Workflow Automation Solutions

𝗛𝗼𝘄 𝗜𝘁 𝗪𝗼𝗿𝗸𝘀: 𝗧𝗵𝗲 𝗥𝗘𝗔𝗟 𝗱𝗶𝗳𝗳𝗲𝗿𝗲𝗻𝗰𝗲 𝗯𝗲𝘁𝘄𝗲𝗲𝗻 𝗔𝗜 𝗪𝗼𝗿𝗸𝗳𝗹𝗼𝘄𝘀, 𝗔𝗴𝗲𝗻𝘁𝘀, 𝗮𝗻𝗱 𝗠𝗖𝗣. ⬇️ This image illustrates the difference with surprising clarity. Let’s break it down: 1. (𝗔𝗜) 𝗪𝗼𝗿𝗸𝗳𝗹𝗼𝘄𝘀: 𝗙𝗼𝗹𝗹𝗼𝘄𝘀 𝗰𝗹𝗲𝗮𝗿 𝗶𝗻𝘀𝘁𝗿𝘂𝗰𝘁𝗶𝗼𝗻𝘀 ➜ An AI workflow is like a recipe. It runs in a fixed order: An email arrives → the content is summarized → a task is created → the plan is sent via Slack. It’s linear, predictable, and doesn’t adapt. No decisions. No context-awareness. Just automation. 2. 𝗔𝗜 𝗔𝗴𝗲𝗻𝘁𝘀: 𝗔𝗰𝗰𝗼𝗺𝗽𝗹𝗶𝘀𝗵 𝗴𝗼𝗮𝗹𝘀 𝗮𝘂𝘁𝗼𝗻𝗼𝗺𝗼𝘂𝘀𝗹𝘆 ➜ An AI agent doesn’t need step-by-step instructions. You give it a goal — for example, “Plan my day” — and it figures out how to get there. It accesses tools, checks your calendar, moves meetings, finds focus time, and adapts the schedule based on what matters. It makes decisions based on context — not just predefined logic. 3. 𝗠𝗖𝗣: 𝗘𝗻𝗮𝗯𝗹𝗲𝘀 𝗿𝗲𝗮𝗹 𝗮𝘂𝘁𝗼𝗻𝗼𝗺𝘆 ➜ The Model Context Protocol (MCP) is the key enabler. It gives the agent secure, real-time access to apps like Calendar, Notion, Slack, and Perplexity. This unlocks cross-app coordination, memory, and adaptive behavior. Not just running commands — but reasoning across systems. 𝗪𝗼𝗿𝗸𝗳𝗹𝗼𝘄𝘀 𝗮𝘂𝘁𝗼𝗺𝗮𝘁𝗲 𝘀𝘁𝗲𝗽𝘀 ➜ 𝗔𝗴𝗲𝗻𝘁𝘀 𝗽𝘂𝗿𝘀𝘂𝗲 𝗼𝘂𝘁𝗰𝗼𝗺𝗲𝘀 ➜ 𝗔𝗻𝗱 𝗠𝗖𝗣 𝗶𝘀 𝘁𝗵𝗲 𝗳𝗼𝘂𝗻𝗱𝗮𝘁𝗶𝗼𝗻 𝘁𝗵𝗮𝘁 𝗺𝗮𝗸𝗲𝘀 𝘁𝗵𝗲 𝘀𝗵𝗶𝗳𝘁 𝗽𝗼𝘀𝘀𝗶𝗯𝗹𝗲!

What’s the difference between RPA vs IPA vs APA? 𝗥𝗼𝗯𝗼𝘁𝗶𝗰 𝗣𝗿𝗼𝗰𝗲𝘀𝘀 𝗔𝘂𝘁𝗼𝗺𝗮𝘁𝗶𝗼𝗻 (𝗥𝗣𝗔) An ideal choice for repetitive, rules-driven processes with minimal need for decision-making. For example, organizations that handle large volumes of data entry, invoice processing, or report generation can benefit significantly from RPA right away. These tasks are often time-consuming and prone to human error, making them perfect candidates for automation. 𝗜𝗻𝘁𝗲𝗹𝗹𝗶𝗴𝗲𝗻𝘁 𝗣𝗿𝗼𝗰𝗲𝘀𝘀 𝗔𝘂𝘁𝗼𝗺𝗮𝘁𝗶𝗼𝗻 (𝗜𝗣𝗔) Processes that handle unstructured data and/or support decision-driven tasks are a match for IPA. For example, document processing workflows that involve extracting relevant information from content in diverse formats. IPA is a solution for improving existing processes while maintaining necessary human oversight and flexibility. 𝗔𝗴𝗲𝗻𝘁𝗶𝗰 𝗣𝗿𝗼𝗰𝗲𝘀𝘀 𝗔𝘂𝘁𝗼𝗺𝗮𝘁𝗶𝗼𝗻 (𝗔𝗣𝗔) APA is uniquely suited for complex workflows that demand a high degree of adaptability and real-time decision-making. APA can manage risk and compliance and dynamically allocate resources with minimal human intervention. Processes that require predictive capabilities or include customer interaction are top candidates for APA. While each has its distinct advantages, many Automation Anywhere customers have adopted a progressive strategy when scaling from RPA to IPA, and ultimately to APA. Whether as a phased or combined approach, RPA can provide immediate efficiency gains, with IPA powering automation as processes become more sophisticated. APA, leveraging the security and execution reliability of RPA and IPA platforms, can be introduced to tackle the most complex workflows, ensuring that automation efforts are both scalable and sustainable. This integrated approach enables businesses to automate a broader range of tasks, from simple to complex, while ensuring the ability to adapt to changing market conditions and operational demands. By combining RPA, IPA, and APA, organizations can achieve greater efficiency, adaptability, and resilience in their operations. Organizations that embrace this comprehensive automation ecosystem are better positioned to navigate the challenges of the modern business environment and drive long-term success.

Over the past 2 years, the Workato Business Technology team has been implementing AI and Agentic solutions … Here are our team’s top 5 lessons learned based on 3 categories (People, Technology, and Prompting): 1. There’s a strong readiness for adoption of new AI technology. The primary challenge lies in setting the right expectations about what the solution is designed to do. 2. On the flip side, blind reliance on AI shouldn’t be encouraged. Verification is especially needed when rolling out new AI solutions. 3. Integrating new tech into someone’s everyday workflow & routine. Habit is one of the biggest hurdles to adoption. 4. Set clear expectations about what the AI will be able to do and not do. 5. There’s a learning curve when it comes to talking to bots or AI. Sometimes, new users have to be reminded to think outside the box and ask open-ended questions like “What can you help me with today?” The solutions? Start small, prove value internally, and build as you go. #BusinessTechnology #BT #AI

This new white paper by Stanford Institute for Human-Centered Artificial Intelligence (HAI) titled "Rethinking Privacy in the AI Era" addresses the intersection of data privacy and AI development, highlighting the challenges and proposing solutions for mitigating privacy risks. It outlines the current data protection landscape, including the Fair Information Practice Principles, GDPR, and U.S. state privacy laws, and discusses the distinction and regulatory implications between predictive and generative AI. The paper argues that AI's reliance on extensive data collection presents unique privacy risks at both individual and societal levels, noting that existing laws are inadequate for the emerging challenges posed by AI systems, because they don't fully tackle the shortcomings of the Fair Information Practice Principles (FIPs) framework or concentrate adequately on the comprehensive data governance measures necessary for regulating data used in AI development. According to the paper, FIPs are outdated and not well-suited for modern data and AI complexities, because: - They do not address the power imbalance between data collectors and individuals. - FIPs fail to enforce data minimization and purpose limitation effectively. - The framework places too much responsibility on individuals for privacy management. - Allows for data collection by default, putting the onus on individuals to opt out. - Focuses on procedural rather than substantive protections. - Struggles with the concepts of consent and legitimate interest, complicating privacy management. It emphasizes the need for new regulatory approaches that go beyond current privacy legislation to effectively manage the risks associated with AI-driven data acquisition and processing. The paper suggests three key strategies to mitigate the privacy harms of AI: 1.) Denormalize Data Collection by Default: Shift from opt-out to opt-in data collection models to facilitate true data minimization. This approach emphasizes "privacy by default" and the need for technical standards and infrastructure that enable meaningful consent mechanisms. 2.) Focus on the AI Data Supply Chain: Enhance privacy and data protection by ensuring dataset transparency and accountability throughout the entire lifecycle of data. This includes a call for regulatory frameworks that address data privacy comprehensively across the data supply chain. 3.) Flip the Script on Personal Data Management: Encourage the development of new governance mechanisms and technical infrastructures, such as data intermediaries and data permissioning systems, to automate and support the exercise of individual data rights and preferences. This strategy aims to empower individuals by facilitating easier management and control of their personal data in the context of AI. by Dr. Jennifer King Caroline Meinhardt Link: https://lnkd.in/dniktn3V

AI Agents started replacing employees. Now, they're trusted with corporate credit cards. Companies are rushing to implement them across all departments. And by 2027, 82% of organisations will rely on them. But the truth is, in most cases, what many believe is an AI agent... is just a clever workflow. Let's run a typical task to illustrate this, through 3 scenarios. For example: Scheduling a quarterly business review with an Important Client. A. Non-Agentic Workflows Humans do everything with tools that act based on their instructions. 1. A manager remembers that it's time for a business review. 2. They check their CRM & their calendar. 3. They prompt ChatGPT to "write them an email to schedule a client call". 4. They copy/paste the output and send the email. Human does all the thinking. AI doesn't take any initiative. It just helps with content. B. Agentic Workflows AI is part of an automated system with triggers and logic. 1. Calendar automation detects it's been 90 days since the latest quarterly business review. 2. The workflow checks availabilities in the manager's calendar. 3. The workflow triggers a draft email (via GPT-4) to schedule the meeting. 4. Email is sent automatically. It's semi-autonomous. It saves time. But it won't adapt to unexpected scenarios (e.g: rescheduling, follow-up) C. AI Agents Agent has a goal-oriented objective: "Keep client engaged & happy". 1. The agent constantly monitors the CRM, past clients' conversations and activity. 2. It detects it's time for a quarterly business review, but also checks recent usage drop and billing tier. 3. It drafts a personalised email ("Hey Maïa, noticed your team added 5 users, but they haven't used the platform as much...") and suggests some time slots. 4. It sends the email and monitors the response. It automatically follows up with alternative time slots if it doesn't get a reply within 2-4 days. 5. Once the meeting is booked, it creates an agenda based on previous conversations and notifies the CS team of potential red flags (e.g: high-risk of churn). It's personalised. Contextual. It coordinates multiple tools. It focuses on a broad goal (client satisfaction) instead of a task (sending an email). Are you trying to automate your work with AI agents at the moment? What are you tackling first? 👇

Most data engineers I know aren’t burned out from data. They’re burned out from duct tape. If you've ever spent more time debugging pipeline failures than delivering insights, you're not alone. Modern data stacks promised us agility—but what we got was complexity. “I’ve been OOO for 2 weeks. What’s changed in this pipeline since then?” Normally, that means scrambling through logs, Slack threads, and dashboards. But not this time. With Ascend .io’s Agentic Data Engineering, the platform tells me what changed: ➤ What data has been updated ➤ Which transforms were auto-managed ➤ Whether anything broke—and if so, what was auto-fixed ➤ Where I need to take action (if any) This isn’t just automation. It’s an entirely new category: Agent-assisted, metadata-driven pipelines that evolve on their own—like an intelligent teammate that’s been watching your data while you were gone. Here’s what makes Ascend.io different: ✔️ AI-powered agents help document, debug, and manage pipeline changes ✔️ Dynamic orchestration driven by real-time metadata, not manual DAGs ✔️ Unified control plane across Snowflake, BigQuery, Databricks & more ✔️ Incremental processing — no reprocessing of unchanged data ✔️ Code-first or low-code flexibility with Git-native workflows Real results from teams using Ascend: ✅ 7x increase in productivity ✅ 83% reduction in processing costs ✅ 87% faster delivery This feels like the shift from DevOps to Platform Engineering—but for data teams. Learn more: https://hubs.li/Q03n44B60 What would change for your team if pipelines could explain themselves?

Few technologies have made as significant an impact as GenAI. A new report from the Deloitte AI Institute [https://deloi.tt/4mCv7zh] explores how GenAI agents are changing the game for automation.    In just two years as an enterprise-ready technology, AI agents are advancing automation from robotic process automation (RPA) to agentic process automation (APA). These intelligent agents understand context and intent, offering personalized automation and decision support.     Next-generation agents are set to collaborate autonomously, handle complex workflows and orchestrate enterprise-wide processes (like supply chain and customer experience management). As strategic advisors, these agents will be able to autonomously automate workflows, manage contracts, and support decision-making—allowing humans to focus on oversight, ethics, and innovation.    This human involvement is crucial for building trust in agentic AI. For organizations—and their workers—to fully embrace AI agents, they must have confidence in their reliability, security, and ethics. They need assurance that these agents will perform tasks accurately, safeguard sensitive data, and make decisions that are fair and transparent. Trust is essential for GenAI’s successful adoption. Using our Trustworthy AI™ framework, we’re helping organizations build the trust that’s needed for transformation.    Excellent GenAI insights and perspectives on automation’s future by my colleagues Prakul Sharma, AJ M., Patricia Henderson, and Camille Chicklis.   

Next-generation AI agents are redefining process automation, moving from traditional robotic to agentic automation. It’s truly exciting to see how organizations are unlocking entirely new possibilities to streamline workflows and drive innovation, leading to remarkable gains in efficiency, creativity, and agility. My colleagues Prakul Sharma, AJ M., Patricia Henderson, and Camille Chicklis explore how collective automation and autonomous AI agents are transforming essential business processes in a new Deloitte Insights report [https://deloi.tt/45guxjC]. To illustrate agents in action, they highlight the integration of AI agents with robotic process automation (RPA) technology in invoicing. While RPA excels at automating the routine tasks, it often struggles with missing or unstructured data and exceptions. Adding AI agents into the mix enables smarter, more adaptive automation—capable of managing exceptions like missing vendor details and learning from each new transaction to continuously improve. By adopting these advances and reinventing everyday processes, organizations enhance efficiency and generate value, positioning themselves for whatever comes next!  

Automation is more than just clicking a button While automation tools can simulate human actions, they don't possess human instincts to react to various situations. Understanding the limitations of automation is crucial to avoid blaming the tool for our own scripting shortcomings. 📌 Encountering Unexpected Errors: Automation tools cannot handle scenarios like intuitively handling error messages or auto-resuming test cases after failure. Testers must investigate execution reports, refer to screenshots or logs, and provide precise instructions to handle unexpected errors effectively. 📌 Test Data Management: Automation testing relies heavily on test data. Ensuring the availability and accuracy of test data is vital for reliable testing. Testers must consider how the automation script interacts with the test data, whether it retrieves data from databases, files, or APIs. Additionally, generating test data dynamically can enhance test coverage and provide realistic scenarios. 📌 Dynamic Elements and Timing: Web applications often contain dynamic elements that change over time, such as advertisements or real-time data. Testers need to use techniques like dynamic locators or wait to handle these dynamic elements effectively. Timing issues, such as synchronization problems between application responses and script execution, can also impact test results and require careful consideration. 📌 Maintenance and Adaptability: Automation scripts need regular maintenance to stay up-to-date with application changes. As the application evolves, UI elements, workflows, or data structures might change, causing scripts to fail. Testers should establish a process for script maintenance and ensure scripts are adaptable to accommodate future changes. 📌 Test Coverage and Risk Assessment: Automation testing should not aim for 100% test coverage in all scenarios. Testers should perform risk assessments and prioritize critical functionalities or high-risk areas for automation. Balancing automation and manual testing is crucial for achieving comprehensive test coverage. 📌 Test Environment Replication: Replicating the test environment ensures that the automation scripts run accurately and produce reliable results. Testers should pay attention to factors such as hardware, software versions, configurations, and network conditions to create a robust and representative test environment. 📌 Continuous Integration and Continuous Testing: Integrating automation testing into a continuous integration and continuous delivery (CI/CD) pipeline can accelerate the software development lifecycle. Automation scripts can be triggered automatically after each code commit, providing faster feedback on the application's stability and quality. Let's go beyond just clicking a button and embrace automation testing as a strategic tool for software quality and efficiency. #automationtesting #automation #testautomation #softwaredevelopment #softwaretesting #softwareengineering #testing

AI isn't just a tool; it's becoming a teammate. A major field experiment with 776 professionals at Procter & Gamble, led by researchers from Harvard, Wharton, and Warwick, revealed something remarkable: Generative AI can replicate and even outperform human teamwork. Read the recently published paper here: In a real-world new product development challenge, professionals were assigned to one of four conditions: 1. Control Individuals without AI 2. Human Team R&D + Commercial without AI (+0.24 SD) 3. Individual + AI Working alone with GPT-4 (+0.37 SD) 4. AI-Augmented Team Human team + GPT-4 (+0.39 SD) Key findings: ⭐ Individuals with AI matched the output quality of traditional teams, with 16% less time spent. ⭐ AI helped non-experts perform like seasoned product developers. ⭐ It flattened functional silos: R&D and Commercial employees produced more balanced, cross-functional solutions. ⭐ It made work feel better: AI users reported higher excitement and energy and lower anxiety, even more so than many working in human-only teams. What does this mean for organizations? 💡 Rethink team structures. One AI-empowered individual can do the work of two and do it faster. 💡 Democratize expertise. AI is a boundary-spanning engine that reduces reliance on deep specialization. 💡 Invest in AI fluency. Prompting and AI collaboration skills are the new competitive edge. 💡 Double down on innovation. AI + team = highest chance of top-tier breakthrough ideas. This is not just productivity software. This is a redefinition of how work happens. AI is no longer the intern or the assistant. It’s showing up as a cybernetic teammate, enhancing performance, dissolving silos, and lifting morale. The future of work isn’t human vs. AI. The next step is human + AI + new ways of collaborating. Are you ready?