack of volume can always be compensated for through a reduction in project scope, but a lack of data quality invariably leads to POC failure”, said Gartner in its 5 Practical Steps to Implement AI Techniques.
You want machine learning that works, not just generates. Therefore, your data must be findable, accessible, meaningful, and modular.
So, how can you best prepare your data to be AI-ready?
We’ve put together a list of six things designed to empower data leaders to shift from reactive cleanup to proactive enablement, turning fragmented data chaos into clarity.
But first, let’s take a step back and talk about agentic workflows.
Agentic AI refers to systems where multiple AI agents work together to automate entire workflows or discrete portions of them. These multi-agent systems operate semi-independently, with each agent responsible for a specific task or decision point.
Think of it like a supply chain of intelligence: the output of one agent becomes the input for the next. But just like a supply chain, an error in one step can compromise the entire outcome, which is why traceability, semantic enrichment, and data quality are critical.
Characteristics of Agentic AI are:
The benefits of Agentic AI are virtually limitless. It can be integrated into any workflow that requires tedious information lookups, which are then processed, contextualized, and transformed into next-step actions without human intervention.
From compliance checks to clinical study analysis to customer response drafting, agentic systems reduce latency, boost consistency, and free human teams to focus on judgment, not data wrangling.
Imagine a compliance officer needs to answer whether a specific regulation applies to a new product launch. In an agentic system, one agent might retrieve relevant documents, another highlights named entities (like product names and jurisdictions), a third compares against known policies, and a final agent assembles a draft response - all with traceability back to original documents. Each step is handled autonomously, and the entire workflow is orchestrated to ensure reliability and auditability.
An agent might interact with individual databases or systems – i.e., a policy system, a product system, supply chain system, etc. Each agent queries a system, the answer is created in a structured, shareable knowledge format, and decision-ready data is passed to the next agent in the chain.
You can imagine how a wrong answer anywhere in that chain will create problems.
An orchestrated workflow like this isn’t limited to compliance. In research, an agent might extract trends from study data; in customer service, another could generate answers based on enriched knowledge bases; in supply chain, agents could monitor and flag risk based on policy changes.
The power lies not in any agent but in how they work together to complete complex, multi-step tasks with speed and context awareness.
Agentic AI isn't limited by imagination - it's limited by your data infrastructure.
Your data needs to be structured, contextual, traceable, and accessible on demand for agents to retrieve, reason, and act across systems.
This means going beyond traditional data pipelines or dashboards. You need to prepare your content for an environment where machine agents continuously query, connect, and build upon knowledge without human babysitting.
So, how do you know if your data is ready?
Start by assessing these six foundational capabilities from our AI Readiness Checklist. They determine whether your data can fuel not just AI but agentic, production-grade outcomes.
Bring your PDFs, spreadsheets, SharePoint files, databases, and internal systems into a single, queriable layer.
AI can't reason with what it can't see - especially when your knowledge is scattered across teams and tools.
Of course, that is easier said than done. The biggest challenge with enterprise data is that it is fragmented across formats, systems, ownership, and vocabulary. Data is spread across SaaS apps, legacy databases, document stores, emails, PDFs, etc., and isn’t designed to talk with each other – let alone share metadata.
Different business units may describe the same entities differently – for example, client, customer, payer – all may mean the same thing (or not), depending on context. This can be solved through metadata.
Data without context has very little value. The more information you can use to describe that data, the more valuable it becomes. If we have a report and know it came from our finance system, we will give it greater credence than if we saw it in a deck.
Semantic enrichment is one of the ways to harmonize data.
Let’s say we are working on a new oncology drug and conducting trials across multiple regions: the U.S., Europe, and Asia. Each site reports adverse effects (AEs), but:
Your semantic enrichment needs to standardize your terms, or map dictionaries, so that a query across your data provides the proper results.
One of the most overlooked challenges in AI systems is provenance. It’s not enough to deliver an answer in regulated industries or high-stakes workflows - you have to show where it came from.
In highly regulated environments, such as GxP-compliant systems in life sciences, traceability isn’t optional - it’s a regulatory requirement. Being able to show where data originated, how it was transformed, and what decisions were based on it is essential for auditability and compliance.
Most enterprises lack a consistent way to track how data moves from its original source (e.g., database or system of record), through reports and transformations, and finally into an AI model or dashboard. Without a clear lineage, teams can't trust or defend automated outputs.
Traceability must be embedded into the enrichment process, not bolted on later. Solutions like openlineage.io offer this for pipelines, which you can then add to your semantic metadata.
The more powerful your AI becomes, the greater the risk of unauthorized or inappropriate exposure. Yet access rights are often fragmented across systems, with no unified model for role-based control. AI agents (or even internal dashboards) can accidentally surface sensitive or restricted data simply because there’s no consistent enforcement layer.
Enterprises need a governance-aware access model that respects identity, geography, and sensitivity down to the query level.
Any solution you build or buy should include widely recognised standards such as RBAC (Role-based Access Controls), where entitlements are consistently enforced across systems. For more dynamic environments, attribute-based access control (ABAC) may provide the flexibility needed.
Regardless of the model, agents and AI systems must respect these constraints natively to prevent data leakage or compliance risks.
GenAI made headlines, but the next wave is agentic—AI systems that can autonomously complete multi-step tasks. For machine learning to work, it requires more than raw content.
Your data must be modular, contextual, machine-readable, and ready to flow through orchestration frameworks.
The problem is that most content remains locked in formats or systems not designed for dynamic handoff. You need to structure and enrich your data to let AI agents retrieve, reason over, and reuse it safely and with context intact. Retrieval-augmented generation (RAG), orchestration tools, and model context protocols (MCP) all depend on structured, traceable knowledge , not just raw content.
Consider exposing enriched outputs as XML or RDF triples so agents and downstream systems can consistently consume, interpret, and act on your data.
According to Gartner, AI agents will soon become integral to business decision-making. As technology evolves, organizations that embed agents into their workflows will be better positioned to adapt to rapid shifts in both markets and technology.
But even the smartest agents are useless if your data isn’t structured, enriched, and ready to serve. Use the AI readiness checklist to transform your fragment data chaos into a sustainable, extensible, and strategic foundation that unlocks AI’s full potential.
Want to turn fragment data into decisions?
If you found our AI readiness checklist helpful, here’s the secret to making all six pillars work in concert: a strong semantic layer.
In our next piece, we explore how a graph-based semantic structure transforms fragmented enterprise data into a powerful foundation for agentic AI, making your systems smarter, more connected, and easier to trust.