AI has quickly become one of the most discussed technologies in business. Attend almost any software conference or industry trade show and it is difficult to find a vendor that is not promoting some form of AI.

We experienced this firsthand at a recent industry trade show. AI seemed to be everywhere. By the second day, it became increasingly difficult to distinguish one AI message from another.

The discussion, however, often starts with what AI can do rather than what is required for AI to work effectively within an existing business process.

For companies that depend heavily on documents, particularly wholesale distributors, Document Management and AI automation need to be considered together.

AI provides increasingly powerful tools for reading, interpreting and acting on information contained within documents. Document Management provides the structure for capturing those documents, organizing them, connecting them to business transactions, controlling their movement through the organization and retaining them for future retrieval.

One without the other leaves a significant gap.

The better question for management may therefore be:

Is our Document Management environment ready to support AI-driven automation?

The AI Failure Rate Should Get Our Attention

There is good reason to look beyond the AI demonstration and consider the underlying infrastructure.

Research from S&P Global Market Intelligence data reported by CIO Dive found that the percentage of companies abandoning most of their AI initiatives increased from 17% to 42% in a single year. The average organization reported scrapping 46% of its AI proof-of-concept projects before they reached production.

Those are significant numbers.

They also suggest that simply having access to good AI technology does not guarantee a successful automation project.

Research from RAND reached a similar conclusion from another direction. RAND interviewed 65 data scientists and engineers about why AI projects fail. Among the leading causes were problems involving the underlying data and infrastructure required to support the AI application.

That should be particularly relevant to companies considering AI for document-intensive business processes.

In Document Management, poor data is not an abstract technical problem. It may be a poorly scanned document, an invoice format that changed without notice, an incorrect purchase order number, missing index information, inconsistent vendor item numbers or information appearing in a different location than expected.

AI can be very good at interpreting information.

But it still needs reliable documents, reliable data and a business process around that information.

The Document Is Often Where the Process Begins

Most business transactions begin with a document.

A purchase order is issued. A vendor acknowledgement comes back. A packing slip or receiver documents the arrival of the product. An invoice eventually requests payment.

The ERP records the transaction, but much of the information required to complete that transaction originates in documents.

This distinction becomes important when considering automation.

Automation does not begin when an invoice is finally posted to the ERP. It begins when the first document enters the process.

That means the ability to capture, identify, classify, index, retrieve and relate documents to one another becomes part of the automation infrastructure—not simply an electronic filing function.

This is where Document Management and AI begin to converge.

Document Management Has Changed

For many years, Document Management was primarily associated with scanning, indexing, storing and retrieving documents.

Those capabilities remain important, but the role of Document Management is changing.

A modern Document Management solution can become the operating layer between incoming documents and the company’s ERP or other business applications.

Documents arrive through email, scanners, uploads and other sources. They must be identified and classified. Relevant information needs to be extracted. That information may need to be validated against ERP data. Related documents need to be connected. Exceptions need to be routed to the appropriate employee.

AI adds important new capabilities to this process.

It can improve document identification, extract information from less structured documents, recognize relationships within the information and assist with decisions that previously required considerably more manual intervention.

But AI still needs an organized environment in which to operate.

That is where Document Management becomes increasingly important.

OCR Is Not Dead—Its Role Is Changing

There has been considerable discussion suggesting that AI makes traditional document capture and OCR obsolete.

I think that gets the relationship backwards.

Before AI can interpret information contained in a document, the document first has to enter the system in a usable form. Documents still arrive as PDFs, scanned images, email attachments and files generated by many different systems. Images can be crooked or poor quality. Vendor formats change. Important information can appear in different locations.

OCR, document capture and AI are therefore not competing technologies.

They are components of the same process.

OCR converts document images into machine-readable information. Document Management provides the structure for organizing and managing the document and its information. AI adds another level of interpretation and automation.

The objective is not to determine which technology replaces another.

The better question is how these technologies work together.

Capture the document. Understand the document. Connect it to the transaction. Then automate the process around it.

The ERP Is Part of the Process, Not the Entire Process

ERP systems are extremely good at managing structured transactional information.

Documents are different.

Consider a typical purchasing and accounts payable process. A purchase order may originate in the ERP, but the vendor acknowledgement may arrive by email. The invoice may arrive before the product reaches the warehouse. A receiver may be generated later. Vendor item numbers may differ from the distributor’s item numbers, and purchasing units of measure may differ from stocking or pricing units.

Employees routinely work through these differences.

For automation to do the same, the Document Management solution needs access to both the documents and the ERP information required to understand them.

This requires integration.

The document cannot exist in one environment while the transactional data needed to validate it remains isolated somewhere else.

Effective AI automation depends upon connecting the two.

Good ERP Data Becomes Even More Important

AI can interpret information, but it cannot eliminate the need for good underlying business data.

Vendor-to-distributor item cross-reference tables are a good example.

A vendor may identify an item as ABC-123 while the distributor identifies that same item as 45678. The vendor may sell it by the case while the distributor stocks it by the each.

An experienced employee may immediately understand the relationship.

An automated system needs a dependable way to establish it.

Maintaining vendor item cross references, units of measure, pricing relationships and other ERP master data may not sound particularly exciting compared with AI, but these fundamentals can determine whether an automation project succeeds.

The better the underlying business data, the more opportunities exist for Document Management and AI to automate the process surrounding it.

This is another reason the conversation about AI readiness cannot be separated from data readiness.

Matching Documents Changes the Opportunity

Document Management also creates something that AI by itself does not necessarily provide: context.

An invoice should not always be viewed as an independent document.

It may belong to a larger transaction consisting of a purchase order, vendor acknowledgement, receiver, invoice and related correspondence.

Once those documents are captured and logically associated, AI can work with considerably more information.

Instead of simply asking:

“What information is on this invoice?”

the system can begin asking:

“Does this invoice agree with the purchase order, acknowledgement and receiver?”

That is a much more valuable automation question.

We have moved beyond simply recognizing and extracting information from a document. We are now using the documents, ERP data and AI together to automate a business process.

Exceptions Are Where Document Management Becomes Critical

No practical automation process will eliminate every exception.

A quantity may not match. A price may be outside an established tolerance. A purchase order may be missing. A vendor may change an item number. An invoice may arrive before the warehouse receives the product.

Something will eventually require human judgment.

This is also where many AI demonstrations can create unrealistic expectations. Demonstrations naturally tend to show clean transactions. Real business processes are rarely that cooperative.

A well-designed Document Management environment should recognize that reality.

Instead of forcing employees to search through email, shared folders and ERP screens to understand the problem, the system should present the exception together with the documents and information needed to resolve it.

AI can identify and help explain the exception.

Document Management can route it, maintain the supporting documents, record what happened and return the transaction to the automated workflow after the issue has been resolved.

The objective is not necessarily to remove people from the process.

It is to remove people from the routine work and allow them to concentrate on the exceptions that actually require their knowledge and experience.

Document Management Creates the Automation Framework

This changes the way management should think about Document Management.

It is no longer simply the place where documents are stored after the work is completed.

Properly designed, it can become part of the infrastructure through which the work is performed.

The process can begin when the document arrives:

Capture → Identify → Extract → Validate → Match → Route → Resolve Exceptions → Update ERP → Retain and Retrieve

AI can improve several of these steps, but Document Management provides the framework that connects them.

That combination is where much of the practical opportunity for AI automation exists.

Evaluating Document Management and AI Readiness

Before beginning an AI document automation project, management should evaluate several fundamental areas:

How are documents currently received and captured?
Can documents be reliably identified and classified?
What information needs to be extracted?
What ERP information is required to validate that information?
Are vendor and customer cross-reference tables properly maintained?
Can related documents be associated with the same transaction?
What matching rules and tolerances are required?
What exceptions occur and who is responsible for resolving them?
Can the Document Management solution communicate with the ERP and other business applications?
How much manual effort currently exists in the process?

These questions are not simply technical requirements.

They help determine the organization’s Document Management and automation readiness.

Start With the Documents and Follow the Process

AI is expanding what can be accomplished with business documents. Tasks that once required substantial manual effort can increasingly be captured, interpreted, compared and routed automatically.

But the growing number of AI projects that never make it from proof of concept into production should remind us that the AI model is only one part of the equation.

Successful automation requires more than attaching an AI tool to an ERP system.

It requires understanding where the documents originate, how they move through the organization, what information they contain, how they relate to other documents and ERP data, and where human judgment is still required.

That is why Document Management and business process AI automation should not be evaluated separately.

Document Management provides the foundation. AI expands what can be done with it. Integration connects it to the business transaction.

For management considering an AI automation initiative, perhaps the first question should not be:

“What can AI do?”

It should be:

“Are our documents, data and business processes organized so AI can actually do something useful with them?”

Answer that question first, and the path toward meaningful automation becomes much clearer.

If you’d like to speak with our team directly, click here to book a call.

Document Management and AI: Building the Foundation for Automation