There is no shortage of conversation about what AI can do in background screening. There are real opportunities to reduce repetitive tasks, process information faster, improve quality, and make complex workflows easier to manage.
But what AI can do and what it takes to make AI work well in background screening are two different questions. The second requires understanding the complexity of screening itself: information comes from thousands of different sources, exceptions are part of everyday workflows, and some tasks still depend on people or information that is not readily available digitally. Those realities shape where AI can help, what it needs to work well, and where other approaches may make more sense.
That context matters because most of us have seen AI produce an answer that is incomplete or simply wrong. In everyday life, that may be little more than an inconvenience. But when the information being processed can affect both the people being screened and those relying on it to make decisions, the consequences can be much greater.
The important question for screening leaders, then, isn’t “Where can we use AI?” It’s “What does AI need in order to work well here?”
Here are five things to look for.
1. Start with the process, not the AI
When new technology becomes available, it’s natural to start looking for places to use it. A better starting point is the process itself. What is it trying to accomplish? Which steps are necessary? Which could be simplified or removed? Which can already be handled through traditional automation?
A useful order of operations is to simplify before you automate, and automate before you add AI.
In many cases, a simple rule or conventional automation may be all that is needed. For example, moving an order to the next step once a required search is complete may only require traditional automation. A task that requires interpreting information or responding to a situation that doesn’t follow a predefined path is a different kind of problem. Even then, the fact that AI can be applied doesn't necessarily mean it should be.
The goal is not to find more places to use AI. It is to choose the right approach for the task at hand.
2. Give AI information it can trust
AI can process large amounts of information quickly, but its output still depends on the quality of the information it receives.
That matters in background screening, where relevant records can reside across courts, government systems, employers, licensing and credentialing bodies, and other independently operated sources. Those sources vary in how information is structured, maintained, and accessed. Once information is found, it still has to be connected to the right person and accurately reflect what the source says before it can be used with confidence.
AI can help read, organize, and process that information more efficiently. But if the underlying information is incomplete, inaccurate, or associated with the wrong person, processing it faster does not resolve the underlying problem.
For screening leaders, that makes the information behind an AI-enabled process every bit as important as what the AI can do with it. Ask where the information comes from, how it is structured and governed, and whether it can be trusted.
3. Design for how screening actually happens
On paper, many screening tasks can look relatively straightforward. In practice, they rarely follow a perfectly predictable path.
Consider employment verification. The basic task sounds simple: contact an employer, reach the right person, ask the necessary questions, and capture the answers.
Anyone familiar with the process knows it rarely works that neatly. A call may go unanswered. It may be transferred to another department. The person who answers may not have the information. Someone may need to call back. A person may not want to interact with an automated system. The conversation may go in a direction the system was not expecting.
Those are not edge cases outside the process. They are part of the process.
That is why applying AI successfully requires more than teaching it how to perform the basic task. The surrounding workflow also has to account for the unanswered call, the unexpected response, the missing information, or any of the other ways a screening process can veer from the expected path.
The same is true when technology is used to extract information from court records. Capturing and structuring the information may reduce manual data entry, but the workflow still has to account for reviewing what was captured, correcting information that is missing or inaccurate, and making sure each relevant case is handled appropriately before the information moves on.
Across screening workflows, a useful application of AI has to work not only when everything goes according to plan, but when it doesn’t.
4. Be clear about where AI stops
AI does not have to take on an entire process to make a meaningful difference. With a clearly defined task and the right context, it may be able to handle some work on its own, help someone complete a task more efficiently, or validate work that has already been completed.
Court records offer a simple example. When a record isn't readily available online, a person may still need to go to the courthouse to access it. But technology can help read and extract the information they capture and turn it into structured data, reducing the manual work required afterward.
The same principle applies when AI encounters something it cannot handle reliably. There needs to be another path, which may mean handing the task to a person. That makes it important to be clear about what AI is being asked to do, what it is not being asked to do, and where its role ends.
5. Measure the outcome, not the AI
Ultimately, the best way to evaluate an AI-enabled process is to look at what it actually improves.
Does it shorten turnaround time? Improve quality? Reduce repetitive tasks or manual effort? Lower cost? Does it successfully handle the exceptions that can slow down a screening process? Does it create a better experience for the people interacting with it?
Those are the measures that matter.
Before treating an AI-enabled approach as an improvement, prove that it is one. The value of AI should be judged by what changes because of it, not simply by the fact that the technology is being used. If an AI-enabled approach does not make the process meaningfully better, the presence of AI itself adds little value.
Ask better questions about AI
Screening leaders do not need to become AI experts to make informed decisions about AI. But they do need to understand the problem AI is being asked to solve, where it can genuinely improve the process, and where its limits are.
The goal isn’t to use more AI. It’s to use it where it can make screening work better.
Heading to the PBSA Annual Conference in Arlington, Texas, September 27–29? Come see InformData at booth #231 to continue the conversation about what AI and technology can be applied around the realities of screening. We’re also hosting a VIP lounge for clients and invited guests, with interactive discussions and demonstrations of how InformData is putting these ideas into practice. Interested in joining us? Ask your InformData representative about access.
