When you get order confirmation documents from multiple suppliers and you have no EDI in place, you often get the same information written in different shapes and forms. Take date formats, for instance.

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One supplier might date an invoice "April 1, 2024," while another might use"01/04/2024," or vaguely put it "beginning of April." While these variations make sense to humans, ERP systems struggle to interpret these inconsistencies correctly, making a mess out of your data, your schedules, and everyday operations.

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Agentic processing of data across various types of documents simply removes this problem, by standardizing and interpreting different date formats automatically. When you deal with tons of unstructured documents daily and you need to filter them for information that makes sense for all departments and internal systems, AI Agents just save your day. And they do it fast and right.

How a simple date format can mess up your work

Date formats vary significantly depending on region, industry, and individual supplier setup. For example:

• MM/DD/YYYY is common in the United States.

• DD/MM/YYYY is used in many European and Asian countries.

• YYYY-MM-DD is a widely accepted ISO standard.

• Relative dates like "next Friday" or "end of Q2" introduce additional complexity.

ERP systems typically require a standardized format, meaning businesses must manually convert these date variations before inputting them into their systems. This process is time-consuming, prone to human error, and inefficient at scale.

How AI Agents solve the problem

Due to LLMs, AI Agents can recognize and standardize multiple date formats based on a common denominator, ensuring they’re compatible with ERP systems. Here’s how they do it:
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1. Pre-built templates for standardization

People can instruct AI agents to recognize and process different date formats based on supplier patterns. Customizable templates allow businesses to define how dates should be structured before they enter the ERP system.

2. Automated recognition and conversion

The LLMs, i.e. the brains of AI Agents, can interpret various date expressions, including explicit dates (e.g., "March 15, 2024") and vague descriptions (e.g., "end of March"). If a supplier specifies a range, AI Agents can default to the first or last possible date based on predefined business rules.

3. Context-based adjustments

AI Agents allow businesses to set rules for interpreting relative dates. For example, if a document states "week 14 of the year," the Agent can assign the correct date based on a standard starting point. People can specify a preferred default, such as setting the "beginning of April" to April 1st unless stated otherwise.

4. Machine learning for continuous improvement

AI Agents improve over time by learning from corrections and feedback. Users can provide good and bad examples, helping the system refine its accuracy.
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What’s to gain for business  

Using AI Agents for processing documents with different date formats comes with:

• Less manual work: Automating date standardization eliminates the need for people to manually correct inconsistent formats.

• More accurate data: AI-driven recognition minimizes errors that could disrupt operations or lead to incorrect scheduling.

• Seamless ERP updates: Normalized data gets smoothly into ERP systems without extra processing.

• Capacity for scaling: AI Agents handle high in number, low in volume documents efficiently, enabling organizations to scale operations without adding more people to the team.

A smarter approach to data processing

The challenge of varying date formats is just one example of how AI Agents can streamline business workflows. By leveraging AI Agents, companies can automate complex data processing tasks, improve accuracy, and enhance efficiency across their operations. As AI Agents get even more specialized, these capabilities will gain a significant advantage in managing structured and unstructured data with precision and reliability.