Remember when whole swaths of work could be called “paper pushing”? The age of computing made those paper-based processes move a lot faster and much of the paper became digital. But, still, the work to be done required “swivel chair” effort: manual inputting, manipulating, and exporting data between people and applications to get things done.
Sure, automating repetitive processes was an ideal, but automation technology was time and IT-heavy to implement and required advanced technical skills to use. Not to mention that processes tended to change long before the automation program was ready to apply, creating a notorious cycle of automation obsolescence.
Fast-forward to the last decade and automation options offer a brave new world of accessible tools. Automation tools have quickly evolved, and merged with each other, providing the means to rapidly automate business processes, often in a matter of minutes, and even with zero coding.
At the forefront of automation technologies is RPA. RPA is about getting the work done. It stands for Robotic Process Automation. “Robotic” is describing the software that you set up to mimic your digital process steps. “Process” refers to the work that you want to get done. And “Automation” is what it sounds like—deploying bots to do the work on their own, on repeat.
RPA represents the unification of many automation-assistive technologies within an intuitive consumer-friendly UI. RPA “bots” are flexible software programs designed to execute digital work across any application or environment, non-stop and without losing fidelity. So RPA can tackle defined repetitive business processes and execute them securely, reliably, and with no errors. Major business value right there.
But on its own, RPA is, well, “robotic”; it can’t handle unstructured information or process variation. RPA paved the way to making automating business processes a normal part of work, but falls short on its own when facing the complexity of most everyday business scenarios that include variation and uncertainty, decision making, and unstructured information.
For example, RPA bots are great at copy-paste, scraping data, opening emails and attachments, extracting and using structured data. But if the email doesn’t follow a prescribed structure, RPA bots (on their own) aren’t able to find the necessary information to, say, update inventory levels in an ERP.
Spoiler alert: the limitations are narrowing. RPA got a skills upgrade with the addition of AI technologies to identify and make sense of information to feed process steps, as well as uncover hidden processes, learn/improve, analyze performance, and predict outcomes or make decisions. AI technologies provide the ability to tackle complexity and work with massive data sets.
Integrating a whole host of cognitive technologies and artificial intelligence (AI) tools with RPA, and alongside humans, is called Intelligent Automation.
Advances in applying AI to everyday business problems have provided means for automation to expand beyond highly defined processes and structured data. AI brought the ability to handle complexity to the automation table.
In the context of business process automation, various AI technologies are combined with RPA to enable automating more flexibly. AI is used to find and identify business processes, extract data, apply reasoning, and predict or forecast based on performance and results.
With so many tools and platforms it helps to understand what intelligent automation means and in particular how AI and data play into it. Instead of looking at this as a technical question though, let’s look at what AI technologies do with examples of AI joining forces with RPA:
Computer vision: Optical Character Recognition (OCR) is one example of helping bots see what people see—recognize, identify, caption, sort—and turn the information into useful data by communicating with ocular tools or legacy storage systems.
Facial recognition: Detects and identifies faces within images to determine who the person is, how they feel, and which image belongs in which group.
Machine learning (ML): ML enables bots to self-learn and keep improving at prediction and classification by generating and continuously honing patterns derived from source data. ML helps bots decrease reliance on human intervention over time.
Speech to text, text to speech, and translation: AI enables effortless speech to text conversion and translation, plus speaker recognition and verification.
Natural language processing (NLP): This refers to the ability to process free-form language to extract the relevant information. NLP supports sentiment analysis too, which is especially applicable to automation within the customer experience.
Connecting artificial intelligence (AI) technologies with RPA software is what makes it “intelligent”. Intelligent automation is about bringing a broad spectrum of technologies to bear to automate many more—and more complex—business process automation scenarios than RPA, or any single tool, can alone.
The ideal version of intelligent automation can automate business processes you don’t even know about by looking for patterns across digital workloads and uncovering them, then suggest automations that apply the right set of AI tools and RPA bots to handle each part of the job, including sourcing and organizing unstructured data and collaborating with people to continuously improve. All helping the enterprise get closer to end-to-end automation in the full sense of the term, as a continuous, expanding, virtuous cycle.
To achieve these end-to-end automation capabilities, intelligent automation is about applying multiple AI/cognitive technologies such as computer vision, natural language processing (NLP) and machine learning (ML) to RPA. Specialized AI tools are what make it possible to automate processes that rely on unstructured or complex information sources or that don’t smoothly follow a structured flow. RPA bots handle actions, while AI technologies set up the information and next actions to point RPA in the right direction to proceed.
Here’s the thing: the real world of business data and information is murky, and like it or not, automation still requires reliable, accessible, organize-able, data.
What enterprise organizations face is a sort of data-automation paradox. Most business information is not structured, and yet having the right, usable, data is what enables AI — and by extension intelligent automation — to work its magic.
In relation to automation, data is categorized by how structured it is, meaning how easily it can be exported/extracted, organized, and stored, so that an automation program can use/apply it.
Highly structured data, like quantitative sales numbers in a spreadsheet, is straightforward to use and analyze. Unstructured data is essentially everything else — from free-form chats, emails, and presentations, to satellite imagery and IoT sensor data — so it can be helpful to subdivide unstructured data into semi-structured data as well.
Semi-structured data is information that contains some structural information (e.g., tags and semantic markers), such as invoices or mortgage application forms, that can provide guideposts that aid processing the data.
All of these data types are growing, but unstructured data is leading the pack. According to Datamation 80%+ of enterprise data is unstructured data, and it’s growing between 55%-65% per year. IDC’s predictions agree, forecasting that 80% of global data will be unstructured by 2025, and going further to call out that organizations across industries from healthcare and manufacturing to financial services, public sector, and entertainment, that unlock the value of data will be the ones to establish market advantage into the future.
Automating processes with real-world complexity requires the ability to harness data of any type. An executive report from IBM Institute for Business Value helps to clarify the role of data in advancing automation, with enterprise automation levels largely defined in terms of the data that can be processed. Basic automation is akin to RPA working alone, able to execute processes with structured data inputs, whereas Advanced and ultimately Intelligent automation require the ability to work with unstructured/any data type.
For the real world of unstructured data today, and the near future of business even more heavily dominated by unstructured information, unlocking the value of intelligent automation requires unstructured data processing.
Super.AI’s solutions break down unstructured data into smaller data components to make the process of labelling faster, more granular, and more accurate. Super.AI combines the intelligence of trained human labellers, which could include your business subject matter experts, with AI to fuel intelligent automation of business processes. The unique pairing of human and artificial intelligence enables Super.AI to offer faster, more accurate unstructured data processing of any data type, relying on minimal historical data for training, and, perhaps most importantly, guaranteed data output quality.