Tech specialist Christopher Surdak explains robotic process automation (RPA) from an investment and a functional perspective. Bots already exist – a macro in Excel is a bot – but RPA systemically optimizes bots to replace human labor. Like much in the tech world, scale proves crucial to successful RPA deployment. With proper deployment and maintenance, bots will be the world’s new labor force, handling mundane tasks so humans can be more human.
- Robotic process automation (RPA) is systemic bot deployment – automating human tasks on a large scale.
- For decades, companies relied on small, incremental process improvements instead of transformative ones.
- RPA users are adopters, adapters or achievers.
- In the bot menagerie, a “snot” could kill your business, while a “dot” is your best friend.
- Myriad solutions exist to combat RPA failure.
- RPA is a capital expense, which provides returns incrementally, over time and is strategic, not tactical.
- RPA produces returns on more than investment.
- Bots are not humans, and their mistakes are not human mistakes.
Robotic process automation (RPA) is systemic bot deployment – automating human tasks on a large scale.
Companies may regard robotic process automation (RPA) as a quick fix for certain processes, but to deliver a reasonable return on investment, RPA must scale. To understand how, executives should understand what a bot is and what it can – and can’t – do, and change their thinking about capital investment in IT. Existing at the low-end of the intelligent automation (IA) spectrum, RPA applies existing technology in a new way.
“From a functional perspective, bots are just simple little programs that run on the user interface of the software you use every day.”
A bot is a small software program that automates a mundane, repetitive task. A bot runs macros in Excel, for example, or auto-fills forms. These functions have existed for years, but they lack cohesion; they mimic human interactions with software, only faster and more efficiently, with fewer mistakes. However, they require sound design, responsible deployment and regular care. When a bot doesn’t work properly, catastrophe may result.
Millions of bots do small tasks, everywhere. RPA harnesses these programs and centralizes them for efficiency and clarity.
RPA may lack the charisma of big data or digital transformation, but has the potential to be disruptive. RPA can be its own digital workforce and for many companies, their first real experience of artificial intelligence (AI). RPA represents a step towards a cybernetic future in which machines handle repetitive tasks at massive scale, and humans focus on what humans do best: strategic thinking and problem-solving. For this to happen, companies must let go of outdated ideas about capital.
For decades, companies relied on small, incremental process improvements, instead of transformative ones.
After decades of Six Sigma principles, Lean and kaizen process improvements, businesses struggle to squeeze more efficiency out of their KPIs, benchmarks and forecasts. Bureaucracy, inefficiency and a slower metabolism all conspire to keep them chasing newer, more nimble digital companies. Pricing power has disappeared, as competitors race to the bottom. Customers are more powerful than ever before, thanks to price transparency on the internet. They can get what they want, when they want it. Business has changed. You must transform how you think about value in a world where instant gratification is the norm.
“If you’re not ready to change how you and your organization behave, reading the rest of this book is moot.”
Deploying RPA through your systems will reveal a lot about your operations and ferret out dysfunction. Bots show where your analog principles slow you down and cost you money. Unlike the incremental process improvements, RPA fuels disruptive transformation that may seem overwhelming. Accounting practices that require several systems and a team of people working long hours may seem ridiculous after a bot takes over the process. If you want different results, fast, let go of analog thinking.
RPA users are adopters, adapters or achievers.
Adopters comprise 60% of RPA users and pat themselves on the back for being forward-thinking, but they want results without making a strong commitment. They waste valuable time and money, and when they inevitably give up, set themselves back years.
“Digital transformation is as much about moving from a capital-scarcity-mentality to an information-abundance-mentality, as it is about deploying bots, AI, a blockchain or a cool new app.”
Adapters invest substantially in RPA and recognize its potential to transform their businesses. They need the patience to get past the “twenty bot barrier” (TTB), which is the threshold past which they might see a return on investment. They discover their bots require more maintenance than they anticipated. They must stay the course and trust that scaling up will address that problem.
Achievers comprise 10% of users, have thousands of bots in production and more on the way, and have created the ecosystem in which they can thrive. They regard RPA as a crucial part of their labor force.
The best bot is the one you don’t have to think about, but which drives efficiency for your enterprise. One large company that already invested in 100,000 bots, for example, intends to double its capacity, eliminating 20 million hours of human labor by 2022 – the equivalent of 20,000 employees. The corporation invested in expertise and dedicated a budget and human resources to achieve company goals. In the 19th century, Chief Electricity Officers implemented that new technology. In the 20th century, Chief Information Officers took on that role. Are Chief Bot Officers next? You must commit to RPA and understand RPA as a structural change, not a cosmetic one.
In the bot menagerie, a “snot” could kill your business, while a “dot” is your best friend.
There are many bots in the RPA universe, and like pets, some require little care and feeding and produce great results, while others are costly and need lots of attention. In this menagerie there are:
- Turtles (“Tots”) – These bots are small, with limited functionality.
- Fish (“Fots”) – These bots are pretty, but expensive. Many companies have Fots and need patience to make them profitable.
- Snakes (“Snots”) – These bots offer low utility and genuine satisfaction, but have the potential to make big mistakes.
- Chickens (“Chots”) – Macros, scripts and apps are chots. Not cuddly, but productive.
“Bots can and will display all of the variation that exists in the natural world; perhaps even more.”
- Parrots (“Pots”) – Online chatbots are usually Pots.
- Dogs (“Dots”) – These bots have high value, but require high maintenance.
- Ox (“Oots”) – The “beast of burden” in the bot world, these bots deliver the same work as humans but at higher rates and volumes, reliably.
- Horses (“Hots”) – These bots – like Oots – are hard-working, but require higher maintenance.
There are hybrid bots that combine different strengths, such as the “CentBot,” which has the speed and strength of a horse and the EQ and IQ of a human.
Myriad solutions exist to combat RPA failure.
Google “RPA implementation failure” and you will find over five million results, but those failures must be put in context:
- Financial – The bot doesn’t deliver value or meet expectations. Finance accounts for 60% of failed implementations. The biggest error is failure to scale due to outdated ideas about value. RPA is a capital expense, not an operational one, and cannot be “Leaned out” as can other operations.
- Governance – These failures derive from poor management and oversight. Governance is RPA’s primary value proposition because it oversees a digital workforce, and as such, requires plans and accountability. Instead of centers of excellence (CoEs) in charge of governance, create a center of support (CoS) to collaborate with the business in decision-making.
“Many RPA ‘failures’ are more like graduating from High School with a ‘B’ grade point average, as opposed to not graduating at all.”
- Operational – These failures usually spring from how individual bots operate, not how they coordinate with other bots. They arise not from poor design, but from poor governance.
- Design – These failures derive from faulty programming due to missed or misinterpreted requirements. The fault lies in hiring in-house programmers with minimal training instead of contracting skilled developers. Testing and maintenance are essential, and can cost up to four times the design cost.
- Technical – While these failures are rare, some RPA software proves incompatible with existing systems. Mostly, RPA enhances legacy technologies and seldom malfunctions. Focus on what the bot can do, not on what they cannot do. For example, bots might seem the perfect option for reading and extracting data from PDFs, but that requires optical character recognition (OCR), which remains unreliable.
RPA is a capital expense, which provides returns incrementally, over time and is strategic, not tactical.
When RPA became the new trend in 2015, there was a predictable race to the bottom on pricing, even though clients sought exotic designs that suited their unique needs. You cannot get something for nothing; vendors offered a $10,000 bot when $50,000 was already a bargain; the failure rate was 95%. Selling RPA as “cheap, fast and good” – when every engineer knows that you can only have two of those three – was like selling crack cocaine to CFOs. Predictably, they suffered disappointment because they regarded RPA as an expense, not an investment.
“I urge you to use RPA to automate processes that enhance revenue generation, rather than simply cut costs.”
The more information you have, the more valuable it becomes. Uber, Google and Facebook, for example, see IT as revenue rather than as expense, and spread the cost of their investments over millions, even billions, of customers. Capital-centered, analog businesses operate on the “scarcity and control” principle, which hinders them regarding RPA as revenue generating, not cost-cutting. As a capital investment, RPA can deliver ROI incrementally, over years. A taxi company doesn’t expect to buy a new taxi with one fare, and a company that buys RPA shouldn’t expect one bot to pay for itself in a few weeks. You must pass the 20-bot barrier to even begin to see a positive ROI.
RPA produces returns on more than investment.
Return on investment (ROI) is not only tricky to calculate, but depends on inputs and assumptions, which in turn depend on the interests of the people pulling the levers on funding.
“Return on time is perhaps the most important, least understood and least valued benefit from RPA.”
Laser focus on ROI draws attention away from other types of returns (ROx). RPA on a small scale may not yield big financial returns, but promises benefits in other areas:
- Return on time (ROT) – This is the most important and least understood benefit. Saving time is valuable because there is only so much of it. You can fake quality and low price, but you can’t fake speed. One bot can reduce the time required for a small task by 80% or more.
- Return on quality (ROQ) – After decades of Lean processes to improve quality, customers expect perfection. When things go wrong, they want solutions immediately. Bots can streamline processes while humans take care of unexpected failures.
- Return on consistency (ROC) – Whereas several people doing the same task introduce small variations, bots do it the same way, every time.
- Return on focus (ROF) – With bots doing the grunt work, humans can focus on more rewarding, complex tasks.
Bots are not humans, and their mistakes are not human mistakes.
Bots are not a new technology; they are a new kind of workforce. As such, do not expect them to behave like the human workforce. They are faster and more accurate than humans and can work around the clock, but only on specific tasks. If even one input changes, they can fail, sometimes spectacularly. Design, therefore, proves supremely important.
Seek out processes with the greatest capacity for improvement, and design for optimal capability. Understand that bots need context to avoid mistakes a human would never make – for example, paying someone for 400 hours a week instead of 40 – and that bots lack long-term memory. Developmental best practices to optimize bot productivity and reduce errors include incremental saves; task batching; minimizing logins; local temporary persistence measures; and adding steps to save time.
“Hence, if you choose to not embrace the new, the cost of eventually giving in will only go up, rapidly.”
You still need people, even those whom the bots eventually replace. Recruit “automation ambassadors” in the company to interpret processes that will be automated, and to explain RPA’s benefits to redundant workers. Money and regulations will always require human oversight. Find opportunities to retrain your human workforce to perform the tasks that, for reasons such as cost, complexity or compliance, limit the return from investing in RPA. In the short term, job losses across the board, especially in white-collar industries, will be painful. But there is no stopping progress. When people no longer have to perform mundane tasks, they will learn new skills – human skills – that will make them competitive in the global race for best talent.
Keep your RPA expectations reasonable. A good RPA developer won’t promise 100% accuracy, because that doesn’t exist. RPA may seem dull compared to fancy AI, but it should, with time, become as ubiquitous as electricity. By properly caring and feeding your bots, you will enjoy RPA’s remarkable benefits, as it drives digital transformation on a massive scale.
About the Author
Christopher Surdak is an industry-recognized expert in mobility, social media and analytics, big data, information security, regulatory compliance, artificial intelligence and cloud computing with over 25 years of experience.
This document is restricted to personal use only.
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