Leveraging Automation in Sprints

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

Project 𝗥𝗘𝗪𝗢𝗥𝗞 ⤵️ 30-40%. 𝗡𝗢 new hires. 𝗡𝗢 new tools. Your team is talented...but your process may not be. Vague requests arrive ➔ Meetings get called to decode them ➔ Decisions stall ➔ REWORK follows ➔ The whole cycle repeats next sprint Nobody planned to waste the week. The workflow made them do it. Brian Galardo, PMP, PMI-CPMAI, PMO-BP, Program Manager and Ops Excellence at Salesforce, broke that loop using 3 things many teams already have. 1) a structured Slack intake form 2) a shared tracker 3) artificial intelligence (aka AI) Before any request touched someone’s calendar, it had to include the problem statement, impacted users, and urgency level. Then AI summarized the request, flagged missing context, surfaced risks, and suggested routing. Teams reviewed asynchronously. By the time everyone met, they weren't decoding the request; they were making decisions. The 𝗥𝗘𝗦𝗨𝗟𝗧? Review meetings were cut roughly in half. ✂️ 𝙍𝙀𝙒𝙊𝙍𝙆 𝙙𝙧𝙤𝙥𝙥𝙚𝙙 𝙗𝙮 𝙖𝙣 𝙚𝙨𝙩𝙞𝙢𝙖𝙩𝙚𝙙 30 𝙩𝙤 40%. Back-and-forth clarification decreased because expectations were clearer before work started. 𝗡𝗢 complex agent build. 𝗡𝗢 shiny new platform. 𝗡𝗢 “digital transformation” theater. Just a cleaner workflow. And that's the part most teams miss. Automation doesn't fix messy processes. It amplifies whatever is already there. Because if ownership is unclear, automation creates faster confusion. And if inputs are vague, automation creates faster rework. On the other hand, if the process is clear, automation creates speed, visibility, and momentum. So, before you automate anything, answer these questions — ► Who owns this? ► What does a good outcome look like? ► What information must arrive before work starts? Get these right and AI can handle the prep. Humans still make the calls though. And that's the real unlock. Remember...the best automation doesn't replace people. It protects judgment from admin work. Ok, over to you. 📌 What's one manual, repetitive workflow your team still runs every week that should've been automated by now? And why? #ProjectManagers #ArtificialIntelligence #Automation

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.

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.

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.

In Agile, automation should be driven by risk, stability, and feedback value not sprint pressure. Not every story should be automated but every story should go through an automation feasibility check. Instead of asking: “Can we automate this quickly?” A better technical question is: “Will automating this give us reliable, repeatable feedback in our pipeline?” When deciding whether a scenario belongs in the automation suite below evaluation shall be checked : - Stability : Is the feature/API/UI likely to change next sprint? - Repeatability : Will this scenario run across builds, environments, or data sets? - Business criticality : Does failure block core user journeys? - Regression impact :Could future changes easily break this flow? - Data variation : Does it benefit from parameterized runs? (DDT) - Execution cost : Is manual execution expensive or time-consuming? Then comes the technical placement decision: • Can this be validated at API/service layer instead of UI? • Does it fit fast CI smoke or deeper regression ? • Will the failure signal be clear and diagnosable? Good Agile automation is not about maximizing script count it’s about maximizing trustworthy feedback per build. How do you decide what gets automated each sprint?

→ The Hidden Pulse Behind Every Successful Sprint: Jira Automation Workflow Have you ever wondered why some teams glide through sprints while others struggle to keep pace? The secret often lies in a well-crafted Jira automation workflow - the invisible engine driving efficiency and focus. Here’s how mastering automation at key sprint moments transforms your agile game: → Pre-Sprint: Laying the Foundation • Automatically prioritize and assign backlog items. • Notify stakeholders of upcoming sprint goals. • Create and link relevant subtasks to streamline execution. • Set due dates and dependencies with precision. → Sprint Start: Kickoff Without Chaos • Trigger status updates to "In Progress" for assigned stories. • Send reminders to team members, ensuring everyone is aligned. • Generate sprint burndown reports automatically. • Lock in sprint scope to prevent last-minute scope creep. → During Sprint: Staying Agile, Staying On Track • Detect stalled tasks and alert assignees instantly. • Auto-update linked issues when blockers clear. • Sync external tools and calendars seamlessly. • Record daily stand-up notes or reminders without manual input. → Sprint End: Closing the Loop Smoothly • Auto-transition completed tasks to "Done." • Compile sprint review reports and share them with stakeholders. • Reset or archive sprint boards efficiently. • Collect feedback via automated surveys for continuous improvement. follow Shraddha Sahu for more content

Being agile is difficult if a team spends two, three, four or more days manually testing each iteration. When that happens, teams either move to longer sprints or they add testing, hardening or stabilization sprints. Fortunately, there is a simple three-step process you can use to pay down a team’s manual testing debt. ✅ The first step is to stop the bleeding, which means stopping the situation from getting worse. I worked with a team that spent about 50 hours every two-week sprint performing manual testing, essentially the last two days of every sprint. If the team didn’t make a change, the 50 hours would become 55 hours, then 60 hours, and more. As the amount of time spent on manual testing debt climbs, it will be harder to pay it off. It was urgent to stop things from getting worse. To do this, team members looked for easy things to automate. They didn’t need to automate features being added in the current iteration—that could come later. The immediate concern was to stop things from getting worse. ✅ The second step in paying off manual test debt is staying current. This is the critical phase when team members add automated tests for every new feature they add to the product. Team members add to their definition of done that every new feature includes automated tests. Adding automation for each new feature isn’t easy, but it helps that team members were able to improve their test automation skills during the stop-the-bleeding phase. A team can remain in the stay-current phase indefinitely. Although they may still have a lot of debt, things are no longer getting worse. ✅ The final step is paying the debt down. During this catch up phase, team members systematically attack the technical debt. Sometimes this can be difficult, because longstanding debt can be hard to remove. However, even incremental progress will help. After all, the team has stopped the bleeding and is even staying current with automated tests for all new features. They are now moving in the right direction. We would obviously like to see debt paid down quickly, but that isn’t always practical given competing needs for the team’s time. Following these three steps can help reduce its over-reliance on manual testing. https://lnkd.in/gmBjk44y