Agentic AI is moving fast from boardroom buzzword to business must-have, promising systems that can reason, act, and orchestrate workflows with minimal human input.
Yet moving from pilot to production is where progress stalls. While 78% of organisations have at least one AI agent pilot in place, yet only 14% have deployed these systems organisation-wide, and more than 40% of agentic AI projects are expected to fail by 2027.
The issue is not the underlying intelligence. Today’s AI systems are already capable of sophisticated reasoning. The real challenge is far more fundamental: getting the right data, in the right place, at the right time.
Without that, even the most advanced agents struggle to deliver reliable, explainable, and actionable outcomes. Context, not capability, is the limiting factor here. Addressing it requires a shift in how organisations connect, contextualise, and consume their data.
Exposing data to AI is causing projects to fail
Modern AI agents cannot operate effectively in fragmented environments. Yet that’s the reality in most enterprises, where data sits across multiple clouds, data centres, legacy systems, and formats. Simply exposing that data to AI systems, without structure and governance, can lead to performance issues and significant business risk.
The organisations making progress on agentic AI are taking a different approach. Rather than centralising all their data and feeding it to AI, they’re bringing it together in a data fabric architecture first. This matters because agentic workflows depend on reliable access to data anywhere: the ability for AI to access, understand, and apply trusted information across cloud, on-premises, and hybrid environments.
Whether an agent is retrieving internal knowledge or triggering downstream actions, it needs a consistent view of the entire data lifecycle. Open architectures and standardised access help make this possible, while shared metadata and access policies ensure control is maintained.
Contextualise data for agents to understand
Connecting data is only the starting point. For agentic systems to work effectively, they need to understand what that data represents, how it is used, and how it relates to other information across the organisation. Without context, agents can retrieve information, but they cannot interpret it with confidence or produce reliable outputs.
It is like sending a highly capable employee into a huge library with no filing system, labels, or permissions. They may find something quickly, but they will not know whether it is current, relevant, approved for use, or connected to the task.
That starts with discovery. Organisations need to automatically identify data sources across cloud and on-premises environments, activate metadata, surface attributes such as structure and format, and track how data moves through pipelines.
This information forms the basis for lineage, showing how datasets are related and change over time. Lineage matters when you need to validate a result, explain an agent action, or trace a broken output to its source. It creates transparency and confidence in the systems agents interact with.
A centralised catalogue then creates a usable structure for both humans and machines. It acts as an enterprise data estate map, capturing not just technical metadata, but also the relationships, dependencies and business logic needed to understand the data and act on it.
Contextualisation enables agents to do more than retrieve information. It allows them to explore patterns, ask better questions, and make decisions with a deeper understanding of their environment.
Consume the right context at the right moment
Even with connected and well-understood data, agentic AI systems will fall short if they cannot access and use that context in a controlled, timely way.
Effective consumption is about orchestration – ensuring agents can act with the right information, under the right conditions. This requires guardrails to protect sensitive data, observability to monitor behaviour, and fine-grained access controls to keep actions aligned with policy.
Different use cases need different approaches. An internal knowledge assistant may use retrieval-based methods to surface current information, while a sales agent may need structured access to CRM data through controlled interfaces. In both cases, the goal is the same: accurate, relevant context without compromising security or governance.
This is how AI agents move beyond experimentation, operating with precision, security, and alignment to business goals.
Agentic AI success starts with data
Agentic AI isn’t a miracle worker. Handing agents fragmented data and expecting them to thrive has not worked – and never will. Like any powerful tool, deploying it on unstable foundations only leads to disappointment.
High-quality, well-governed data enables agents to reason accurately, act safely, and produce outcomes that can be trusted. Without laying this foundation, even the most promising experiments will struggle to scale.
Ultimately, organisations that connect and understand their data will be well placed to benefit from agentic AI.
