For the past decade, organisations have focused their digital transformation efforts on modernising systems of record, automating workflows and expanding customer engagement channels. But the rise of agentic AI—autonomous software agents capable of taking actions, orchestrating tasks and optimising journeys—is shifting the source of competitive advantage yet again.
According to recent industry analysis in the Martec for 2026 report by Chiefmartec, 56% of companies cite poor data quality as the top inhibitor to AI effectiveness. Even more point to integration friction, fragmented systems and gaps in organisational readiness. As organisations accelerate AI adoption, these constraints are becoming existential.
Agentic AI does not replace the enterprise data stack—it amplifies its weaknesses. And it turns what was once an operational nuisance into a strategic bottleneck.
AI agents are proliferating across the enterprise. Marketing teams are deploying content-generation agents; sales organisations are experimenting with AI-driven prospecting; customer experience teams are layering agents into service channels; and IT teams are exploring autonomous workflows for integration and orchestration.
But as adoption expands horizontally, a new pattern is emerging: AI performance is only as strong as the data foundation beneath it.
The new generation of agents requires:
These ingredients are not optional. They determine whether an AI agent makes the right decision or the wrong one.
Legacy systems of record were never designed for this. And even modern cloud systems remain fragmented and inconsistent across functions (e.g., CRM, CDP, CDW, MAP, MDM). Despite years of investment, enterprises still struggle to maintain a single source of truth, let alone operationalise it dynamically for AI-driven actions.
As iPaaS tools morph into agent-building platforms, integration complexity is rising, not declining. Organisations that once relied on point-to-point automation now face:
More agents means more data movement, more data movement means more failure points and each failure dilutes the value of every downstream AI initiative.
In fact, integration friction is now a top-three barrier to AI adoption according to the Martec for 2026 report by Chiefmartec, particularly in enterprises with heterogeneous stacks and deep institutional technical debt.
The promise of AI autonomy collides quickly with the reality of enterprise data sprawl.
As models converge and commoditize, the differentiator shifts from intelligence to context. Leading organisations are discovering that agentic systems require more than raw data. They require contextualised data—high-fidelity information enriched with meaning, structure and lineage that allows agents to reason, decide and act safely.
This is sparking the rise of what industry analysts call the System of Context, a layer that sits between systems of record and AI agents, assembling:
This “context layer” is not a CDP, warehouse or MDM system. It’s a dynamic, operational data integrity layer purpose-built for AI decision-making. In this sense, context engineering becomes the new discipline enterprises must master. Without it, agents cannot perform reliably. With it, they unlock exponential value.
Each new agent (whether designed for content, analytics, orchestration or customer interaction) introduces additional:
Organisations quickly learn that scaling AI isn’t about deploying more models; it’s about orchestrating more data-driven decision flows, each of which must be validated and safeguarded end-to-end.
As enterprises adopt more agents, they inevitably route more datasets through their pipelines: CRM and CDP data, marketing assets, email corpora, knowledge bases, product catalogs and increasingly warehouse-level data.
This volume creates opportunity, but also risk. Without a strong data integrity layer, even a single corrupt or misaligned data point can propagate across dozens of agent workflows.
The lesson emerging across industries is clear:
AI cannot scale faster than the organisation’s ability to supply it with clean, consistent and contextual data.
This is why data integrity, often overlooked in the first wave of automation, is now becoming a board-level priority. It is the gating factor for AI performance, efficiency and trust.
In the past, organisations could tolerate fragmented, partially governed data. AI changes that. Agents will act on whatever data they are given (correct or not).
Forward-leaning enterprises are responding by:
Where cloud adoption created the need for identity platforms and security frameworks, agentic AI is now creating the need for data integrity platforms.
For leadership teams, the implications are significant:
Boards and executives can no longer evaluate AI initiatives solely on model choice, vendor selection or expected ROI. They must evaluate:
Data integrity becomes the foundation for:
In other words, data integrity becomes the prerequisite for AI-driven transformation.
The shift toward agentic AI signals a transition in enterprise architecture. Systems of record remain essential, but they are no longer sufficient.
To operate effectively, AI requires:
Enterprises that invest early in these capabilities will unlock faster AI deployment, safer automation and greater competitive advantage. Those that don’t will find themselves constrained not by a lack of models, but by a lack of trustworthy data.
AI is accelerating and agent capabilities are expanding. But the enterprise’s ability to supply clean, contextual, reliable data is not keeping pace. As organisations enter the next chapter of AI adoption, data integrity will shape who wins, lags and fails to achieve meaningful ROI.
The companies that thrive will be those that treat data integrity not as a technical hygiene problem, but as a strategic control point—the foundation upon which all agentic intelligence must be built.
Insights