Historically the most sought after automation tool for most enterprises, RPAs are slowly and steadily becoming irrelevant as businesses are moving towards automating complex, end-to-end processes from the simple, rules-based repetitive tasks they were used for.
It is evident that this is a result of the inherent limitations of RPA Solutions. This blog discusses, in depth, these limitations, and how modern tools and technologies like Generative AI can help you automate better.
Unstructured Data, as the name suggests, refers to all information acquired via sources such as text-heavy documents, emails and media formats like images, videos, etc. Processing this unstructured data, however, is RPA’s Achilles Heel! As per a report from Gartner, about 80% of all organizational data is unstructured.
RPA’s reliance on rigid rules and templates does not allow it to read information from documents such as contracts and bills that do not conform to a set template. It is, therefore, not surprising that many of the limitations RPAs face are stemming from, in one way or the other, this inability to process unstructured data.
RPAs depend on manual resources to process unstructured data. This leads to slower processes reducing the overall organizational agility, ultimately leading to increased inefficiencies.
As per Forrester, conventional automation tools such as RPAs necessitate $5 in services for every $1 spent on the automation tools themselves. The expense balloons further in processes with many document variants or exceptions to business logic, leaving many RPA projects on the shelf. Most of the cost of maintaining traditional automations comes from the cost of handling exceptions.
As an organization grows, it needs to scale its automations keeping in mind its increasing size. However, as it grows, so does the volume of unstructured data. RPA’s inability to process this data thus becomes a major problem for these organizations when scaling.
Another problem created by not being able to process unstructured data is the lack of cognitive skills. Unstructured Data contains very valuable insights that any business could leverage to improve their knowledge and make better business decisions. With RPAs, however, businesses miss out these insights and the opportunities attached with them.
Another major problem associated with RPAs is the inability to handle exceptions. When an RPA encounters an unanticipated problem in its working, it throws an error that needs to be addressed by software developers and the likes. Yikes!
The question that arises then, is, if not RPA, then what? The answer to this, in simple words, is Intelligent Automation.
Intelligent Automation refers to the next generation of automation wherein technologies such as Generative AI are leveraged to address the shortcomings of legacy automation solutions. This empowers automation solutions to process both structured and unstructured data, allowing them to automate more complex tasks with minimal dependence on manual resources, such as IT/Tech Teams, etc.
These technologies democratize the power of automation to business users while maintaining IT governance and controls in place. Natural Language Processing Automation, for example, allows even non-technical employees to build, check and verify their automations, allowing businesses to significantly reduce their costs on the maintenance of their automation solutions, as was the case with RPAs.
Additionally, this unlocks hundreds of potentially crucial use cases such as Optical Character Recognition (OCR) and Intelligent Document Processing. But what just might be a gamechanger is Exception Handling: a major source of inconvenience for existing RPA users.
The future, it seems, belongs to those who adapt with the times. And the times: they’re a-changin! AI is changing the way business is done across functions in companies in every industry. The ramifications are huge, and so are the opportunities. It is up to organizations to decide if they still want to go ahead with an outdated technology, or give automations an upgrade they deserve in today’s day and age.
But Binny was right. And he could not have been more eloquent in putting forward the value proposition Kognitos offers its enterprise customers. As he moved on to discuss how Kognitos is helping businesses automate complex processes and workflows better, using Natural Language and LLMs, while discussing the use cases for which Kognitos was used by Fortune 50 Companies like PepsiCo, the audience was galvanized; something we felt and thoroughly enjoyed throughout the 3 days.
Disrupt is one of the largest annual technology conferences hosted by TechCrunch, that brings together founders, investors, developers, technologists, and business people from all over the world to learn about the latest trends in technology and beyond.
As the team pulled up to the Moscone Center In San Francisco, the enthusiasm was unprecedented. Such was the interest and excitement amongst the audience that most of our team members could not find the time to eat their lunch! As a Platinum Sponsor, Kognitos’s booth took center stage at the Expo Hall, where the team interacted with hundreds of prospective clients, showcasing how Artificial Intelligence (AI) is reshaping the way we think about how we leverage Automation and RPAs to influence customer and employee experiences. The team also hosted a meet-and-greet with their existing and potential clients, partners and other attendees at Disrupt.
As the 3 days flew right past, one thing was certain: Automation is at the cusp of being revolutionized, and that it is Kognitos that has the potential to do so. Looking back, we can say with absolute certainty that enterprises are aware, and taking note of the value Kognitos can add for them.
Looking back, we are very excited by the advances in Kognitos Conversational Exception Handling, and the ever increasing number of use cases that customers themselves can imagine. This was brought to our attention during Binny’s fireside chat with Jim. You can go through the interaction here:
Thrilled and excited, we are ready to redefine what it is to automate. And we’d like to thank you for your support.
But Binny was right. And he could not have been more eloquent in putting forward the value proposition Kognitos offers its enterprise customers. As he moved on to discuss how Kognitos is helping businesses automate complex processes and workflows better, using Natural Language and LLMs, while discussing the use cases for which Kognitos was used by Fortune 50 Companies like PepsiCo, the audience was galvanized; something we felt and thoroughly enjoyed throughout the 3 days.
Disrupt is one of the largest annual technology conferences hosted by TechCrunch, that brings together founders, investors, developers, technologists, and business people from all over the world to learn about the latest trends in technology and beyond.
As the team pulled up to the Moscone Center In San Francisco, the enthusiasm was unprecedented. Such was the interest and excitement amongst the audience that most of our team members could not find the time to eat their lunch! As a Platinum Sponsor, Kognitos’s booth took center stage at the Expo Hall, where the team interacted with hundreds of prospective clients, showcasing how Artificial Intelligence (AI) is reshaping the way we think about how we leverage Automation and RPAs to influence customer and employee experiences. The team also hosted a meet-and-greet with their existing and potential clients, partners and other attendees at Disrupt.
As the 3 days flew right past, one thing was certain: Automation is at the cusp of being revolutionized, and that it is Kognitos that has the potential to do so. Looking back, we can say with absolute certainty that enterprises are aware, and taking note of the value Kognitos can add for them.
Looking back, we are very excited by the advances in Kognitos Conversational Exception Handling, and the ever increasing number of use cases that customers themselves can imagine. This was brought to our attention during Binny’s fireside chat with Jim. You can go through the interaction here:
Thrilled and excited, we are ready to redefine what it is to automate. And we’d like to thank you for your support.
In this day and age, process automation is a must-have for any business looking to stay ahead of the curve. By leveraging technology, organizations can quickly reduce errors, save money and time on mundane tasks – freeing up their valuable human resources in the meantime. Moreover, advancements in tech have made it far more accessible so that even smaller companies not only have access but also get to benefit from automated processes ranging from basic data entry all the way to complex multi-system workflows! With automated processes, organizations can build an edge on the competition by staying ahead in rapidly changing markets and delivering impeccable customer service.
Businesses are increasingly relying on Robotic Process Automation (RPA) to take care of routine, everyday tasks – allowing human employees to focus their efforts elsewhere. While RPA is largely productive in these endeavors, it may sometimes stumble upon anomalies that fall outside its programming parameters; a “human intervention” scenario known as an exception. When this happens, the whole operation can be put at risk: delays mount up and efficiency decreases while costs skyrocket.
There are two major types of exceptions in process automation.
Business Exceptions
Application Exceptions
Business Exceptions happen when a bot is unable to process further due to programmed instructions. For example, a bot could only be programmed to process expense invoices upto $1000. In such a case any expense above $1000 would cause the bot to throw a “business exception alert” to a supervising human.
Application Exceptions happen when a bot encounters a technical issue like a server crash, network malfunction or a good old software bug. In such cases the usual strategy adopted by RPA bots is to just retry the process.
Exception Handling is Expensive!!
To minimize the impact of exceptions, organizations need to have robust exception handling strategies in place. This may involve training RPA bots to handle common exceptions using artificial intelligence (AI) or machine learning algorithms, or incorporating human oversight into the process to ensure exceptions are resolved quickly and efficiently. Additionally, organizations should continuously monitor and fine-tune their RPA bots to ensure they are functioning optimally and handling exceptions effectively.
But implementing robust exception handling strategies is very expensive and time consuming. Handling business exceptions not only requires business users to plan as many exceptional scenarios as possible, but it also involves them explaining such situations to bot developers who have limited business domain knowledge. Application exceptions might not always be resolved by retries, and such a scenario involves intervention of the IT team to analyze and fix the issues, which can be very time consuming. Such time overheads can add up over time, leading to significant losses in productivity and profitability.
Kognitos offers a unique and innovative solution for process automation – a botless system that uses an LLM-based interpreter to understand and execute processes written in plain English. This system acts as an IT layer for businesses, allowing them to simply explain their processes in English, eliminating the need for complex IT configurations and freeing up valuable resources. For example, in the case of invoice processing, Kognitos would communicate any exceptions directly to the business user in plain English and allow for resolution with a simple prompt. This drastically reduces the time and cost associated with planning and handling exceptions with traditional RPA solutions, as well as making it possible to handle exceptions that may have been impossible to plan for. The result is a more streamlined, efficient, and cost-effective process automation solution for businesses of all sizes.
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While the potential of ChatGPT and other NLP models to automate certain tasks that involve processing and generating human language is exciting, it is important to understand the limitations of this technology. One limitation is that ChatGPT and other NLP models are not adept at mathematical or logical reasoning. Additionally, these models can sometimes generate responses that are inappropriate or offensive, particularly if they are trained on a dataset that includes such language. Furthermore, NLP models like ChatGPT are not able to fully replace human workers, as they do not possess the ability to think and reason in the same way that humans do. This is because it is trained to generate human-like text based on a given prompt or conversation without a deep understanding of what is right and what is wrong. Tasks that involve critical thinking, such as math or business processes, are also hard for LLMs to do because they require precision and repeatability which isn’t a strong suite of LLMs.
Even if Generative AI is able to overcome the above mentioned issues, one major problem that remains with ML driven automation systems is the problem of “opaqueness”. The ML systems would just execute actions based on some logic deeply embedded in one of the model parameters, but we would never know the “why” part for any action. For example, an ML automation system could erroneously send out wrong invoices to your customers, and you would be left wondering exactly what caused it to do so. This means that resolving any bug or issue in the automation would be a nightmare for the IT team. Not just that, but business process automations are inherently logical and procedural. Using Generative AI (like ChatGPT) in this use case would just introduce non-determinism in such tasks that could cause unintended problems.
We need an AI system that is able to execute actions in a deterministic and auditable manner. Traditional programming languages already do this. But less than 1% of the human population knows how to even read code. Hence, there is a need for computers to natively understand statements in native language and know how to execute them (in the same way they know how to execute programming languages like python or java). However, this approach presents several challenges.
A language like English is very contextual. The same word could mean two very different things based upon the context it is spoken in. For example “Capital” can refer to financial assets or the city where a company is headquartered. Programming languages cannot handle such context based actions, and would require separate algorithms for each case.
Native languages are also very ambiguous. These languages were developed in a way that resolved such ambiguities via conversations. For example, if you say “we should call that employee”, if your listener has some doubt about which employee you are talking about, she would just ask you to clarify. Now programming languages are not built to be run in a conversational way. They just run a specific action, and any ambiguity that arises in the course of that would cause an exception.
One other difference between native and programming languages is the difference in their grammar rules. People do not think in terms of “functions” or “classes”. They think in terms of “actions”, “concepts” and “knowledge”. This is because programming languages are inherently mathematical, whereas native languages developed long before humans even had rudimentary knowledge of basic math.
One solution to this problem is Kognitos, which directly understands native language and is able to process it. Kognitos is able to overcome the challenges associated with native language by understanding the context, resolving ambiguity, and understanding the rules of grammar in a way that is similar to how humans understand them. Additionally, it is able to understand and process domain-specific language, making it more effective at automating tasks that involve human language. And, just like programming languages, it also provides a detailed auditable view into its runs, which the business users can use to gain insights into why an action happened or what might have gone wrong in case of an exceptional situation. Kognitos hence provides a way for businesses to reliably automate their tasks while leveraging the power of the latest LLM technologies.
In conclusion, while ChatGPT and other NLP models have the potential to revolutionize the way we interact with machines, it is important to understand their limitations. By enabling humans to directly communicate with computers in their native language and using technologies like Kognitos, we can overcome these limitations and make businesses more productive. As this technology continues to evolve, we can expect to see more advancements in the automation of tasks that involve human language.
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Any business can be thought of as a collection of employees with unique skills which can be composed into new business recipes. Unlike computers, humans are flexible and their capabilities are applicable across a wide variety of scenarios and they learn and become more efficient over time.
The same cannot be said about computers although we have made significant progress over the last 70 years. Computers have offloaded human labor for routine work, but when something new needs to be offloaded, we need developers or automation specialists to come in and build the automation. Reusability, which eventually leads to composability, is one of the hardest problems in computer science. It is also the holy grail of running a business efficiently. We need to make automation recipes reusable across enterprises and withstand the test of time – like Grandma’s recipes.
As a business’s goals evolve, the business itself needs to reorganize like transformer bots, reusing existing parts but changing the overall business to meet the new requirements of the day. That physical transformation is hard and painful. It isn’t a surprise that many businesses are still undergoing Digital Transformation. Why is it so hard? What is the missing piece of the puzzle? Why can’t business be like Transformer Bots?
Reusability of Business Logic – A Brief History
The Era of APIs and Microservices
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Automation programs costing more than anticipated has several negative results (outside of the extra $ spent).
1. ROI on projects disappears
2. Disillusionment with automation sets in, slowing down efforts to expand automation.
3. Many processes remain manual as new costs estimates and higher TCO impact the candidacy of different processes.
Despite these costs, thankfully new technologies like large language models (LLM) and generative AI are now coming online and enabling new automation platforms to solve these challenges. Large Language Models and Generative AI (such as GPT-3) utilize machine learning to enable outputs to be created directly from language, thus eliminating the step of a user having to
“Program” in another coding language. Furthermore, unlike traditional automation, these technologies can learn and improve over time, becoming more resilient, flexible and creative (just like people).
In this 4-Part Blog Series, each of these traditional sources of cost will be evaluated in greater detail with 3 segments:
Cost 1: Implementation: RPA has long sought to bring automation to the business user to move automation away from the purview of developers and equip subject matter experts with the ability to build automations themselves. Attempts at “Citizen Development” unfortunately have largely fallen short for one primary reason: traditional RPA tools are still very technical. To become a skilled RPA developer still requires weeks or months of training, something the average business user can ill afford. As a result, either RPA developers are hired in house, or consulting firms are used to build automations and to make matters worse, as RPA developers (like most coding skillsets) are in short supply in the market, the cost is high.
Solution 1: LLM Empowered Development
LLMs create a direct link between human thought (expressed in language) and computer code (expressed in coding languages). By creating this direct link, the time and effort required to translate from thought to creation (in the form of art, images, movies, writing or even code) is greatly reduced. Furthermore, the need to take training or “learn” a programming language (code) is reduced or in some cases eliminated all together. This is already happening in more technical forms or programming with tools like “Github Co-Pilot” and is now available in process automation with Kognitos.
Kognitos uses both proprietary and open source LLMs to enable users to build automation on average 10X faster than traditional RPA. Kognitos is entirely built in English, step by step, the same way a person would list out how they perform their work. No new interface or tool needs to be learned, and a far larger pool of talent can comfortably build their own automations.
As LLMs are dynamic and learn, Kognitos learns the nuance of an organization’s language, operations and processes over time, making them more resilient and creating “examples” that can be leveraged to short-cut the building of other processes within the same organization in the future.
Why This Matters: Faster Implementations = Lower Costs = Higher ROI = More Viable Automation Candidates
With LLM based automation platforms like Kognitos, businesses can more rapidly develop and deploy automation all while incurring a lower labor cost in the implementation stage. This not only helps COEs build momentum to automation programs and exceed internal goals, but reduces the upfront cost required to launch any automation. Speed of implementations not only result in less labor hours required, but also accelerate the payback period of an automation project. Additionally, if the up-front cost of automation is lower, then more processes within a business may now potentially have enough of an ROI to meet internal thresholds.
As mentioned above, we will cover in three additional blog posts the other costs of automation programs, but the reduction of implementations costs by using LLM based automation is a critical step for expanding automation in a business. LLMs are now making this possible by introducing speed to the implementation process and opening up the labor pool for building automations. Both resulting in on average 5X lower TCO then traditional RPA.
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Top Reasons Logistics Companies Struggle to Automate Document Heavy Processes
With Traditional RPA/OCR:
Variability: Logistics companies and freight brokerages receive a wide variety of documents from an even larger number of vendors. Traditional automation solutions use OCR templates or models to try and extract the necessary information from Bills of Lading, invoices, freight payments etc. and upload them into a system of record. These approaches require lots of time to train or set up, and are inflexible. When documents are received with unexpected fields/ tables or are damaged, OCR without local logic throws exceptions. The process breaks and is now “Brittle” requiring lots of services and eliminating the hopeful ROI.
With the exception handling available to traditional RPA, developers in an automation COE or IT, must either route the exception to a subject matter expert, ask how the finance or accounting professional would handle the situation and/or create a new template for future reference. In a recent conversation with a large logistics company, an automation COE leader recently told us, “The business user keeps asking, ‘Why do we have to keep having these meetings? I thought we fixed this already?’”. Because of the constant maintenance work required to keep these processes running, and the time-suck it has on business units, processes with lots of different document types fail to meet ROI thresholds and remain stubbornly manual. Wouldn’t it be easier if logic could just be added on top of the OCR or automation for future exceptions?
Unknown Requirements Up Front: In traditional approaches to RPA, the first step is to map out a process and try to identify all of the possible variations which may occur. Typically led by outsourced consultants, this takes time and money to identify as many variations as possible in the hope that processes don’t break. Not only is this time consuming, costly and inefficient, but often if asked, the business users can’t tell the development team all of the variations. They are stored in a user’s memory, but are hard to articulate until it is needed. When requirements aren’t mapped up front, processes break frequently, causing frustration and requiring expensive maintenance services. Because of this, most automation teams steer clear of processes with unknown requirements, leaving a huge portion of documents un-automated and frustratingly manual.
But when you train a new employee how to conduct a process manually, you don’t spend the time and money to train them on all potential edge cases on their first day. Instead, you train them on how a process should occur, and then enable their intuition and problem solving to learn and handle the rest as new documents are encountered. Automation should work the same way…and now it does.
The New Approach:
Kognitos built a new approach to automate document processing from end to end 10X faster and over 5X cheaper than traditional RPA or OCR tools. How? By approaching a process the same way a human would. If a front-line team member in the AP department of a freight broker received a BOL from a new vendor, in a totally new format, they would still be able to understand it based on past experience, and extract the needed information. If they didn’t understand it, they would walk over to their manager’s desk and receive instruction on how to proceed. After receiving instruction, that employee would jot it down on a sticky note, or remember the logic needed to handle that document type in the future.
With Kognitos, we have made handling exceptions and adding local logic on top of OCR/automation as simple as having a conversation. When Kognitos encounters an exception, it creates a question or request (in English) for the business user. The business user simply needs to respond in English with basic instructions like “For invoices from this vendor, the supplier number is always under the document ID”. Kognitos’s brain processes this logic (just like a person), remembers it and applies it anytime the situation is encountered again. Processes don’t break, they just pause, wait for instruction and then continue.
Value of Conversational Exception Handling:
The key is to have an automation tool that automates the same way humans approach documents and that tool is Kognitos.
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“Manufacturers face the challenge of retaining top talent and attracting new workers while minimizing disruption in their organization. Using technology to drive efficiency and innovation is vital”- 2022 Marcum National Manufacturing Survey [ii]
The Problems with Old Solutions:
The problems facing manufacturing finance teams are well documented. Adding to the challenge, many of the traditional responses CFOs are considering can have unpalatable consequences and side-effects.
Increasing budgets for compensation can effectively create a “compensation arms race” at the same time when risks of “Wage spirals” are rising.
“Over the next two-year period, 77% of survey respondents plan to increase average wages by 5% or more, and of that 77%, a quarter are looking to increase average wage by more than 10%. So at least a quarter of your competitors are looking to increase wages by 10% or more in the next two years”[iv]
Investing in new data analytics capabilities is important, but by itself often slow to implement. Raising prices unfortunately can reduce competitive positioning and risk losing market share. So if many of the traditional options can have negative side-effects, what should finance teams do? What if instead, finance teams had a tool to help offset some of these side-effects, and do so quickly?
The Opportunity for Finance Teams to Be Heroes:
Kognitos is designed for business users, giving finance teams the power to automate processes in hours or days, by themselves. This drastically reduces cost and helps make analytics and forecasting processes far more efficient and accurate. With Kognitos, CFOs may not need to resort as much to some of the tough options above.
In the past, automation tools were either too simple to drive meaningful productivity, or required too high of a TCO, pricing out many business processes. If an automation tool requires a vast team of IT staff or “Center of Excellence” to manage and maintain it, it’s ability to be widely adopted will be constrained.
Instead, in manufacturing, automation should be moved to the functional role itself. Finance professionals in manufacturing spend a significant amount of time performing manual processes that are repetitive, or have to fill the gaps between applications that do not integrate properly. As a result, these hardworking professionals often become disillusioned with their work as they are not able to focus on the work that distinguishes them: problem solving and helping deploy the company’s capital for long-term growth.
But this changes when F&A professionals are empowered with the ability to automate away repetitive work, and focus on higher level, human centric work. With Kognitos, F&A departments can automate through regular conversational English. Finance professionals simply tell Kognitos what they wish to have automated such as “I want you to take all invoices received from Customer X, and process them through Epicor for payment.”. Kognitos then builds an actionable automation plan, and after approval executes this plan. This can be done in less than a day, while delivering ROI in excess of 200%. Numerous use cases for automation in manufacturing finance teams exist (see more examples here: https://v.fastcdn.co/u/f8b11a40/62179020-0-Process-Automation-i.pdf) and can be deployed rapidly.
“Increasing productivity was the most popular business priority for the coming year, with 60% ranking it among their top three priorities.”[v]
If manufacturing CFOs need better forecasting, more efficient operations, and high levels of retention to make it through the current crisis, finance teams can be the heroes. By automating manual, repetitive work quickly, and seeing ROI immediately, finance professionals can provide their leadership with the knowledge they need to make critical decisions and help make their company more competitive by eliminating cost, all while making their own jobs more enjoyable.
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[i] Grant Thornton, “As Inflation Soars, CFO Optimism Sinks”, 24 May 2022
[ii] Marcum, “2022 Marcum National Manufacturing Survey”, 4 August 2022
[iii] Grant Thornton
[iv] Marcum
[v] Marcum
Late last year, I wrote a blog on RPA’s dirty secret while believing that it will take a few years for the markets to correct course, but the current conflict in Europe accelerated it in a way I did not expect. Today, robotic process automation (RPA) technology remains unsuitable for the masses. All the airport signs with the cute robots did excite the business travelers, but when the rubber hit the road, it was a hard technology to use. Why?
That brings me to the perfect analogy for the situation, YouTube.
You may ask, “What does YouTube have to do with any of this?”
Prior to YouTube, video content creation and distribution required:
Today, armed with just our cellphones and YouTube we can immediately create unique content and share it .. and it goes viral. Gone is the steep barrier to entry to create, consume, and recreate. Also gone is the complexity at scale as YouTube solves this for us.
The man with the camera is what robotic process automation (RPA) looks like today. The concert experience is what the world is craving for when it comes to automation. The art of automation needs to be democratized, atomized, and commercialized. Just like the art of video creation was.
The record breaking IPO of UiPath had marked the attempt of the RPA vendors to democratize the technology into a larger base of users, not just the top of the pyramid. “A bot for everyone.” The idea was to break into the mid-market and below.
The challenge they faced, however, was that even with the aid of AI wrappers on top of the legacy RPA technology, the fundamental issues around speed or cost of automation remained. While the cost of the RPA software on the surface seems affordable by the mid-market, the required professional services, which can be 3-4x the cost of the RPA software licenses, and the overheads of creating centers of excellence and hiring consultants for process discovery, make the technology a non-starter in the mid-market and also for a large chunk of automatable processes in large enterprises.
The RPA vendors find it hard to “land and expand” as they run out of high ROI business processes soon after the first 5-6 workflows are automated at an enterprise. That doesn’t mean that there isn’t the appetite or opportunity to automate more. In fact, what has been automated today is just a tip of the iceberg.
The real appetite for automation today far exceeds the ability of our society to produce automation engineers.
There are only about 18M coders in the world and a very small fraction of them are engaged in business process automation. However, there are about 1B business professionals willing to teach a computer something new about their business — if only the computer was smart enough to understand. These one billion professionals would like to automate with the same ease as someone who uses their cell phone at a concert to record a video, then post it to YouTube. To do that, however, the automation engines have to be rewritten from scratch with both an AI-first and a cloud-first design.
It is no surprise that UiPath has been struggling to democratize the technology beyond the top of the pyramid.
Credit: https://seekingalpha.com
If one looks at the trajectory of UiPath earnings since Q2 2021 (IPO), it shows that the cash infusion from the IPO was not able to accelerate growth. The top of the pyramid was getting saturated while the lower portions were not accessible due to the large cost of the professional services. To make things worse, the war has put a huge question mark on the supply of additional automation professionals needed for future growth. I have spoken with many service providers who are struggling to satisfy their customers’ automation needs today because they are short on developers. In a $38B TAM for RPA, with ~$1B revenue, if the leading RPA platform is struggling to grow, a fundamental shift in approach is required.
While the RPA market is struggling due to lack of developers who can drive automation with the existing technology, the overall $600B TAM for hyper-automation is waiting for the “YouTube” moment in the automation space. We can’t overstate the following fact:
There just aren’t enough coders in the world.
So how do we get the “YouTube moment in automation?” I see three secular trends that will lead us there.
Trend #1: Democratization
Automation will be come accessible to a larger portion of a billion business professionals. The low code/no-code movement promised such a solution, but it has its own issues. In short, business users do not want to learn the complex menus in the general purpose automation tools, and yet on the other hand, feel constrained if the tools are too opinionated.
The automation tools that will succeed will be less menu-driven and more natural language driven. The time is near when everyone will be able to teach a computer a new skill.
You might have heard that the attention span of humans is reducing with every generation. From hand written letters that would take weeks to deliver, to emails, to tweets and Instagram. From 2 hour movies to 30 minute TV programs, to 2 minute YouTube videos, down to 5 seconds on TikTok.
The atomization of creation and consumption of content is a multi-generational secular trend that will also shape the future of the automation space. Once the tools that can truly democratize the ability to automate are available, we will see the atomization of automation emerge. From software that we released in 18 months, to every 3 months, to every day today with CI/CD. We will see a new breed of “instant automations” that enable a business user to globally release a new automation to their audience in under 5 minutes using natural language instructions to machines.
Every business is a set of differential business processes and every business employs engineers to automate some of those processes. While the creation of the automations is already hard, the distribution and consumption of built automations is severely anemic to make matters worse. The automation platforms of the future will directly solve for the global scalable distribution of automation just like YouTube did for our videos. At the same time, discoverability and accessibility of the automations will be solved as well. I posit the Action Bar – just like the “search bar” from 25 years ago, will emerge allowing massive consumption of automation by end users at a global scale.
The businesses will finally focus on the business logic. They will not waste time on how or where the automations run or how their customers, partners, and employees get access to automation. In the end, these automation distribution platforms will enable a new economy of micro-automations, and instead of downloading 200 apps on our phones, we will all be using a single Action Bar with a billion micro-skills backed by a billion users.
One Action Bar – for everything.
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