Automation In Daily Tasks

From MIT SMR - how 14 companies across a wide range of industries are generating value from generative AI today: McKinsey built Lilli, a platform that helps consultants quickly find and synthesize information from past projects worldwide. The system integrates with over 40 internal sources and even reads PowerPoint slides, leading to 30% time savings and 75% employee adoption within a year. Amazon deploys AI across multiple divisions. Their pharmacy division uses an internal chatbot to help customer service representatives find answers faster. The finance team employs AI for everything from fraud detection to tax work. In their e-commerce business, they personalize product recommendations based on customer preferences and are developing new GenAI tools for vendors. Morgan Stanley empowers their financial advisers with a knowledge assistant trained on over a million internal documents. The system can summarize client video meetings and draft personalized follow-up emails, allowing advisers to focus more on client needs. Sysco, the food distribution giant, uses GenAI to generate menu recommendations for online customers and create personalized scripts for sales calls based on customer data. CarMax revolutionized their car research pages with GenAI, automatically generating content and summarizing thousands of customer reviews. They've since expanded to use AI in marketing design, customer chatbots, and internal tools. Dentsu transformed their creative agency work with GenAI, using it throughout the creative process from proposals to project planning. They can now generate mock-ups and product photos in real-time during client meetings, significantly improving efficiency. John Hancock deployed chatbot assistants to handle routine customer queries, reducing wait times and freeing human agents for complex issues. Major retailers like Starbucks, Domino's, and CVS are implementing GenAI voice interactions for customer service, moving beyond traditional phone menus. Tapestry, parent company of Coach and Kate Spade, uses real-time language modifications to personalize online shopping, mimicking in-store associate interactions. This led to a 3% increase in e-commerce revenue. Software companies are integrating GenAI directly into their products. Lucidchart allows users to create flowcharts through natural language commands. Canva integrated ChatGPT to simplify creation of visual content. Adobe embedded GenAI across their suite for image editing, PDF interaction, and marketing campaign optimization. For more information on these examples and to gain insight into how companies are transforming with GenAI, read the full article here: https://lnkd.in/eWSzaKw4 images: 4 of the 20 I created with Midjourney for this post. #AI #transformation #innovation

Klarna’s AI-powered customer service is a masterclass in how to scale CX without sacrificing quality. OpenAI helped them automate 66% of their CX workload and add $40M in profit to their bottom line. Here's how it went down: When they rolled out their AI assistant (powered by our friends at OpenAI), Klarna wasn’t just testing the waters—they were making a huge bet to transform their customer service. With over 150M customers worldwide, this was a bold move. But it paid off. According to Klarna's CEO, Sebastian Siemiatkowski, AI agents got them some wild outcomes: → 2.3 million conversations handled in 1 month (2/3 of their total service chats) → Replaced the need for 700 full-time human agents → 11-minute resolution times down to 2 minutes with CSAT scores rivaling human agents AI in customer service can be a double-edged sword: If it works, it’s transformative. If it doesn’t, you lose customer trust—and fast. Klarna understood this and made their AI assistant feel like an extension of their brand. How? → Made it available 24/7 in 23 markets and 35+ languages → Matched the AI with the brand’s tone and style to make interactions consistent → Designed core features like the personal financial assistant to align with Klarna’s values of smart banking Their success highlights a bigger trend: as AI agents rapidly become more capable, brands that leverage them well will have a competitive advantage by exceeding customer expectations. This involves really molding the AI around your business logic to look up data, take actions, and more. That is exactly what we do at Decagon. Klarna would never have been able to add $40M in bottom-line revenue without using AI agents in their CX motion and I'm seeing more and more brands have the same realization: AI agents are the most effective and proven path to efficiency and quality at scale in CX.

🚨 Taking SOC investigations to the next level: Introducing an AI-powered Phishing Investigator built on n8n workflow automation! ⚡ Imagine sending a phishing email for analysis and instantly getting a full investigative report — including insights from Splunk and AI-driven analysis — all orchestrated automatically. 📮 How it works (step by step): • GDrive: Downloads suspicious emails • Zamzar(Custom Built integration): Converts attachments to PDF for uniform analysis • Gemini: Builds queries & integrates with Splunk to investigate and fetch results. Also for performing investigations & generating report. • Splunk: For performing investigations. • Any.Run(Custom Built integration): Analyzes suspicious files and outputs detailed behavior • Aggregator AI: Compiles all insights, runs a final investigation, and generates a comprehensive report 💼 Business Value: • Faster phishing investigations ⏱️ • Reduces repetitive manual work 🎯 • Delivers AI-driven analysis in a single, automated workflow 🤖 • Bridges multiple tools seamlessly for SOC efficiency 🔐 🛠 Tools Used: • n8n (Orchestration) • Splunk • Gemini • GDrive & Zamzar • Any.Run 📂 GitHub: https://lnkd.in/gNH2uuQk ⚠️ Note: This is a POC. Next, I’ll be expanding the workflow with more datasets and advanced AI models for deeper intelligence. #CyberSecurity #SIEM #Splunk #SOC #AIinCyberSecurity #Automation #GenerativeAI #SecurityOperations #n8n #PhishingInvestigation #Gemini

🤖 HexStrike AI MCP Agents – Automating Cybersecurity with AI ⚡ HexStrike AI MCP Agents is an advanced Model Context Protocol (MCP) server that connects AI agents (Claude, GPT, Copilot, etc.) with 150+ cybersecurity tools. It’s designed to support automated penetration testing, vulnerability discovery, bug bounty workflows, and security research — all in authorized environments. 💡 Key Highlights: 1️⃣ AI + Security Tools – Seamlessly bridges LLMs with real-world cybersecurity utilities 🔗 2️⃣ Automated Testing – Streamlines vulnerability scanning & reporting 🛠️ 3️⃣ Bug Bounty Support – Helps researchers find and responsibly disclose issues ethically 🎯 4️⃣ Security Research – Ideal for labs, red/blue team exercises, and academic studies 📚 5️⃣ Productivity Boost – Saves time by orchestrating multiple tools under one framework ⚡ 🌟 Why It Matters: AI isn’t just powering productivity — it’s also transforming cyber defense and security research. Frameworks like HexStrike help professionals test smarter, fix faster, and stay ahead of evolving threats. ⚠️ Disclaimer: This content is for educational and research purposes only. Tools and frameworks like HexStrike must be used only on systems you own or have explicit written permission to test. Unauthorized use is illegal and unethical. #AISecurity #CyberSecurity #EthicalHacking #PenTesting #BugBounty #AIResearch #InfoSec #SecurityTools #AIandCyber #TechInnovation

🔐 90% of Cybersecurity Work Happens with These Tools — Let Me Prove It If you want to break into cybersecurity or upgrade your tech stack, save this. This is the toolkit that’s powering real-world SOC teams, Red Teams, and Threat Analysts at companies like Microsoft, Cisco, and CrowdStrike. 🧠 What Most Security Posts Miss — This Covers: ✅ Networking Surveillance Use tools like Wireshark and Nmap not just to map networks, but to detect unusual port behavior and packet anomalies before IDS triggers. ✅ App Vulnerability Scanning BurpSuite, ZAP, and Veracode allow developers to embed security testing inside CI/CD — saving hours of patching post-deploy. ✅ Cloud Security Monitoring Cloud-native tools like Prisma Cloud and AWS Security Hub automatically scan cloud misconfigs — one of the top causes of data breaches. ✅ Incident Response Stack Tools like TheHive, MISP, and SANS SIFT are used in SOCs for rapid triage, evidence collection, and threat intel correlation. 🔐 Insider Insight: What the Pros Actually Use Here’s how actual teams combine tools in the field: 🔹 John The Ripper + Hashcat 👉 Used in Red Team assessments to simulate credential compromise. 🔐 Industrial Use: Password audits on enterprise Active Directory exports. 🔹 SolarWinds 👉 Often used for system log forensics, especially in hybrid environments. 💡 Tip: Pair it with EnCase for deep-dive investigation in malware-laced systems. 🔹 WiFi Pineapple 👉 PenTesters use it to demonstrate real-world Man-in-the-Middle (MITM) attacks — yes, even in corporate cafeterias. 🔹 Cobalt Strike 👉 Used by both defenders and attackers. It simulates Advanced Persistent Threats (APT) — now part of many blue team training scenarios. 🧪 Pro Tip: Combine These Tools for Real-World Impact a) Scan → Nmap / Nessus b) Exploit → Metasploit c) Report → TheHive d) Harden → Checkmarx, Veracode e) Monitor & React → Prisma Cloud + Lacework That’s how CloudSec & DevSecOps teams run secure pipelines today. 🛡️ Why This Matters in Industry ==> 70% of breaches happen due to misconfigurations or known CVEs. ==>Top companies automate 80% of vulnerability scans. ==>Security engineers are now expected to know tools AND automate with them (Python/Go scripting). 🚨 You don’t need to memorize tools — you need to know how & when to use them. 💥 Final Thought If you’re a: 🎓 Fresher → Start with Wireshark, BurpSuite, and Metasploit 🧑💻 Developer → Learn OWASP ZAP, Veracode, and Snyk 🧠 Security Pro → Master TheHive, MISP, and threat intel platforms Cybersecurity isn't optional anymore. It's baked into every layer of modern tech — from mobile apps to microservices. 👀 Follow me Mazharuddin Farooque for more tech stacks decoded like this.

For months, one of our biggest operational challenges was the mandatory human touchpoint needed to route customer interactions. Every new support ticket required a Tier 1 agent to read the description, classify the Intent, judge the Sentiment, and then manually route it to the correct specialist or seniority level. This delay was a drain on agent time and, worse, a source of customer frustration. In the last few days we've successfully implemented an AI-powered system using the Gemini API to solve this problem. We trained a model on our historical data to automatically and accurately classify every incoming interaction in real-time. The Model Now Automatically Determines: 🎯 Intent: Is this a 'General Inquiry,' 'Subscription Cancellation,' or 'Billing Inquiry'? 😠 Sentiment: Is the customer 'Neutral' or 'Critical Negative'? 📈 Priority Score: A dynamic score (1-5) that combines intent and sentiment. The Impact is Immediate and Measurable: Eliminated Triage Bottleneck: Senior agents now spend 100% of their time solving problems, not reading tickets. Faster Crisis Response: Critical issues (Priority Score 5) are routed directly to the L3 team in seconds, not minutes. Improved Customer Satisfaction (CSAT): By routing complex issues immediately, we're cutting down on resolution time and reducing the need for costly agent transfers. This shift is a game-changer for our customer experience and a prime example of how targeted AI tools can drive real operational efficiency.

AI + HI = Improved CX In today’s digital world, businesses strive to deliver exceptional customer experiences (CX) to stand out. While artificial intelligence (AI) has revolutionized CX by enabling automation, personalization, and efficiency, it cannot fully replace the human touch. AI enhances CX by processing vast amounts of data in real time, predicting customer preferences, and providing instant responses through chatbots, recommendation engines, and self-service options. It reduces wait times, offers 24/7 support, and ensures consistency across interactions. However, AI alone has limitations—it lacks emotional intelligence, creativity, and the ability to handle complex, nuanced customer concerns. Human agents bring empathy, critical thinking, and problem-solving skills that AI cannot replicate. When combined with AI, human agents become more efficient, as AI handles routine tasks, provides insights, and allows them to focus on high-value interactions. Impact on BPO KPIs 1. First Call Resolution (FCR) Improvement: • AI-driven knowledge bases and predictive analytics equip human agents with real-time solutions, reducing repeat calls. • Virtual assistants handle routine inquiries, allowing human agents to focus on complex issues. 2. Reduction in Average Handling Time (AHT): • AI-powered tools like speech analytics and automated summaries minimize the time agents spend on after-call work (ACW). • Virtual assistants can gather customer information before handing over to a live agent, speeding up resolutions. 3. Increased Customer Satisfaction (CSAT): • AI ensures faster response times and personalized interactions based on past behavior. • Human agents, equipped with AI-driven insights, can provide more empathetic and accurate solutions, improving overall satisfaction. 4. Enhanced Agent Productivity and Utilization: • AI automates repetitive tasks such as data entry, ticket classification, and FAQs, freeing up agents for complex interactions. • Sentiment analysis tools help agents adjust their approach in real time for better engagement. 5. Lower Cost Per Contact: • AI-driven self-service options reduce the volume of inbound calls and chats, lowering operational costs. • Intelligent routing ensures the right agent handles the right query, optimizing workforce efficiency. 6. Improved Net Promoter Score (NPS): • Personalized AI-driven recommendations and proactive outreach enhance customer engagement. • The combination of AI efficiency and human empathy fosters long-term customer loyalty. The synergy of AI and HI leads to an improved CX by ensuring speed, accuracy, and emotional connection. AI-driven insights empower human agents to offer proactive solutions, while human empathy ensures customers feel valued. AI and HI are not competitors but collaborators. Businesses that successfully integrate both will deliver superior CX, optimize BPO performance, and achieve sustainable growth in an increasingly digital world.

Customer support is highly personalized, requiring empathy and nuanced understanding—qualities that many believe AI cannot replicate. As part of our course, AI in Business Applications, my team and I worked on a project that leverages Generative AI to enhance, not replace, the human aspect of customer support. By combining Large Language Models (LLMs) with human oversight, we created a scalable, efficient, and context-aware system tailored for support-heavy environments. ▶️The Reality of AI in Personalized Support AI tools like LLMs are not here to replace human agents but to complement them. However, skepticism remains due to the following limitations of LLMs: 1. Lack of Empathy: AI struggles to understand emotional nuances, which are often critical in support scenarios. 2. Generic Responses: LLMs may offer answers that lack the deep personalization customers expect. 3. Hallucinations: AI can occasionally generate inaccurate or misleading responses when context is unclear. 4. Complexity of Issues: AI might fall short in handling multi-layered or highly sensitive customer queries. 💡Our Solution: Human-AI Collaboration To address these challenges, we implemented a hybrid system that leverages AI’s efficiency and human agents’ empathy and expertise: Fine-Tuning for Accuracy: By training the AI on domain-specific data (e.g., product manuals, FAQs, past conversations), we ensured it could handle routine inquiries with precision. Retrieval-Augmented Generation (RAG): This framework enhances the AI’s reliability by pulling accurate, up-to-date information from a structured knowledge base before generating responses. Escalation to Human Agents: For personalized or emotionally charged cases, the AI seamlessly hands off the conversation to a human agent, ensuring customers feel heard and valued. 🎯How This Enhances Customer Support Efficiency: AI handles repetitive, straightforward queries, freeing human agents to focus on complex, high-value interactions. Scalability: With AI assisting in routine tasks, businesses can scale support operations without compromising quality. Empowered Human Agents: By providing agents with AI-curated insights, they can deliver faster, more informed, and empathetic solutions. Round-the-Clock Support: AI ensures customers receive instant responses to basic queries, even outside business hours. ⚖️A Balanced Approach The key takeaway? AI is not a replacement but a tool to enhance human capabilities. While it streamlines processes and improves efficiency, the human touch remains central in building trust and loyalty with customers. This project deepened my understanding of how AI can solve business challenges while respecting the personalized nature of customer support. By combining Generative AI with thoughtful design and human collaboration, we can create systems that are both powerful and people-centric. #AI #GenerativeAI #CustomerSupport #HumanAI #BusinessInnovation #HybridApproach #AIinBusiness

🎉 New blog post is out! This one took me a couple of weeks to write. I put together 8 years of experience building AI tools for #CustomerSupport teams into one detailed overview. 14 different AI solutions for customer service in 2026. From fully automated chatbots to internal AI tools that help your team work faster. Covers three layers of AI customer service, with screenshots and examples: ↳ Fully automated AI agents that can resolve issues end-to-end ↳ AI solutions with human-in-the-loop ↳ Internal AI tools for support teams Each solution has specific tools, use cases, and a framework for deciding where to start. This should give you a good overview of how your customer service team can benefit from AI and what AI tools you can use. 📌 Write + in the comments and I'll send you the link. ↳ Make sure we are connected so I can send you a DM Hope you like it! 👋 I'm Lena, CEO & Founder at ParsLabs, helping businesses scale their communication with AI solutions, including voice agents, AI assistants and AI automations. Follow to stay up to date with latest updates in AI

Over the last 6 months, the conversation about AI in customer service has evolved into a really nuanced one. From talking about AI simply replacing entire customer service teams an year back, we're now looking at really thoughtful and impactful application of AI in customer service. I recently spoke to Christian Sokolowski from Rebuy Engine about how his team built AI for customer service that doesn’t just automate, but amplifies human judgment. They use daily tuning, empathy guardrails, and real-time sentiment analysis to keep the experience personal, even when a bot starts the conversation. The result? Faster resolutions, less burnout, and a team that feels more connected not less. Tune in to the full conversation here: https://lnkd.in/gs8Y5YJY [I didn't "sit down" with Christian - I just spoke to him :) I think 'sit down' is to speak is what peruse was to read.]