AI Brings a CIO’s Old Visibility Challenge Back to the Forefront: The Lack of Visibility
Information systems have been built in successive layers. ERPs, CRMs, industry applications, historical databases, cloud services, and shared files were not conceived as a single, homogeneous whole. This complexity had already hindered the urbanization of the CIO’s architecture and migration projects. It takes on a new dimension when copilots and agents multiply connection points.
To respond correctly to a request, an AI must access the right information and interpret its context. To act, the constraint grows: it must know the systems involved, their dependencies, and the rights it possesses.
Yet a CIO does not always have this detailed view of their landscape. A source may remain accessible even if it no longer serves as a reference. An application may contain duplicated data. A local configuration can escape documentation that has become obsolete. The misalignment becomes apparent when two applications describe the same business object under different names, with no clear rule to differentiate them. These visibility issues existed before, but their impact gains a new dimension as AI uses multiply.
Being “AI-ready” doesn’t simply mean having enough data
AI readiness is often linked to data volume. Yet quantity and exploitable quality are not the same. Useful information must have identifiable meaning: where it comes from, when it was last updated, which application has authority, who is responsible, and what uses are permitted?
Consider an agent tasked with helping analyze a customer incident. Finding several reports mentioning the same device is not enough. One must distinguish a recent diagnosis from an old note, a validated procedure from a technician’s hypothesis, and information that is still applicable from outdated history.
Having the ability to read data does not guarantee its quality or the right to reuse it for a new purpose. The reasoning extends to infrastructure as well. A recommendation based on an incomplete inventory or an outdated configuration may be technically coherent but ill-suited to the actual situation. Multiplying connections without improving governance thus exposes AI to the inconsistencies already present in the IT landscape, with the risk of spreading them on a larger scale.
Alexis de Saint-Jean, Product Marketing Director at SoftProject, revisits this notion of AI Readiness: “Behind the term ‘AI ready’ lies a challenge that has always existed: knowing your data, understanding which data are usable and which are not. AI, and especially agentive AI, simply makes the answer more urgent. And this answer begins with a data catalog.”
From the technical catalog to a comprehensible map of data heritage
An inventory of databases and applications is a first step. For it to be usable, this knowledge must also be understood by business users. The data catalog thus brings together technical metadata, business definitions, responsibilities, and the provenance of information.
A finance team should be able to retrieve the agreed definition for a KPI; a data engineer, its source; a governance lead, its owner and the associated rules. Documentation helps settle two conflicting figures, identify sensitive information, or exclude a source that no longer holds authority.
To structure this knowledge, platforms like SoftProject and its approach to the data catalog bridge technical metadata, business definitions, responsibilities, and information traceability. This work requires keeping definitions up to date as applications evolve: a static catalog ages with the systems it describes. The goal is to have a usable map, not merely a directory reserved for data specialists.
This visibility eases trade-offs before any AI deployment: which sources to open for a given use case, which to cleanse, and which to keep out of reach. It remains incomplete, however, if one ignores the state of the equipment and environments on which applications run.
Visibility must also stay operational on the infrastructure side
A catalog created a few months earlier does not necessarily describe the current fleet. devices change configuration, software evolves, patches fail, and new alerts appear. For operations teams, knowledge of the IT landscape must keep pace with this reality.
RMM (Remote Monitoring and Management) tools bring this operational dimension. They centralize the inventory of devices, their monitoring, alerts, and various maintenance tasks. The Septeo IT Solutions software suite, therefore, combines device and server monitoring, alert management, patching, and asset inventory within a single console.
RMM complements the data catalog and the application map by providing an operational read: the condition of devices, missing patches, incidents, and past interventions. An alert gains value when it is correlated with patches and configuration changes. For an AI intended to assist technicians, simply knowing a server exists is not enough; its state and recent incidents can alter the diagnosis.
With agentive AI, knowing the context of the IT landscape becomes even more critical
This requirement grows when AI moves from consultation to action preparation. A vague answer in a knowledge assistant can mislead a user. An operational recommendation based on poor context can lead a technician to intervene on the wrong device or apply an unsuitable fix.
RG System Suite illustrates this evolution with Brain, its AI integrated into the RMM. It leverages the information available on the platform to contextualize incidents, help prioritize them, and prepare interventions. The technician retains control over execution: having richer context does not automatically authorize action without validation.
As Nadine Pilote, General Director of Septeo IT Solutions, explains: “AI readiness is not just about data: it also implies continuous visibility of the IT environment in which AI will seek its context and possibly act. The more AI becomes operational, the more the quality of the context we provide becomes strategic. This is the case for our Brain AI focused on business intelligence, which uses RMM-provided context to analyze and prepare interventions.”
The reliability thus depends on two linked conditions: having up-to-date information and clearly defining what the AI can consult, suggest, or prepare, and what requires a human decision.
From a static map to a living knowledge of the IT landscape
AI unites two disciplines often treated separately: knowledge of data heritage and visibility into the infrastructure. The catalog clarifies what information means, where it comes from, and who is responsible. Supervision describes devices, their configurations, and events that affect their operation.
As copilots and agents access the information system, these two readings must be updated and used together. An initially reliable piece of information can become obsolete. Likewise, a correctly inventoried device may present undetected incidents. In both cases, the diagnosis risks being biased. For a CIO, being “AI-ready” means maintaining this knowledge over time, as close as possible to real-time operations.
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