How to Prepare for ECM 2.0
Once upon a time, companies thought digitizing documents was the finish line. They scanned their paper files, uploaded them to an ECM (Enterprise Content Management) system, and patted themselves on the back. "Look at all these files we can search now!" someone declared.
But here’s the problem: those documents are still just sitting there. Digital? Yes. Useful? Not really. It’s like moving from a paper filing cabinet to a digital filing cabinet — but nobody told the files to get to work.
ECM 2.0 doesn’t just store files. It understands them, acts on them, and most importantly makes it easier for downstream systems and teams to make critical decisions. Imagine workflows that alert teams when contracts are about to expire, escalate invoice processing based on predefined thresholds, and ensure claims are ready for adjudication. Unlike systems that directly approve payments or claims, Adlib’s role is to support these decisions by identifying, validating, and routing critical information. This ensures that downstream decision-making is more accurate, timely, and complete.
This blog will show you how to assess if your ECM is ready for AI, what “ECM 2.0” really means, and how solutions like Adlib are turning static files into dynamic, AI-driven data engines.
It means your ECM isn't just a storage unit — it’s a content intelligence platform.
Here’s what happens when AI meets ECM workflows:
- Files don't just sit there. They get read, identified, and tagged automatically.
- Workflows don't wait for you. They trigger actions, approvals, and escalations automatically.
- Decisions are supported, not made. AI can recognize patterns (like missing data in claims) and ensure the right data is available for human or system-based decision-making.
- Data lakes get richer. Extracted data is fed into data lakes for analysis, reporting, and AI model training.
It’s not just “store it and search for it later” anymore. It’s “store it, process it, and analyze it” with data lakes and AI-driven insights.
So how does Adlib turns a static file into a workflow engine? It comes down to three core capabilities:
The old way: Drop files into a shared folder, have someone tag it as "Invoice" or "Contract" (if they remember).
The new way: Adlib automatically identifies document types, even if they all look the same.
How It Works:
• Adlib reads the document’s content (not just its title) to understand what it is.
• Adlib recognizes the difference between a claim form, a purchase order, or a supplier invoice, even if they all come in as PDF files with cryptic names like “Scan_000012.pdf.”
The result: No more manual tagging or misfiled documents.
This is where Adlib really shines. It takes unstructured content (think: invoices, claims, forms) and extracts the essential details. We’re talking names, dates, amounts, signatures, and more.
The old way: Download the invoice. Manually copy-paste the invoice number into a database. The new way: Adlib extracts the data automatically and feeds it directly into your ERP, CRM, or other systems.
Example Use Case:
• An invoice comes in.
• Adlib extracts the invoice number, amount, and due date.
• That data is sent to your ERP, and it’s also fed into a data lake for broader analysis on cash flow, payment patterns, and supplier risk.
• No manual data entry. No bottlenecks.
What happens after Adlib extracts the data? This is where routing and validation workflows kick in. Adlib’s workflows route the document based on business rules and ensure data completeness.
The old way: Someone emails an invoice to the finance team, asking, “Can you approve this?” The new way: Adlib Workflow automates routing and validation to ensure no bottlenecks or errors.
Here’s How It Works:
• If required fields are missing from the document, it’s sent back to the submitter with an automated message.
• If all required data is present, it’s routed to the next step in the process, which could be a human for review or an automated system.
• If specific conditions are met (e.g., the invoice is over a set amount), it’s flagged for additional review.
With Adlib’s intelligent routing and validation, the process is 100% touchless but always auditable.
Here’s where things get really exciting. Traditional ECMs only store files, but AI-driven ECMs feed structured data into data lakes.
Imagine this:
• Adlib extracts purchase order data (PO numbers, amounts, vendor names) from PDFs.
• Adlib routes the approvals and sends the data to a data lake.
• Your analytics tools (like Power BI or Tableau) pull insights on vendor performance, cash flow, and purchasing trends.
The result: Your finance, procurement, and audit teams have real-time analytics at their fingertips.
If you’re thinking, "This sounds great, but how do Iget there?" — here’s your plan:
1. Audit your ECM: Can it identify, extract, and route files? If not, let’s talk.
(Note: Many companies have scanned hardcopy files into image-only PDFs or TIFFs. Others have files in source formats like Word or Excel. While digital, these files may not be AI-ready. Adlib converts file types and optimizes the file’s content so that AI can process it.)
2. Leverage Adlib for AI-driven extraction: This is how you convert unstructured data into usable content.
3. Integrate with data lakes: Extracted data shouldn’t just sit in your ECM. Push it to data lakes for cross-enterprise analysis.
4. Build workflows: Decision trees make workflows smarter and more adaptable to change.
Want to know if your ECM is ready for AI? Let’s talk.
Leverage the expertise of our industry experts to perform a deep-dive into your business imperatives, capabilities and desired outcomes, including business case and investment analysis.
