When an employee reports that an AI assistant cannot find a company policy or has returned an answer that conflicts with a business system, the problem may not lie with the tool itself.
The cause could be outdated source material, an access restriction, incomplete data or a failed connection between the AI tool and the system holding the information. For service desk teams, an AI-related ticket requires a broader investigation than a conventional application fault.
Nishanth Sirikonda, Cloud Solutions Architect at Texas-based charity FirstDay Foundation, said employees commonly report that an AI assistant cannot find a policy or that its response does not match information available in a business platform.
“That symptom can have several causes: outdated source content, a permission restriction, a connection problem or incorrect interpretation,” Sirikonda told UC Today.
The service desk’s first task is to establish what the employee was trying to do, then compare the AI response with the authoritative source. This is especially important when employees expect an AI assistant to work like a conventional search engine, rather than a tool limited by the information and access rights it has been given.
Direct Access Does Not Guarantee AI Access
Permissions should be among the first areas examined when an employee expects an AI tool to retrieve information from a system such as Workday.
Employees do not all have access to the same company information. Access can depend on job title, role and security settings, while payroll, absence and personal data may be restricted to specific positions.
“It all depends on permissions first, because not everybody will have the same permissions,” Sirikonda said.
The fact that a record exists in a source system does not mean it should be returned to every employee through an AI assistant. Nor does an employee’s ability to view information directly automatically mean that the AI tool has been authorised to retrieve it.
“Direct access does not automatically mean the AI should have access,” he said.
Sirikonda said the investigation should begin with three checks: whether the source information is correct, whether the employee can access it directly, and whether the AI connection is authorised to retrieve it.
Those checks can establish whether the issue lies with the source content, an employee’s access rights or the connection between the AI tool and the underlying business system. They also help teams avoid treating every missing result as an AI failure.
Better Tickets Support Faster Investigations
A ticket stating only that an AI assistant has given the wrong answer gives the service desk little to investigate.
Teams need system logs, login timing information and a clear description of where and when the issue occurred. They also need to determine whether the problem is recurring, limited to one employee or affecting a broader group.
Sirikonda said this context is necessary to establish whether an incident is caused by an individual’s permissions, a problem with a source system or a wider issue with the AI connection.
Analysts should also record what the employee asked, the response the tool provided, the information they expected to find and whether they can retrieve that information in the underlying application.
This matters where sensitive information is involved. An employee may know that a record exists without being entitled to receive it through an AI assistant. In that case, the tool may be operating as intended rather than failing.
The objective is not simply to decide whether the employee received an unsatisfactory answer. It is to determine whether the response was shaped by an access rule, a missing connection, incomplete source material or an unclear request.
