In part two of this series, we take a look at how no code is enabling machine learning to tackle complex workflows previously resistant to process automation.
Last week, we examined hyperautomation through the lens of Robotic Process Automation (RPA), a class of technology geared towards automating labor-intensive computing tasks that involve exclusively structured data (i.e. data that fits snugly in spreadsheets). The unambiguous, rule-based nature of these tasks translates easily to boolean logic: although RPA bots effectively copy the actions of humans, the humans previously compelled to carry out RPA-grade work must have felt as if they were playacting robots, performing the same rote operations over and over again.
RPA may sit at the base of the hyperautomation pyramid, but as we established in last week’s post, hyperautomation represents a movement beyond RPA, whose fundamental limitation is that it only addresses workflows involving structured data, whereas today the vast majority of enterprise data is unstructured. Unstructured data workflows, involving images, videos, documents, and even richer file formats for 3D/AR/VR, introduce new levels of ambiguity and are less rule-based than “guideline-based.”
The sheer volume of unstructured data flooding today’s enterprises—literally breaking their DAMs—creates a catch-22: on the one hand, enterprises rely on human discretion to process unstructured data (e.g. tag images and videos, police usage rights, transcribe text from documents); on the other hand, there is too much unstructured data for humans to process in an efficient manner. Fortunately, there is a way out of this bind.
With hyperautomation, machine learning (ML) slides in alongside RPA as a means to better manage unstructured data. Unlike RPA, ML models can do more than copy humans making either/or decisions. They can learn to reason like humans, or at least reproduce more nuanced judgment calls when faced with a wider range of media. However, ML introduces problems of its own—problems that have delayed its widespread adoption as the successor to RPA in the world of business process automation.
These barriers to successful training are a big part of the reason why so many enterprise AI projects fail. So can these problems be solved? We at Impira certainly think so, and what’s more, we think they can be solved simultaneously.
A quick analogy: suppose you were looking to play a new sport, take up an instrument, or learn another language. How would you go about acquiring this new skill? Surely you wouldn’t attempt to “cram” a bunch of learning into a single weekly session, but would rather set up a steady, consistent practice of an hour here, an hour there, as many days per week as possible—ideally with an instructor present to guide your progress and propose subtle tweaks.
This sort of ongoing learning approach (as opposed to cram-style batch training) offers clear-cut advantages for ML models as well. By integrating the learning and implementation phases, ongoing learning enables trainers to correct their models’ mistakes in real-time, thereby enhancing the models’ ability to reproduce human abilities such as nitpicking, making subtle distinctions, and picking up on context clues.
That said, an ongoing learning model still requires a human to train it, and so the first of the two problems raised above remains unsolved. This is where no code enters the picture.
The basic promise of no code is to empower users from diverse lines of business (LOBs) to develop new applications and solve problems without relying on IT. In today’s sprawling enterprises, the ever-growing demand for niche LOB apps inevitably exceeds the supply of IT resources. No code closes that gap by giving non-IT folks with little to no coding experience the ability to quickly build productivity-boosting tools.
Webflow comes to mind as a successful and easy-to-grasp no code tool. Webflow enables users to build websites via intuitive drag-n-drop commands, eschewing the need for HTML chops and lowering barriers to entry for would-be site builders. This same logic can be applied to ML, as evidenced by our latest Mango Beta product. By enabling end users to train ML models to process a wide range of documents and visuals, we help LOBs dealing with huge quantities of unstructured data drastically increase productivity.
The applicability of no code to this next frontier of hyperautomation is clear since the onus to process unstructured data tends to fall on people in creative, marketing, and product teams who may be unversed in writing SQL and algorithms. These same people are the most sensible candidates to train the models that will be assisting them since they know the naming conventions and taxonomic subtleties that ML models need to learn in order to be effective. Training ML models to take over these tedious and time-intensive workflows enables our most creative peers to divert more time toward what they do best.
No code offers tremendous value as an accelerator of ML adoption, and as such will play a critical role in hyperautomation in the years to come. Yet hyperautomation is only one example of no code’s extraordinary potential, which we’ll unpack further in future posts. In the meantime, we’d love to hear your thoughts. Connect with us at firstname.lastname@example.org or on Facebook, Instagram, LinkedIn, and Twitter.