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*Artificially generated 💡 content by GPT4o* If RPA ~ DC & GenAi enabledRPA ~ AC From Static to Dynamic: The Evolution of Automation with Generative AI The shift from Direct Current (DC) to Alternating Current (AC) revolutionized energy distribution, offering flexibility, scalability, and efficiency. Today, automation faces a similar transition. While Robotic Process Automation (RPA) has been a transformative tool for structured, repetitive tasks, its static nature limits its ability to address dynamic and unstructured challenges. Enter Generative AI (GenAI)—poised to redefine automation much like AC did for electricity. RPA: The "Direct Current" of Automation RPA operates within pre-defined rules, excelling at: 1) Data entry 2) Reconciling accounts 3) Generating routine reports However, its limitations include: a) Inability to handle unstructured data (e.g., emails, images). b) Dependency on static inputs and rigid workflows. c) Lack of cognitive decision-making capabilities. Like DC, RPA struggles to adapt to dynamic scenarios, limiting scalability and versatility in complex environments. Generative AI: The "Alternating Current" of Automation Generative AI brings adaptability, context-awareness, and creativity to automation. It can: Process Unstructured Data: Understand and analyze text, images, and videos. Learn and Adapt: Improve over time by recognizing patterns. Solve Creatively: Generate new solutions and insights. Applications include: Writing reports and creating content. Powering intelligent chatbots. Adapting workflows in real time. Generative AI mirrors AC’s flexibility, scaling automation to meet complex and evolving demands. Synergy of RPA and Generative AI Together, RPA and GenAI create a hybrid model: Efficiency Meets Intelligence: RPA handles structured tasks, while AI addresses unstructured inputs. End-to-End Automation: AI augments workflows, enabling decision-making. Human-Centric Processes: AI’s adaptability enhances automation without constant reprogramming. For instance, RPA extracts data, while AI interprets it, analyzes discrepancies, and provides insights. Transforming the Future of Automation Generative AI is a paradigm shift, offering: Scalability: Handling structured to unstructured data workflows. Creativity: Drafting marketing content or simulating strategies. Adaptability: Adjusting to evolving business conditions. Human-Like Interaction: Enhancing decision-making and collaboration. Conclusion The transition from RPA to AI-powered automation is as transformative as the move from DC to AC. While RPA laid the foundation for efficiency, Generative AI adds adaptability, creativity, and intelligence. Together, they redefine automation, enabling dynamic, human-centric systems that drive innovation. Are we ready to embrace the alternating current of automation?

Automation 360 v.32 Boosts Generative AI and Cloud Capabilities We’re thrilled to announce the latest version of our industry-leading cloud-native Intelligent Automation platform, Automation 360™ v.32. This version adds even more generative AI capabilities, cloud automation resiliency, connections with advanced AI models, and more. The world of generative AI is expanding quickly, with big tech companies and innovative startups all developing exciting new capabilities, and we’re bringing more of this advanced power to the Automation Success Platform. Let’s jump into v.32 to see what’s new. Generative AI-enhanced Document Automation Document Automation already offers AI-powered intelligent document processing to quickly extract and embed document data into any workflow. In v.32, we’ve dramatically improved document extraction using generative AI so you can effortlessly extract table data from unstructured documents with natural language queries, making document processing faster and more accurate. It’s available for on-premises solutions, too! Generative Recorder Automations can be impacted or even fail when a cloud service is upgraded. Generative Recorder lets developers configure automation resiliency to address these and similar scenarios using generative AI to provide insights on fallback decisions. Developers can then use these recommendations to update automations automatically. Developers can also boost automation resiliency by capturing the most recent five minutes of automation execution, streamlining the troubleshooting process, and swiftly pinpointing the root cause of issues. “Any changes in the underlying application environment typically lead to challenges in automation deployment. We tested Generative Recorder on our customer’s CRM application, and it has led to smoother automation deployment and improved developer productivity.” -Prasanth Selvaraj, Application and Product Manager at Bosch Service Solutions Note that Generative Recorder is only available to cloud customers and requires an additional license. Contact your Automation Anywhere representative to learn more. Command packages for Google Gemini and OpenAI Assistants For a more streamlined and efficient connection to advanced AI models like Google Gemini and OpenAI Assistants, v.32 introduces native command packages. This offers built-in and plug-in capabilities for easy integration of AI assistants into automation workflows and enhances productivity as you unlock new possibilities for automation, natural language processing, and multi-modal interactions. Shared by Gabriel Zaharia Centrul de Informare pentru Finantari Nerambursabile

"Agentic AI unlocks new opportunities for businesses, offering smarter, more adaptive automation when paired with the right tools and implementation strategies." Wondering how agentic AI can enhance your existing automation tools like RPA? Learn about the future of agentic process automation in Dr. Marlene Wolfgruber's article for Intelligent Document Processing (IDP) Community: https://hubs.li/Q030Hrdx0 #AI #ArtificialIntelligence #AgenticAI #RPA #IntelligentAutomation #Automation #PurposeBuiltAI #IDP #IntelligentDocumentProcessing #APA #businessoperations #continuousimprovement #customerexperiences #processautomation #processintelligence #processdiscovery #processmining #digitaltransformation #digitalenterprise #digitalintelligence #digitalbusinesstransformation #digitalautomation #intelligentautomation #processtransformation #processmining #processoptimization #futureofwork #innovation #strategy #contentintelligence #processes #artificialintelligence #machinelearning #technology #automation #rpa #ai #roi #ml #nlp #business #success #data #analytics #ABBYY

How does AI integration happen? Part 2/2 6. Automation and Workflow Integration For AI to provide value, it needs to work seamlessly within established workflows. Automation: Automating repetitive tasks (e.g., using robotic process automation (RPA) combined with AI for decision-making). Business Process Integration: Making sure AI outputs are fed into business workflows, reports, or decision-making processes. 7. Continuous Monitoring and Maintenance Once deployed, AI systems require ongoing monitoring to ensure they are performing as expected: Monitoring: Continuously tracking the performance and outcomes of the AI model to detect issues such as drift (when the model's performance degrades over time). Model Retraining: Periodically updating the AI model with new data to ensure it remains accurate. Feedback Loops: Collecting user feedback or results to refine the AI system and improve its accuracy or effectiveness. 8. Scalability and Optimization As AI is integrated into more parts of an organization or used by more users, it is important to scale the system to handle larger volumes of data or increased usage. Optimization might involve: Infrastructure Scaling: Using cloud computing, distributed systems, or edge computing for more efficient AI deployment. Performance Tuning: Reducing model latency and improving response times, especially for real-time applications. 9. Compliance and Ethical Considerations It is also addressing ethical, legal, and regulatory concerns: Data Privacy: Ensuring that AI systems comply with laws like GDPR, HIPAA, or other data protection regulations. Bias and Fairness: Monitoring and mitigating biases in AI models to ensure fair and equitable outcomes. Transparency: Making AI decision-making processes understandable and explainable, especially in critical areas like healthcare or finance. 10. User Training and Adoption For effective AI systems, users within an organization (or customers, in the case of consumer products) must understand how to interact with and trust the system. This step includes: Training: Providing training to employees or customers on how to use AI-enhanced tools or interfaces. Change Management: Addressing any resistance to adopting AI technology and ensuring smooth transitions to new processes. Tools and Platforms for AI Integration Several platforms and frameworks can facilitate AI integration, including: Cloud Platforms: AWS, Microsoft Azure, and Google Cloud provide a range of AI services and tools. AI Frameworks: TensorFlow, PyTorch, and Scikit-learn are popular tools for building and training AI models. Enterprise Solutions: Salesforce Einstein, IBM Watson, and others provide pre-built AI solutions for various industries. Conclusion AI integration is a multi-step process with careful planning, model development, deployment, and ongoing optimization. Successfully aligning AI with the organization's goals and ensuring that the technology works seamlessly with existing systems and workflows

AI's impact on the SAP industry is significant, transforming how businesses operate and optimize their processes. Here are several key areas where AI influences SAP environments: 1. **Enhanced Data Analytics:** - **Predictive Analytics:** AI algorithms can analyze historical data to predict future trends, enabling more accurate forecasting and decision-making. - **Real-Time Insights:** AI-driven analytics provide real-time insights into business operations, helping companies react quickly to changes. 2. **Automation of Routine Tasks:** - **Robotic Process Automation (RPA):** AI-driven RPA automates repetitive tasks such as data entry, invoice processing, and report generation, reducing manual effort and errors. - **Intelligent Document Processing:** AI technologies can automatically extract and process data from documents, such as invoices and purchase orders. 3. **Improved Customer Experience:** - **Chatbots and Virtual Assistants:** AI-powered chatbots can handle customer queries, provide support, and improve service efficiency. - **Personalization:** AI can analyze customer data to offer personalized recommendations and tailor experiences. 4. **Optimized Supply Chain Management:** - **Demand Forecasting:** AI models can enhance the accuracy of demand forecasting, leading to better inventory management and reduced stockouts. - **Supply Chain Optimization:** AI algorithms can optimize routes, predict disruptions, and improve overall supply chain efficiency. 5. **Enhanced Decision-Making:** - **Decision Support Systems:** AI can provide advanced decision support by analyzing complex data sets and offering actionable insights. - **Scenario Planning:** AI helps in simulating various business scenarios, enabling better strategic planning. 6. **Improved Resource Management:** - **Predictive Maintenance:** AI can predict equipment failures before they occur, reducing downtime and maintenance costs. - **Workforce Management:** AI can optimize workforce scheduling and allocation based on demand forecasts and historical data. 7. **Advanced Reporting and Analytics:** - **Natural Language Processing (NLP):** AI-driven NLP can generate intuitive reports and summaries, making complex data more accessible. - **Anomaly Detection:** AI can identify anomalies and irregularities in data, helping in early detection of potential issues. 8. **Enhanced Integration and Interoperability:** - **AI-Driven Integration:** AI can facilitate seamless integration between different SAP modules and external systems, enhancing overall system interoperability. Overall, AI enhances SAP systems by improving efficiency, accuracy, and decision-making capabilities, leading to more agile and intelligent business processes.

Meet Foundry: An AI Startup that Builds, Evaluates, and Improves AI Agents The development of AI agents as autonomous tools capable of handling complex tasks has led to a significant advancement in artificial intelligence. Foundry, a Y Combinator-backed startup, aims to be the “Operating System” for AI agents, making AI automation more accessible, manageable, and scalable. Let’s take a closer look at what Foundry is, how it works, and why it matters. What is Foundry? Foundry is a platform that enables companies to create, deploy, and manage AI agents with ease. These agents can autonomously handle tasks ranging from customer support to workflow automation, utilizing advanced large language models like GPT-4. Foundry aims to remove barriers to AI agent adoption by providing tools that reduce technical overhead while increasing transparency and control. Simplifying AI Agent Development Foundry offers an environment for both developers and non-developers to develop AI agents tailored to specific needs. The platform abstracts much of the complexity of training, parameter tuning, and infrastructure provisioning. Users can create agents that understand context, respond to prompts accurately, and evolve with additional data and interactions. Foundry’s no-code capabilities make it accessible to non-engineers, allowing them to use pre-built templates and tools to build, customize, and deploy AI agents, enabling broader departmental automation. For developers, Foundry supports in-depth customization, including API integration and third-party services, allowing agents to scale from simple bots to complex entities. Monitoring, Debugging, and Trust A crucial component of Foundry’s offering lies in its monitoring and debugging capabilities. One of the challenges facing any AI application, especially in an enterprise setting, is trust and transparency—understanding what the AI is doing and why. Foundry addresses this by providing monitoring tools that give users real-time insights into their agents’ decision-making processes. This ensures that agents operate as expected and allows for efficient problem diagnosis and resolution. Foundry’s transparent feedback mechanism enables users to refine agent behavior, improving reliability. Its debugging approach resembles traditional software debugging, making it intuitive for developers. Integration with Existing Systems One of Foundry’s key features is its integration capabilities. AI agents are most effective when they are well-integrated into existing systems, databases, and workflows—allowing them to pull and push data effectively and interact with other software. Foundry offers APIs that enable agents to communicate with external software, such as CRM and ERP tools. This allows companies to integrate AI agents without overhauling existing systems, thereby reducing cost and time barriers. Vision for AI Automation The market for automation tools and AI-driven business solutions is growing. In this...

AI Agents are disrupting automation In a recent article by Praveen Akkiraju, Sophie Beshar, and Hunter Korn, the authors explore the impact of AI agents on automation. Here are their 5 key predictions: 👉Assistants for All: Soon, everyone, from consumers to knowledge workers, will have their own AI assistant. These versatile helpers will break down barriers between software applications, automation tools, and IT services, creating exciting opportunities for entrepreneurs. 👉Human-in-the-loop is the operative framework for deploying generative AI solutions. When we use generative AI (like GPT), it’s necessary to involve humans in the process. They guide and oversee the AI’s actions. Right now, most applications of generative AI are in the experimental or early production stage. They focus on providing advice and assisting users rather than fully autonomous decision-making. Large Language Models (LLMs) such as GPT have some limitations. They can’t predict or reason as reliably as humans. However, they are still useful for specific tasks during the design phase of automation platforms. 👉Automation is a hard problem and is often underestimated. Established companies are integrating AI to enhance platform efficiency and user experience. Leading Large Language Model (LLM) providers empower users to rapidly build AI agents (like GPTs). Emerging companies should reimagine workflows grounded in unique datasets and prioritize a straightforward UX. 👉Deployment of Automation with AI will take a “Crawl, Walk, Run” approach. Deploy automation with AI incrementally. Start with simple tasks, refine strategies through experimentation, and ensure proper data and tools are in place. 👉Code generation has emerged as a foundational element. Code generation, a foundational element, aligns well with LLMs. These code-gen models will shape agent architectures. More on this can be found here👇👇 https://lnkd.in/eyYHpe_8.

With the advent of Generative AI, there has been tremendous excitement about the possibility of automating enterprise workflows and unlocking value. Agentic workflow and RAG (Retreival-Augmented Generation) are buzz words that cannot be ignored. Yet, workflow digitisation and automation has been at the heart of enterprise transformations and value creation efforts for the last decade or two - with the aim of scaling revenue without increasing costs proportionately, while reducing human induced errors and improving user experience. To accomplish this goal, the typical approach taken has been quite logical - digitise and automate the mundane tasks associated with the “happy path” flow and then have humans focus on managing exceptions or the “unhappy path” and on more value adding activities. So, what is the workflow automation hoopla all about! Being part of both B2C and B2B enterprise transformation initiatives, here is my attenpt to put a framework around the enterprise automation conundrum and make sense of where Gen AI can play a massive role. The dot com innovation allowed B2C enterprises to engage with their customers digitally, by digitising (and standardising) and automating their customer engagement workflow to a large extent. The unhappy path flow still required human action and intervention, though. Automating workflows in B2B enterprises has been harder, primarily due to challenges in standardisation of processes, but also because the huge amount of digitisation effort needed and due to lack of availability of data. To get this right, a maniacal focus on process standardisation, digitisation and automation of the flow of data is needed, which can result in automation of much of the happy path. The quest, however, continues beyond the happy path flow automation and the upside can be unlocked via three broad efforts: i) address information gaps by pulling data that may not be readily exposed to operators or customers, ii) decrease the unhappy path scenarios - by improving data quality or enforcing policies, for example; and iii) automate as much of the unhappy path as possible. And this is where Generative AI has the potential to be a major game changer. The ability to have the context and ability to extract data from various data sources is a powerful way to bridge the information gap or reduce the unhappy path flow scenarios. And, the ability to act like a human agent and find the solution for the customer in an unhappy path flow scenario is where the maximum upside is. Unlocking the unhappy path automation challenge, among other things, is what the hoopla is all about! How are you thinking about workflow automation? Has Gen AI been part of your toolkit already? #digitaltransformation #worflowautomation #unhappypath #GenAI

Artificial Intelligence Development Services Accelerate your business operations with generative AI and machine learning development services As a trusted AI Development Company, we can help you maximize the value of your data by integrating AI into your existing workflows to enhance decision-making, improve operational efficiency, and uncover actionable insights that drive growth and innovation. Get an AI Readiness Assessment with our AI Development Services Leverage our AI development and integration services to evaluate your infrastructure, data systems, and workflows for process automation and smart decision-making. We are a responsible AI and ML development company that can help you transform into a data-driven and intelligent enterprise. Our comprehensive AI development services assist you in integrating AI into your business processes to automate and streamline complex tasks, reduce downtime, and analyze real-time data to accelerate decision-making. Our collaborative process begins with our AI experts asses your current AI capabilities and readiness to create a roadmap for successfully integrating with your existing infrastructure. You can rely on our AI experts to understand your business-specific goals and challenges. Our team of elite data engineers, ML engineers, and data scientists draws years of AI experience to navigate the complexities of custom AI development projects. Whether you need assistance with deploying machine learning models, delivering bespoke NLP solutions, LLMs, custom computer vision/OCR solutions, RAG for enterprises, or generative AI for content creation and summarization, our AI accelerator framework integrates the best practices of MLOps and continuous delivery to realize your AI vision. Our team is equipped to provide end-to-end, on-demand customization to help your business achieve AI-enabled modernization. With decades of experience enhancing customer experiences for diverse clients, from startups and SMBs to enterprises in industries like fintech, healthcare, AdTech, logistics & SCM, we are well-versed in fulfilling your business requirements for sustainable results and long-term success. AI and ML Development Services We Offer Our AI software development services can help you achieve tangible business outcomes by utilizing the full AI capabilities in modern business applications. Our deep expertise in AI and cross-sector versatility ensures that we help you at every stage of your AI journey to support your business goals.

Traditional API development is rigid, modern system need 𝗖𝗼𝗴𝗻𝗶𝘁𝗶𝘃𝗲 𝗔𝗣𝗜. After working on several enterprise AI projects, I've noticed an interesting shift in how we build APIs. Let me share my thoughts on what I call "Cognitive APIs" Traditional APIs vs. Cognitive APIs For decades, we've built APIs with rigid input and output schemas. As developers, we'd pull these schemas and structure our entire program logic around them. It's like building with Lego blocks - every piece has to fit exactly right. But something interesting is happening with AI and LLMs. We're moving towards more flexible, intelligent systems that can adapt their behavior at runtime. I started calling these "Cognitive APIs" after implementing several agentic AI systems for enterprises. Let me share a project where I built a sales qualification system. Traditionally, we would define a fixed schema for lead information - name, company, budget, timeline, etc. But here's the problem: no matter how comprehensive your schema is, you'll always miss something important. Instead, I took a different approach: 1. Started with a basic schema containing only essential fields 2. Used an LLM to dynamically evaluate and expand the schema during runtime 3. Built a system that could adapt to this flexible input and store it in a structured format (Excel in this case) The key difference? The API wasn't constrained by predefined structures. It could understand and process information more like a human sales rep would. Key Components of Cognitive APIs Through this experience, I've identified three essential components for building effective cognitive APIs: 1. 𝗟𝗼𝗼𝘀𝗲 𝗖𝗼𝘂𝗽𝗹𝗶𝗻𝗴: Your system needs to handle dynamic changes at runtime. Think of it as building with rubber bands instead of rigid metal bars - there's flexibility built into the design. 2. 𝗦𝗰𝗵𝗲𝗺𝗮 𝗡𝗼𝗿𝗺𝗮𝗹𝗶𝘇𝗮𝘁𝗶𝗼𝗻: I use a smaller, faster model as a "translator" to convert unstructured LLM outputs into consistent JSON formats. This helps maintain compatibility with existing systems while allowing for flexibility. 3. 𝗧𝗲𝘀𝘁𝗶𝗻𝗴 𝗼𝗳 𝗖𝗼𝗴𝗻𝗶𝘁𝗶𝘃𝗲 𝗔𝗣𝗜: For my sales system, I implemented an LLM-based scoring mechanism. When a lead's score falls below 80%, it automatically triggers human review. This creates a feedback loop that helps maintain quality while allowing for flexibility. As we build more AI-powered systems, this shift from "dumb" to intelligent APIs seems inevitable. 𝘛𝘩𝘦 𝘢𝘣𝘪𝘭𝘪𝘵𝘺 𝘵𝘰 𝘩𝘢𝘯𝘥𝘭𝘦 𝘧𝘶𝘻𝘻𝘺 𝘪𝘯𝘱𝘶𝘵𝘴 𝘢𝘯𝘥 𝘰𝘶𝘵𝘱𝘶𝘵𝘴, 𝘤𝘰𝘮𝘣𝘪𝘯𝘦𝘥 𝘸𝘪𝘵𝘩 𝘳𝘶𝘯𝘵𝘪𝘮𝘦 𝘪𝘯𝘵𝘦𝘭𝘭𝘪𝘨𝘦𝘯𝘤𝘦 𝘧𝘳𝘰𝘮 𝘓𝘓𝘔𝘴, 𝘰𝘱𝘦𝘯𝘴 𝘶𝘱 𝘯𝘦𝘸 𝘱𝘰𝘴𝘴𝘪𝘣𝘪𝘭𝘪𝘵𝘪𝘦𝘴 𝘧𝘰𝘳 𝘣𝘶𝘪𝘭𝘥𝘪𝘯𝘨 𝘮𝘰𝘳𝘦 𝘢𝘥𝘢𝘱𝘵𝘪𝘷𝘦 𝘢𝘯𝘥 𝘤𝘢𝘱𝘢𝘣𝘭𝘦 𝘴𝘰𝘧𝘵𝘸𝘢𝘳𝘦 𝘴𝘺𝘴𝘵𝘦𝘮𝘴. I am working on building and exploring cognitive API for domain specific areas like cloud security, healthcare and for me cognitive API+Knowledge Graph seems to be the future of AI systems.