Overwhelmed By Documentation? AI Agents Can Help.

Today’s business settings are increasingly complex, and operations teams are drowning in documentation. Complicated data in the form of emails, fragmented SaaS data, outdated systems that lack APIs and tasks that require manual reconciliation create an operational drag that legacy automation can’t address.

Today’s technology can help navigate this data mess. Not only can AI agents handle fragmented data, but they can reason and make decisions across diversified systems and integrate the way a human would. They can do so even when no APIs are available. This not only cuts costs, but it enables skilled professionals to do higher level work.

Enterprises are bogged down

The “swivel chair” problem is just one reason that operations teams are drowning in documentation. Others include the increase in SaaS and data silos; the API misconception; and the rise in user expectations.

The “swivel-chair” problem

Although the volume of the data is staggering, the primary cause of the costly bottleneck is the data’s complexity. A human has to serve as the bridge between the disparate information: an email, a PDF contract and a legacy ERP system. It’s the “swivel chair” problem, and it is simply not scalable.

The rapid growth of SaaS and separate storage systems 

With the introduction of each new application comes a new, separate data silo. As such, one simple request may need changes in multiple systems, resulting in even more logs and support tickets.  

The misconceptions around API

The healthcare industry largely relies on outdated systems with limited APIs. The result? Work piles up, and teams must do it all manually.

Rising customer demands

It’s not uncommon to expect prompt service, and the days of waiting lengthy periods of time for a basic update or to secure data access are over. These rising customer demands further weigh down back-office teams already maxed out.

The effects

There are additional effects resulting from the volume and complexity of data.

Revenue losses 

Revenue loss is the most significant. In response to a build-up of requests for materials or expensive items that remain unbilled, a hospital’s revenue losses can reach into the millions. 

In procurement, the bottom line is significantly impacted when a backlog of invoice reconciliation tickets prevents discounts for early-pay. Some estimates indicate that these inaccuracies cost more than $2,500 annually per hospital bed.

Operational gridlock & delayed care

Backlogs build on themselves; delayed requests in disparate systems, for example, also delay patient care. Imagine that a nurse places an order in Epic on a Thursday for a procedure the following day, but an entry error translated into a fulfillment delay. A human wasn’t able to review the ticket until the following week, delaying surgery and keeping the patient in pain for longer than necessary.

Turnover and declining employee performance

The most experienced analysts and IT professionals are spending their days doing low-value, repetitive “detective work,” where they hunt for data and perform manual entry. When experienced technical staff work on trivial tasks, like searching for data, it leads to burnout and turnover. 

In fact, the TIAA Institute Healthcare Workforce Survey found that “40% of surveyed healthcare employees say that staffing shortages and a lack of resources are a big problem, and 30% say stress and burnout are a big problem. These concerns are especially high among those at elevated risk for turnover, rising to 63% and 56%, respectively.”

High price for unproductive labor

To tackle the backlog, companies often choose to hire expensive workers by bringing in contractors or using business process outsourcing (BPO) services. This makes a data issue a big, ongoing expense in the budget without actually solving the main problem. The costs of running healthcare administration are estimated to exceed $1 trillion each year.

Old solutions didn’t work. Here’s why.

Robotic Process Automation 

While Robotic Process Automation seemed like a strong solution for basic tasks like screen-scraping, the bots aren’t able to think or make decisions. When a website’s user interface changes, or a request needs some decision-making (i.e.: “Is ‘PulsePoint USA’ the same as ‘PulsePoint Medical’?”), the bot stops working; it can’t manage the “swivel chair” issue.

Offshore business process outsourcing and contract workers 

Hiring more people means paying a premium for extra work, but that doesn’t improve any processes. Additionally, hiring new employees means training hours, and security issues persist.

iPaaS platforms

Platforms that connect apps, data and services enable businesses to automate processes and improve operations by ensuring that all systems communicate effectively. Tools like Workato are great for linking systems that have clear and updated APIs, but they struggle when it comes to the final step: they can’t read a confusing email, understand a blurry product picture or fill out a complicated Excel VBA template. While they address the issue of connecting APIs, much of the behind-the-scenes work doesn’t begin with a straightforward API request. 

How AI agents handle large data sets

AI agents are capable of understanding unorganized data; when they read an email from a doctor that says, “We need to add the ‘Zimmer NexGen knee component’ used in operating room 4,” agents can understand the meaning of the message and identify important details without requiring a specific format. This removes the need for manual organization.

According to Microsoft, cognitive AI “mimics human thinking and decision making by learning from data, adapting to new information, and refining its approach to problem solving.” It’s how AI agents can conduct complicated detective work across different systems. For example, if an invoice gets stuck in a “ghost queue,” an agent can identify that the vendor is “PulsePoint Medical.” They can then look up purchase orders linked to “PulsePoint USA” in the ERP system, compare invoice items to a PDF contract and determine the right match. This job needs reasoning, not just numbers.

AI agents also use integrations that work like humans. When a legacy system lacks an API, the agents keep trying. They automate the same steps that a human would take by completing and running the official VBA Excel template used by the supply chain team and adapts to the situation.

As an always-available digital workforce, AI agents work all day, every day and can handle tasks that clear up delays. A goal at one healthcare system is to ensure its items list is updated and completed before the next business day starts – something the human team was unable to accomplish. 

AI agents effectively manage backlogs 

AI can seamlessly sort through and prioritize tasks so that teams can better keep track of their work. That’s why AI agents are a great option for handling the documentation backlog.

Immediate effect on profits

Every year, hospitals lose millions of dollars. However, by automating the process of matching non-catalog items, they can secure discounts and stop these losses. This not only makes things run more smoothly but contributes to the bottom line

Growing without additional hires

Agents offer a flexible way to manage the increasing volume of data. One AI worker can do the job of up to 10 full-time employees, enabling an organization to expand without increasing expenses for support staff.

Complete ability to track and follow rules 

An agent-driven workflow creates a permanent record, while a manual process relies on emails and memory. Every action taken by an agent is recorded – and it can be reviewed. This is a big advantage for industries with strict regulations, like healthcare. 

Reassigning skilled workers 

When repetitive tasks are automated, skilled workers are freed up to focus on more important, strategic activities. For example, supply chain analysts can concentrate on negotiations rather than searching for purchase order numbers. At the same time, the IT team can spend less time resetting passwords and more time working on high-value projects.

How use AI agents successfully 

Begin by addressing an issue that causes significant friction, like losing money or catching up on unpaid invoices, where failing to fix it could make a significant financial dent. Automate that first. To gain the trust of the team, a small trial to demonstrate value is imperative before expanding on the use of AI agents.

Humans should make important choices 

Before implementing independent AI agents, it’s important to have a human supervisor. While critical decisions can involve agents to help resolve issues, a person should give the final approval. This process builds trust and ensures accuracy. 

Concentrate on results instead of just the technology 

The aim isn’t simply “to use AI;” it’s to “prevent revenue leakage” or “ensure vendors are paid on time.” The whole project should be evaluated based on a specific business goal. If the final result cannot be measured, redirect the focus. 

Collaborate with an expanded team 

Work with a partner who can closely examine the company’s processes and systems. Knowing the business well is essential for the success of an AI agent. A simple, ready-made solution probably won’t be able to manage the complicated needs of a complex business setting. 

The bottom line

AI agents help organize complicated work across disparate systems and make it easier to see results. They allow healthcare organizations to grow while enhancing compliance and increasing profits. By ending the swivel-chair work, teams can reassign team members to focus on strategy and ensure that IT operations coincide with business goals.