Business Process Automation

Focusing on AI’s hype might cost your company millions… (Here’s what you’re overlooking) Every week, new AI tools grab attention—whether it’s copilot assistants or image generators. While helpful, these often overshadow the true economic driver for most companies: AI automation. AI automation uses LLM-powered solutions to handle tedious, knowledge-rich back-office tasks that drain resources. It may not be as eye-catching as image or video generation, but it’s where real enterprise value will be created in the near term. Consider ChatGPT: at its core, there is a large language model (LLM) like GPT-3 or GPT-4, designed to be a helpful assistant. However, these same models can be fine-tuned to perform a variety of tasks, from translating text to routing emails, extracting data, and more. The key is their versatility. By leveraging custom LLMs for complex automations, you unlock possibilities that weren’t possible before. Tasks like looking up information, routing data, extracting insights, and answering basic questions can all be automated using LLMs, freeing up employees and generating ROI on your GenAI investment. Starting with internal process automation is a smart way to build AI capabilities, resolve issues, and track ROI before external deployment. As infrastructure becomes easier to manage and costs decrease, the potential for AI automation continues to grow. For business leaders, identifying bottlenecks that are tedious for employees and prone to errors is the first step. Then, apply LLMs and AI solutions to streamline these operations. Remember, LLMs go beyond text—they can be used in voice, image recognition, and more. For example, Ushur is using LLMs to extract information from medical documents and feed it into backend systems efficiently—a task that was historically difficult for traditional AI systems. (Link in comments) In closing, while flashy AI demos capture attention, real productivity gains come from automating tedious tasks. This is a straightforward way to see returns on your GenAI investment and justify it to your executive team.

This is why AI agents are exploding in adoption—they deliver real business value by turning LLM intelligence into automated action. They are becoming the backbone of automation in customer support, operations, sales, and internal workflows, replacing repetitive tasks that humans perform by clicking buttons and following rules. Instead of just generating text, AI agents orchestrate actions, making them far more valuable in real business environments. A perfect example is customer-support order-tracking. Every day, support teams receive hundreds of emails asking, “Where is my order?” A human agent reads the message, extracts the order number, searches in the backend system, checks the shipment status in the carrier portal, decides what’s wrong, and finally replies or creates a follow-up ticket. This manual process takes 2–3 minutes per email—highly repetitive and expensive at scale. An AI agent can now automate this entire workflow end-to-end. It first extracts the order ID from the customer’s message, then calls the lookup_order tool to fetch order details, and the check_tracking_status tool to get carrier updates. Next, it analyzes the status and determines whether delivery is delayed, lost, or on track. Based on the result, it triggers the right action, such as create_internal_ticket, initiate_carrier_trace, or reschedule_delivery. Finally, the agent generates a personalized reply to the customer with the latest status—without any human involvement. With memory, it can even handle future follow-ups intelligently. Read more on the internal architecture of an AI Agent in detail: https://lnkd.in/gEhVX5cY Build Your First AI Agent in 10 Minutes! (No Code Needed): https://lnkd.in/gjNf5yyr

There’s been a lot of discussion about how Large Language Models (LLMs) power customer-facing features like chatbots. But their impact goes beyond that—LLMs can also enhance the backend of machine learning systems in significant ways. In this tech blog, Coupang’s machine learning engineers share how the team leverages LLMs to advance existing ML products. They first categorized Coupang’s ML models into three key areas: recommendation models that personalize shopping experiences and optimize recommendation surfaces, content understanding models that enhance product, customer, and merchant representation to improve shopping interactions, and forecasting models that support pricing, logistics, and delivery operations. With these existing ML models in place, the team integrates LLMs and multimodal models to develop Foundation Models, which can handle multiple tasks rather than being trained for specific use cases. These models improve customer experience in several ways. Vision-language models enhance product embeddings by jointly modeling image and text data; weak labels generated by LLMs serve as weak supervision signals to train other models. Additionally, LLMs also enable a deeper understanding of product data, including titles, descriptions, reviews, and seller information, resulting in a single LLM-powered categorizer that classifies all product categories with greater precision. The blog also dives into best practices for integrating LLMs, covering technical challenges, development patterns, and optimization strategies. For those looking to elevate ML performance with LLMs, this serves as a valuable reference. #MachineLearning #DataScience #LLM #LargeLanguageModel #AI #SnacksWeeklyonDataScience – – –  Check out the "Snacks Weekly on Data Science" podcast and subscribe, where I explain in more detail the concepts discussed in this and future posts:    -- Spotify: https://lnkd.in/gKgaMvbh   -- Apple Podcast: https://lnkd.in/gj6aPBBY    -- Youtube: https://lnkd.in/gcwPeBmR https://lnkd.in/gvaUuF4G

Large language models (LLMs) can improve their performance not just by retraining but by continuously evolving their understanding through context, as shown by the Agentic Context Engineering (ACE) framework. Consider a procurement team using an AI assistant to manage supplier evaluations. Instead of repeatedly inputting the same guidelines or losing specific insights, ACE helps the AI remember and refine past supplier performance metrics, negotiation strategies, and risk factors over time. This evolving “context playbook” allows the AI to provide more accurate supplier recommendations, anticipate potential disruptions, and adapt procurement strategies dynamically. In supply chain planning, ACE enables the AI to accumulate domain-specific rules about inventory policies, lead times, and demand patterns, improving forecast accuracy and decision-making as new data and insights become available. This approach results in up to 17% higher accuracy in agent tasks and reduces adaptation costs and time by more than 80%. It also supports self-improvement through feedback like execution outcomes or supply chain KPIs, without requiring labeled data. By modularizing the process—generating suggestions, reflecting on results, and curating updates—ACE builds robust, scalable AI tools that continuously learn and adapt to complex business environments. #AI #SupplyChain #Procurement #LLM #ContextEngineering #BusinessIntelligence

Recovery speed matters. Recovery sequence matters more. In automated plants, bringing systems back quickly is only half the work. The harder questions are: What comes back first? Under whose approval? Under what operating conditions? Production restarting before telemetry is stable leaves teams operating blind. Control systems restarting before quality checks are ready puts output integrity at risk. Supplier feeds resuming before validation lets risk back into the environment. Recovery planning needs a sequencing lens. Sequencing discipline rests on four priorities: 🔹 Restore visibility before velocity Telemetry, logs, and monitoring come back early. Teams should never recover blind. 🔹 Define critical process order Every system does not deserve equal priority. Recovery follows business and safety impact. 🔹 Set control gates before restart Production resumes only when minimum trust conditions are met. 🔹 Rehearse the sequence, not just the timeline A fast recovery that restarts systems in the wrong order creates new risk. In automated environments, resilience is measured by how safely and intelligently operations return.

To deploy Agentic AI for cyber recovery, organizations must integrate autonomous, goal-driven agents into their cyber resilience architecture to continuously monitor, detect, and respond to threats with precision and speed. These agents should be embedded within key systems, including backup, orchestration, threat intelligence, and analytics, and empowered to autonomously initiate recovery workflows based on predefined cyber recovery point objectives (CRPOs) and cyber recovery time objectives (CRTOs). Agentic AI dynamically evaluates the integrity of backups, identifies logic corruption or ransomware patterns through AI-based anomaly detection, and launches validated recovery paths from clean data repositories. By simulating and learning from attack scenarios, the AI agents refine recovery playbooks and ensure real-time decision-making during crises. This self-directed capability not only accelerates recovery timelines but also embeds resilience as a continuous, adaptive force, reducing human error, increasing readiness, and ensuring that cyber recovery is intelligent, autonomous, and always aligned with business continuity goals. Readiness remains our main protection. #resilience #readiness #protection #CRTO #CRPO #threat #intelligence #analytics #Agentic #AI #backup 

🚨 Building a Production-Grade Disaster Recovery Platform That Survives Anything Here's the reality: 96% of businesses that experience major data loss shut down within 2 years. That statistic haunts me. So I'm building something to prevent it. 💥 THE CHALLENGE: Design a cloud-native disaster recovery platform that can: - Survive complete region failures - Achieve RTO (Recovery Time) under 5 minutes - Maintain RPO (Recovery Point) near-zero - Automate failover without human intervention - Meet ISO 22301 compliance standards 🛠️ WHAT I'VE BUILT SO FAR: Primary Region: - Azure Kubernetes Service with 3-replica HA - PostgreSQL database with persistent storage - Velero automated backups (daily at 2 AM) - ArgoCD for GitOps deployment - Azure Blob Storage for backup retention - Production voting app serving real traffic Current Status: 99.8% backup success rate, first restore test completed in 4 min 37 seconds. WHAT I'M BUILDING NEXT (Weeks 4-5): Disaster Recovery Region: - Secondary AKS cluster in West US - PostgreSQL streaming replication - Cross-region automated failover - Azure Traffic Manager for DNS routing - Prometheus + Grafana monitoring - Terraform for Infrastructure as Code THE TECH STACK: Cloud & Orchestration: Azure (AKS, Blob Storage, Traffic Manager) Backup & Recovery: Velero Database: PostgreSQL with streaming replication GitOps: ArgoCD Automation: Terraform, Ansible (planned) Monitoring: Prometheus, Grafana (implementing) Compliance: ISO 22301, NIST SP 800-34 💡 WHY THIS MATTERS: When disaster strikes (and it WILL): ❌ Manual recovery takes hours/days ✅ Automated recovery takes minutes ❌ Data loss destroys businesses ✅ Continuous replication means zero loss ❌ Panic and errors during crisis ✅ Orchestrated automation handles it This isn't just a project. It's learning how enterprises protect their most critical systems. 📊 TARGET OUTCOMES: - RTO: < 5 minutes (currently 4:37 ✅) - RPO: Near-zero via continuous replication - Automated failover: No human intervention - Cost-effective: ~$500/month for complete DR Part of my Diploma in Artificial Intelligence Operations (AIOps) - where AI meets infrastructure resilience. Building in public. Following along? Drop a 🚀 below! Questions? Ask away I'm documenting everything. 👇 #DisasterRecovery #CloudEngineering #Azure #Kubernetes #DevOps #AKS #Velero #ArgoCD #BusinessContinuity #InfrastructureAutomation #AIOps #BuildingInPublic #CloudNative #PostgreSQL #Terraform

What Experience and Technology Teaches Today’s RCS Operations After 30 years in revenue cycle — through DRG rollouts, ICD‑10, payer consolidation and the move to value — the core lessons held true: clean data, end‑to‑end processes, clinical alignment, and people/governance win the day. Now intelligent automation (IA) — RPA + ML + NLP + intelligent document processing — is rewriting how we capture and protect revenue. What IA enables - Turn unstructured payer and clinical documents into structured data. - Automate eligibility checks, prior authorizations and claims scrubbing before submission. - Prioritize AR work with predictive triage so teams focus on highest-recovery opportunities. - Surface contract underpayments and drive automated recovery workflows. - Forecast cash flow and denial risk with predictive analytics. Five strategic shifts for executives 1. Automate end‑to‑end, not just tasks — orchestrate workflows across registration, coding, billing and collections. 2. Measure outcomes (days in A/R, net collections, denial root‑cause) instead of throughput. 3. Transform the workforce — move talent from repetitive work to exceptions, revenue integrity and payer strategy. 4. Prevent denials upstream with embedded checks and real‑time decision support. 5. Treat data as a strategic asset — build a unified revenue data layer for IA to work against. Governance is non‑negotiable: model validation/versioning, audit trails, security, and change management must accompany every automation rollout. A pragmatic roadmap - 90 days: value‑stream assessment + quick wins (eligibility, registration validation, prior auth reminders). - 6–12 months: deploy intelligent document processing, claim scrubbing and predictive AR triage (human‑in‑the‑loop). - 12–36 months: scale end‑to‑end orchestration, embed decision support at point‑of‑care, and create a revenue intelligence unit. Metrics that matter: days in AR, net collections %, denial rate by root cause, cost per claim, and contract underpayment recovery. Bottom line: decades of revenue cycle discipline combined with IA deliver a step‑change in predictability, cash velocity and patient experience. For C‑suite leaders, the choice is simple — treat IA as a strategic core capability or concede growth and margin to those who do. If you’re driving revenue cycle strategy and want to discuss practical first steps or case examples, let’s connect.

The Rise of Autonomous AI Agents: Transforming Knowledge Work with Language Models ... Researchers from Renmin University of China have published a survey on a new paradigm in AI: autonomous agents powered by large language models (LLMs). This study provides a taxonomy for constructing these agents and highlights their potential to revolutionize industries by automating complex cognitive tasks. 👉 A New Era of AI Assistants LLMs have demonstrated remarkable abilities in natural language understanding and generation. By integrating these models with key components like memory and planning modules, researchers can create autonomous agents capable of perceiving, reasoning, and acting to accomplish complex objectives. The proposed framework encompasses four modules: 1. Profiling: Defines the agent's role using methods like handcrafting, LLM-generation, or dataset alignment. 2. Memory: Enables agents to store and retrieve information using operations like reading, writing, and reflection. 3. Planning: Empowers agents to decompose tasks and generate plans using strategies like single-path reasoning, multi-path reasoning, and planning with feedback. 4. Action: Translates decisions into specific outputs by recalling memories or following plans, leveraging both internal LLM knowledge and external tools. LLM agents could automate a wide range of knowledge work and decision-making tasks, boosting productivity and innovation across sectors. The proposed framework offers a roadmap for designing more sophisticated AI assistants and chatbots. 👉 Early Killer Apps The survey showcases several promising applications of LLM agents: - Social science research: Analyzing datasets, generating hypotheses, and automating experiments.  - Software engineering: Code generation, debugging, and documentation. - Industrial automation: Optimizing manufacturing, predicting maintenance, and enabling flexible production. - Robotics: Enhancing robot perception, planning, and interaction capabilities. As the technology matures, we can expect to see more high-impact use cases emerge, improving efficiency, decision-making, and tackling previously intractable problems. 👉 The Road Ahead While the potential of LLM agents is vast, challenges remain: - Role-playing capability: Accurately simulating less common roles or capturing human psychology.  - Generalized human alignment: Aligning agents with diverse human values. - Prompt robustness: Improving resilience of complex prompt frameworks. - Hallucination: Mitigating false information generation. - Knowledge boundary: Constraining LLM knowledge to match human users. - Efficiency: Improving slow LLM inference speeds. Evaluating the safety and robustness of autonomous LLM agents is an open research question. As we refine these technologies and address the challenges, LLM agents could become indispensable tools, ushering in a new era of intelligent automation and discovery.

Optimization, machine learning, and LLMs are NOT competitors. They are three different layers of the same decision system. Machine learning helps us understand the world as it is likely to unfold. It turns messy historical data into signals, probabilities, forecasts, classifications, and patterns. It helps answer questions like: What is likely to happen? What demand might show up? Which customer behavior is changing? Where is risk increasing? Where is the system drifting? Optimization helps us decide what to do about it. That distinction matters. A forecast is not a decision. A prediction is not a plan. A dashboard is not an operating model. Knowing that demand may increase, supply may be constrained, or a customer may churn does not tell you what action to take when capacity, cost, timing, inventory, labor, service levels, and business tradeoffs are all competing at the same time. This is where optimization becomes essential. It translates predictions into economically coherent actions. It forces tradeoffs into the open. It asks: given what we believe, what should we do now? What should we buy, build, route, allocate, price, schedule, or prioritize? Then LLMs add another layer. LLMs help humans interact with the decision system. They explain. They summarize. They retrieve context. They help users interrogate assumptions. They generate scenarios. They make complex systems more usable. In many cases, they become the conversational interface between the business and the machinery underneath. But an LLM by itself is not a decision engine. It can sound intelligent while having no real concept of feasibility, constraints, economic tradeoffs, uncertainty, or downstream consequences. It can describe a plan beautifully without being able to prove the plan can actually work. That is why the future is not “AI versus optimization.” The future is systems where machine learning estimates uncertainty, optimization makes disciplined decisions under constraints, and LLMs help people understand, challenge, and interact with those decisions. The companies that win will not be the ones that simply add a chatbot to a dashboard. They will be the ones that build decision intelligence systems where data, prediction, optimization, simulation, and human judgment work together. Because the goal was never to predict the future perfectly. The goal is to make better decisions before the future arrives.