Robotic Process Automation Guide

New thesis here at Andreessen Horowitz: We believe AI will automate operations and eat the world of RPA. Every company has ops work – whether it's data entry, doc extraction, info transfer, etc – that is essential for achieving business goals (e.g., booking a customer), but is highly mundane / repetitive and not the best use of employee time. Though some companies have attempted to use Robotic Process Automation (RPA), RPA was an imperfect solution since the tech just wasn't advanced enough yet. Thanks to AI, though, ops work can now be truly productized. In the future, we believe AI agents will be prompted with an end goal (e.g., book an appointment) and be empowered with the right tooling and context to take those actions. They’ll be adaptable to various data inputs and will be able to handle process changes. Because of this flexibility, they will be far easier to implement and maintain than traditional RPA systems, making them more accessible for companies to use. This is a massive market! Ops spend far outstrips most traditional software spend and is also greenfield (there are no legacy ops software incumbents), making it a particularly appealing opportunity for startups. Read more about our thesis below. If you're building in intelligent automation, we would love to chat. https://lnkd.in/gaMh68Yq

What is the future of RPA jobs?? Especially UiPath?? I get this question so much in my inbox, so I thought to answer it here. We’re moving from RPA (Robotic Process Automation) → to Agentic Automation where bots don’t just follow rules, they reason, learn, and decide. Old RPA: bots mimic clicks and keystrokes New APA: intelligent workflows that combine RPA + AI + APIs UiPath is already leading this shift with tools like: AI Center : brings ML models into automations Document Understanding :extracts data from unstructured files Autopilot : helps build automations with GenAI Integration Service :connects UiPath with any API So if you’re in RPA today, here’s what to do next: Learn AI + RPA integration Start exploring how to embed AI models in your automations. UiPath AI Center Overview UiPath Document Understanding Get comfortable with APIs Automations are no longer limited to desktop actions , they integrate across systems. check UiPath API Integration Tutorial Design for scalability and orchestration Build frameworks, not one-off bots , logging, retry, and modular design matter more than ever. check UiPath Automation Cloud Orchestration Guide Experiment with GenAI inside UiPath Combine UiPath + OpenAI to summarize emails, classify text, or extract insights. check Autopilot What do you think ? #uipath Sarah Ghanem

The future of scaling RPA will very likely not include RPA at all. At least when it comes to the bottom up approach of makers building stuff for their own personal productivity. As much as many RPA vendors have actually invested into making their tools more business-user-friendly, it still takes effort, time and skill to learn and apply those tools in any meaningful way. Sure, one may not need to be a programmer with years of experience in IT to build RPA flows, but it's still not exactly a walk in the park either. One would still need to spend a considerable amount of time to learn and build a flow that actually does something useful AND works more than once or twice. And I feel Microsoft has actually done a great job at making Power Automate pretty much the easiest tool to pick up and learn. Not only because of its pricing, but also because of the UI/UX actually being the least techy of the overall bunch, and the friction eliminated as much as possible. And still, in order to really have every user in the org automating stuff, RPA is probably not the right tool. Agents are. Now, I'm not one of the AI influencers that spit nonsense around AI replacing people or vibe coding being the next big thing. But I do see how in a year or so, Computer Using Agents (still hate the name) could really be used easily by many more people than RPA. It could be very simple to automate user interfaces of legacy apps and web pages using natural language, without ever needing to do any of that drag-and-drop we all do in RPA. And that's the future of truly democratizing automation. It's not about making the UI easier to understand, or adding more connectors for easier integrations with systems. It's definitely not in adding more powerful features or improving performance. Those are all great, but it's mostly important to the pros, not the every day business user who may want to automate their own personal tasks. It's all in making it possible to ask an agent to do something, and having that agent actually perform the task. All in natural language. Now, I still don't see CUA replacing RPA any time soon (if ever). Its premise does not really seem to be fit for actual business critical flows (and yes, contrary to what most cloud advocates would like, there are plenty of business critical RPA flows running every minute all over the world). It's best for personal productivity solutions. You wouldn't want agents running things where sticking to the rules is more important than making it accessible. But CUA could easily be the way to scale UI automation across entire organizations, and potentially even become a data source for process mining to find the processes that really need to be automated via RPA. We just need to give it time. And I don't even think we need lots of that. It seems to be pretty close. I, for one, definitely see myself using it, even if I consider myself to be rather proficient in RPA.

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 no longer just about doing things faster—it’s about doing them smarter. But to lead the future, we must navigate the present with clarity and caution. RPA + Agentic AI is a force multiplier—but only when done right. Pitfalls to Watch Out For 1. Automating Broken Processes RPA is fast and efficient—but only if the underlying process is well-designed. Many organizations make the mistake of automating chaotic, inefficient workflows, leading to faster failure, not better outcomes. Fix the process before you automate it. 2. Overestimating AI’s Capabilities Agentic AI is powerful, but not magical. It still requires large volumes of quality data, proper training, and ongoing governance. Expecting AI agents to “figure everything out” autonomously is unrealistic. Without data and structure, AI is just another buzzword. 3. Scalability Roadblocks What works in a pilot doesn’t always scale. Integrating RPA bots and AI agents across departments or geographies often hits a wall due to fragmented systems, change resistance, or lack of skilled talent. Think scale from day one—governance, architecture, and ownership matter. 4. Compliance and Ethics Risks As autonomous AI agents make decisions, there are increasing concerns around accountability, transparency, and bias. Without clear guidelines, companies risk reputational damage or legal fallout. AI governance isn’t optional—it’s essential. 5. Underestimating Change Management Intelligent automation transforms jobs, not just tasks. Without proactive communication, upskilling, and cultural readiness, even the best technologies will face resistance. Automation without people enablement is automation at risk. #RPA #AgenticAI #IntelligentAutomation #DigitalTransformation #AIethics #AutomationPitfalls #FutureOfWork #Leadership

We automated 60% of our back-office operations using AI agents. We saved $2M/year. Now we’re open-sourcing how. Why? Because traditional “automation” is broken. RPA scripts crack with the smallest change. One UI shift, and you’re back to square one. That’s why 50% of RPA projects fail quietly. We needed a system that adapts. Learns. Self-heals. So we built one. With real agentic AI. What changed? → Repetitive ops didn’t just disappear — they evolved → Agents now read data, act on it, and adjust in real-time → Every Slack ping, CRM task, and internal update — handled, end-to-end This isn't a slide deck or a blog. It’s the actual blueprint that runs our business. And we’re opening it up. Here’s what you’ll get: ▪️Full backend system map (how agents flow across infra) ▪️Security + DevOps stack (audited for enterprise) ▪️Real prompt templates, fallback logic, error flows ▪️Screenshots from our own playbooks + dashboards This is the stuff we charged $100K for. You’re getting it free. 👇 Want the blueprint? Comment “Ops” and I’ll send it to you. 🔁 Repost to help more teams automate the boring stuff. Follow Gaurav Bhattacharya for more no-fluff GTM + AI system drops.

𝗬𝗼𝘂𝗿 𝗥𝗣𝗔 𝘄𝗶𝗹𝗹 𝗰𝗿𝗮𝘀𝗵 𝗮𝗻𝘆𝘄𝗮𝘆! You can not avoid this simple fact when automating systems out of your control. Instead of looking to RPA errors as a consequence of some weakness in your team, you should address it as a part of the process. Robotic Process Automation (RPA) error handling has its particularities. Usually, we develop UI automation in systems that can change for multiple reasons. Therefore, in cases like web systems that are updated very often, RPA crashing is part of the game. Since it is tough to predict and prepare for those changes, it is better to detect and react faster to re-deploy a new version of your automation with less downtime. 𝗥𝗲𝗮𝘀𝗼𝗻𝘀 𝗳𝗼𝗿 𝗖𝗵𝗮𝗻𝗴𝗲𝘀 𝗶𝗻 𝗨𝗜 𝗪𝗼𝗿𝗸𝗳𝗹𝗼𝘄𝘀 𝗔𝗱𝗱𝗿𝗲𝘀𝘀𝗲𝗱 𝘄𝗶𝘁𝗵 𝗥𝗣𝗔 In most cases, the RPA team can not predict or be aware of changes in the target systems the bots will face in the subsequent execution, as we discuss as follows. 𝗡𝗲𝘄 𝗦𝘆𝘀𝘁𝗲𝗺 𝗩𝗲𝗿𝘀𝗶𝗼𝗻 Many modern systems deliver updates without user consent. It simply updates its features, UI experience, and informs users through a release note or a log of changes. The intention is to constantly deliver system improvements to users who can figure out how to use the system after minor changes in the UI. However, if your bot automates UI actions based on component ID or is sensitive to component layout, it will no longer work properly. 𝗡𝗲𝗲𝗱 𝘁𝗼 𝗨𝗽𝗴𝗿𝗮𝗱𝗲 𝘁𝗼 𝗔𝗰𝗰𝗲𝘀𝘀 𝗡𝗲𝘄 𝗙𝗲𝗮𝘁𝘂𝗿𝗲𝘀 In some systems, users need to authorize upgrades or do it manually, giving more control to the IT department regarding system changes. It is not just automation; many ERPs have layers of customization and integrations that system updates might impact. Nevertheless, updating systems is a good practice to reduce security risks, improve performance, and have access to new features. Large companies use several systems in each department. Therefore, it is common for RPA teams to get surprised by systems upgrades by IT teams without being notified. 𝗨𝘀𝗲𝗿 𝗜𝗻𝘁𝗲𝗿𝗳𝗮𝗰𝗲 𝗮𝗻𝗱 𝗪𝗼𝗿𝗸𝗳𝗹𝗼𝘄𝘀 𝗜𝗺𝗽𝗮𝗰𝘁𝗲𝗱 𝗯𝘆 𝗖𝘂𝘀𝘁𝗼𝗺𝗶𝘇𝗮𝘁𝗶𝗼𝗻 Many systems support field and business rules customization for better modeling your company's needs. It is not unusual to see your bots crashing due to some new customization that your team was not notified. Your company departments want autonomy to get the most out of the systems they use. 𝗗𝗲𝘁𝗲𝗰𝘁𝗶𝗻𝗴 𝗦𝘆𝘀𝘁𝗲𝗺 𝗨𝗽𝗱𝗮𝘁𝗲𝘀 𝘁𝗵𝗮𝘁 𝗗𝗲𝗺𝗮𝗻𝗱 𝗔𝘂𝘁𝗼𝗺𝗮𝘁𝗶𝗼𝗻 𝗨𝗽𝗱𝗮𝘁𝗲 Not every system update will crash your bot... (...) === 𝗙𝘂𝗹𝗹 𝗔𝗿𝘁𝗶𝗰𝗹𝗲 - 𝗬𝗼𝘂𝗿 𝗥𝗣𝗔 𝘄𝗶𝗹𝗹 𝗰𝗿𝗮𝘀𝗵 𝗮𝗻𝘆𝘄𝗮𝘆 – 𝗠𝗼𝗻𝗶𝘁𝗼𝗿 𝗮𝗻𝗱 𝗿𝗲𝗮𝗰𝘁 𝗳𝗮𝘀𝘁𝗲𝗿 𝘁𝗼 𝘂𝗻𝗽𝗿𝗲𝗱𝗶𝗰𝘁𝗮𝗯𝗹𝗲 𝗲𝗿𝗿𝗼𝗿𝘀 https://lnkd.in/dUPhVRuW #rpa #roboticprocessautomation #intelligentautomation

Automate the Boring, Focus on the Meaningful! A friend of mine recently reached out with a manual task that had been eating up hours of their time every single week—a repetitive workflow that looked something like this: 1️⃣ Log into a website 2️⃣ Click on "All Orders" 3️⃣ Click on "Action" → Select "Export CSV" 4️⃣ Add a shipping date range 5️⃣ Check a couple of boxes 6️⃣ Click on "Export CSV" again 7️⃣ Format the CSV 8️⃣ Upload that CSV to a specific tab in a Google Sheets file 9️⃣ Repeat this every 30 minutes! Once I understood the full process, I started thinking: 🔹 Could a headless browser be required? 🔹 Are there APIs being triggered that we could leverage? 🔹 How can we make this as efficient as possible? A few hours later, I had a Python-based script running that now does all of this in under 10 seconds—automatically, every 30 minutes. 🎯 💡 Time saved? If this took just 3 minutes manually (being optimistic), that’s 6 minutes per hour → 48 minutes per workday → 4 hours per week → ~16 hours per month! That’s 2 full workdays every month freed up for something actually meaningful. This got me thinking—how many of us are still stuck doing repetitive, mindless tasks that could easily be automated? How much time are we losing? If you have any manual processes at work that you suspect could be automated, let’s chat! 😃 #Automation #RPA #Python #Efficiency #WorkSmarter #SaveTime

𝐅𝐫𝐨𝐦 𝐑𝐏𝐀 𝐭𝐨 𝐀𝐠𝐞𝐧𝐭𝐢𝐜 𝐀𝐈 - 𝐓𝐡𝐞 𝐏𝐫𝐨𝐜𝐞𝐬𝐬 𝐀𝐮𝐭𝐨𝐦𝐚𝐭𝐢𝐨𝐧 𝐄𝐯𝐨𝐥𝐮𝐭𝐢𝐨𝐧 From following rules to make decisions and context understanding: The evolution of automation is evolving. 🤖 → 🧠 → 🌟 1️⃣ 𝐑𝐏𝐀 (𝐑𝐨𝐛𝐨𝐭𝐢𝐜 𝐏𝐫𝐨𝐜𝐞𝐬𝐬 𝐀𝐮𝐭𝐨𝐦𝐚𝐭𝐢𝐨𝐧) Think of RPA as a digital worker that follows strict, predefined rules to perform repetitive tasks. Key characteristics: • Rule-based • Works well with structured data and stable processes • Excels at high-volume, routine processes Example: Data entry/form filling. 2️⃣ 𝐈𝐏𝐀 (𝐈𝐧𝐭𝐞𝐥𝐥𝐢𝐠𝐞𝐧𝐭 𝐏𝐫𝐨𝐜𝐞𝐬𝐬 𝐀𝐮𝐭𝐨𝐦𝐚𝐭𝐢𝐨𝐧) IPA represents the fusion of RPA with AI capabilities. It's like upgrading our digital worker with a brain that can learn things and improve over time. Key improvements: • Integration of AI technologies such as NLP and ML • Can handle semi-structured and unstructured data • Ability to learn and improve over time Example: Intelligent document processing and predictive analytics. 3️⃣ 𝐀𝐏𝐀 (𝐀𝐠𝐞𝐧𝐭𝐢𝐜 𝐏𝐫𝐨𝐜𝐞𝐬𝐬 𝐀𝐮𝐭𝐨𝐦𝐚𝐭𝐢𝐨𝐧) And now comes Agentic AI, that shows a shift from automated execution to autonomous problem-solving. It can understand context, plan actions, and work towards goals autonomously. Key improvements: • Autonomous decision-making and planning • Understanding of context and objectives • Ability to break down complex tasks into manageable steps Example: AI assistants that can plan and execute marketing campaigns. What's your take on this evolution?

You set up a Process Automation CoE to streamline workflows, boost ROI, and accelerate digital transformation—yet you’re still wrestling with low-impact initiatives, fragmented tech stacks, and skill gaps that stifle progress. Sound familiar? In every RPA CoE, these roadblocks are all too common. But what if you could unlock a blueprint that not only crushes these obstacles, but also turns your CoE into a well-oiled, innovation-driven powerhouse that consistently delivers tangible business value? Pain Points in Process Automation CoEs 1. Lack of Vision and Strategy: Misaligned objectives and absence of a scalable automation roadmap. 2. Limited Stakeholder Buy-In: Resistance to change and poor communication of the CoE’s value. 3. Weak Governance: Lack of policies, standards, and compliance frameworks for automation. 4. Skill Gaps: Inadequate technical expertise in advanced automation, RPA, AI, and ML tools. 5. Fragmented Technology Stack: Poor integration with legacy systems and underutilization of AI and predictive analytics. 6. Poor Process Selection: Automating low-impact processes with minimal ROI. 7. Scalability Challenges: Limited reusability of automation components across business units. 8. Change Management Issues: Resistance to automation and insufficient employee upskilling. 9. Inadequate Performance Monitoring: Limited tracking of ROI, productivity gains, and KPIs. 10. Security and Compliance Risks: Gaps in data governance and adherence to industry regulations. 11. Leadership Deficiency: Absence of a skilled technical leader to align CoE with business goals. Strategies to Strengthen the CoE for ROI and Growth 1. Set Clear Goals: Align CoE objectives with organizational KPIs and define a phased automation roadmap. 2. Build Robust Governance: Standardize policies, compliance frameworks, and success metrics for sustainable automation. 3. Foster Stakeholder Engagement: Conduct workshops, showcase automation success stories, and secure leadership buy-in. 4. Invest in Skills: Upskill teams in RPA,AI/ML. 5. Modernize Technology: Integrate tools into a unified platform and leverage advanced capabilities like AI and IoT. 6. Prioritize High-Impact Processes: Use data-driven methods to identify and automate processes with maximum ROI. 7. Plan for Scalability: Develop reusable automation components and build a sustainable pipeline of opportunities. 8. Change Management: Reskill employees, address resistance, and communicate automation benefits effectively. 9. Monitor Performance: Implement dashboards to track KPIs, optimize processes, and measure ROI. 10. Ensure Security & Compliance: Strengthen data governance and adhere to industry-specific regulations. 11. Appoint Skilled Leadership: Hire a seasoned CoE leader with expertise in process automation, AI, and strategy. #IntelligentAutomation #RPA #AI #ML #DigitalTransformation #CoE #AutomationROI #Leadership #cognitbotz #Innovation #AutomationStrategy #BusinessGrowth