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Your AI Agent is recommending that your reps call a customer about a renewal. The recommendation looks reasonable on the surface: the account is active in the CRM, the opportunity is open, and the customer has used the product for three years.
However, their contract expired last week. Finance has placed the account on credit hold. Support has opened an escalation that morning and the partner portal lists a different account owner.
You can say that this is a case of AI hallucination, but more likely this is due to the AI piecing together a plausible answer from business systems that contradicts.
That is the overlooked problem in AI readiness. Organisations often ask whether they have the right model, enough trained staff, or the right assistant, copilot, or agent. Those questions matter. But before an AI system can make a reliable decision, it needs a consistent view of the business.
Adding AI will not resolve the underlying problem of poor data. It will just confidently produce the wrong answers from incomplete, outdated, or contradictory information.
Why AI exposes data problems
Moving from simple chatbots to the addition of AI Agents magnifies this issue further. Chatbots can function with a single knowledge base. Agents, on the other hand, are expected to interpret a situation, retrieve information from several systems, apply business rules and sometimes, take action.
That requires more than just generating answers, it requires permissions, understanding of standard process and the ability to link relevant information together.
Consider this situation; your renewal agent recommends calling a customer whose contract was cancelled that morning. The model is not hallucinating. It is reading three systems that disagree about the account.
If each system uses a different customer identifier or definition of “active partner”, the agent will not be able to reliably derive accurate answers.
This is the gap between an AI system that generates a plausible response and one that can operate safely across a real business process.
Recent infrastructure research illustrates the scale of the problem. Google Cloud’s State of AI Infrastructure Report, found that 83% of organisations surveyed believed they needed infrastructure upgrades to support production-grade agentic AI. In addition, 43% of IT leaders identified difficulty integrating legacy APIs and data sources as their biggest agentic AI infrastructure gap. Basically, if you want to elevate your AI systems to become a dependable operational system, you need to put in substantial effort beyond just having AI in your system.
What is data coherence?
Data coherence means that the systems an AI workflow relies on use consistent identities, definitions, relationships, permissions, and current information for the same business entities.
It does not necessarily mean clean data, a single database, or a completed integration project. A central database can still contain duplicate records, missing information and conflicting definitions.
Coherent data has several important qualities.
1) Shared identity
The organisation must recognise the same customer, partner, product, contract, and transaction across systems.
“Acme Ltd” in the CRM, “Acme Corporation” in billing, and “ACME-1047” in support may all refer to the same customer. The systems need a shared identifier or a reliable identity-matching process that connects them.
Without shared identity, an agent may combine information from two different accounts or treat one account as several unrelated customers.
2) Shared meaning
Teams must agree on what important terms and measures mean.
Definitions such as these should not vary by department:
- qualified opportunity
- active partner
- customer health
- renewal date
The AI system cannot resolve a definition that the organisation itself has not agreed on.
3) Current information
An agent needs to know what is true now, not merely what was true when a data export was created.
A customer may have entered escalation this morning. A renewal may have been approved an hour ago. A product may have been discontinued. A payment status may have changed since the last CRM synchronisation.
Every AI workflow needs a freshness requirement. A customer-success agent may need support events within minutes. A quarterly planning assistant may work with daily data. “Real time” is not a universal requirement, but “fresh enough for this decision” is.
4) Controlled permissions
An agent must know what it is allowed to see, retrieve, change, and approve.
An agent with access to everything is not necessarily more capable. It may be a security risk. Permissions should apply to:
- the user making the request;
- the agent’s role;
- the data source;
- the type of action;
- the customer or region involved;
- the approval level required.
Read access and action access should not be treated as the same thing. An agent may be allowed to read a contract but not change its renewal status. It may recommend a discount but require human approval to apply it.
5) Connected relationships
Information must be linked to the records around it.
A contract should be connected to the correct customer, product, partner, region, service agreement, and opportunity. A support escalation should be linked to the account and the relevant product. A meeting note should be associated with the correct customer and opportunity.
Without those relationships, an agent may locate a document but fail to understand its business significance.
Where coherence breaks down
Most data problems begin before AI enters the picture.
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Fragmented CRM systems
Large organisations often operate different CRM systems across countries, regions, acquisitions, or business units. Each system may use different customer names, ownership rules, lifecycle stages, and definitions of revenue.
An agent searching across those systems may find several apparently valid records and have no reliable way to determine which one is authoritative.
Unclear ownership
Many organisations have data policies but no clear owner for the definitions and records that matter most.
Someone should be accountable for questions such as:
- Who owns the definition of an active partner?
- Who approves changes to the customer lifecycle stages?
- Which system is authoritative for contract status?
- How quickly must support escalations appear in the CRM?
- Who resolves duplicate customer identities?
If nobody owns the answer, an AI agent cannot be expected to choose the right interpretation consistently.
Duplicated working data
Employees create local spreadsheets, presentations, documents, and databases when official systems do not contain the information they need. These copies may help people complete their work, but they create competing versions of operational truth.
A spreadsheet that was correct last month may still appear authoritative to an agent if it is indexed and lacks a clear expiry date.
Disconnected information
Contracts, emails, meeting notes, support conversations, and partner documents often contain information that does not appear in structured databases.
Search can make those documents visible, but visibility is not the same as context. A document must be connected to the right customer, product, contract, opportunity, and date. It also needs appropriate permissions and a clear indication of whether it is current.
If your CRM data is difficult for people to trust, it will be even harder for AI to use effectively. Read our practical guide, How to Fix Messy Customer Data in Your CRM: Data Migration and CRM Hygiene, to learn how to create a healthier foundation for your CRM.
A practical data-readiness model
Organisations can assess data readiness through four stages.
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Stage one: Accessible
Data can be located, but access is inconsistent and often depends on individual knowledge. AI tools remain isolated and depend on manual searches, or prompts.
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Stage two: Connected
Core systems exchange data through APIs, pipelines or integration platforms. However, identity, definitions and ownership remain inconsistent.
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Stage three: Coherent
The organisation has shared entities, governed metadata, clear lineage and agreed business definitions. Agents can retrieve relevant context with reasonable confidence and understand the sources of data.
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Stage four: Adaptive
Data quality checks, event-driven updates, permissions and feedback loops are built into the operating model. Agents can act across systems while recording why they acted and what changed.
There is no need to clean every piece of data before starting. A better approach is to choose one important workflow that crosses several systems. This could be partner deal registration, customer onboarding or contract renewal.
Coherent data is the real starting point
An agent cannot create reliable context from duplicate companies, incomplete contact records or conflicting account histories. It can only work with what the organisation gives it.
When records are accurate, connected and kept up to date, AI systems have a much better chance of understanding what is happening and suggesting useful next steps. This applies whether your organisation uses HubSpot, another CRM or a wider collection of connected business systems. The platform matters, but the quality of the information flowing through it matters more.
Clean data may not be the most exciting part of an AI strategy, but it is what makes the strategy workable.