An office manager or legal assistant prepares documents for digital signature, designates the correct signatories, sends the document via an e-signing platform, and stores the signed result in the document management system. At first glance, this is a repetitive, easily followed sequence of steps. Yet the answer to whether AI can take this over is: partly, and only under conditions.
Structuredness scores high at 4: for a fixed document type, such as a standard contract or a supply agreement, it is precisely known which fields need to be filled in, where the signature boxes are located, and in what order signatories take their turn. This is a task that can be captured well in rules, and therefore suitable for automated processing. Volume (4) reinforces this picture: companies that have many similar documents signed benefit from a system that takes the repetitive work off their hands.
But structuredness only applies as long as the document type is fixed. As soon as an organization works with varying contract forms, negotiated attachments, or documents where the signatory differs case by case, the basis for automation falls away. Then someone first has to determine which template and which process applies, and that is precisely the part that cannot be captured in a fixed route.
The two axes that really determine this judgment are error costs (2) and compliance (2), and these are unfavorable. A signing process touches on the legal validity of a document. If the system designates the wrong person as signatory, if a signature ends up on the wrong version of a contract, or if a signed document is not archived correctly, problems arise that cannot be resolved with a simple correction. An incorrectly signed employment contract or a supply agreement with the wrong authorized signatory can have legal consequences that far outweigh the time saved through automation.
That is why the role of AI here is limited to support: preparing the document, filling in standard fields, and sending it via the e-signing platform can be done with rules and robotic process automation (RPA). But designating the correct signatory, especially when signing authority, function, or mandate play a role, requires a judgment that is not simply left to a system. That is precisely the part where category two from the taxonomy comes into play: AI can make a proposal, but a human must approve or reject that proposal, with reason.
Whether automation works here depends on two things. First: is there a fixed signing process per document type? For a standard agreement with a fixed structure, this can be automated; for documents that are compiled case by case, it cannot. Second: can the correct signatory be identified correctly based on fixed data, such as function or role in a system? If that identification is simple and unambiguous, a system can support that step. As soon as signing authority depends on context, such as the size of a contract amount or a specific power of attorney, human judgment is needed.
This also explains why the outcome differs per organization. A company that has hundreds of similar supplier contracts signed per month, with a fixed authorized signatory per contract type, can automate a large part of the process and limit the human role to spot checks. An organization that mainly processes custom agreements, merger documents, or contracts with varying powers of attorney has little use for automation: there, every step is unique and the risk of a costly mistake is too great to leave to a system.
The comparison with other controlling tasks is illuminating here. Just as with screening customers and partners against sanctions lists, the same applies: AI can generate a proposal or signal, but the final judgment and responsibility remain with a human once the consequences of an error are serious. The signing process has that same characteristic: the costs of an error are high enough to advise against full automation, even though the task is well structured on paper.
When it comes to archiving already signed documents, the picture does change, however. Storing and making signed documents retrievable closely resembles the task discussed on the page about digitizing and making a paper archive accessible, and there the opportunities for automation are considerably more favorable than with designating signatories.
Please note: if this task is related to changes in job roles or workforce deployment, separate legal requirements apply; this page only concerns the task itself, not the consequences for employees.
The conclusion is that preparing and sending standard documents for signature can be supported with rules or RPA, but that designating the correct signatory and overseeing the result remain human work as long as the error costs and compliance risks are high. Would you like to know how this plays out for your own organization, including other tasks performed by office managers or legal assistants? The free quickscan at ftetoai.com provides, in twelve questions, without an account, an indication of which part of the hours in your profile can be taken over by AI today. The full workscan, with an in-depth analysis per task, is still under construction.
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