Enterprise organizations are constantly seeking ways to optimize operations and improve their return on investment (ROI). Kognitos helps companies achieve incredible returns by both consolidating costs and creating opportunities for top-line growth. By addressing the inefficiencies of legacy robotic process automation (RPA) and other forms of automation, while aligning investments with outcomes, Kognitos delivers superior value, agility, and productivity.
One of the clearest ways Kognitos delivers ROI is through direct cost savings. Many legacy RPA solutions come with high implementation and maintenance costs, including consulting fees, infrastructure investments, and ongoing payroll for RPA developers. With Kognitos, these costs are significantly reduced or removed from the equation entirely.
1. Lower Development Costs
A $20,000 RPA bot and license can easily lead to an additional $120,000 or more in annual costs related to development, specialized consultants, or outsourced firms. Kognitos’ AI-driven platform eliminates the need for external consultants and developers by creating processes in plain English, reducing complexity and speeding up time to production.
2. Reduce Infrastructure Costs
Traditional RPA systems require substantial infrastructure to operate, including servers, maintenance contracts, software licensing, and testing environments. Kognitos’ cloud-based, scalable architecture and use of APIs eliminates infrastructure expenses, further driving down total cost of ownership.
3. Alleviate Pressure on Workforce Shortages
Kognitos supplements the need for full-time-employees by automating routine, manual tasks, allowing companies to repurpose their workforce to focus on higher-value activities such as problem-solving and innovation. This reduction in the need for operational headcount translates into significant cost savings over time.
4. Reduce Costly Churn and Improve Employee SatisfactionWhen processes are automated, employees are paid on time and routine tasks are completed without delays. This reduces the need for emergency hires or the stress of the hire-and-fire cycle, improving employee satisfaction and reducing HR costs associated with churn, recruiting, and training.
5. Eliminate Downtime
Errors in business processes can have significant financial impacts, ranging from compliance fines to lost revenue. Kognitos eliminates downtime by automating processes with precision, ensuring greater accuracy and reliability. Kognitos-powered processes don’t break when they meet exceptions. Instead, they pause to await human intervention, allowing unaffected process runs to continue uninterrupted.
6. Reduction of Technical Debt and Vendor Consolidation
With fewer applications to manage, organizations reduce their technical debt, which eases the burden on IT teams. Kognitos’ flexible platform handles multiple automation needs, from document processing to system auditability, allowing for vendor consolidation by minimizing the need for costly additional third-party tools.
While direct savings from cost reductions are important, Kognitos also delivers substantial indirect savings, which further amplify its ROI potential.
1. Boost Customer Satisfaction
Failing to meet SLAs can lead to strained vendor relationships and lost customers, both of which can have a ripple effect on a company’s reputation and bottom line. Kognitos helps organizations meet their SLAs consistently by automating processes at scale with higher accuracy, reducing associated penalties and improving customer satisfaction
2. Avoid Integration Nightmares
In fast-paced industries like finance or retail, companies often face integration challenges, especially during mergers and acquisitions. Kognitos helps streamline these processes by automating the integration of business systems ensuring continuity across different platforms.
3. Create Corporate Memory
Kognitos helps organizations build a valuable “corporate memory” by documenting, standardizing, and storing business processes reducing the cost of disruptions when key employees leave or change roles. Companies can avoid steep learning curves and process slowdowns that often occur with employee churn, contributing to long-term cost savings.
4. Improved Compliance
The built-in auditability of processes run on Kognitos helps organizations stay compliant in highly regulated industries like banking and healthcare. This control and visibility can help organizations to reduce the risk of potential fines or legal issues that can hurt a company’s bottom line and negatively impact the share price.
ROI isn’t just about reducing costs—it’s also about driving revenue growth. Kognitos supports top-line growth in several ways:
1. Customer and Vendor Expansion
By automating processes that impact both customers and vendors, Kognitos ensures that payments are timely, services are delivered accurately, and communication is seamless. Happy customers and vendors are more likely to remain loyal or even expand their services, increasing customer retention rates and strengthening vendor relationships. This, in turn, drives repeat business and supports long-term revenue growth.
2. Market Perception and Strategic Advantages
In today’s market, companies leveraging AI and automation have a competitive advantage. Kognitos enables organizations to market themselves as cutting-edge and forward-thinking, which can boost shareholder confidence and improve market perception. This can lead to new business opportunities, enhanced market share, and, ultimately, greater revenue.
3. Faster Decision-Making
AI automation allows businesses to make faster, more informed decisions. By eliminating manual work and reducing the risk of human error, Kognitos ensures that companies can act quickly and accurately on important data.
Unlike traditional RPA platforms that lock customers into expensive licensing agreements and come with a myriad of indirect costs, Kognitos offers a consumption-based pricing model that aligns customer investments directly to ROI.
Costs are directly tied to outcomes, because companies only pay for what they use. This removes the financial risk associated with traditional RPA platforms, where companies might invest heavily upfront without knowing whether they’ll see a return.
Customers can start small, automating a few key processes, and then scale their automation efforts as they prove ROI. This flexibility allows companies to grow their investment in support of their business goals, ensuring that the platform delivers maximum value. The more organizations invest in automating their processes, the more they stand to gain in terms of cost savings and revenue growth.
Kognitos is revolutionizing the way organizations approach automation by delivering direct cost savings, indirect benefits, and top-line growth opportunities. Its consumption-based pricing model ensures that investments are aligned with business outcomes, giving organizations the flexibility and scalability they need to thrive in a competitive market. As companies increasingly look to adopt AI and automation, Kognitos offers a compelling solution that drives long-term ROI and positions them for success in the digital age.
Artificial Intelligence (AI) has significantly enhanced our lives, from driving our cars to automating business processes. According to a report by McKinsey, generative AI is expected to contribute up to $4 trillion annually to the global economy. With this immense potential, about 67% of senior IT leaders prioritize generative AI for their organizations, according to a Salesforce survey. However, as the adoption of this technology accelerates, ethical concerns are also on the rise. How can organizations understand the ethical implications of generative AI and ensure its responsible use?
Generative AI gained significant attention in 2023, and its adoption skyrocketed in 2024. Experts refer to it as a game changer, a once-in-a-lifetime phenomenon. Corporate leaders are eager to leverage generative AI to add tangible value to their business processes and gain a competitive edge. This enthusiasm has spurred rapid adoption across enterprises. A McKinsey report, “The State of AI in 2024,” found that 65% of respondents regularly use generative AI, nearly double the previous year’s figure. Additionally, 75% believe that generative AI will significantly impact their industries in the future.
Despite the excitement, there is growing public concern about AI’s role. A Pew Research Center survey highlights that the explosive growth of generative AI has caused significant angst among stakeholders due to the risk of irresponsible and unethical use. The Salesforce survey revealed that 79% of respondents believe generative AI brings potential risks, and 73% are concerned about bias. Moreover, many business leaders are unsure about the ethical considerations of generative AI, which could lead to a trust gap between organizations and AI.
The effectiveness of generative AI models depends on the quality of the training data. If these data sets are unreliable or biased, the AI’s output will also be flawed. Organizations must ensure that the data sets used to train AI models are reliable and free from bias to avoid discriminatory outcomes.
One of the biggest concerns for enterprises is the unauthorized use of private data. Generative AI models, especially those trained on private data sets, can pose significant privacy and security risks. These data sets often contain sensitive information, including personal details of individuals (PII) and intellectual property (IP). Unauthorized access or misuse of such data can lead to severe privacy violations and potential legal repercussions. It is crucial to ensure that AI models comply with stringent data privacy policies, guidelines, and regulations to bridge the AI trust gap and protect both PII and IP.
Another major concern for enterprises is the phenomenon of hallucinations, where the AI model produces factually incorrect outputs, leading to misinformation. These errors can stem from insufficient training data, inaccurate assumptions, or biases. It is crucial for the AI model to recognize when it does not know the answer to a question or when it is not highly certain about the accuracy of its response. This self-awareness is essential to prevent the spread of misinformation and maintain trust in AI systems.
As generative AI adoption accelerates, new use cases will emerge, reducing deployment costs and increasing value for enterprises. However, with great power comes great responsibility, and ensuring consumer safety and security is paramount. Ethical, trusted AI is a promise that must be upheld to truly add value for all stakeholders. Corporate leaders must understand AI principles to create tangible benefits and mitigate risks by implementing robust policies and decision-making structures. Prioritizing ethical considerations and responsible use will allow us to harness generative AI’s full potential while safeguarding stakeholder interests.
To learn more about Ethical, Trusted AI, register for our webinar on AI Trust and Safety for the Future of Intelligent Automation in the Enterprise, on Thursday, July 25th at 9 AM Pacific Time.
Terms like Generative AI and Large Language Models often dominate discussions today, and are frequently used interchangeably. However, grasping the precise relationship and distinct capabilities of Generative AI vs. Large Language Models is paramount. A nuanced understanding empowers astute technology investments and unlocks advanced capabilities within core business processes.
This article aims to clarify the critical distinction between Generative AI vs. Large Language Models (LLMs). We will precisely define both technologies, delineate their intricate relationship, and explain their operational dynamics—both individually and synergistically—to unlock profound advancements in business workflows. By showcasing various practical applications, including intelligent automation, novel content creation, elevated customer service, and sophisticated data analysis, this content delivers a comprehensive overview, enhancing comprehension of these cutting-edge AI paradigms. In essence, it serves as a foundational resource for enterprises seeking to harness these technologies effectively, championing their combined role in fostering greater innovation and efficiency.
Generative AI represents a broad and exciting category of artificial intelligence. Its defining characteristic is the ability to produce new, original content that has never existed before. Unlike traditional AI that might analyze or classify existing data, Gen AI creates. This capability extends across various modalities:
The “generative” aspect means the AI learns patterns, structures, and styles from vast amounts of existing data and then uses that knowledge to generate novel outputs that are statistically similar to its training data but are not direct copies. This transformative power underscores the significance of Generative AI in shaping future business capabilities.
A Large Language Model (LLM) is a specific type of artificial intelligence primarily focused on understanding and generating human language. The “Large” in their name signifies their immense scale, being trained on colossal datasets of text (billions to trillions of words from books, articles, websites, etc.) and comprising billions to trillions of parameters. This extensive training enables LLMs to grasp context, semantics, and grammatical structures with remarkable proficiency.
Key characteristics of an LLM:
Prominent examples of LLMs include models like OpenAI’s GPT series, Google’s Gemini, and Meta’s LLaMA. Their ability to interact with and produce human-like text has made them pivotal tools in many modern applications.
The core difference between Generative AI and a Large Language Model lies in their scope and specificity. All LLMs are a form of Generative AI, but not all Generative AI models are LLMs.
So, while an LLM can generate a marketing email (which is a Gen AI capability), a Gen AI model might also create a realistic image of a product that has no text involved. This is the fundamental difference between LLM and Gen AI. The term Gen AI vs LLM highlights this distinction between the broader field and its highly prominent subset.
Both Gen AI and LLMs operate on sophisticated neural network architectures, primarily transformers, which allow them to process data in parallel and learn long-range dependencies.
The power stems from their ability to grasp complex patterns from data and apply that understanding to generate novel, coherent, and often highly creative outputs.
The combined power of Generative AI and LLMs is unlocking advanced capabilities across numerous business processes, offering significant efficiency gains and innovation opportunities.
These applications illustrate how the synergy between Generative AI and LLMs is reshaping enterprise capabilities across various functions.
Kognitos stands at the forefront of intelligently applying Generative AI and LLMs to revolutionize enterprise processes, s is a safe AI automation platform that uniquely leverages the power of Generative AI vs. Large Language Models to provide natural language process automation.
Kognitos integrates Gen AI and LLM capabilities fundamentally by:
By intelligently applying Generative AI and LLMs, Kognitos delivers advanced, intelligent Business Process Automation solutions that unlock unparalleled productivity, strategic agility, and true digital transformation.
The relationship between Generative AI and LLMs will continue to deepen, driving innovation across various fields. As LLMs become more sophisticated, they will power even more advanced Gen AI applications, leading to:
Understanding the difference between LLM and Gen AI and leveraging their combined strengths will be crucial for organizations to thrive in this AI-driven future.
The supply chain industry faces rising complexities from consumer demands, trade uncertainties, natural disasters, and more. To stay competitive, supply chain leaders must leverage AI to dramatically improve productivity, agility, and resilience
AI is no longer just hype – leading organizations like Amazon, Walmart, FedEx and UPS are already using it throughout their supply chain operations. According to Gartner, 50% of supply chain organizations will invest in applications that support artificial intelligence and advanced analytics capabilities through 2024. The firms that transform their supply chains with AI first will gain a major competitive advantage.
AI offers three main benefits for supply chain productivity:
Many supply chain processes involve repetitive, low-value tasks that can be automated entirely by AI. For example:
AI applies predictive analytics and machine learning to demand sensing and planning activities. This provides:
AI can optimize logistics decisions instantly by processing millions of variables and constraints.
And these companies are not alone in driving innovation across supply chain and inventory management processes. In a research conducted by McKinsey & Co, the company predicts the potential productivity gain of 1.2 to 2.0 percent of annual revenues, or $400 billion to $660 billion by AI streamlining processes and automating key functions such as inventory and supply chain management.
It is clear that artificial intelligence is rapidly transforming the supply chain industry and companies that fail to adopt AI will likely be left behind. The consequences of not adopting AI include:
On the other hand, companies that adopt AI will drive the next productivity frontier.
We are at the dawn of the AI era for supply chain management. Early adopters have only scratched the surface of AI’s potential. Over the next decade, AI will catalyze innovations we cannot yet envision, taking supply chains from vulnerable to antifragile. The future belongs to the supply chains that embrace AI first.
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.
Book a demo now.
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.
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.
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.
Book a demo now.
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.
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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.
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