One of the lesser talked-about issues large organizations face is the siloes that their various business units operate in. It’s a peculiar situation born from the specificity of tasks and objectives assigned to hyper-specialized teams within an enterprise.
This separation of tasks inevitably leads to critical dependencies between teams. However, a lack of coordination between these teams can lead to a host of inefficiencies, including information lag, duplicated efforts and wasted time.
The obvious solution is to increase communication between teams that rely on each other. Yet, the path to increased communication is usually unpopular. Can you remember the last time you were excited to find that you received an exclusive invitation to another weekly cadence meeting?
Can’t somebody else do it?
One solution to improving communications and reducing inter-departmental friction is to hand the task over to AI agents. AI agents are systems powered by AI that perform complex tasks or make informed decisions with varying human involvement. They surpass traditional chatbots and large language models (LLMs) by integrating data and advanced analytics tools, making them more adaptable and capable of complex reasoning across various industries. They are touted for their ability to perform repetitive tasks that are less desirable for humans.
AI agents: What they are and why they matter
They don’t join meetings. They don’t forget to send updates. And they don’t lose track of who needs what information.
A real example: Payment integrity in health care
Take a health care payer’s payment integrity (PI) team. Their job: identifying overpayments – whether due to adjudication system errors, policy discrepancies, provider contract complexity or intentional abusive billing by bad actors.
Fraudulent activities, such as providers intentionally submitting false claims, inflating billing codes, or exploiting prior authorization, further complicate the detection of overpayments and highlight the need for vigilant coordination among teams. That means constant coordination with claims processing, provider networking, policy, Special Investigations Unit (SIU) and prior authorization.
It is common practice to hold regular cadence meetings among these teams to increase coordination.
Now imagine the workflow:
- PI identifies a strange spike in payments for a particular procedure.
- Analysts dig in, build queries and analyze trends.
- Hours (or days) later, someone in a meeting mentions a recent policy update or contract change that explains everything.
Time wasted. Work duplicated. Risks overlooked.
An AI agent, acting as the communication layer between teams, could surface policy changes, contract updates, or SIU activities automatically – long before PI analysts go down the rabbit hole. It could draft summaries, distribute updates, and cross-check changes across business units. It can even highlight overlaps in PI and SIU work so both teams avoid chasing the same pattern independently.
That’s real, measurable value without changing the core workflows.
Avoiding the “lethal trifecta” of AI risk
One advantage of this type of agentic AI deployment is that it mitigates some of the highest risks associated with AI and automation. Recently, researchers have begun to discuss AI and the Lethal Trifecta for AI deployments.
- Access to private data.
- Exposure to the open internet.
- The ability to communicate externally.
When all three combine, the risk skyrockets. Fortunately, AI agents used solely for communication endpoints between internal business units avoid two out of the three conditions, making them relatively low-risk.
You used to call me on your cell phone
Communication breakdowns are ubiquitous. The payment integrity scenario is only one example, but agentic AI can streamline communication without adding more meetings to already-overlooked calendars.
If organizations want to increase information flow without increasing friction, agentic AI can become the connective tissue between teams – quietly fixing one of the most persistent operational challenges in enterprise health care.
