Nov 5, 2024
Robotic Process Automation (RPA), has an intimidating title but isn’t as hard to understand or use as most outside the software industry probably think. You probably use RPA solutions already, even if you don’t know them by that name.
We know that our typical customer isn’t a software engineer or even an IT specialist. Thankfully, recent exciting advances in RPA and Generative Ai (GenAi) allow virtually anyone to start implementing RPA into their business without any prior technical experience.
At DoFlo, we help our customers to design, implement, and run process automations at whatever scale is required. For our customers, building process automations has never been easier. What might once have taken months or years and significant investments to accomplish can now be designed and tested in hours, using no special software. This allows individuals to become far more productive, and even more critically, it enables professionals without a programming background to become their own type of “software developer.” Those with hard-won domain expertise can now build process automations that help them do their jobs.
Far from being the threat to employees that many consider it, we believe that our RPA design software should be seen as a means of enabling experts to leverage automation whenever possible so that they can excel at things computers will never be able to do. It should be seen as a tool of increased focus and, as we will discuss, a “time machine” or cheat code for your business.
In this blog post, we will give you a brief historical overview of RPA, focusing on the reasons for its recent surge in popularity and accessibility. Then we’ll drill down to a few things about RPA that we think everyone should know
TL;Dr:
RPA is not about replacing people
RPA requires careful planning
RPA is about maintaining quality processes
RPA can (and should) be done in small steps
RPA saves businesses 25-80% on costs
RPA will not solve poor business processes
What is RPA Really?
Despite the eye-catching title, coined by technologist Pat Geary in around 2012, RPA is not a specific technology, like a coding language or a piece of software. It’s robotic, but it’s not a physical robot, and it’s not a chatbot like Chat-GPT, though it can involve either one.
Neither is RPA, as companies like Blue Prism or UiPath strive to imply, an “army of virtual workers executing business-critical tasks.” Overblown descriptions of RPA as virtual human beings do a disservice, particularly to the small businesses that can benefit so much and so immediately from the automation of simple, repetitive tasks.
Ultimately, that is all RPA is: the automation, via a robotic process, of a simple task that would otherwise take time and attention and could be better spent elsewhere. That’s it. No science fiction. No magic to speak of. Just a great deal of potential savings to individual workers, particularly in the reduction of simple clerical errors.
We mentioned before that you might already use RPA solutions without knowing it. If you use a password service like LastPass, or even just a built-in keychain application like Apple Keychain or Google Password Manager, when one of those applications automatically signs you into a website or an app, that is a form of RPA; the simple task of looking up a long password for one of a thousand websites is done instantly, saving you time that would be otherwise wasted.
Three S’s: Simplicity, Security, Speed
Password managers are a great example of the benefits of this kind of automation. You can have a secure, unique password for every online service, but not have to remember or input that password during day-to-day tasks. Ideally, RPA solutions simplify your operations, enhance security, and allow for greater speed and ease of use.
Other examples of RPA in your life may be converting an invoice with an attached QR code directly into a pay order via your online banking app, or even just clicking to pay via Apple Pay.
Examples don’t stop with smartphones or computers. Technologies like scantron, or barcode scanners in grocery store checkouts are examples of RPA.
Some readers will probably remember a time when not every store had barcode scanners. Either clerks would have to manually enter the prices of each item into the register separately, or teams of checkers would call out the numbers of each item, while a colleague entered the numbers into a computer terminal, and before that, a paper receipt with carbon copies.
This process led to many errors in transcription or pricing, and also caused problems for store inventory reporting, since every item couldn’t always be accounted for this way. Fraud was also a widespread problem, as being unable to easily check that a given item had the correct tag allowed shoppers to easily switch tags and in that way, steal from a retailer. Employees too had the opportunity to collect payments for items without entering them into the accounting system
These processes are repetitive, prone to error, and require a great deal of attention from the user with no large reward associated with success. Automating them just makes sense.
Savings
The sum of these three S’s is a fourth: savings. One of the greatest threats to a small business comes from within: employee fraud, embezzlement, mistakes, and waste are the leading causes of business failure.
RPA can reduce opportunities for fraud and theft, eliminate errors, and cut waste while making your life and your business significantly easier.
None of this is as sexy as the image of an army of digital agents doing your work for you. Nevertheless, savings is the name of the game in RPA.
Boosters of Generative AI and the more exuberant members of the RPA community like to imagine a world where emails are automatically answered and entire customer journeys from initial requests to final billing are entirely automated… this is still largely a fantasy. And let’s be honest: what kind of a business would you be running if you weren’t personally interacting with your customers? Not the kind of business we’d like to work with.
A Time Machine
Tech people seem to love to imagine of their technology replacing people.
At DoFlo, we think this sprouts more from an innate introversion in the typical software developer than from a real-world desire to eliminate human contact and human input from business processes. We want more human interaction and more of a human touch, not less of one.
The question is more: what do the actual humans in your organization actually do that machines can’t? That’s probably how they should be spending their time.
RPA is not about eliminating people: it’s about giving people more time for people.
An entirely automated business will almost never make sense. After all, anything you can automate, a competitor can also automate and potentially do even more cheaply and effectively than you. Therefore RPA doesn’t necessarily convey an unfair advantage to its users. Rather, it allows its users to apply their real world advantages (such as customer care skills, domain knowledge, experience, and brand equity), without wasting their own time on low-value activities.
It remains your job as a business owner or a manager, to maintain your real-world advantages while leveraging available technology to eliminate unforced errors and time-wasting activities.
How RPA Got Started
RPA’s history goes back to the first mainframe computers that were developed for large corporations. These companies found that while computers aided in doing certain core business tasks, like financial projections or billing, they also entailed a great deal of manual work that introduced errors into the process.
Imagine you’re a data technician entering information from handwritten or typed sheets into a mainframe computer. You might be able to transcribe at 100 words per minute, but if you have an error rate of even 1%, the numbers that come out of whatever program you’re running will be worthless. Either you will need multiple manual checks of the data, a way for the program to recognize potential errors, or a way to reliably add data accurately the first time.
In the 1950s, one of the first RPA technologies to emerge was OCR: Optical Character Recognition. A technology used to recognize printed or handwritten characters and transcribe them to a computer program.
OCR allowed computer technicians to input vastly more data, and specialized papers and fonts were even developed to aid in this process. Today OCR technologies developed 50 years ago form the basis for many modern programs, such as the ones used by the U.S. postal service, Social Security Administration, or the test grading machines used in high school and college standardized tests.
Today, in education, finance, and government, automation programs that were written as long as 50 years ago remain in use, proving that the cost and error-reduction savings from RPA are of great value.
The Toll of Stress:
Another ‘S’ to consider is nearly as important to your business as the others: stress.
RPA technologies allowed for less human work in the entry of data, but they usually did not eliminate human jobs.
What they did eliminate was stress, either emotional or physical.
Specific tasks targeted by RPA solutions were often the worst ones that workers were required to carry out, because of their tedious nature. Not much was understood about repetitive stress trauma in the 1950s, but we now know that the effects of highly repetitive tasks on the human body and the brain can be very unhealthy.
Scientific research suggests that repetitive and emotional stress are linked in their effects on the brain. Carpal tunnel syndrome or arthritis may be seen as a physical syndrome, but we now understand that repetitive stress and emotional stress are deeply linked. This connection plays out in the ongoing development of the brain, where stress hormones respond to the demands we place on our bodies and our minds in subtle and unseen ways.
Just as in physical development, a certain amount of stress is required for the brain to adapt and grow. Not all stress is “bad.” It can also prompt us to grow stronger in mind and body. So much so that we place children and young people into physically and emotionally stressful situations as a means of encouraging their mental development. However, sustained stress, as well as repetitive and tedious activities that do not encourage positive growth, can hurt the brain’s ability to adapt to new situations.
Lifelong Consequences of Stress
Stress can even cause a person who have lifelong learning deficits. From the above Harvard paper:
“Sustained activation of the stress response system can lead to impairments in learning, memory, and the ability to regulate certain stress responses.”
What we might once have referred to as “mental blocks,” or “oppositional defiance,” in children, may now be better understood as real, neurochemical responses in the brain, to overstimulation from stress hormones.
The scientific literature now even talks of the effects of fetal exposure to stress hormones in the womb, warning that maternal stress can shape the activation of genes in unborn children and even their descendants, by playing upon the development of their reproductive system, via the epigenome: the chemical process by which a person’s cells can “mark” its genetic code with instructions on which genes to activate or deactivate.
To put it simply: stress hormones seem to play a role in how an individual’s cells build them into a human being. The yet emerging science of epigenetics calls great attention to the effects of stress on people who have not even been conceived, but who nevertheless will experience the consequences of how their ancestors dealt with stress.
These phenomena are most studied in children, but it would be foolish to assume that such problems only affect the very young. Common complaints about Millennial and Gen Z work habits may be better contextualized with these scientific findings. It may be that Zoomers, focused as they are on emotional well-being, have got something fundamentally right. Workplace stress and tedium are a real threat to business success.
Enhancing Human Engagement
Technologies that help to alleviate stress and boredom are therefore bound to improve productivity and overall performance. We can take this as practically axiomatic. But it’s important to emphasize that productivity is not necessarily the end goal of RPA.
Enhanced productivity may help your business turn out 10% more widgets, but it will not necessary bolster your business fundamentals as much as you may hope. That’s because all productivity comes at an inevitable cost: either in the complexity of the technology used or in the workload of the human beings you employ.
Business fundamentals are virtually always best served by an engaged and creative workforce, that experiences the right amount of stress and stimulation. Neither too much nor too little should be the goal.
In this context, RPA is better understood as a way to support the human element of your business, by alleviating the stressful tasks that get in the way of higher-value activities, and lead to worker misery and disengagement.
We understand many of these lessons reflexively: how often have your best ideas come out of the blue, usually in a quiet moment of reflection, or while doing something completely unrelated? Too much stimulation and too many distractions can add up to a workforce that doesn’t have time to stop thinking and start being creative and inspired.
The False Productivity Promise
“If the automobile had followed the same development as the computer, a Rolls Royce would today cost $100 and get a million miles per gallon, and explode once a year killing everyone inside.” —Robert X. Cringely, InfoWorld magazine”
There was a time, not that long ago, when it was assumed that having computers in the workplace would lead to unimagined leaps in overall economic prosperity and productivity of individual workers. In some quarters, double-digit yearly jumps in productivity were seriously entertained as possibilities, particularly in the 1970s and 80s, when it was becoming clear that computerization was a major disruptive force in business.
Going back even further, exuberant early 20th century predictions such as that of economist John Maynard Keynes, held that by the year 2030, automation would mean that a typical work week would consist of just 15 hours of actual work.
Certainly the mainframe, then the microcomputer, and then finally the PC and the internet-connected tablet and smartphone, vastly transformed how we work day to day. But what’s interesting about the history of computing in the workplace, is that actual gains in overall economic productivity were modest compared with the predictions.
Today, a modern worker may feel that they really do do only 15 hours a week (or less) of actual work. Nevertheless, we do not spend 15 hours a week at work. In many places, people work longer hours today than they did a century ago.
Productivity and prosperity did rise, especially in developing countries where hundreds of millions of people were lifted out of poverty and into the service of a global consumer economy. But in developed countries like the United States, Great Britain, and the EU, productivity largely stagnated.
An End to Hypergrowth
Why was that? Why did computerization and automation not vastly improve localized economic output in the last 50 years? Why did the amount of time people had to spend at work not steeply decline, while economic output continued to rise?
We have the ability to do so much more than we ever did before, and yet our lifestyles don’t tell a story of a world that has banished long hours and stress from our work lives.
In his book, The Rise and Fall of American Growth, Robert J. Gordon explains the gap between the promise and the reality. Specifically, he shows that the era of so-called “hypergrowth” in the western world which extended from about 1870 to 1970 was not a fundamentally sustainable model for the future:
“Advances since 1970 have tended to be channeled into a narrow sphere of human activity having to do with entertainment, communications, and the collection and processing of information. For the rest of what humans care about—food, clothing, shelter, transportation, health, and working conditions both inside and outside the home—progress slowed down after 1970.”
Essentially, as modern lives became more convenient and more focused on careers in a knowledge economy, the opportunities for huge advances in productivity became fewer. Even though knowledge focused technology like the internet exploded in use, this did not have as big an impact on the basic living conditions than previous innovations had. When most of the work of clothing and feeding people is automated, there is less overall productivity to be gained.
There is also the troubling tendency, as we explained in another post, for the products of a highly automated society to become "Enshitified," or drop in quality as higher overall production and consumption is prioritized over quality and sustainability.
Automation is a Thing of the Past
Gordon argues that automation, far from being a promise about the future, is largely a thing of the past.
Most of the real work in developed countries had already been automated by 1970, leaving very little room for growth in the fundamental economic drivers of the modern world: that is, products produced and food grown. When viewed from this perspective, the economic output of the developed world has stagnated because we have solved many of the fundamental problems of feeding and clothing human beings.
It’s a little counter-intuitive, but it makes some sense. In essence: you cannot make people more prosperous simply by producing more food and basic necessities. Once we have enough, then growth becomes a major challenge. This is one of the reasons for the growing problem of obesity in the western world (not to mention that it’s becoming a problem in formerly underdeveloped regions too).
If your economic system is based on always making and selling more than you did the year before, you will need to get people to consume everything you make. Once everyone has “enough,” more or less, then you will need to convince people that they need more than enough.
Whereas agriculture, mining, and the machine industrial complex saw massive growth in complexity and productivity for a century, by 1970, a tiny fraction of the workforce was employed in these parts of the economy. Further automation became less valuable because human work had already been largely eliminated. Totally unsupervised farming and manufacturing may not be feasible, but automation has already eliminated most of those jobs.
The focus of innovation then turned to the technology and entertainment sectors, where there was still the promise that machines would disrupt business in unseen ways.
Limits to Automation: Choice and Attention
“Computers have become our jobs, rather than our jobs being done by computers.”
It was believed by many that the personal computer revolution would bring untold prosperity to the developed world.
But here, with the introduction of computers to the daily lives of knowledge workers, automation found fundamental limitations that were unexpected. Limitations that had not been faced with the introduction of machinery to farm labor, or of the assembly line to industry.
In the process of introducing computing to knowledge professions like law, finance, education, or marketing, the tech industry created an incredible variety of applications that each individually required some amount of the user’s attention to operate. This rapid disruption and balkanization of tasks and specializations among uncountable software firms created the necessity for specialized IT professionals, whose sole job was to tend to the computers and keep them updated with the latest and greatest software and hardware, allowing businesses to compete.
But the rapidity of the changes caused nearly as many problems as it solved. While companies could do much more, and do it much more quickly, each company was forced to contend with a marketplace in which customer expectations constantly changed, along with the technologies that served those customers.
In effect, every company became a technology company, and most jobs became, in effect, technical professions.
Better is the Enemy of the Good
IT equipment, unlike farming or manufacturing equipment, was useful and competitive for months, or a few years at most, before it needed upgrading. Development and release cycles became shorter, for both hardware and software. This added yet more complexity to the workplace. No longer could a software provider be expected to maintain and support a program for years or decades, as had been the case in industry. Now programs lived and died on the scale of single-digit years.
Each company was also faced with the challenge of training and managing an army of people who needed access to dozens of different pieces of software, all of which operated on different standards and required their own specialized programmers, implementation, and staff to monitor them.
In effect, whether they wanted to or not, all of the knowledge professions became technology-oriented jobs, requiring banks to become IT companies, law firms to become cybersecurity experts, marketing firms to become SEO specialists, and entertainment companies to become special effects producers.
Even worse, as the barrier between locally hosted software systems and cloud run products offered on a subscription basis became blurry by the 2010s, the tools of knowledge work became even more changeable and out of local control. Cloud based services alleviate the need for dedicated IT departments inside a company, but they take ultimate control of a company's operations away from the company premises.
Computers Become Work
While the whole point of computers in the workplace was to increase productivity, the complexity of the resulting work environment often led workers to become less efficient in their work. Productivity might have risen, but the amount of actual work being done also rose, contributing to less overall productivity per worker per hour.
Workers were expected to work longer hours, with more and more time being filled with duties that don’t directly contribute to the business of the firm. Waiting for IT support became something of an international pastime. Business development, in many cases, became slower, and more costly, when computers had once promised to make it swifter and cheaper.
Few people reading this will recall a workplace without computers, yet every one of us will likely recognize that too much contact with computers contributes to a loss of productivity in our organizations at certain times.
The ready access to email and instant messaging is distracting, can lead to longer and more difficult production cycles, allow for constant interruptions or last minute changes, and encourage workers to become active on certain problems at the wrong point in a business process.
In short, computers become one’s job, rather than computers doing one’s job for them.
“I’m a Teacher not Tech Support!”
Many companies struggle with the reality that every employee must become something of a generalist, with knowledge of technologies that don’t directly contribute to their success at work. This leads to frustration and the trend of so-called “quiet quitting” among workers who feel that their jobs have become little more than tending to computer systems that are constantly in need of improvement.
Whereas at one time an executive might have had a secretary to keep track of their tasks and meetings, today they may be expected to do all of this work themselves, and more besides, while waiting for IT support to help fix problems with their email, or their security systems, or any number of other odds and ends that help run modern businesses.
Today, startups are typically threatened by the tendency to over-invest in IT infrastructure, building for a scale that is never really justified by the market for their services.
The corporate workplace is often no better. Constant software updates lead to more downtime as workers adjust to new functionalities, or wait for bug fixes, or find that their existing work processes have been broken along the way.
Professions that once relied on specialists with specific training and education, such as teachers, architects, or lawyers, must now contend with complex systems that contribute little to their actual output. In the worst cases, teachers feel more like IT support people than educators, and other professions waste half their days dealing with tasks that ought to be handled by someone else.
Enter RPA: Screen Scraping Edition
All of these stressors and blockers have come to a head in the 2020s, leading many in the technology industry to hail GenAi and Cognitive RPA as a solution to the mounting problem.
How did we get here?
RPA as a more mature software technology began to take shape in the early 2000s. Known then as “screen scraping,” early software RPA did essentially that: it “scraped” data from the information displayed via a screen originally designed to be used by a human.
This made some sense at the time: software for the past 30 years had largely been focused on human-mediated interactions between software programs. In a way similar to how OCR technology once automated data entry, now screen scraping allowed one program or service to read the outputs of another program or service without any specialized interconnecting software.
This form of RPA was effectively taking a world of software and websites made for humans, and allowing machines to connect them together. There were obvious “white hat,” or “legal” uses for this tech, but also “black hat” or illegal uses, such as gathering information from around the web to spy on or spam people.
A Cheat Code for Your Business
Screen-scraping was originally a “quick and dirty” solution for connecting two programs that aren’t designed to deal with each other.
This era of RPA was often seen as a low-value, low-effort way to write software. The advent of something called Transformer technology, an area of AI and so called “Deep Learning” which we will dig into in a later post, changed that judgement among computer scientists, and eventually led to the proliferation of self-service, Cognitive RPA technologies… like DoFlo.
To summarize, this type of AI led to programs that were able to read web pages and other information far more efficiently than before, and to do so regardless, in most cases, of how that information was formatted, meaning that even if websites and software products changed and were updated, the integrations between them would continue to work uninterrupted.
If a human could interact with and process information, then Cognitive RPA could do the same. The use of Neural Networks (NN), allowed programs to learn, just as humans do, by experience and exposure to information over time.
This led to the ability for different data sources and tools to deal with each other as if they were human: speaking to each other in a common language that doesn’t falter if one or the other has a different “accent” or a different way of speaking.
That brings us to today: when RPA can be seen as a kind of cheat code for business: a way of getting complex software tasks to take care of themselves, and reduce the complexity of a worker’s daily tasks, giving them more time for individual or person-to-person value-generating activities.
Lawyers can read the law. Sales people can talk to clients. Teachers can teach.
No Replacement for People
It may be that the long-mythic promise of the 15 hour week enabled by technology might be possible at last. We might be seeing the dawn of a time when computers stop being a distraction, and start doing what we need them to do, regardless of whether we understand how.
But this is where we must sound a note of caution for the claims of the current RPA sector. Screen scraping, Transformers, and GenAI don’t replace people.
Plainly put: we don’t think they ever will.
This is because technologies like GenAi are not what they appear to be: they don’t think, or create, or innovate. They merely commodify and perpetuate human outputs. There are also some sound arguments as to why current GenAi technology may be a technological “dead end.” This concerns the energy requirements and fundamental limitations of LLM and Transformer technologies, which we can explore in a future blog post.
Yes, they appear to be thinking. Yes, they appear to be creating. But all they are is the sum of an enormous pool of existing data. Until some future time when the fabled “General Artificial Intelligence,” inflection point is reached -and we don’t say that’s inevitable- that’s all AI will be: a rehashing of existing material and ideas.
Today, “prompt engineering” is an emerging skill in the field of GenAi, and we look upon that development with much reservation and concern.
We don’t think people should have to be focusing on how to say the right words to get a computer program to produce a rehashing of existing data. That doesn’t seem to us to create something of genuine human value. It does not, in short, produce anything truly new.
What RPA Really Promises
That’s the unhealthy side of the automation hype cycle. But let’s focus on the healthy side.
RPA can replace fundamentally broken processes that humans should never have been doing in the first place, but nevertheless had to do, because the market or the business rather demanded it. People were forced to develop technical abilities that did not add to their institutional or domain knowledge.
Technical ability is not institutional knowledge, or at least not of a long-lasting kind. People know why they do things. Computers will only ever understand how things are done. So RPA promises to make technical ability largely irrelevant to doing one’s job.
An RPA solution can eliminate and simplify tasks, but this doesn’t mean RPA has become human like in many other ways.
Computers may be able to understand each other, but that doesn’t mean they understand the same things we do as people. We encourage you not to think of RPA as creating virtual humans. That’s honestly an icky image that smacks of a god complex among some weird individuals in today’s tech culture. Yet it’s also a commonly used PR device that scares consumers and threatens professionals with redundancy.
Today thousands of people are losing their jobs because of GenAI and RPA, and we think their organizations will probably be worse off as a result of losing all that human capital and institutional knowledge.
RPA is not virtual human beings.
“Too much fun is no fun at all”
Instead, let’s return to the notion of creating a kind of cheat code for your business. RPA (and DoFlo) will, if used correctly, eliminate many of the reasons why you lose time and attention, and allow you to plan your days more effectively.
It will not place you in the position of playing prompt engineer for a wonky Ai. Instead, it will ideally allow you to spend much less of your time staring at a screen, trying to get the information you need to do serious creative problem-solving.
Or you might look at it as installing a piece of machinery: it is something specially designed to make a difficult task easy. Instead of a long process required to find key information, you have the click of a button. Instead of a long chain of steps to check and pay an invoice, again you have a system that runs all the checks for you. You remain in control of the process as a whole, but you stop doing the fiddly little things that tend to introduce the most errors.
And the best part: if done correctly, systems built using our technology will be able to adapt and continue to function regardless of changes to the way that various programs behave. We are bringing back the years-long product lifecycle, and allowing you to create machines that will last a long time, providing you great value and savings over many years.
Of course, like the use of any cheat code, too much fun is no fun at all. If you try to automate your whole business, then you will lose the things that make your business unique and irreplaceable: the human values it represents, and which reside in the human members of that team.
Innovation?
The word “innovation” gets thrown around far too much in our business, without ever really being explored. But what does innovation actually mean? It doesn’t mean doing new things. It means making things (things that already exist!) new.
So innovating in your business is not necessarily about creating amazing new automations that do things nobody has ever done. That wouldn’t make a lot of sense for most businesses because most businesses are built by people with experience; experience at doing things that they have done before.
Experience is your main business advantage. It’s the thing that makes you better at what you do than anyone else.
Amazon did not create a market for books, or a way to buy things online. These existed. It innovated around an existing market for books, and existing ways to buy things. That’s it. That was “innovation,” enough. An innovative business just applies its experience to new problems.
Yet, people often have a kind of funny faith in technology, particularly when they don’t really understand what that technology is doing. They start to think that technology will allow them to do things that they could never do before.
Yet the history of technology is far less about doing things never done before, and far more about just stopping to do things that don’t work, or that make life suck just a little bit too much.
Sure, technology is full of firsts, but it’s more full of seconds and thirds, and better versions of existing things. Do you want to be the first to ever have done something, or do you want to be the company that did things the right way?
Experience.
We believe that a great injustice is now being done in the name of automation. That is: using the promise of the technologies we’ve discussed here to justify “force reductions,” as layoffs are now called in many industries. The fact of the matter is that more often than not, layoffs occur for any reason other than that technology actually replaced people.
But when people are laid off, institutional experience and domain knowledge are lost, and getting those back will not be a question of better software. It will be a question of time and effort.
What we have here outlined is what we hope the future of RPA will look like. It is the future we are working toward, and we hope you’ll stick with us to see it come to fruition.
DoFlo will be a technology that helps people apply their experience to the problems they understand best, while offloading the technical challenges and barriers to getting things done.
DoFlo is not planning to be a place where you can invent entirely new, completely unimagined things. Sure, you will be able to do that. But our experience in both technology and business tells us that businesses usually know very well what they need to happen. It’s just a question of how badly they need those things, and what they’re willing to go through to get them done.
Our version of RPA is just about making that process as easy as it can possibly be, so that experts in their own fields will never need to become experts in ours.