CIOs Are Rebuilding Their Architecture. Only 27% Planned for AI Agents.
most redesigns leave out the data foundation agents need
Most organizations treat AI agent readiness as a platform decision. Pick the orchestration framework, connect the models, and agents will slot into the architecture once it has been modernized.
The larger problem is that most modernization plans do not include agents at all. In Accenture's CIO Pulse survey, published November 3, 61% of CIOs expect significant transformation or fundamental redesign of their enterprise architecture within three years, yet just 27% have agentic architecture in scope for that redesign. And 51% cite the absence of a trusted, governed data foundation as a barrier to scaling AI.
That puts a recurring job on your data team's desk. Every system consolidated, migrated or retired over the next three years changes where definitions live, who owns each dataset and how records flow downstream, and someone has to keep that knowledge intact.
And it gets harder as you scale. One redesign without agents in scope is a gap. Three years of migrations without them means agents arrive later to a platform whose meaning was never written down for them to read.
As agents move from pilots into the core of the enterprise, every agent needs to know what it is working with, who owns it and which other agents it can trust. That is what we are building at OpenMap: The Agent Network, the connective tissue for the world's AI agents, starting with every person and every local business. We are raising a £5M seed. Meet us at openmap.ai/investors.
Why Do Architecture Roadmaps Leave Agents for Later?
Picture a three-year modernization program. The roadmap has clean boxes for the cloud migration, the ERP consolidation and the new integration layer. AI agents sit in a box marked "phase three."
Along the way, two customer systems are merged, a legacy reporting database is retired, and a dozen revenue fields are renamed.
The architecture gets cleaner.
The meaning behind it gets lost.
This is not an architecture problem. It is a data context problem.
The Cost Isn't a Late Agent Program. It's Manual Approvals That Never End.
When agents do arrive, they find systems they can connect to but data they cannot vouch for. So people stay in the loop.
The same Accenture survey found that 70% of CIOs use AI for individual productivity, against 37% for end-to-end orchestration through agents. In IT operations, 49% still run agents with manual approvals.
Before an agent can act without a human checking each step, someone usually has to:
Confirm which system holds the authoritative version of a customer, product or supplier
Trace where a field came from and which reports and models depend on it
Name the owner who can approve an action on each critical dataset
Resolve duplicate and conflicting records that disagree about the same entity
Check whether a business definition changed during the last migration
None of these tasks are particularly difficult. They are slow, they cross team lines, and they usually depend on the few engineers who remember how the old systems worked.
So every agent decision waits for a person. The agent is in production, but the work is not automated.
How to Put AI Agents Into Your Architecture Plan in 30 Days?
You do not need to redesign your data platform, or restart your modernization roadmap, to make agents part of it.
Within the first month, focus on the domains your first agents will touch, usually customer, product and finance data, and the systems scheduled to change first.
Start by discovering what lives in those systems and mapping how critical datasets flow into reports, models and applications. Then name one owner per critical dataset, and record the business definitions each migration must preserve.
Next, expose this context through the catalog or knowledge layer your architects and agents already use, so every migration decision is checked against the dependencies agents will rely on later.
DataManagement.AI accelerates this process by automatically discovering metadata, mapping lineage across the modern data stack, identifying downstream dependencies, and creating a centralized knowledge layer that stays continuously updated as your environment evolves. It unifies master data, governance and operational trust into one continuously maintained system, resolves duplicate and conflicting records, and keeps ownership and policy attached to every entity as systems move.
Instead of spending months documenting assets manually after the redesign is done, your architects, data owners and agents work from the same map while it happens. Agents arrive to a foundation they can read, not one they have to guess at.
Agent Readiness Is Actually a Data Foundation Problem
Architecture programs will keep modernizing the infrastructure: cloud platforms, integration layers and agent frameworks. Accenture's numbers show how much of that work is already planned.
But no framework can tell an agent which record is authoritative, what a metric means or who is accountable for a change. That knowledge has to be built into the foundation.
Organizations that scale agents successfully do not just add an agent layer to the architecture. They build the data foundation the agents will stand on.
Warm regards,
Shen and Team