Binny Gill, our founder and CEO, alongside Biplab Adhya, Managing Partner at Wipro, a leading technology and services company with $11 billion annual revenue, took center stage during a keynote panel discussion to discuss why learning the language of humans is key to enable generative AI for automation. The panel explored the practical applications of generative AI within enterprise automation processes, drawing in the audience with real-world use cases. During this time, they discussed how Kognitos specializes in leveraging generative AI to automate business tasks and processes, including invoice processing, collection accounting, financial and accounting process automation, center of excellence processes and more. On the panel, they shared that one of the key advantages of utilizing Kognitos is our human language interpreter for conversational AI capability. By incorporating natural language processing and conversational AI, we eliminate the need for upfront documentation of all process variations.
Additionally, we had the privilege of hearing from leading enterprises such as Workday, Capital One, Squaretrade (an Allstate company), CBRE, Forbes, Turkish Arlines and many more who shared their perspectives on the challenges with Robotic Process Automation, and their enthusiasm for adopting generative AI automation within their organizations.
Upon leaving this event, we are filled with a sense of assurance that the future of generative AI automation is already here and that now is the time to adopt this revolutionary technology. It is with great pride that we continue to lead the way in this transformative industry alongside such esteemed customer organizations like PepsiCo, Norco Industries, Century Supply Chain Solution, Brown Industries and more!
As businesses continue to enter the digital age, Gartner’s recent blog posts have indicated an impending shift from Enterprise Resource Planning (ERP) Platforms to Enterprise Run-Time Platforms as a necessary piece of agility and scalability. This major change will require companies looking towards ERP strategies they can tailor towards faster & more adaptive models that keep up with their ever changing needs – such as Digital Core/Lean Core/Agile Core options. It is clear this new era in business operations has just gotten underway!
The era of big, expensive on-premise ERP systems has been rapidly replaced by cloud and SaaS solutions. These applications offer businesses access to new types of enterprise software with faster implementation times at minimal cost due to subscription models. In the HCM space, this shift is almost complete with cloud being an industry norm for many companies such as Salesforce and Workday who pioneered the technology; even legacy vendors like Oracle and SAP have adopted a “cloud first” development strategy in order to stay competitive within their field. This architectural shift makes it easier to open their core systems via APIs. AI can then be able to interact with these systems in a much more natural, reliable and robust way.
ERP software has historically had a steep learning curve. Not only that, but the maintenance cost of this software has been a pain for almost all businesses relying on them. This means that new age businesses are increasingly looking to move away from the traditional offerings and adopt a leaner and more agile solution. This is where Generative AI comes in. With its ability to understand natural language based business domains, and its ability to natively talk to machines, it can help bridge the gap between domain experts and software. Businesses would only need to train their employees in how the business works – not how software works.
Thus by utilizing ERP systems and Generative AI automation, businesses can achieve efficiencies that lead to increased profitability. By leveraging the power of technology in this way, companies create an optimized workflow for data management with greater accuracy and scalability – ultimately enabling them to make more informed decisions backed by reliable intelligence! This combination is enabling organizations to become more agile and responsive, allowing them to rapidly adapt as necessary in this ever-changing business climate.
Business users now have access to smarter, more user-friendly enterprise software through the integration of generative AI tools. No longer do they need to spend hours learning a new application – instead, domain knowledge and AI will collaborate in order for businesses to get the most out of their ERP solutions. This opens up an exciting opportunity where Enterprise Generative AI enablers & ERP vendors can join forces and provide cutting edge products that promote success within enterprises all over the world!
At Kognitos, we are already walking the walk for Norco – one of our esteemed clients. We believe in showing results not just talking about them; so let’s explore how this vision could become a reality with concrete examples.
Gartner Article: https://blogs.gartner.com/tonnie-van-der-horst/erp-is-dead-part-1/
Want to Unlock the Power of Generative AI for Your Business Today
The Answer: Empower your skilled workers with automation and the ability to focus on higher level, meaningful work. Kognitos’s automation platform learns business logic from the business user and automates them without the need for a developer or a steep learning curve of a tool. Users can automate even exception laden tasks so they can focus on relational and meaningful work.
Another McKinsey 2021 study shows compensation alone will not retain employees. While having compensation rates commensurate with industry bands is important, many employees highlight other reasons for leaving their current employers including autonomy, and the desire to engage in more relational work. This is especially true of millennials and GenZ, who combined will soon represent over half of the workforce. However, many business processes are not designed to provide skilled employees with this desired work style. Instead, top talent is bogged down in manual processes, and even when attempts at automation occur, is forced to wait weeks or months for results due to backlogged automation teams. Frustrated, they look elsewhere for more meaningful work, costing companies a tremendous amount in recruiting, training and cultural costs. But if automation thrives at reducing manual work (and thus eliminating the problems described above), and if many companies today have some form of process automation, why hasn’t it given employees the autonomy and freedom to engage in the more relational work they so desire?
Traditionally, Automation has been focused on manual, repetitive, rules-based tasks. Naturally, this led to the automation of many back-office tasks previously either outsourced or handled by relatively unskilled labor. But automation has failed at large to take root with more skilled employees, the same employees that managers are struggling so mightily to retain. There are a few reasons for this. The first, is that the day-to-day work handled by a high skill employee often requires lots of exceptions. Processes have variance, and it is for this reason employers pay these employees to problem solve and handle these variations. In fact, this highlights one of the other risks with employees leaving, if an employee who handles a great deal of exceptions leaves, the knowledge of how to best navigate such exceptions departs with them, a significant loss for a company. The number of exceptions present in processes, even processes that are routine and manual, discourages the use of traditional automation, as the cost and maintenance of these processes outstrips their value. Furthermore, employees become frustrated when exceptions need to be built into the automation, as it can often take weeks for a dedicated automation team to build corrections into the workflow. This does not provide the type of autonomy employees desire, but instead leaves them dependent on backlogged development teams and losing faith in automation to benefit their day-to-day work.
In contrast to traditional automation, Kognitos’s platform provides the solution managers need and provides employees with the work experience they desire. Using patented NLP, Kognitos enables employees to problem solve exceptions in conjunction with automation, and through English instruct the automation software as to how to handle exceptions. The platform then learns from this experience and can automate these processes and exceptions in the future. With Kognitos, employees are given the autonomy they desire, no longer have to do much of the work they loathe, and have more time to focus on higher value, more relational work. In turn, greater retention and a happier workforce helps to turn the challenge of the Great Resignation into a truly strategic opportunity for managers.
At Clear Ventures, we looked into the reasons behind the widespread dissatisfaction with today’s RPA industry. After several discussions with large enterprise companies in our own portfolio, it became apparent that RPA’s poor success rate can be boiled down to three primary factors:
• High upfront cost: Contrary to Humans, who learn over time by doing and adapting, RPA ossifies current business processes in software. This mandates a great deal of upfront work to ensure that the business and associated exception cases are clearly captured and programmed. Upfront costs are significantly higher both for bots and, commonly, business consultants who first must optimize the current business process prior to automation.
• High maintenance cost: In theory, a diligent job upfront can reduce ongoing maintenance cost, but the reality turns out to be quite different. Many business processes are simply too complex to effectively document all of the exception cases upfront. And because business requirements change, the processes do as well. This leads to high maintenance costs that often turn out to be the hidden “Achilles’ heel” of RPA.
• Finger-pointing (the blame game): When the automated process fails to work as expected, a blame game often ensues. This RPA project finger-pointing typically is a three-way exercise between the business user, the RPA tool vendor, and the RPA tool programmer. Undermining RPA project failure accountability aggravates the frustration and further increases costs.
RPA at a Crossroads RPA is at a crossroads, and we need a different approach as nobody except perhaps the RPA software vendor is being served well in the current environment. As we looked beneath the proverbial “tip of the iceberg”, we came across deeper reasons for the poor success rate of current RPA implementations including:
It became obvious to us at Clear Ventures that the RPA market is primed for disruption. We evaluated several startups and eventually came across Kognitos which takes a radically different approach from RPA. Kognitos’ solution is centered on two pillars:
• Natural Language programming: Kognitos enables bot and human interaction based upon natural language, eliminating or dramatically reducing the need for experienced programmers. Using natural language slashes both maintenance cost and time to value for the line of business.
• Run-time learning: Current RPA approaches simply crash or freeze when faced with exceptions and edge use cases. When a Kognitos bot encounters unexpected situations, it interacts directly with the business user. Similar to the way in which humans resolve unexpected hurdles, the bot asks questions, learns, and then applies the learnings to modify and continue the process.
We have partnered with Kognitos since the days when the concept was just a glimmer in the eyes of Binny Gill, the gifted product author and architect of the vision. We are thrilled that Binny and his team have come out of stealth and have launched their first product. We look forward to continuing this exciting journey with the Kognitos team as they move into the next phase of the journey to reinvent the RPA industry.
Please visit Kognitos.com for more reading.
Let us dig deeper and find out why.
The promise of RPA has always been that it can do mundane tasks that otherwise would have to be done by a human. In general, that is the primary purpose of computers. The real difference with RPA is in the expectation of who the developer is. In RPA, and also the low code/no code systems, there is the expectation that the business user or the citizen developer will build the automation. There is no requirement of formal computer programming experience from these business users — at least that is the initial marketing pitch, and the demos demonstrate how easy it is for a non-programmer to create an automation via a drag-n-drop UI. And when it works it does seem magical!
But, the dirty secret about any automation is that the initial creation of the software is just the marriage ceremony, married life is what happens next.
The initial creation of automation (or the marriage ceremony), is a beautiful well orchestrated demonstration of good intent and promises. It is the happy path or sunny day scenario. Seasoned software developers know that it is only 20% of the work.
The Day 2 — Day N of automation (or married life), is full of surprises, edge cases, exceptions, changes in requirements, changes in expectations, changes in environment, changes in people, growth and acquisitions. Or as developers call it, “an average day at work”.
The complexity of Day 2 and beyond forced business users to hand over the RPA tools to the IT department which created a Center of Excellence where developers (either in house or consultants) were hired to maintain the automations. That still does not explain the large rate of failure and frustration in RPA projects. We don’t hear of general software projects failing that often. What gives?
Let us double-click into what challenges IT faces when implementing RPA.
RPA systems, more often than not, fill in the functionality gaps between existing business tools. For example, taking an invoice from an email and extracting data from it, performing validations and updating the general ledger; or receiving a record from Salesforce and creating a corresponding record in Netsuite after cross-checks and validations. There are many such processes that humans are manually doing today that RPA helps automate.
However, there is a reason why these gaps exist. Either it is hard to configure the existing products to fill in these gaps, or the existing products don’t have the requisite features. The latter happens when the diversity of integrations required are too large for the software vendors to grapple with, or the environment is quite fluid and unpredictable. For example, a website could change its look and feel breaking automation that depends on it; or a payroll system might change its behavior without coordinating with any financial planning tools that depend on it.
Humans, who fill in those product gaps, are adaptive and don’t crash when a website moves the “Submit” button to a different spot on the page, or when the payroll system rolls out an improved version of their software.
Dealing with a bunch of third party tools and services is hard, and that’s what RPA is tasked to do.
The inherent instability of these environment makes implementation hard, even for the very best developers. The automations created regularly break and are constantly hungry for fixes. This results in “bots” being hungry for “baby sitting”, and the realization that we can only have a few bots. Did we say “married life”?
I have built software products all my life and I know that gathering requirements is not easy. There is a reason why “product manager” is a full-time job. If we don’t invest in figuring out what the requirements are up front, it will lead to later changes which will be more expensive to do. Some of the requirement for a product manager is fulfilled by process mining tools that are deployed in conjunction with the RPA solution. But deploying these tools is also a chore and RPA projects inevitably require many meetings with various stakeholders and business users across the organization with the objective of figuring out the business processes in great detail. Then, based on the stability, complexity and value to business of the process, it is determined whether a process is fit for automation.
If we simply ask the business user to describe a business process in detail, more likely than not, they will miss some key scenarios. Humans have evolved to very quickly figure out the next best step in any situation. But, we have had no evolutionary need to chronicle all the possible paths to take in a complex workflow. Planning is hard, reacting is easy.
Business requirements also change often. In a growing business, processes are created and modified almost every quarter to help the company evolve to the next level of scale and maturity. Acquisitions and mergers can force changes to processes. Governmental regulations may throw a wrench into business process as well. These factors make it uneconomical to produce automation for processes that are subject to change.
Finance, Sales, Marketing, HR and Support are not software development organizations. When business leaders decide to bring in automation tools, they have to decide between hiring developers in-house or leveraging consultants. Today, it is hard to hire talent in the automation space as there are not enough developers trained in these specialized tools. Further, the business users are trying to reduce their workload by offloading it to a machine, and it seems antithetical to the idea to hire a team of humans that now you have to manage. Most enterprises chose to have a combination of consultants and in-house developers to build out the Center of Excellence.
Creating highly maintainable software is very hard when most of team are part-time consultants. Long term owners of software are critical for the success of any software product. If there is churn in the staff maintaining automation, it reflects in poor maintenance of the automation leading to poorer long term results.
RPA succeeds in automating repeated tasks that are narrow in scope and don’t change much. As long as that process is not in an unstable environment and the requirements are not going to change, it is likely to succeed in saving a lot of time for humans. However, those kind of processes are a small fraction of what could and should be automated. Gartner estimates that hyper-automation market to reach $600B by 2022, and RPA will barely scratch the surface of that.
We need to think beyond RPA.
No. It isn’t RPA.
Notice that the fundamental reason for all of the problems around automation is that automation needs to evolve on Day 2, and that is hard to do with RPA. The humans that are currently driving the business processes manually, are inherently able to handle and adapt to unforeseen conditions and are able to rewrite the process as they go along.
The biggest difference between humans and machines is how they behave when something goes wrong.
Can we build such a system that has human-like error handling?
Yes. With the recent advances in deep learning and compiler design, this has become achievable.
Below are the 5 requirements for automation to become human-like.
The above table shows the fundamental difference between machines and humans. When a piece of automation realizes it does not know how to do an action, it just crashes. A human on the other hand, would simply ask the manager, learn the new fact or skill, and continue. There is a reason why we do not need to do “process discovery” before hiring a new employee. The expectation is that the employee will ask if anything is confusing and learn from the answer. So, the training cost of a human gets spread out over time, unlike software engineering which front-loaded with a lot of the development cost.
The table below shows a qualitative representation of this observation. Each business will have different numbers but the picture helps visualize the problem better.
Humans can be trained incompletely and yet be productive to the business.
Machines have to be trained more thoroughly upfront because of cost of making changes later in the lifecycle is steep.
Good software practice is to articulate all edge cases up front and build that into the software. That leads to a large up front cost.
Humans with time get more useful to the business without explicit effort in retraining them. We call them “experienced employees”.
Machines, on the other hand, get ossified and hard to change, and after some time require a rewrite of logic.
If we can handle errors without crashing and change automation without a development cycle, we can fundamentally change the TCO of automation.
Such a system would exhibit:
In a previous article I had articulated the journey towards building such a system that dramatically increasing success rates and reduces TCO of automation. There are two breakthroughs that such a system will possess:
We, at Kognitos Inc., have built such a system and foresee that many others will come forward with similar solutions. The business process automation industry will finally be able to tackle the TCO and scaling problems around automation, leading to more efficient and dynamic businesses in the future.
