Kognitos is attending ITC Vegas, October 14-16 in Las Vegas. See us at booth #454 or join us for dinner at Momofuku.
Kognitos is attending ITC Vegas, October 14-16 in Las Vegas. See us at booth #454 or join us for dinner at Momofuku.
By using Kognitos, our customers are answering this question for themselves with use cases in Logistics, Manufacturing, Telecom and CPG, among others. Customers with sophisticated automation COEs, including PepsiCo and Wipro, are seeing the value of Kognitos in automating use cases that can’t be handled with RPA. And with Conversational Exception Handling , the truly non-technical business SMEs are handling exceptions and teaching Kognitos how to handle them in the future. This frees up IT and COE resources to build new processes.
Not only are customers recognizing how Kognitos is changing automation, but investors and thought leaders too. The greatest evidence of this is that Kognitos is now listed alongside the legacy, twenty-year-old players in automation like UiPath and Automation Anywhere in the Sequoia Capital: Generative AI Act 2 map.
Calling it “our generation’s space race,” the article discusses the current state of technology in the AI space and the challenges it faces. how Generative AI has the potential to revolutionize a number of industries, attributing it to customer demand and a huge number of use cases across industries.
The author opens by discussing the history of Generative AI, from its early roots in machine learning, to its advances in deep learning today, before moving on to the challenges it faces: from the difficulty in training these AI models to the potential threat of the entry of bias in their output. Despite these challenges, the article suggests that Generative AI would play a crucial role in our future.
The author further elaborates on how the market is moving towards “Act Two” where he says, “We now believe the market is entering “Act 2”—which will be from the customer-back. Act 2 will solve human problems end-to-end. These applications are different in nature than the first apps out of the gate. They tend to use foundation models as a piece of a more comprehensive solution rather than the entire solution”.
The article introduces Sequoia’s updated Generative AI Market Map for 2023. The map is organized by use cases instead of model modality, reflecting Generative AI’s evolution, as well as the multi-modal nature of its applications.
The Market Map features Kognitos in the Enterprise: Horizontal segment under RPA/Automation, and we are proud to be included only after two years in this list with the major players in automation.
Our goal to make automation simple and accessible for business users is what guided us in Sequoia’s Market Map for Generative AI. With Kognitos, humanity is coming out of the dark ages of computer language literacy. We envision a future where all business apps will be written in English. As we approach the last quarter of this year, we stand by our mission to enable machines to understand human language natively, and we’re working harder to solve for this fundamental problem.
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.
Generative AI is the modern approach to automating business processes, reducing costs associated with mundane and repetitive tasks, and increasing employee productivity which leads to increased revenue. It is advanced automation that offers businesses greater accuracy and agility than traditional robotic process automation (RPA). With generative AI, business and IT users can describe what they want to automate and it will present a plan of action in plain human language, that the users can review and edit before deciding to build automation. It keeps users and organizations in control while still providing them the flexibility and speed to build and fully manage automations in real-time using human language. Generative AI self-learns and adapts to existing business processes using conversational exception handling capability, which would be impossible for an RPA. In this article, we will discuss the differences between generative AI and RPA, explore the benefits of replacing RPA with Generative AI, and provide some tips on how to make a smooth transition from RPA to generative AI. If you’re interested in learning more about how your business can benefit from a private and safe Generative AI for Automation solution, meet with a Kognitos solution expert today!
In summary, generative AI and robotic process automation (RPA) are two distinct forms of automation that offer businesses the opportunity to automate processes more quickly and efficiently. Generative AI is more flexible, adaptive, and creative than RPA and can be used in scenarios where RPA would not be able to make decisions. Companies should assess their needs carefully before deciding which type of solution would best suit their requirements for automated business processes.
The advantages of generative AI automation over robotic process automation (RPA) are undeniable. Generative AI is far more adaptable and accurate than RPA, allowing for rapid training and evolution in dynamic environments. Furthermore, this type of automation can be deployed on an enterprise-wide basis, reducing costs associated with manual labor. Generative AI also offers faster deployment times than traditional methods, as programming or manual input is not required. It allows businesses to reap the rewards of automation sooner rather than later.
Along with these time-saving and cost-efficient benefits, generative AI also provides access to previously hidden insights from data patterns that may go unnoticed by humans or other automated solutions such as RPA. It makes it a powerful tool for decision making and fraud detection, among many other applications.
In summary, the advantages of generative AI over RPA are numerous: better accuracy and adaptability; quicker deployment; reduced costs; and greater insight into previously unseen data patterns. Businesses should carefully consider their needs before deciding which type of automation solution is best suited for them – but when it comes to speed, accuracy, cost-efficiency, and access to new insights – generative AI is hard to beat!
Generative AI stands out as a cutting-edge form of automation that can help businesses stay ahead of the curve. By leveraging its powerful learning and evolving capabilities, organizations can take advantage of cost savings while optimizing their processes more effectively than ever before. With reinforcement learning techniques such as Proximal Policy Optimization (PPO), generative AI algorithms can adjust to changing circumstances and quickly adapt when needed. It means that businesses can create custom solutions tailored specifically for their needs without needing to conduct a complete overhaul. Furthermore, the system is capable of analyzing large amounts of data quickly, which allows it to uncover new opportunities and insights that would have otherwise been missed. As such, generative AI is the key to unlocking increased efficiency in the workplace and giving companies an edge over their competitors in today’s digital world.
Making the transition from robotic process automation (RPA) to generative AI can be a complex and daunting prospect. However, with careful planning, organizations can ensure a smooth transition while reaping the full benefits of this powerful technology. Here are some tips for making the transition to generative AI as seamless and successful as possible:
1. Identify processes and tasks that are ripe for automation
The first step in transitioning from RPA to generative AI is to identify processes and tasks that are suitable for automation. Organizations should take an inventory of their current business processes and analyze which ones have the highest potential for improvement through automation. It will help them determine which areas they should focus on first when implementing a generative AI solution.
2. Evaluate potential solutions
Once you’ve identified areas where generative AI could offer improvements, it’s important to evaluate potential solutions carefully before making any final decisions. Researching different types of solutions available, understanding pricing models, and talking with product experts can help organizations make sure they choose the best option for their needs.
3. Map out a timeline for transitioning from RPA to generative AI
Organizations need to create a roadmap for successfully transitioning from RPA to Generative AI, taking into account how long each step in the process might take and allowing enough time for adequate training of employees on the new systems before implementation begins. Establishing specific timelines will help keep everyone on track and ensure that all deadlines are met so that the organization can start achieving its goals as soon as possible after implementation is complete.
4. Develop a plan for training employees
Developing a plan for training employees on new systems is essential when transitioning from RPA to Generative AI – especially if employees with existing knowledge about robotics automation need to learn new skills or technologies associated with generative artificial intelligence systems.. Organizations should include all relevant stakeholders in employee training sessions by providing clear explanations of what changes are being made, why those changes are necessary, what impact they will have on day-to-day operations, and how best practices must be followed going forward to get the most out of new systems.
5 Monitor performance closely after implementation
Last but not least, organizations should monitor performance closely after implementing generative AI solutions in order to determine if adjustments need to be made or additional features added over time in order to optimize results even further. By taking proactive steps such as tracking KPIs, running experiments, monitoring customer feedback, and regularly reviewing system performance data, companies can ensure that any issues arising during implementation or use can quickly be addressed.
Taking the first steps towards incorporating generative AI into business processes can seem daunting. However, understanding the core principles of this technology and exploring its various use cases can help organizations identify which type best suits their needs. After that, they should consult a product expert to determine which platform or tool will be most suitable for their particular requirements. By taking these measures, businesses can make sure they get the most out of this powerful technology and benefit from all of its advantages as they automate their processes with generative AI.
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By using Kognitos, our customers are answering this question for themselves with use cases in Logistics, Manufacturing, Telecom and CPG, among others. Customers with sophisticated automation COEs, including PepsiCo and Wipro, are seeing the value of Kognitos in automating use cases that can’t be handled with RPA. And with Conversational Exception Handling , the truly non-technical business SMEs are handling exceptions and teaching Kognitos how to handle them in the future. This frees up IT and COE resources to build new processes.
Not only are customers recognizing how Kognitos is changing automation, but investors and thought leaders too. The greatest evidence of this is that Kognitos is now listed alongside the legacy, twenty-year-old players in automation like UiPath and Automation Anywhere in the Sequoia Capital: Generative AI Act 2 map.
Calling it “our generation’s space race,” the article discusses the current state of technology in the AI space and the challenges it faces. how Generative AI has the potential to revolutionize a number of industries, attributing it to customer demand and a huge number of use cases across industries.
The author opens by discussing the history of Generative AI, from its early roots in machine learning, to its advances in deep learning today, before moving on to the challenges it faces: from the difficulty in training these AI models to the potential threat of the entry of bias in their output. Despite these challenges, the article suggests that Generative AI would play a crucial role in our future.
The author further elaborates on how the market is moving towards “Act Two” where he says, “We now believe the market is entering “Act 2”—which will be from the customer-back. Act 2 will solve human problems end-to-end. These applications are different in nature than the first apps out of the gate. They tend to use foundation models as a piece of a more comprehensive solution rather than the entire solution”.
The article introduces Sequoia’s updated Generative AI Market Map for 2023. The map is organized by use cases instead of model modality, reflecting Generative AI’s evolution, as well as the multi-modal nature of its applications.
The Market Map features Kognitos in the Enterprise: Horizontal segment under RPA/Automation, and we are proud to be included only after two years in this list with the major players in automation.
Our goal to make automation simple and accessible for business users is what guided us in Sequoia’s Market Map for Generative AI. With Kognitos, humanity is coming out of the dark ages of computer language literacy. We envision a future where all business apps will be written in English. As we approach the last quarter of this year, we stand by our mission to enable machines to understand human language natively, and we’re working harder to solve for this fundamental problem.
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.
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!
In summary, generative AI and robotic process automation (RPA) are two distinct forms of automation that offer businesses the opportunity to automate processes more quickly and efficiently. Generative AI is more flexible, adaptive, and creative than RPA and can be used in scenarios where RPA would not be able to make decisions. Companies should assess their needs carefully before deciding which type of solution would best suit their requirements for automated business processes.
The advantages of generative AI automation over robotic process automation (RPA) are undeniable. Generative AI is far more adaptable and accurate than RPA, allowing for rapid training and evolution in dynamic environments. Furthermore, this type of automation can be deployed on an enterprise-wide basis, reducing costs associated with manual labor. Generative AI also offers faster deployment times than traditional methods, as programming or manual input is not required. It allows businesses to reap the rewards of automation sooner rather than later.
Along with these time-saving and cost-efficient benefits, generative AI also provides access to previously hidden insights from data patterns that may go unnoticed by humans or other automated solutions such as RPA. It makes it a powerful tool for decision making and fraud detection, among many other applications.
In summary, the advantages of generative AI over RPA are numerous: better accuracy and adaptability; quicker deployment; reduced costs; and greater insight into previously unseen data patterns. Businesses should carefully consider their needs before deciding which type of automation solution is best suited for them – but when it comes to speed, accuracy, cost-efficiency, and access to new insights – generative AI is hard to beat!
Generative AI stands out as a cutting-edge form of automation that can help businesses stay ahead of the curve. By leveraging its powerful learning and evolving capabilities, organizations can take advantage of cost savings while optimizing their processes more effectively than ever before. With reinforcement learning techniques such as Proximal Policy Optimization (PPO), generative AI algorithms can adjust to changing circumstances and quickly adapt when needed. It means that businesses can create custom solutions tailored specifically for their needs without needing to conduct a complete overhaul. Furthermore, the system is capable of analyzing large amounts of data quickly, which allows it to uncover new opportunities and insights that would have otherwise been missed. As such, generative AI is the key to unlocking increased efficiency in the workplace and giving companies an edge over their competitors in today’s digital world.
Making the transition from robotic process automation (RPA) to generative AI can be a complex and daunting prospect. However, with careful planning, organizations can ensure a smooth transition while reaping the full benefits of this powerful technology. Here are some tips for making the transition to generative AI as seamless and successful as possible:
1. Identify processes and tasks that are ripe for automation
The first step in transitioning from RPA to generative AI is to identify processes and tasks that are suitable for automation. Organizations should take an inventory of their current business processes and analyze which ones have the highest potential for improvement through automation. It will help them determine which areas they should focus on first when implementing a generative AI solution.
2. Evaluate potential solutions
Once you’ve identified areas where generative AI could offer improvements, it’s important to evaluate potential solutions carefully before making any final decisions. Researching different types of solutions available, understanding pricing models, and talking with product experts can help organizations make sure they choose the best option for their needs.
3. Map out a timeline for transitioning from RPA to generative AI
Organizations need to create a roadmap for successfully transitioning from RPA to Generative AI, taking into account how long each step in the process might take and allowing enough time for adequate training of employees on the new systems before implementation begins. Establishing specific timelines will help keep everyone on track and ensure that all deadlines are met so that the organization can start achieving its goals as soon as possible after implementation is complete.
4. Develop a plan for training employees
Developing a plan for training employees on new systems is essential when transitioning from RPA to Generative AI – especially if employees with existing knowledge about robotics automation need to learn new skills or technologies associated with generative artificial intelligence systems.. Organizations should include all relevant stakeholders in employee training sessions by providing clear explanations of what changes are being made, why those changes are necessary, what impact they will have on day-to-day operations, and how best practices must be followed going forward to get the most out of new systems.
5 Monitor performance closely after implementation
Last but not least, organizations should monitor performance closely after implementing generative AI solutions in order to determine if adjustments need to be made or additional features added over time in order to optimize results even further. By taking proactive steps such as tracking KPIs, running experiments, monitoring customer feedback, and regularly reviewing system performance data, companies can ensure that any issues arising during implementation or use can quickly be addressed.
Taking the first steps towards incorporating generative AI into business processes can seem daunting. However, understanding the core principles of this technology and exploring its various use cases can help organizations identify which type best suits their needs. After that, they should consult a product expert to determine which platform or tool will be most suitable for their particular requirements. By taking these measures, businesses can make sure they get the most out of this powerful technology and benefit from all of its advantages as they automate their processes with generative AI.
To illustrate the risks associated with using generative AI by itself for automation, let’s consider an example. We attempted to use GPT-4 to perform a simple task: find the sell price of a product based on a list price and a partner’s discount rate. We utilized the following prompt:
On running the above prompt with two different inputs: “$100, 3” and “$100, 30“, GPT-4 provided the answers $300 and $130, respectively. In the first case, its bias lead it to assume the rate as an absolute number, while in the second case, it treated the rate as a percentage. This subtle discrepancy, present in just a few steps, could have far-reaching consequences when scaled to an enterprise end to end process.
Let’s take a look at a different example. We asked GPT to be a medical insurance claims processor. The user will provide the claim details and the patient’s insurance policy and it will have to approve a reimbursement amount based on the insurance policy. We then tested out the following GPT prompt:
Since the deductible has not been met ($300 from Dr. Alex Dunphy and $200 from Dr. Prabhat Kumar, totaling $500), the remaining deductible amount is $0.
As Dr. Jane Doe is assumed to be an in-network provider, the insurance will cover 80% of the cost after the deductible has been met. In this case, the total cost of the claim is $200.
Total Cost: $200
Insurance Coverage: 80%
Reimbursement Amount: $200 * 0.8 = $160
However, since the deductible has just been met, the reimbursement amount will be $0 for this claim. The patient will be responsible for the full $200 cost of this claim.
Please note that if Dr. Jane Doe is an out-of-network provider, the reimbursement calculation would be different.
One can clearly see that not only did GPT’s bias make it automatically assume Dr. Doe to be an in-network provider, but it also incorrectly reasoned that the deductible wasn’t fully paid, when in fact John had already paid the $500 deductible in his previous two visits.
As illustrated by the above two examples, it’s clear that GPT-4 can sometimes incorporate its own biases or opinions while processing user requests, leading to potential discrepancies in the output. This underlines the challenges that emerge when using generative AI by itself for automation or code execution, particularly when deterministic and unbiased execution is crucial for automating manual processes. Within an Enterprise, mistakes such as those noted above could have significant effects and are not tolerable for automation.
The inherent creativity of generative AI models is both their strength and weakness. While they excel at tasks requiring imagination and ideation, they may introduce biases when it comes to executing code in a predictable and repeatable manner.
The issue of AI bias in computation is more serious than many might realize. So far, the focus has primarily been on addressing racial and social biases in AI, but the risk of biased computation is just as significant. To ensure the safety and reliability of code execution, we must be vigilant in exposing and addressing these risks.
For instance, imagine a scenario where an HR system calculates an employee’s raise based on GPT-4’s understanding of what is considered a “normal” raise. In this case, GPT-4 might unintentionally introduce biases stemming from its training data, leading to unfair or even discriminatory outcomes.
In addition to the risk of biased computation in AI, generative models like GPT-4 also perform poorly at repetitive tasks. The nature of these models is to generate diverse and creative outputs based on patterns identified in their training data. However, this can lead to inconsistencies when it comes to tasks that require strict adherence to a specific pattern or repetition. This could lead to incorrect or incomplete results, which can have serious consequences, especially in safety-critical applications. Also, Generative AI models are computationally expensive, especially when applied to repetitive tasks. The amount of resources required to process and generate results might not be proportional to the task’s simplicity, leading to inefficiencies in resource utilization.
Thus, there is a need for a system with “computational intelligence” – one that is able to understand human language and reliably execute instructions just like traditional software.
In a follow up blog we will dive into the details of “Computational Intelligence” and how Enterprises can still leverage the benefits of Generative AI, in a way that is precise and consistent.
Want to Unlock the Power of Generative AI for Your Business Today
For example, let’s consider a large scale industrial manufacturer based in the United States with Accounts Receivable of $1 Billion. In 2020, the manufacturing industry averaged 51 days for Accounts Receivable to be collected.
Let’s assume the company used a Generative AI automation tool to automate the collection, processing and approval of purchase orders, generation of invoices and routine follow up of collections. If post-automation is able to conservatively reduce its average accounts receivable collection period to 51 days (or roughly 10% improvement) over $100 Million of capital would be unlocked for the business. With high interest rates, this $100 Million of additional capital can have profound effects not only on the leverage of a company, but on the interest paid during inflationary periods.
The company can use this cash to pay its suppliers (and possibly take advantage of discounts), potentially reduce its own discounts given to customers for early payment and invest in growth opportunities without needing to borrow additional funds or tie up as much cash in working capital. The reduced need to rely on short-term lending makes the organization more agile, and better prepared for any potential recessionary environments that may occur in the future
So why haven’t more businesses automated more of their accounts receivable processes to date? One word: exceptions.
Processes within accounts receivable are document heavy and can have numerous rules and idiosyncrasies depending on the customer. Differences in payment terms, variations in formats across documents and errors often throw exceptions that break legacy automation tools like RPA. Fixing and maintaining these processes with legacy automation can cost 5X in services the cost of automation licenses making the ROI on many processes low or negative.
However, Generative AI Automation like Kognitos, and the introduction of Conversational Exception Handling changes this dynamic. With Conversational Exception Handling, operations and finance team members within AR are able to quickly resolve any errors that occur without any needed training on an automation tool. If exceptions are recurring, the AR processor can teach Kognitos with simple English instructions how to handle a situation in the future. This removes the need for developers and the traditional 5X cost to maintain automations, leading to higher, positive ROI. Here is an example of such Conversational Exception Handling: Conversational Exception Handling In Claims
Businesses rightfully should focus on the benefits of cost reduction when implementing automation, but also should consider second-order effects on the organization’s finances. In the high interest rate environment of today, with inflation persisting throughout the economy, reducing accounts receivable in days is critical. Automating the manual steps, error handling and collection follow ups of the AR process not only eliminates labor cost in the business, but can free up hundreds of millions of dollars in cash and reduce interest expense from short term borrowing. Conversational exception handling makes automating these exception heavy processes possible, and should be a key focus for automation efforts in 2023.
Traditionally, process automations have been created by software developers for business users. Dealing with software maintenance and exceptional scenarios, such as unanticipated behavior (e.g., a scanned document of poor quality causing the software to halt since it cannot find a particular field, or the system not supporting a new currency but needing to convert it to USD), has been a costly and labor-intensive process. This often requires looping in software engineers (…Yes RPA Developers are Software Engineers) and going through a conventional software development lifecycle, which over time, complicates the software and leads to higher turn-around times. Although business users may understand what went wrong or what is going on, they are still at the mercy of code written by software developers.
This in turn is a hugely frustrating experience for business users and subject matter experts. Even though they know how to handle a situation, because they cannot code, they cannot immediately address the problem themselves. And despite claims of “Citizen Development”, very few finance, accounting or other professionals have time to learn a new coding language (including RPA).
The advent of Generative AI, and models like GPT-4 however, changed the game. Conversational exception handling allows exceptions to be managed through natural language interactions, enabling automation platforms to intelligently communicate with users and learn from their interactions GPT-4 can further be called when necessary to help solve problems, and converse with business users to fix things through conversation.
Take, for example, an AI-powered automation tool like Kognitos, which can engage with users in English when confronted with a problem or an exceptional scenario. Let’s consider a real-world example of an invoice processing system. Suppose the system encounters a smudged or illegible invoice date. Kognitos would ask the user, “Hey, in Invoice #142, I couldn’t find the invoice date. It seems that the date is either missing or illegible. Can you tell me how to proceed?” The user can then respond with the correct date, such as “the invoice date is 12/2/2022,” if it is a one off scenario, or if it is an exception that will repeatedly occur, teach the AI how to handle it in the future by giving English instructions: “For this vendor, the invoice date is always below the vendor name”. This innovative approach to exception handling results in a more adaptive, flexible, and efficient automation process. No developer expertise required.
1. Simplified and adaptive automation process: Conversational exception handling allows businesses to focus on automating the “happy path,” without the need for exhaustive planning for every potential exceptional scenario. As the AI system becomes more adept and experienced, it can manage an increasingly diverse range of situations, leading to a more agile and responsive automation process. For example, a retail company using AI-powered automation for inventory management would only need to automate standard procedures while leaving the AI to handle any discrepancies in stock counts through conversations with the staff.
2. Substantial reduction in maintenance costs: Companies no longer need to rely on hiring specialists for software maintenance when using AI systems leveraging GPT-4 and other LLMs like Kognitos. For instance, a business using AI for customer service can eliminate the need for a dedicated developer team to manage and maintain the system, as the AI will converse with the customer service team to resolve any issues or handle exceptional cases. This translates into significant cost savings, allowing businesses to channel resources into other growth opportunities.
3. Organic and scalable automation growth: The conversational approach to exception handling enables businesses to start with partial automation and expand gradually as the AI system learns more about the company’s processes. For example, a manufacturing company can initially automate a small portion of its engineering operations like Bills of Materials,, and as the AI learns more about the process, it can be expanded to cover additional aspects of production planning and design, scaling up or down as required. This fosters a more natural, customized automation experience, adapting to the ever-evolving business landscape without massive upfront investments.
The future of business automation is headed towards more intelligent, conversational exception handling leveraging LLMs like GPT-4.. Advanced platforms like Kognitos enable companies to streamline their processes more effectively, minimize maintenance expenses, and adapt their automation strategies to the dynamic business environment. Conversational exception handling removes the need for developers to “Maintain” automations, and instead empowers the business users from the beginning. As Generative AI continues to evolve, businesses will reap the benefits of a more nimble and versatile automation experience, revolutionizing the way we approach business process automation and paving the way for unprecedented growth and success.
