Planning Sprints Effectively

Some processes look small on the surface. But when they repeat multiple times a week across teams - they quietly eat up time, attention, and energy. At Shikho, our CRM helps track every learner’s journey. Both Sales and Customer Experience (CX) teams engage with students, and occasionally a student under CX ownership ends up in an active conversion conversation with Sales. To ensure the right person gets recognition for the outcome, Sales Managers often request a transfer of ownership - based on a few aligned criteria (e.g. no recent CX follow-up, active sales touchpoint, etc.). The old process looked like this: - A WhatsApp message is dropped with a CRM link in a common group - CX Lead clicks the link, checks behavior logs - If criteria are met, they do the transfer (2 clicks) - Then update the group: “Done” Harmless at first glance. But prone to delay, distraction, and dependency. It felt like a process running on favors - not systems. And that needed to change. A simple automation was set up: Google Form → 2 inputs: CRM link + Sales Manager name On submission: - API call fetches student behavior - Criteria validation happens instantly - If qualified, transfer executes automatically - No back-and-forth. No waiting. Just done. The key insight? - All manual CRM steps were already backed by API calls. - If a human could click and validate, a script could too. This wasn't pushed into a future sprint in ClickUp. It wasn’t gated behind a roadmap discussion. It was operationalized within 1 hour - using tools already in hand and logic already understood. AI (ChatGPT) accelerated the build. Now, a working version is live. Soon, usage data from these form submissions can be handed off to Product - making the case to embed this directly in the CRM. No bottlenecks. No approvals. Just initiative and systems thinking. Efficiency is no longer a bonus trait. It’s a leadership habit. With the right mindset + tools, nearly any repetitive task can be rethought, redesigned, and rebuilt - without waiting for permission. What's one process in your org that should be automated but isn't (yet)? #LessonsInBuilding #ProcessDesign #EfficiencyFirst #AutomationMindset #StartupOps #Shikho #CRMWorkflow #LowCode #AIForOps #TeamEnablement

In the world of small and midsize businesses (SMBs), the phrase “AI adoption” often sounds lofty, expensive, and long-term. But what if you could move from pilot to value in 3 days? Here’s how a lean SMB turned that possibility into reality: quietly, effectively, strategically. Day 1: Diagnose-and-Prioritise The company had a simple but urgent question: where is time being wasted? - Manual data entry. - Customer support backlog. - Lead follow-ups are slipping. Using lightweight analytics and stakeholder interviews, they mapped the top three pain points in under half a day. They chose a candidate use-case with clear ROI, minimal tech debt and an obvious win-win. Day 2: Configure-and-Pilot Rather than build from scratch, they leveraged existing tools (CRM + spreadsheet + a no-code AI module). Within hours: - A chatbot responded to basic support requests. - A lead-scoring model flagged the highest-probability leads. - A dashboard surfaced overdue tasks and bottlenecks. They ran the components live, capturing real-time data and user feedback. Day 3: Review-and-Scale On the final day of the sprint: - They reviewed the pilot data: reduced response time, better lead prioritisation, and freed up one full-time equivalent human capacity. - They conducted a mini-workshop: “What worked, what didn’t, what's next?” - They built a 30-day roadmap: incremental roll-out, metrics to monitor, and roles to assign. Why this Matters 1/. SMBs can adopt AI quickly: research shows 91% of SMBs with AI say it boosts revenue. 2/. Focused diagnostics beat broad ambitions: less is more when the use-case is tightly scoped, especially in lean organisations. 3/. Speed builds confidence: early wins open doors, waiting for perfection kills momentum. Key Takeaways for SMBs ✅ Start with high-impact, low-complexity problems. ✅ Use existing tools versus building from scratch. ✅ Run short sprints (3-day or even 1-week) to validate before scale. ✅ Define metrics up-front: time saved, response improved, leads converted. ✅ Build a roadmap,  today’s pilot becomes tomorrow’s foundation. If your SMB hasn’t yet started AI adoption,  ask this one question today: Which process, if improved by even 10%, would make the biggest difference to your business this quarter? Want to explore a 3-day AI diagnostics sprint for your business? I’d be happy to help you map the use-case, tools, and roadmap.

“What #Formula1 Pit Stops Teach Us About Lean Excellence” 🏎️ The #Lean #Power of an #F1 Pit Stop 🏁 Formula 1 is not just about speed, it’s about precision, teamwork, and continuous improvement. Nowhere is this clearer than in the pit stop, where crews execute a 3-second tire change with surgical precision. But how do they achieve this level of excellence? The answer lies in Lean principles. Let’s break it down: 🔧 1. #Standardized #Work – Every Move Has a Purpose F1 pit crews function like a well-rehearsed orchestra. Each member has a clearly defined role, refined through thousands of repetitions. The standardization of every motion ensures: ✅ Minimal variability ✅ Predictable, repeatable results ✅ Reduced errors under pressure In business, the same principle applies: clear work standards and well-defined roles lead to efficiency and quality. 🚫 2. #Waste #Reduction – Every Second Counts In Lean, waste is anything that doesn’t add value. In an F1 pit stop, even microseconds matter, so crews eliminate: ❌ Unnecessary movements ❌ Inefficient handovers ❌ Delays caused by poor positioning By applying 5S (Sort, Set in Order, Shine, Standardize, Sustain), everything has its place, tools are instantly accessible, and there is zero wasted motion. 🤝 3. #Team #Coordination – Flow Over Speed Pit stops aren’t about raw speed, they’re about synchronization. Each movement is precise, and teams rely on deep trust and clear communication. One misstep can cost valuable time. For businesses, this highlights the importance of: 🔹 Clear communication 🔹 Cross-functional collaboration 🔹 Practicing under real conditions (like simulations in F1) 🔄 4. #Continuous #Improvement (#Kaizen) – Always Getting Better No F1 team is satisfied with yesterday’s performance. They analyze every pit stop, identify tiny inefficiencies, and make incremental improvements. Businesses can adopt this mindset by: ✔ Encouraging a culture of improvement ✔ Using data-driven insights to refine processes ✔ Involving the team in finding better ways to work ⚡ Bringing F1-Level Efficiency to Your Business Whether you’re running a production or a warehouse, managing logistics, supply chain, hr, sales or leading an office team, the lessons from an F1 pit stop apply everywhere. Speed comes from precision, not just effort. 💡 How can you fine-tune your team’s processes like an F1 pit crew? Share your thoughts! #Lean #Kaizen #Formula1 #Efficiency #ContinuousImprovement #StandardizedWork #ProcessOptimization

As the leader of an Intelligent Automation CoE, I’ve had the privilege of guiding enterprise teams in their evolution from RPA and low-code platforms to AI-driven decisioning and orchestration. Across industries, a few core principles consistently enable scalable, precise, and impactful automation. Here are five principles I’ve seen consistently deliver results: ✔️ Start with a high-impact use case: Identify a process with clear ROI and measurable outcomes. Automate it end-to-end before expanding. ✔️ Iterate fast, automate faster: Build automation in agile sprints. Test early, deploy often, and refine based on real user feedback. ✔️ Don’t fear manual effort early on: Use low-code tools, RPA, and human-in-the-loop models to validate automation before scaling. Doing things that don’t scale helps you learn what will. ✔️ Embed automation into existing workflows: Design bots and AI agents to integrate seamlessly with enterprise systems (ERP, CRM, ITSM). Automation should feel like an enhancement, not a disruption. ✔️ Build a strong automation foundation: Hire engineers and architects who understand both business processes and automation platforms. Early talent sets the tone for scalability and governance. These principles can help you move from isolated wins to enterprise-wide impact. Whether you're just starting or scaling your automation journey, these fundamentals hold true. What worked (or not) in your automation journey? 🎯 Follow my AI & IA - Art of the Possible newsletter for insights: https://lnkd.in/g5TkS8pv #IntelligentAutomation #AutomationCoE #DigitalTransformation #AI #RPA #EnterpriseAutomation #Leadership #AgileAutomation P.S. The content of this post reflects my personal viewpoints, not those of my employer.

From Ceremony Manager to Value Driver: My Scrum Master Transformation 📊 I'll never forget my first retrospective as a new Scrum Master – the team was going through the motions, but we had no data to back up our feelings. That moment sparked my journey from simply facilitating ceremonies to truly driving team improvement through meaningful metrics. These KPIs transformed how I coach teams and demonstrate value to stakeholders. Here are the metrics that changed everything: 📈 Sprint Velocity & Burndown: Tracking consistency and predictability in delivery. ⏱ Cycle Time & Lead Time: Measuring flow efficiency from request to delivery. 🐛 Defect Density & Escaped Defects: Focusing on quality and technical excellence. 😊 Team Happiness & Collaboration: Because culture eats strategy for breakfast. 🎯 Sprint Goal Success Rate: Ensuring we're delivering valuable outcomes, not just outputs. These metrics don't just measure performance – they create conversations that drive continuous improvement.

🤔 I’ve always felt the Scrum Master role is one of the most misunderstood by companies and teams. Too often it gets reduced to a part-time job that’s mostly meeting facilitator. The result? Teams fall into Agile Theatre 🎭 Going through the motions of stand-ups, refinement, and retros without real improvement. But here’s the thing: A great Scrum Master isn’t a box-checker. They’re an expert at spotting the elephants in the room, the unspoken issues holding a team back, and bringing them into the light in a way that sparks discussion, not blame. That takes time. It means: - Understanding how the team really works - Knowing where problems like to hide - Using metrics to uncover patterns the team doesn’t see while heads-down in their work If you’re in a part-time Scrum Master role and it feels like you’re stuck in this rut, here’s an exercise I recommend: 👉 Look back at your team’s last 4 to 6 sprints and ask: - What’s the team’s velocity? - How often do we commit above it? - How many points are pulled in mid-sprint? - How many points carry over from sprint to sprint? - How many tickets have been carried over more than once? - How many backlog tickets (not in the sprint) are already “in progress”? These are my go-to questions when I start with a new team. More often than not, this is where the elephants are hiding. 🐘🔍 Once you answer these questions, share the results with your team in demo or before your retro. If the numbers look great, celebrate! 🎉 If not, you now have a clear, data-driven way to start an honest conversation. Because when you show smart people bad numbers, they’ll want to make them better. 📈 👉 Will you give this a try? If you did, what did you discover? #Agile #Scrum #ScrumMaster #ContinuousImprovement #AgileLeadership #TeamPerformance

AI adoption doesn’t happen through slide decks or when leaders buy subscriptions to a copilot—it happens when people feel the impact in their own work. 𝐈𝐧𝐭𝐞𝐫𝐧𝐚𝐥 𝐀𝐮𝐭𝐨𝐦𝐚𝐭𝐢𝐨𝐧 𝐃𝐞𝐬𝐢𝐠𝐧 𝐒𝐩𝐫𝐢𝐧𝐭 At a recent company offsite, we ran an automation design sprint using n8n to help our departments eliminate repetitive tasks, free up time for high-impact work, and get hands-on with AI. We are definitely biased, but it seems like it was a solid success. 𝐒𝐞𝐭𝐭𝐢𝐧𝐠 𝐭𝐡𝐞 𝐒𝐭𝐚𝐠𝐞 • Focused on one tool – People are overwhelmed by the speed of AI and all the tools and capabilities. We did the research, chose n8n as our automation platform (others include Make, Zapier), and simplified the choice for them. • Assigned an Automation Lead – Gave them time to ramp up, set up preconfigured APIs, and prep the environment. • Pre-reads & videos – Our automation leader met with departments in advance and shared primers so teams weren’t starting cold. 𝐄𝐱𝐞𝐜𝐮𝐭𝐢𝐨𝐧: 𝐀𝐮𝐭𝐨𝐦𝐚𝐭𝐢𝐨𝐧 𝐢𝐧 𝐀𝐜𝐭𝐢𝐨𝐧 • Breakout sessions – Departments identified pain points and mapped potential automations. Each team had an assigned engineer to help execute or clear roadblocks. • Rapid prototyping – 1-hour workflow design → timeboxed builds. • Show & tell – Teams presented their automations, the "why" behind them, and their progress. Many were fully functional by the end. 𝐊𝐞𝐞𝐩𝐢𝐧𝐠 𝐭𝐡𝐞 𝐌𝐨𝐦𝐞𝐧𝐭𝐮𝐦  A month later, live automations are running across all teams—with more in the pipeline. And to make automation stick, we put an initial structure in place: • Automation Lead role formalized. • Department-level automation roadmaps created. • Engineering leads assigned until teams are self-sufficient. • Focus on training team members in each department. • Regular check-ins between teams and automation leads. • “Automation of the Week” updates to highlight wins. We’ll share more on what’s working (and what’s not) as we scale this. I am curious what other teams are doing on this front and how they are executing. Would love to hear in the comments or directly from folks.  

Most teams are implementing AI in Scrum backwards. They're using it to automate the easy parts. Generating material for user stories, summarizing meetings, tracking throughput. But they're keeping the hard parts manual, the uncomfortable conversations, the difficult trade-offs, the moments where teams can learn. Experimenting with AI in the Scrum events has taught me that, usually, the real issue teams run into isn't efficiency it's avoidance and a reluctance to rock the boat. Scrum events fail because teams systematically avoid the conversations that matter. Sprint Planning becomes feature Tetris instead of value negotiation. Daily Scrums become status updates instead of problem-solving. Reviews become demo parties instead of outcome validation. Retros become complaint sessions instead of improvement engines. But AI can make these difficult conversations unavoidable. In Sprint Planning AI surfaces value tensions. Analysing customer behaviour data, technical debt patterns, and market signals to present conflicting priorities. For example: "Customer usage data suggests Feature A, but technical debt analysis suggests refactoring will deliver 3x more capacity for Feature B next quarter." AI focuses attention on what does value mean to us right now? In Daily Scrums AI flags collaboration patterns, handoff delays, and knowledge gaps in real-time to surface friction. For example: "Three stories are blocked on review, but I can see a lot of external meetings today, and similar patterns happened in 4 of the last 6 sprints." AI forces the conversation on how is our system of work working? In Sprint Reviews AI presents leading indicators of feature success/failure alongside the demo, forcing teams to align on what working product means. For example: "Feature demo looks good, but early CX data shows 40% of users need support to complete the workflow, and it's increased our support ticket volume by 15%." AI raises transparency on how we validate and build confidence if this sprint actually delivered value? In Retrospectives AI surfaces patterns across sprints that teams may miss or side-step. For example: "Sprints with >25% carryover consistently happen after weeks with 3+ urgent stakeholder requests." AI brings team attention to the systemic changes that could be made to move the needle. The counter-intuitive angle here is these teams have more conflict, not less. But it's far more productive conflict about the things that matter. Although using AI this way really helps I'd encourage you not to start by adding AI to all of your events at once. Start with the event your team avoids or plays safe in the most (usually it's Retros). And lastly a question you can ask to gauge if your team are ready to leverage AI this way: Can your team have a 30-minute argument about priorities and still respect each other afterward? If not, fix your psychological safety first. AI will likely amplify whatever dynamics already exist.

Most teams focus on automating regression tests, ensuring that existing functionality remains intact. But if automation is only catching regressions, it’s always a step behind. By the time issues are found, they’re already in the product. What if automation worked with development instead of trailing behind? In sprint automation changes the game. Instead of waiting for a full sprint to end before automating, why not start automating as features are being built? • Automate unit and component level checks while development is in progress. • Build testability into the code from the start, reducing late-stage defects. • Catch issues early, making fixes cheaper and faster. • Reduce dependency on heavy post sprint regression cycles. • Align automation efforts with business goals, not just code changes. Automation isn’t just about efficiency, it’s about impact. When testing and automation are embedded in the sprint, teams move faster, ship with confidence, and deliver real value. Regression automation is necessary. But relying only on it is like wearing a seatbelt after a crash. Shift left, automate smart, and make testing a continuous process. #softwaretesting #softwareengineering #testautomation #agile #qualityengineering #brijeshsays

Scrum Variations: Deviate to Innovate The Scrum Guide is clear: if you modify, misapply, or omit any element, you’re not practicing Scrum anymore. But as teams mature, they often wonder what they can change. Many add complementary practices, like planning poker, story points, velocity, team boards, pair programming, TDD, etc. These practices enhance Scrum without altering it. But what happens when you fundamentally change Scrum? Once you change the central elements, you may still be an Agile Team, but you're not a Scrum Team. And that's ok. Sometimes, intentional deviations from the framework can lead to meaningful innovation. The key is to respect the principles of transparency, inspection, adaptation, collaboration, and value-orientation. It’s less about abandoning the rules and more about evolving the practices to fit your team’s needs. Just a Few Ideas... Variable Sprint Lengths Scrum’s sprint cadence comes at the expense of flexibility. Shorter sprints might work well for research-heavy tasks, while longer sprints may be better suited for delivery-focused goals. The key is sticking to regular inspection and adaptation, even if the timebox changes. On-Demand Retros Why wait until the sprint ends to improve? If an issue arises, hold a quick retro to address it. Or, maintain a backlog of improvement ideas and trigger a retro when it reaches a certain size. This keeps adaptation timely without constant interruptions. Rotating Scrum Master Rotating the SM accountability among team members fosters empathy and shared ownership. This approach lets everyone experience the SM challenges and responsibilities. Maybe rename to "Team Coach." Incremental Sprint Reviews Why showcase everything at the end of the sprint? Hold mini-sessions throughout. You can share high-priority increments as soon as they're completed. Earlier stakeholder feedback helps the team adapt faster. Hybrid Sprint Planning Sprint Planning can be exhausting. Split it into two parts: one session for the Sprint Goal, another for the development plan. The second session could even be asynchronous, letting team members prepare individually. Keep collaboration high while reducing meeting fatigue. Cross-Team Sprint Planning When multiple teams work toward shared goals or burn a common backlog, joint Sprint Planning can improve alignment and reduce dependencies. Teams maintain autonomy but collaborate early to avoid surprises later. Skipping the Daily Scrum The Daily Scrum is helpful, but some days it adds no value. Let the team skip it when there are no plan updates or impediments. This respects their time and focuses attention on meaningful work. Innovate With Purpose Altering Scrum isn’t a decision to take lightly. The framework exists for a reason: it works. But thoughtful experimentation can lead to improvements. So, ideate, experiment, measure, reflect, and iterate. Apply Scrum’s principles to Scrum itself, honoring its values but evolving its practices.