Most organizations have far more information than they realize. The problem is that knowing the information exists and actually being able to find it are two very different things.
Think about a company that has been in business for 30 or 40 years. Over that time, it may have accumulated thousands of project files, contracts, reports, engineering drawings, meeting notes, correspondence, customer records and other documents. Some may still be on paper, while newer information is scattered across shared drives, databases, email accounts and different software systems.
Then someone asks a fairly simple question: Have we ever worked with XYZ Company?
Finding the answer shouldn’t be difficult, but it often is. Someone may remember working with the company years ago but not remember when. Another employee may know there are old project files somewhere. A search of the shared drive might turn up a few PDFs, while additional records are sitting in boxes that haven’t been opened in years.
The information is there. It just isn’t very useful if no one can get to it.
Going Digital Is Only Part of the Solution
Digitizing paper records solves an important problem. It protects the information, reduces the need for physical storage and gives an organization an electronic copy that can be accessed without finding the original piece of paper.
But anyone who has searched through a folder containing hundreds of scanned PDFs knows that digital doesn’t automatically mean easy to find.
If you have to know the project name, customer, year or exact terminology used in the document before you can find what you’re looking for, there’s still a lot of work involved.
This is where the combination of properly organized information and AI becomes interesting. Instead of requiring an employee to figure out which files might contain the answer, the employee can start with the question they actually want answered.
What Would It Look Like to Ask Your Records a Question?
Let’s go back to XYZ Company.
An employee wants to know whether the organization has worked with them before. Rather than searching through several systems and opening files individually, they ask:
“Have we ever worked with XYZ Company?”
The AI searches the information it has access to and finds records associated with the company. Maybe it discovers projects going back to 2008, along with contracts, reports and correspondence.
Now the employee has another question: “What projects did we work on with them?”
From there, they might want to know whether any problems were documented during those projects or see the reports associated with the most recent one.
That’s very different from typing “XYZ Company” into a search box and receiving 137 files to sort through.
The employee is looking for an answer, not a list of documents.
You Still Need to Know Where the Answer Came From
There is an important piece of this that shouldn’t get lost in the excitement around AI.
If an employee is going to use information to make a business decision, they need to be able to verify it.
An AI system shouldn’t simply say that your company completed six projects with XYZ Company and expect everyone to trust the answer. The employee should also be able to see the records the system used to reach that conclusion and open them when more detail is needed.
This is especially important when you’re dealing with contracts, engineering information, compliance records or decisions that may have been made decades ago.
AI can make finding the information dramatically faster, but the original records remain the source of truth.
The Questions Will Be Different for Every Organization
The XYZ Company example is intentionally simple. The real value depends on the information an organization has and the questions its employees regularly need to answer.
An engineering team might want to know whether a particular equipment failure has happened before and what was done to correct it.
Someone managing a facility could ask what work has previously been performed on a specific building or piece of equipment.
A compliance team may need records related to a policy during a particular period.
Leadership might want to know what previous initiatives addressed a particular problem and what the outcome was.
Even a question like “Why did we stop doing it this way?” could lead to information buried in meeting minutes, correspondence and project documentation that gives today’s team valuable context.
In many cases, organizations already have the information needed to answer these questions. Employees simply don’t have a practical way to search across all of it.
Don’t Forget About the Records That Are Still on Paper
This becomes especially important for organizations with a long history.
There may be decades of valuable information that has never made it into a modern system at all. Engineering drawings may be stored in flat files. Project documentation may be boxed in a warehouse. Old correspondence, reports, photographs and meeting minutes may exist only on paper.
Scanning those records is the beginning, not the end.
Once the records are captured, they can be classified and organized so they become part of the organization’s larger information environment. Existing electronic records can be incorporated as well, bringing information that previously lived in different places together.
That’s when an old document stops being something the organization is simply preserving and starts becoming information that can be used again.
AI Is Only as Useful as the Information Behind It
It’s easy to focus on the AI because that’s the part people see. Someone types a question and receives an answer in seconds.
What makes that experience possible is everything happening behind it.
The organization’s records have to be captured accurately. The information has to be organized in a meaningful way. Relationships between records need to be understood, and appropriate security and access controls have to remain in place.
If the underlying information is incomplete or disorganized, adding AI doesn’t magically fix it.
For organizations exploring AI, that makes the information they already own a good place to start. Before asking what an AI platform can do, it may be more useful to ask what information the organization would want employees to be able to access through it.
There May Be More Value in Your Records Than You Think
Companies spend a lot of time thinking about the new information they’re creating, but years of existing records can contain an enormous amount of knowledge.
They document relationships with customers and vendors, previous projects, decisions, successes, failures and lessons that employees may otherwise have to learn all over again.
Making that information accessible doesn’t mean someone has to sit at a computer and manually catalog every answer the organization might need someday. The goal is to create a well-organized information foundation that allows people to find what they need when they need it.
So the next time someone asks, “Have we ever dealt with this before?”, finding the answer doesn’t have to depend on who happens to remember.
The organization already did the work. The records already captured the story. Now we have much better ways to put that knowledge back to work.
ExtractIQ helps organizations capture, organize and make their existing information searchable, so the answers buried in years of business records can become useful again.