Ingesting data with automation tools
In our workforce today, if someone captures information—such as with a scanner, a fax machine (which is really a scanner), or even a cell phone camera—they’re left with a PDF or image document. Not much can be done with data in a picture file. They’re just pixels, which is a form of unstructured data.
With automation tools, machines can convert unstructured data into structured data, enabling humans to do more with the information they capture.
Types of automation tools
1. Optical character recognition (OCR) converts the text in images to machine readable text
2. Natural language processing (NLP) and/or AI technologies enable computers to read, comprehend, and process human language
3. Categorization is the process through which a machine can use NLP/AI to evaluate a document and classify it based on its content, tonality, and other characteristics
4. Summarization is a feature of AI that reads large amounts of data and condenses it into short, detailed summaries
Once automation and AI have been used to structure, evaluate, categorize, and summarize a document, we now have a richer piece of content with greater context about the subject matter within. With this data, we can now answer important questions about any captured document:
• Who’s it about?
• What type of document is it?
• What does it say?
• What action comes next?
To use an example from the healthcare industry: If a document is faxed to a health system, the unstructured fax waits until a team member can manually review it and decide which patient or clinician needs the information based on these important questions.
With AI, a task can read the document; determine it’s about a particular patient, John Doe; categorize the document as a prior authorization approval for an MRI; condense the data into a short, but detailed summary; decide which department it must be routed to for scheduling and treatment; and then send it on—all without taking time away from the healthcare system’s human team.
Automation and AI help close the processing gap to more efficiently get data where it needs to go.
Across every industry, AI reduces the amount of human effort needed for a workflow. From the moment someone prints or faxes a document, AI accelerates the time to process, evaluate, edit, and file the data in its final destination, whether that’s a system for enterprise resource planning (ERP), customer relationship management (CRM), electronic medical records (EMR), or any other system of record.
There’s still opportunity from input to output
From initial document capture to data routing, the opportunities that exist with automation and AI are vast.
Multifunction devices are an entry point for data capture. Although printing paper production has been on a steady decline, with printing-writing paper capacity falling 6.9% between 2013 and 2023, multifunction devices (MFDs) and multifunction printers (MFPs) play a pivotal role in data capture.
If paper is used at any point in a business process or workflow, it must be ingested before automation can work on it, making MFDs integral to capturing unstructured documents and content.
Many MFDs are now being equipped with direct integrations that allow end users to scan documents and route them through cloud-based integrations for AI processing or directly to their final destination. These direct integrations save users from having to sweep documents from a folder of scanned images into their system of record. Instead, AI can take a document captured via an MFD, read, categorize, and deliver it directly to a team member—reducing data processing time and removing uncertainty and delays due to failed faxes.
At a time when printing documents is on a slow decline, implementing direct integrations enables MFD companies to remain relevant and critical to data capture in this AI age. The opportunity to make physical documents increasingly available for AI processing delivers greater value for end users, streamlines manual workflows, and ultimately keeps data more secure.
Sending more data via the cloud improves downstream security. In addition to upstream data capture, greater reliance on machines for processing and data transfer improves downstream security.
Human processes are inherently flawed. As a human workforce, we routinely write down or print out data that often deserves high levels of protection. But too often, due to breakdowns in data ingestion and processing, a team member from one department may need to print out a sensitive document and deliver it to another department where it waits to be scanned back in. In every human process where sensitive data sits in a tray, on a desk, or even as a password on a sticky note, there is a chance information may be leaked or have errors introduced during translation.
Closing the data processing gap between data ingestion, transfer, and filing increases a company’s security, reducing the number of touch points where information can be accidentally mishandled. Giving end users faster, easier, and more efficient workflows with automation and AI streamlines workflows and reduces administrative costs. It also minimizes the human processes that open organizations up to security risks, allowing each person to spend time on more important tasks.
A period of refinement is ahead for AI. Despite the myriad new abilities AI has delivered to date, AI capabilities are only just beginning to prove themselves in the workplace. The outlook is promising, but there’s still a lot of opportunity on which we’ve yet to capitalize. In the coming year, we’ll be embarking on a period of refinement for AI.
Many automation systems will be able to take vast amounts of information and turn them into something manageable, but there will still need to be human validation and oversight of the information the AI tools provide.
These technologies are still too new to be trusted inherently, so the next phase of the AI boom will be conducted in tandem with humans to review AI outputs and provide feedback. Human oversight will ensure the technology is providing consistent, reliable results. And then, at the discretion of the those who implemented the technology, AI systems will be able to operate with less and less human validation for the tasks they set out to complete.
Though a word of caution: we must refrain from asking too much of AI — not for concerns about it learning too much and becoming sentient, but because if we have unrealistic expectations or demands of AI, we’ll see it fall down on the job. The machine will fail to produce the required output or deliver erratic results. For example, we wouldn’t ask a self-driving car to drive through a crowded market with people or a park full of children. It’s too large of a task, but we do believe it will get there through the process of refinement.
Instead, we need to pursue systems that are reliable and go through cycles of refinement and training, until we have the confidence that the AI is doing the task at hand correctly and consistently. With AI, it’s a two-way street. We can’t just focus on the possibilities of what it can do. We must also prioritize giving feedback, using it in a practical manner, learning from it, and becoming better at how we use it.
