The information needed to prepare a contract often already exists.
It is sitting in an email thread.
A customer may have agreed the price in one message, confirmed the contract term in another, provided their company details elsewhere, and attached the document they want signed. Someone then has to read through the conversation, work out what was actually agreed, transfer the information into a contract, configure the signing process and send it to the right people.
The contract itself may take minutes to prepare. Finding and assembling the information can take much longer.
This is where an AI agent can change the workflow.
Rather than treating email as something that sits outside the contracting process, the agent can use the conversation itself as the starting point for preparing the agreement.
The email thread becomes the context
The workflow starts by connecting an email account to the agent.
The user then locates the relevant conversation and gives the agent access to that thread. There is no need to manually copy the important information into a form.
The conversation becomes the agent’s context.
The agent reads the thread, understands the discussion and identifies information relevant to the contract. This can include the parties, commercial terms, dates, pricing, payment arrangements, signatories and other details that have been agreed during the negotiation.
Importantly, the agent is not simply extracting keywords.
A negotiation can contain several proposed prices, dates or terms before the parties settle on a final position. The agent needs to understand the conversation well enough to distinguish between proposals, counter-proposals and the terms that were ultimately agreed.
That is where semantic understanding becomes more useful than conventional field mapping.
From conversation to document
Once the relevant information has been identified, the agent examines the document that needs to be prepared.
This can be a predefined contract template or an ordinary document supplied by the user.
For a template, the agent can identify the fields that need to be completed from the list of fields the user has registered, and look through the email thread for information that could populate them.
For an ordinary document, the agent can analyse its structure and identify areas that may need to be completed or amended. It can then compare those requirements with the information found in the conversation.
The important difference is that the workflow does not depend entirely on a rigid CRM schema or a document template created in advance.
The agent works from the meaning of the conversation and the structure of the document.
That makes the approach more flexible when the business encounters a document or transaction that was not anticipated when its workflow was designed.
The agent proposes; the user decides
For a contract workflow, automation should not mean removing the human from important decisions.
The agent can identify proposed changes and show them to the user for confirmation.
For example, it might identify a company name, contract value or start date from the email conversation and propose inserting that information into the document. If the user accepts the suggestion, the change can be applied. If the information is ambiguous or incorrect, the user can reject it or provide an alternative.
This creates a useful human-in-the-loop model:
Email conversation → AI interpretation → proposed changes → human approval → signed agreement
The agent does the work of finding and preparing the information. The person remains responsible for approving the result.
This distinction is particularly important in legal and commercial workflows, where accuracy and accountability matter. Thomson Reuters’ 2025 research found that contract drafting was already one of the leading professional uses of GenAI, with 51% of legal professionals identifying it as a use case.
Dan Schaeffer, Senior Specialist Legal Editor at Thomson Reuters, has similarly described AI’s role in contract lifecycle management as helping to “streamline and automate processes” while emphasising the need for human oversight and involvement.
Attachments become part of the process
The same contextual approach can be applied to attachments.
An email may contain a contract, statement of work, purchase order or other supporting document. The agent can identify relevant attachments and use them as part of the workflow.
If the user confirms that a particular document is the one that should be signed, the agent can continue processing it rather than requiring the user to download it, open another application and start the process again.
This creates a continuous workflow:
Conversation → documents → contract preparation → e-signature
The user is no longer manually coordinating each stage.
Preparing the signing request
Once the document has been prepared and approved, the information already extracted from the conversation can also be used to configure the e-signature request.
The agent can pre-fill relevant workflow information such as the recipient’s email address, signing method, reminders and other available options.
If something essential is missing, it can ask the user.
Once the required information is available and the user has approved the document, the agent can initiate the signing request.
The process then continues beyond document preparation. The agent can provide updates as recipients complete their parts of the signing workflow.
The result is not simply an AI document generator.
It is an end-to-end workflow that starts with an existing business conversation and ends with an agreement being sent for signature.
Why this is different from traditional automation
Traditional business automation generally depends on structured data.
A CRM has fields. A contract template has fields. An integration maps one to the other.
This works well when the process is predictable.
But commercial negotiations are rarely predictable.
New terms appear. Documents change. Customers send their own contracts. Information is revised during negotiations. A business may need to execute a transaction for which nobody has previously built a workflow.
Keeping traditional automation working in these circumstances can require constant configuration and maintenance.
An AI agent offers a different model.
Instead of requiring the business to anticipate every possible variation, the agent can interpret the context at the point where the work needs to be done.
That is one reason agentic AI is becoming particularly interesting for professional workflows. Thomson Reuters describes agentic AI as moving beyond generating content to carrying out multi-step work involving information gathering, reasoning and execution.
The bigger opportunity: turning conversations into actions
The significance of this approach extends beyond contracts.
A large amount of business information remains embedded in email, chat and documents. Microsoft found in its 2025 Work Trend Index that employees are interrupted by meetings, emails or chats roughly every two minutes during working hours, while 46% of leaders said their organisations were already using agents to fully automate workflows or processes.
The opportunity for AI agents is therefore not simply to answer questions or generate text.
It is to take the information already present in a business and turn it into action.
For contract signing, that means an employee no longer needs to manually reconstruct a deal from an email thread.
They can give the agent the conversation and the objective:
“Prepare this agreement for signature.”
The agent interprets the discussion, identifies the relevant information, works with the document, proposes the necessary changes, asks for human approval where required, prepares the signing workflow and initiates the request.
The human remains in control.
But the administrative work between agreement and signature can largely be handled by the agent.
That is the real promise of agentic automation: not simply making individual tasks faster, but allowing business processes to begin with intent and context rather than forms and fields.
