No matter the industry you are in, chances are high that you deal with documents on a daily basis. From creation to reviews and approvals to filing and storage, documents play a crucial role in how information is shared, and business is done. How these documents are managed has undergone a gradual shift as digital solutions and automation paved the way for collaboration across locations.
To help you understand where things are headed with intelligent document management, we’re taking a look at the past, present and future of engineering document management systems (EDMS) and the role of artificial intelligence (AI) in document management.
Related Read: Document Management Software in 2025
A Brief History of Engineering Document Management
The 1980s and 1990s saw an early digital wave with businesses starting to scan things like blueprints, CAD designs and specs into computer systems to reduce paper usage. This was the first instance where physical filing rooms started to get smaller. In the 2000s, there was an emergence of engineering document management systems (EDMS) coming on to the scene that let companies start to digitally manage document workflows and permissions. File servers and on-premises systems dominated this era.
Heading into the 2010s brought an acceleration of cloud adoption as cloud-based systems became secure enough for critical files. Industrial design platforms gained traction and engineering companies leaned heavily into real-time collaboration and versioning, as well as global access for teams working all over the world. The need for remote work during the COVID-19 pandemic greatly accelerated more cloud adoption in the early 2020s.
Now, AI and automation are bringing about another wave of change, supercharging actions like indexing and searching. Cloud-native EDMS have become the norm for many medium-to-large engineering organizations, supporting sustainability goals and regulatory needs. Today, cloud and AI adoption are the standard.
4 Key Challenges of Document Management
While centralized EDMS platforms are making work easier for you and your staff, there are still roadblocks that need to be considered. Here are four common challenges that can surface, even when using a modern EDMS platform.
- Data Siloes: Limited integration capabilities, poor metadata and tagging practices, overly strict access permissions and departmental or vendor-specific repositories all contribute to potential data siloes.
- Human-Reliant Workflows: Many platforms don’t automatically route documents and still need humans to handle administrative aspects such as tagging, data entry, approvals, version control and project assignments.
- Compliance risks: Just because everything is stored in one place, doesn’t mean it’s up to date. Additionally, document retention requirements, incomplete audit trails, external sharing and access controls all contribute to regulatory and compliance risks.
- Human Error: Even with a centralized EDMS solution, the system is only as strong as the data that’s uploaded to it. Manual data entry in metadata and tags can lead to more errors that may not be caught by the human eye, restricting users from locating vital records and resulting in costly rework.
Related Read: How to Automate Asset Tagging and Referencing with Machine Learning
AI in EDMS Brings About a Document Management Inflection Point
Automation and AI in document management are quickly being adopted into EDMS platforms to help solve for the challenges above.
Connect and Interpret Information Across Departments
AI like natural language processing (NLP) and machine learning (ML) can index, interpret and operationalize documents across platforms. Information can be found no matter where it is stored or where the person looking for that data is located. It can also help different teams and departments understand certain documents. For example, someone in legal may need to be able to understand schematics to help build a defense, which AI can help with. Automation helps further break down siloes by integrating workflows to get rid of bottlenecks and foster more seamless collaborations across teams, departments and locations.
Redefine How Workflows Operate
Many of the current document workflows you may see are either manual or rule based. While rule-based engines certainly decrease the need for human-reliant workflows, its programming still needs some manual input. This is where predictive AI, automation and ML can come in to take it a step further. Machine learning brings value in its ability to adapt to complex patterns and exceptions that aren’t explicitly coded into the system. AI can also learn from historical workflows, identifying bottlenecks, predicting approval delays and recommending process improvements. Updates and approvals are able to move more efficiently ensuring timely work across teams.
Keep Audit Trails and Regulatory Compliance up to Date
A mix of rule-based AI, NLP and ML go to work monitoring documents in real time to make sure regulatory requirements are met. Anomaly detection flags missing or outdated certifications and can compare 3D models with scans and photogrammetry to detect changes, alerting teams when files are outdated or have errors that make them non-compliant. It also maps all content to relevant standards like ISO or OSHA. An AI-powered document management system helps to ensure audit trails are always updated, and documentation is retained based on policy standards.
Get Rid of Manual Data Entry to Minimize Mistakes
Using computer vision in the form of optical character recognition (OCR) combined with NLP allows information to be pulled directly from images, drawings, specs, diagrams and blueprints without the need for human input. This means metadata from PDFs or scanned drawings can be quickly retrieved without the risk for error that comes with manual data extraction. Machine learning detects duplicate uploads, mismatched versions and documents that are misfiled.
The Evolution of AI in Document Management
As it becomes more common to leverage an EDMS with AI capabilities it’s important to understand key examples of AI in document management. Here are four critical ways AI and automation are further evolving EDMS solutions and document management at large.
- Intelligent Document Categorization
Computer vision models can be trained to recognize specific elements that differentiate documents such as floor plans versus P&IDs versus diagrams, all the way to invoices and inspection forms. This allows for automations that can intelligently categorize documents with proper naming and tagging conventions without the need for human intervention.
- Context Aware Search and Discovery
With AI like semantic NLP your system would be able to understand not just specific elements or keywords in a document but the meaning behind the language used. For example, say you ask the system for “blueprints A and B for building XYZ.” While traditional NLP will look for exact keyword matches to that search, semantic NLP can go deeper returning results like a file or image labeled “Unit B schematics.” Because this type of AI can understand context, it is able to move beyond basic keyword searches to bring more accurate results.
- Anomaly Detection
A next level in error detection, AI would be able to spot inaccurate annotations and other errors immediately. Computer vision has the potential to do this, identifying certain types of mistakes that the human eye may miss, such as pressure-safety valves that are missing an upstream shut-off valve or sensors that may not be tied into their proper piping loops. This saves time a human typically needs when they are reviewing documents, helping reduce downtime that can occur if errors go unnoticed
- Automated Approvals
Similar to how AI technology is already supporting workflow efficiency, we may also see it evolve to support approvals. While there are still many aspects to the approval process that require human oversight, AI can be enabled to approve small and low risk changes against a criteria. This has the potential for much greater efficiency along approval workflows.
Related Read: Five Multi-National Companies Thriving with Engineering Document Management Systems (EDMS)
AI for Document Management Debunked
If you take anything away from what’s been shared above, let it be this: AI is here to augment your expertise, not replace it. Where it’s really helping is in removing the need for human workers to handle repetitive, low-value tasks that are often a drain on your time and energy.
Rather than spending even an hour in your day searching for files, tagging documents, or cross-checking multiple file versions, AI lets you actually focus on innovation and decision making. Think of AI as an assistant that can handle the routine tasks of your day while you put more focus on critical thinking.
Want to know more? We’ll be sharing more content on the future of AI in document management systems, so stay tuned with us.
In the meantime, curious about the AI applications in our EDMS solutions? Find out more and request a demo here.
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