Speedy Problem Resolution

Support teams face constant pressure to resolve cases faster without overloading engineering. For one Glean customer, valuable resources were tied up in avoidable tickets, MTTR (mean time to resolution) hovered at nearly two days, and agents spent hours manually triaging cases. Their goal: boost self-solves, improve MTTR, and reduce R&D reliance – without adding more tools. So they embedded Glean in Zendesk, giving agents prompts to quickly gather knowledge across all company data. In triage, agents use Glean to find similar tickets, summarize runbooks and past Jira investigations, and compile clear updates for customers or well-packaged escalations. That streamlined process now drives faster resolutions, smoother knowledge transfer, and consistent workflows—leading to: • 34% increase in self-solves with more future automation planned - this is incredible progress • 24% faster MTTR (1.9 → 1.5 days) • 2–4 hours saved per week for 85% of users (13–26 business days/year) • Reduced R&D involvement in lower-tier tickets By streamlining resolutions, knowledge transfer, and process consistency, the team achieved remarkable results – proof of what’s possible when AI is embedded into everyday workflows. Stories like this are energizing – showing how teams are using Glean to reimagine what they can accomplish.

The best people in any team don’t waste time pointing fingers. They focus on fixing the fire - then making sure it never happens again. Let me show you what I mean. Last month, a few students messaged our support team saying they couldn’t access their course videos right after purchasing. The issue? A glitch during payment confirmation. The system marked them as paid, but the course wasn't assigned. Now imagine two people jumping in. Person A starts with: “Who set up the payment flow?” “Wasn’t this flagged before?” “This shouldn’t have gone live like this.” Person B starts with: “Let’s manually assign the course for now.” “How many students are affected? Let's fix them now.” “Can we write a quick script to patch the cases while engineering investigates?” Same situation. Different starting point. The second approach didn’t ignore the root cause. They just knew when to solve and when to reflect. Later that week, they debugged the trigger logic and helped product prioritize a permanent fix. No drama. No blame. Just fast fixes, followed by long-term improvements. That’s the skill I rate very highly. The ability to walk into chaos and calmly say, “Let’s fix this now, and make sure it doesn’t happen again.” Whether you're in sales, ops, product, or support - this mindset transforms how teams move. #ProblemSolving #SolutionOriented #ProcessThinking #Startups #OperationalExcellence #FixFirstReflectNext

Two technicians, same equipment, same failure mode. One diagnoses it in 30 minutes. The other takes three hours. The difference is almost never tools, training, or experience. The difference is what they do in the first ten minutes. The fast diagnosis starts with a question: "What changed?" Not "what's broken." Not "what does the alarm say." Just, what changed. The fast tech walks to the area before touching anything. They ask the operator who was on shift. They check the maintenance log for what was done last week. They look at the trend data for the last 30 days. They build a picture of what was different about today before they ever pick up a tool. The slow diagnosis starts with motion. The slow tech sees the alarm, opens the cabinet, starts replacing components in order of suspicion. Two hours in, they've swapped three parts that weren't the problem and they're starting on the fourth. By the time they get to the actual cause, they've created their own collateral damage with the parts they shouldn't have touched. Speed in troubleshooting doesn't come from working faster. It comes from thinking longer before working at all. The best troubleshooters I've watched all share one habit: they're suspiciously still for the first ten minutes. They look. They ask. They listen. They build a hypothesis before they pick up a wrench. The slow ones look productive. The fast ones look almost lazy at the start — until you notice they're done while the others are still swapping parts. — From The Art of Troubleshooting | The Smart Maintenance 4.0 Library #Maintenance #Reliability #Troubleshooting #ManufacturingExcellence #MaintenanceTechnician

𝗗𝗮𝗶𝗹𝘆 𝗠𝗮𝗻𝗮𝗴𝗲𝗺𝗲𝗻𝘁: 𝗪𝗵𝗮𝘁 𝗶𝗳 𝘆𝗼𝘂𝗿 𝘁𝗲𝗮𝗺 𝗰𝗼𝘂𝗹𝗱 𝘀𝗼𝗹𝘃𝗲 𝗽𝗿𝗼𝗯𝗹𝗲𝗺𝘀 3𝘅 𝗳𝗮𝘀𝘁𝗲𝗿, 𝘀𝘁𝗮𝗿𝘁𝗶𝗻𝗴 𝘁𝗼𝗺𝗼𝗿𝗿𝗼𝘄? In my work with leaders, I discovered 𝘁𝗵𝗿𝗲𝗲 𝗰𝗿𝗶𝘁𝗶𝗰𝗮𝗹 𝗰𝗵𝗮𝗹𝗹𝗲𝗻𝗴𝗲𝘀 that were secretly sabotaging their team's performance: 1. 𝗜𝗻𝘃𝗶𝘀𝗶𝗯𝗹𝗲 𝗣𝗿𝗼𝗯𝗹𝗲𝗺𝘀: Teams were experiencing cascading issues that went undetected for months, creating massive hidden inefficiencies. 2. 𝗥𝗲𝗮𝗰𝘁𝗶𝘃𝗲 𝘃𝘀. 𝗣𝗿𝗼𝗮𝗰𝘁𝗶𝘃𝗲 𝗠𝗮𝗻𝗮𝗴𝗲𝗺𝗲𝗻𝘁: Leaders were constantly firefighting instead of systematically preventing problems. 3. 𝗦𝘁𝗿𝗮𝘁𝗲𝗴𝗶𝗰 𝗠𝗶𝘀𝗮𝗹𝗶𝗴𝗻𝗺𝗲𝗻𝘁: Execution was disconnected from strategic objectives, causing significant performance gaps. These challenges taught me a 𝗽𝗿𝗼𝗳𝗼𝘂𝗻𝗱 𝗹𝗲𝘀𝘀𝗼𝗻: Daily management isn't just a process—it's the heartbeat of organisational excellence. By implementing a structured daily management system, leaders can transform reactive cultures into proactive, high-performance machines. My 𝗯𝗿𝗲𝗮𝗸𝘁𝗵𝗿𝗼𝘂𝗴𝗵 came when I introduced a simple, yet powerful daily management framework. We created visual management, implemented 15-minute daily huddles, and established problem-solving to act when we saw issues. The 𝗿𝗲𝘀𝘂𝗹𝘁𝘀? Dramatic improvements in response times, team alignment, and strategic execution. For senior executives, this isn't just about efficiency—it's about 𝗰𝗿𝗲𝗮𝘁𝗶𝗻𝗴 𝗮 𝗰𝘂𝗹𝘁𝘂𝗿𝗲 𝗼𝗳 𝗰𝗼𝗻𝘁𝗶𝗻𝘂𝗼𝘂𝘀 𝗶𝗺𝗽𝗿𝗼𝘃𝗲𝗺𝗲𝗻𝘁 𝘁𝗵𝗮𝘁 𝘀𝗲𝗽𝗮𝗿𝗮𝘁𝗲𝘀 𝗴𝗼𝗼𝗱 𝗰𝗼𝗺𝗽𝗮𝗻𝗶𝗲𝘀 𝗳𝗿𝗼𝗺 𝘁𝗿𝘂𝗹𝘆 𝗴𝗿𝗲𝗮𝘁 𝗼𝗻𝗲𝘀. 🔑 𝗧𝗮𝗸𝗲𝗮𝘄𝗮𝘆: Start with a 15-minute daily team huddle focused on identifying and solving problems in real-time. Watch how this simple practice can revolutionise your organisation's performance.

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.

Recently I witnessed a perfect example of how different teams can handle the same type of problem. An internal payment system couldn't get event statuses because another team's service was burning through a shared API rate limit. Instead of 4 calls per day as recommended, they were making erratic calls through inefficient polling. The fix? Simple. Change from continuous polling to the recommended schedule. Maybe 60 minutes of work for one engineer, even without extensive contextual knowledge. Their response? "Please submit this as an Aha idea for our next planning cycle." Meanwhile, at Aurora, we handle operational issues completely differently. When we discover inefficient processes affecting system performance, we: Immediately assess business impact and technical scope Deploy hotfixes within hours, not planning cycles Document the fix and implement monitoring to prevent recurrence Save formal process for actual feature development This approach has helped us maintain 4 9s of uptime on critical data pipelines while other organizations wait for roadmap discussions. The difference? We treat production issues as operational incidents, not feature requests. When systems break or perform poorly, rapid resolution takes priority over process compliance. Good organizations distinguish between different types of work: Feature development follows formal product processes with discovery and planning Operational issues get fast-tracked through engineering channels with immediate triage Performance optimizations get handled as technical debt within existing sprint capacity At Aurora, our leadership empowers our teams to make quick decisions on operational issues while maintaining appropriate governance for new features. It's one of the things that makes working here effective and why I love leading our Enterprise Data & Automation practice. The result? Our data systems stay reliable, our business operations stay smooth, and we can focus planning cycles on actual innovation instead of firefighting. Process should enable better outcomes, not slow them down. The best teams know when to follow process and when to cut through it. What does rapid operational response look like at your organization?

We analyzed 1000+ incidents. Here's what separated 15 min fixes from 2 hr outages: Most teams focus on getting better tooling or more data access. But here's what actually determines resolution speed: → Knowing which questions to ask first → Understanding what patterns indicate problems → Having context about past failure modes → Seeing how similar issues were solved before Think about your best engineer during an incident. They're following an investigation pattern built from years of experience: → "Last time this happened, it was a connection pool issue" → "When I see this error pattern, I usually check..." → "This metric spike typically means..." This is the "senior engineer algorithm" - and it's usually invisible to everyone else. Making these investigation patterns visible and reusable in the throws of an incident is huge: → Knowledge transfer happens naturally → New engineers learn actual debugging patterns → Teams discover common failure modes → Investigation steps become reusable These days tools and data aren't the bottleneck. It's scaling your team's incident investigation knowledge.

You answer calls within 20 seconds? Congratulations. You're doing the bare minimum. The pandemic fundamentally transformed customer expectations. Those endless hold times? Your customers won't tolerate them anymore. In a competitive market, they'll simply leave. ----- Speed of answer has become the baseline, not the victory many businesses think it is. ----- Think about it: What good is answering quickly if you can't actually solve the customer's problem? It's like showing up to a fire with an empty water bucket – you were quick, but not exactly helpful. The real masters of customer experience know that true success comes from a two-part formula: 1. Answer as quickly as possible 2. Resolve the issue during that first interaction When you fail to resolve issues promptly, you create a high-effort experience. Your customers feel it – and they remember it. Oh boy, do they remember it. ----- 3 Ways To Become A Resolution-First CX Operation ----- 1. Empower your front line: Give your agents the authority to make decisions that solve problems without transfers or escalations. 2. Implement "resolution-first" technology: Use AI tools (like our Agent Assist) that provide agents with real-time suggestions and information specifically designed to solve problems, not just document them. Documenting problems without solving them is basically just journaling. 3. Measure what matters: Stop obsessing over average handle time and start tracking first-contact resolution rates. What gets measured gets improved. And what gets ignored gets... well, ignored. ----- The economics are clear: ----- Companies focused on first-contact resolution see operational cost savings while improving customer satisfaction and loyalty. Just because a call was answered quickly doesn't produce the same result as a call that was resolved quickly. If we respond fast AND resolve issues, we create satisfaction and loyalty – and that's where the true ROI lives. Is your team still celebrating speed metrics while customers remain frustrated? It might be time to refocus your approach before your customers ghost you faster than a bad first date.

Last month, a field technician spent almost 5 hours trying to find the root cause of an equipment issue. 5 hours. Calls. Photos on WhatsApp. Different software. Waiting for approvals. Calling experts again. Sending reports back and forth. And the worst part? The problem itself was not that complicated. The real problem was the process around it. Everything was disconnected. The technician had one tool. The supervisor had another. Knowledge was sitting in PDFs. Experts were on separate calls. Operations teams were waiting for updates. There were gaps in communication, unclear information, and delays at almost every step. Now compare that to what happened recently with the same kind of issue. Diagnosis time: Under 1 hour. What changed? The technician was the same. The equipment was the same. Even the experts were the same people. The difference was having one connected system instead of fragmented tools. With Telepresenz, the technician could instantly connect with the right expert, pull up the right knowledge, follow guided workflows, and collaborate live, all inside one platform. Instead of jumping between apps and calling different people, the technician had everything in one place. The expert could see the issue live, the right documents were already available, and decisions were made on the spot. Everything happened in one continuous flow: - Issue identified - Expert joins live - Knowledge accessed instantly - Guided troubleshooting followed - Decision made in real time - Problem resolved Simple. When operations, collaboration, and knowledge are connected together, something powerful happens: People stop wasting time managing the process… and start solving the actual problem. That is where real operational efficiency comes from. But removing friction between teams, systems, and decisions. And when you reduce diagnosis time from 5 hours to less than 1, the ROI becomes very obvious. P.S. How much time is your team losing every day, because people are jumping between tools instead of solving the problem?

"Sorry, we need more time for research." Ever heard that only to find out the decision was already made while you waited? I still remember when a product leader confessed to me: "By the time our research team delivered insights on the checkout redesign, we'd already finished building it. We just needed to check a box." This isn't a research problem. It's a process problem. The traditional research cycle (plan for 2 weeks → recruit for 2 weeks → conduct for 1 week → analyze for 1 week) simply can't keep pace with today's product development cycles. After working with dozens of product teams facing this exact challenge, we developed the SPEED framework: S - Systematize your research calendar (weekly cadence, not project-based) P - Prioritize questions that block immediate decisions E - Extract insights from ALL customer touchpoints (not just formal studies) E - Enable cross-functional access to insights (break down the researcher bottleneck) D - Document decisions alongside supporting evidence One Director of Product at a fintech company was frustrated with research that consistently arrived too late. Their team implemented SPEED and reduced their decision cycle from 3 weeks to just 3 days. The key shift? Moving from "we need to launch a new study" to "let me check what we already know." Their team now has a centralized insights engine that instantly answers questions like: • "What's causing friction in our onboarding?" • "Why are enterprise customers underutilizing feature X?" • "What language do customers use to describe their problems?" I know this isn't a small change,  but for teams where the current approach simply isn't working, incremental improvements won't cut it. You need a system overhaul. I know that this isn’t a small change, but for many teams the current way of doing things isn’t working. If that’s the case, you may need to do a significant overhaul. If you want to talk through the best way to implement this framework for you, here’s my Calendly: https://bit.ly/43AL8Qe Happy to chat!