Automated Customer Support

Power Automate Work Queues are not built for scale! That's a fact. When you think about scalability in Power Automate, one thing that will definitely come to mind at some point is queues and workload management. While you might be able to survive without them in some event-based transactional flows that only process a single item at a time, but whenever you process tasks in batches, or when RPA gets involved, you'll need queues. Power Automate comes with Work Queues out of the box. And you would think that's your go-to queueing mechanism for scaling. After all, it's at scale that you really need those queues - to de-couple your flows and make it easier to maintain, support, debug them, as well as make them more robust and efficient. Queues is a must even at medium scale. Heck, we use them even in small scale implementations. But the surprising thing about Power Automate Work Queues is that they are not fit for high scale implementations. And that is by design! The docs themselves (link in the comments) explicitly state that if have high volumes or if you dequeue (pick up work items from the queue for processing) concurrently, you should either do it within moderate levels or use something else. If you try and use Power Automate Work Queues for high scale implementations (more than 5 concurrent dequeue operations or hundreds/thousands of any type operations involving the queues), you'll get in trouble. There can be all sorts of issues that could happen - your data may get duplicated, you may accidentally deque the same work item in multiple concurrent instances, or your flows might simply get throttled or even crash. This is because of the way they're build and the way they utilize Dataverse tables for storing work items and work queue metadata. So, if you do want to scale, it's best to use an alternative. And, obviously, Microsoft wouldn't be Microsoft if they didn't have an alternative tool to do that. The docs themselves recommend Azure Service Bus Queues for high throughput queueing mechanisms. Another alternative could also be Azure Storage Queues, but that only makes sense if the individual work items in your queue can get large (lots of data or even documents) or when you expect your queue to grow beyond 80GB (which is possible in very large scale implementations). Otherwise, Azure Service Bus Queues are absolutely perfect for very large volumes of small transactions. On top of that, they have some very advanced features for managing, tracking, auditing and otherwise handling your work items. And, of course, there's a existing connector in Power Automate to use it. So, while I do love Power Automate Work Queues, I'll only use them in relatively small scale implementations. And for everything else - my queues will go to Azure. And so should yours.

For decades, businesses have built call centers, service teams, and help desks to fix issues faster. Yet speed alone never created loyalty. The real measure of service has always been how it makes people feel: heard, understood, and valued.   Now, with AI transforming how we engage with customers, that emotional foundation is being redefined. 62% of customers now say they prefer chatting with a bot over waiting for a human, as long as it provides faster, more accurate service, according to Salesforce.   This statistic shows that people still seek empathy and understanding, but they also want quick, smart responses. That’s where AI chatbots and virtual assistants come in.    So, what is the role of AI chatbots and virtual assistants in improving customer support? Here are a few key roles they play: ▪Immediate Understanding: 🔅 AI can analyze tone, sentiment, and keywords to understand the customer's state of mind instantly. This allows responses to feel timely and considerate, not robotic. ▪Faster Resolutions with Context: 🔅 Virtual assistants can resolve repetitive tasks instantly while passing complex cases to human agents with full context, so customers never need to repeat themselves. ▪Consistency Without Fatigue: 🔅 Unlike human agents, AI doesn’t get tired or lose patience. It brings calm, consistent support anytime, in any language, across any channel. ▪Empathetic Language Modeling: 🔅 The latest AI models are trained to respond with warmth and tact, saying things like “I understand how frustrating this must be” or “Let me take care of that for you,” just like a well-trained agent would. ▪ Boosting Human Support: 🔅 By handling the routine, AI allows human agents to focus on high-emotion, high-stakes moments where real connection is needed, creating a more powerful hybrid model. Are chatbots naturally empathetic? Not yet. But they can be designed to behave empathetically, and that’s a game-changer for CX. Support today focuses on meeting people where they are, not just directing them where the system wants. In regions like Saudi Arabia, where expectations for digital transformation and real-time service are rapidly growing, support becomes a strategic necessity. When technology understands people and people trust technology, customer support becomes more effective. #Customerexperience #CX #AI #Chatbots #Virtualassistants

Emotion AI in Customer Support: Why Tone Is the Missing Signal in Financial Conversations Customer support fails when tone is ignored. In financial services, that is not a UX issue. It is a risk issue. The same message “I need help” can mean very different things: → Calm → Angry → Anxious Traditional systems treat them the same. Emotion AI does not. Emotion AI analyzes: → Text sentiment → Voice stress → Response urgency This allows support teams to act before frustration turns into churn, complaints, or regulatory escalation. Why this matters in finance: → Money is emotional → Delays create anxiety → Errors trigger anger → Stress signals often precede disputes and fraud Emotion AI helps financial institutions: → Detect emotional signals in real time → Prioritize high-risk conversations → Assist agents with empathetic responses → Reduce burnout and improve first-contact resolution This is not about replacing agents. It is about augmenting human judgment with emotional intelligence at machine speed. Tone is becoming a new data layer. Empathy is becoming a system capability. The future of customer support is not scripted. It is adaptive. It is proactive. It is emotionally intelligent. That future is Emotion AI.

The rapid development of artificial intelligence (AI) is outpacing the awareness of many companies, yet the potential these AI tools hold is enormous. The nexus of AI and emotional intelligence (EQ) is emerging as a revolutionary game-changer. Here’s why this intersection is crucial and how you can leverage it: 🔍 AI can handle data analysis and repetitive tasks, allowing humans to focus on empathetic, creative, and strategic work. This synergy enhances both productivity and the quality of interactions. Imagine a retail company struggling with high customer churn due to poor customer service experiences. By integrating AI tools like IBM Watson's Tone Analyzer into their customer service process, they could identify emotional triggers and tailor responses accordingly. This proactive approach could transform dissatisfied customers into loyal advocates. Practical Application: AI-driven sentiment analysis tools can help businesses understand customer emotions in real-time, tailoring responses to improve customer satisfaction. For example, using AI chatbots for initial customer service interactions can free up human agents to handle more complex, emotionally charged issues. Strategy Tip: Integrate AI tools that provide real-time sentiment analysis into your customer service processes. This allows your team to quickly identify and address customer emotions, leading to more personalized and effective interactions. By integrating AI with EQ, businesses can create a more responsive and human-centric experience, driving both loyalty and innovation. Embracing the combination of AI and EQ is not just a trend but a strategic move towards future-proofing your business. We’d love to hear from you: How is your organization leveraging AI to enhance emotional intelligence? Share your thoughts and experiences in the comments below! #AI #EmotionalIntelligence #CustomerExperience #Innovation #ImpactLab

A junior reached out to me last week. One of our APIs was collapsing under 150 requests per second. Yes — only 150. He had tried everything: * Added an in-memory cache * Scaled the K8s pods * Increased CPU and memory Nothing worked. The API still couldn’t scale beyond 150 RPS. Latency? Upwards of 1 minute. 🤯 Brain = Blown. So I rolled up my sleeves and started digging; studied the code, the query patterns, and the call graphs. Turns out, the problem wasn’t hardware. It was design. It was a bulk API processing 70 requests per call. For every request: 1. Making multiple synchronous downstream calls 2. Hitting the DB repeatedly for the same data for every request 3. Using local caches (different for each of 15 pods!) So instead of adding more pods, we redesigned the flow: 1. Reduced 350 DB calls → 5 DB calls 2. Built a common context object shared across all requests 3. Shifted reads to dedicated read replicas 4. Moved from in-memory to Redis cache (shared across pods) Results: 1. 20× higher throughput — 3K QPS 2. 60× lower latency (~60s → 0.8s) 3. 50% lower infra cost (fewer pods, better design) The insight? 1. Most scalability issues aren’t infrastructure limits; they’re architectural inefficiencies disguised as capacity problems. 2. Scaling isn’t about throwing hardware at the problem. It’s about tightening data paths, minimizing redundancy, and respecting latency budgets. Before you spin up the next node, ask yourself: Is my architecture optimized enough to earn that node?

🚀 Apex ➕ Agentforce ➕ Salesforce Data Cloud : The power combo transforming Salesforce I built a Customer Sentiment Analyzer Agent in Salesforce that detects at-risk clients based on real-time signals like CSAT, NPS, and email sentiment. When a case is closed with low satisfaction, the agent automatically: • Fetches customer signals from Data Cloud • Invokes an agent via Apex • Returns actionable follow-up recommendations • Records feedback to improve future predictions In this video, I walk you through the full architecture and show you the implementation live. 🧠 Agent intelligence meets CRM logic. This is the future of proactive customer success! What other use cases would you build using Salesforce + Agentforce? See repo and Apex classes in the comments 👇 #Salesforce #Apex #Agentforce #DataCloud #SalesforceArchitect #SalesforceDevelopers #AI

I see nobody doing this with AI voice agents. So I did. This is unlocking a whole new layer of intelligence on your AI voice calls. What is it? Sentiment Anlalysis. Why does this matter? Because most businesses are sitting on a goldmine of voice data... but they’re not extracting the emotional signals that drive real outcomes. Here’s where sentiment analysis actually adds value: ✅ Customer Experience Monitoring Spot unhappy customers early. Trigger an automatic follow-up if a call turns negative. ✅ Agent Performance Tracking See how sentiment shifts across reps, scripts, or time. Is your team actually creating positive experiences? ✅ Trend Recognition Negative sentiment = higher churn? Now you've got predictive insights. ✅ Training & QA Flag poor sentiment calls for review. Let AI highlight the moments that caused friction. But it's not always worth your time... Sentiment analysis is useless if: → You're not acting on the data. → Your calls are too short or robotic. → You don’t have enough volume to find patterns. → Your domain needs custom sentiment tuning (sarcasm, mixed languages, etc.). Want to make it actually useful? Here’s how: → Link sentiment to outcomes like conversions or renewals. → Create real-time alerts or dashboards for your team. → Fine-tune the model on your transcripts, not generic ones. → Combine it with other signals like talk-time, interruptions, and keywords. The emotional layer of your calls is where the real insight lives. Curious how this works in practice? I’m happy to show what I built today. Drop a “curious” below or shoot me a message.

OpenAI doesn’t measure support the way you do. They’re not chasing CSAT or time-to-close. They rebuilt support — and what they came up with changes everything. Here’s the shift: A ticket opens. It gets solved. It closes. And most of the knowledge dies there. OpenAI saw that model couldn’t scale. Support wasn’t just a volume problem — it was an engineering and operational design problem. So they built something different: a system where every interaction improves the next. It starts with three building blocks: 🔲 Surfaces. Where support lives: chat, email, voice, and increasingly embedded directly in-product. 🔲 Knowledge. Not static docs, but living guidance that evolves with real conversations, policies, and context. 🔲 Evals & classifiers. Shared definitions of quality built by humans + software, continuously running to steer the system. These pieces form a loop. A pattern spotted in an enterprise chat updates the knowledge base. An eval created for one case trains the model for thousands more. And because the same primitives power every channel, improvements scale automatically. And here’s what really struck me: At OpenAI, reps aren’t just responding to tickets. They flag interactions that should become test cases. They propose new classifiers. They even prototype lightweight automations to close workflow gaps. Training shifts too — from just “policies” to spotting structural gaps and feeding improvements back. The result? Support isn’t measured by throughput, but by its capacity to evolve. And the loop doesn’t stop there. Each interaction compounds: Evals turn daily conversations into production tests. They codify what “great” means: not just solving, but solving politely, clearly, consistently. Patterns flow back into knowledge, automation, and product design. Every resolution strengthens the system. Every pattern spotted improves future answers. Every classifier scales across channels. And the org itself learns alongside the AI — reps shape classifiers, contribute datasets, and watch quality improve in real time through observability dashboards. What does all this point to? A blueprint for the future of support. Glen Worthington put it best: “Support has never really been about replying to just tickets. It’s about whether people get what they need, whether it actually serves them well.” That’s the profound shift: Support specialists are recognized not just for solving problems, but for refining knowledge, improving models, and extending the system itself. The future isn’t support as a destination. It’s support as an action — woven into every product surface. Here’s the uncomfortable question for every support leader 👇 If you look at your last 100 tickets… How many made tomorrow’s support better than today’s? Because in the future, the answer needs to be: all of them. Jay Patel Shimul Sachdeva

AI-powered sentiment analysis can revolutionise how contact centres understand and improve customer satisfaction. As you can see from the chart, senior leaders are - rightly or wrongly - judging success by NPS and CSAT. Personally, I think first-contact resolution is woefully underestimated, but that's what's being said in the real world... By analysing every interaction through natural language processing algorithms, businesses can now capture real-time insights into customer sentiment across all channels, moving beyond traditional random sampling or manual reviews. The technology excels at identifying patterns that human analysis might miss. When customers repeatedly express frustration during specific journey stages, AI flags these operational pain points for immediate attention. Product development teams receive actionable feedback about recurring complaints, while managers can identify which agents consistently generate positive sentiment and which need additional support. Real-time capabilities are particularly powerful. AI can detect escalating customer frustration mid-conversation, enabling agents to adjust their approach or escalate appropriately. This immediate feedback loop helps prevent satisfaction scores from deteriorating and creates opportunities for service recovery. However, the regulatory landscape is evolving rapidly. The EU AI Act introduces important restrictions that will shape how sentiment analysis operates in European markets. My understanding is that the Act prohibits emotion recognition systems that rely on biometric data and bans their use in workplace settings except for medical or safety purposes. I'd be interested to hear people's views on this, as I'll admit I haven't been through the Act with a fine toothcomb... I think it's likely that sentiment analysis will increasingly focus on text-based natural language processing rather than vocal tone analysis, facial recognition (for video calls) or other biometric markers. While this narrows the technical scope, it doesn't diminish the value proposition. Text-based sentiment analysis remains highly effective at identifying customer satisfaction trends, process inefficiencies and training opportunities. For contact centres, this regulatory clarity actually provides a helpful framework. By focusing on linguistic patterns and word choice analysis, organisations can be confident in building compliant AI systems that deliver meaningful customer insights while respecting privacy boundaries. Our report, "AI for Customer Satisfaction" looks at how AI can measure and improve CSAT in more depth. It's available for free download at https://lnkd.in/ea26U6ct #AIAnalytics #CustomerExperience #ContactCentre #EUAIAct #SentimentAnalysis Five9 Krisp Shara M. Davit Baghdasaryan Jonathan Buckley Anita Stein Nicole Friedrich

If I were the VP of Support at an enterprise company dealing with repetitive customer support tickets, here’s how I’d use AI to power KCS and improve ticket resolution while turning my support agents into “heroes”: First, some context: - Most support tickets are recurring, yet agents have to field every single one of them individually (this is unscalable).  - Agents are only rewarded based on the number of tickets resolved and have a hard time improving support quality (can be unrewarding) The best way to go about this problem? Enabling agents to externalize documentation on their own and improve support quality with every logged request, using AI to power Knowledge-Centered Support (KCS) Here’s how I’d implement this at an enterprise company: 1) Democratize knowledge creation Support agents know customer issues best, so it doesn’t make sense to wait for technical writers (who are already swamped) to create knowledge articles. With the help of AI, you can enable support agents to generate knowledge articles on their own, just by clicking a button. 2) Externalize new knowledge All new knowledge articles can be pushed to your external customer help center/knowledge hub right away. With that, customers can either resolve issues on their own or ask an AI Chatbot (that has immediate access to all knowledge articles). 3) Iterate & improve knowledge Now that recurring tickets are handled, support agents can dedicate their time to tickets that *actually* need human help. AI can then help them update existing articles as similar requests come in. This is WAY more efficient than relying on technical writers because your agents are already “on the ground.” 4) Gamify support process On the backend, AI can track & display: - Which customer issues were resolved  - Which knowledge articles were referenced - How many customers were assisted by each agent - How many tickets were resolved or deflected This makes it easier to boost support morale because agents see the REAL impact of what they’re doing for customers and the company – in short, they become “heroes.” (We do this ourselves at Ask-AI) TAKEAWAY An AI-powered KCS will help you improve your overall customer experience. You can resolve customer issues faster, your support agents are empowered – and the VP of support can report better TTR and CSAT metrics. Any thoughts on this?