How Bardeen and InConceptLabs revolutionized QA automation with AI.

I wanna share a proud moment of our partnership with Bardeen, but this post is more about how AI is changing the QA game. The case study is not very detailed for a Getting Started tutorial, but has the key elements there to understand the process. Here are some points it is worth to mention for the professional community: - AI is completely changing the QA Automation process, but 99% of teams and companies are still using the old way - QA Automation is no longer the automation test engineers pure responsibility, it requires more versatile skillset (AI engineering, prompt engineering, MCP development, DevOps, Solution Architecture) - While the all AI tools are accessible for everyone, bringing them together and making to work is still not straightforward To achieve significant (5 to 10x) productivity gain on automated tests generation it took 12 months, endless experiments from one of our strongest development teams to setup and fine-tune the process. Thanks to Bardeen team, to help with their AI expertise and push as hard to implement which seems even impossible. #inconceptlabs #ai #qaAutomation

Developers hate waiting on QA. Testers hate chasing unstable builds. But what if AI could make both sides happy? 🤔 At QA Bolt, we’re seeing teams cut regression time by up to 70% using intelligent test agents that learn from product behavior. These agents don’t just follow scripts, they make testing smarter every sprint. The question isn’t if AI will transform QA... it’s when will your team adapt? 🔗 See how it works: www.qaboltservices.com What’s your biggest bottleneck in testing right now? #AI #SoftwareTesting #Automation #Innovation #DevTools

Smart QA is evolving the way we approach quality. It’s not only about executing test cases but about building intelligent strategies that prevent defects early. By blending automation, AI, and data-driven insights, QA teams can predict risks, optimize test coverage, and accelerate delivery. This shift allows organizations to release faster without compromising reliability. Smart QA is where efficiency meets innovation — and it’s becoming the backbone of modern software engineering. 🚀 #SmartQA #Automation #AI #SoftwareTesting #QualityEngineering

QA is changing fast. AI is now writing test cases, predicting bugs, and automating regression runs. But it’s not replacing testers — it’s changing what good testers do. The real skill now is knowing how to use AI tools without trusting them blindly. QA isn’t about clicking buttons anymore. It’s about strategy, data, and user impact. Adapt fast, or get left behind. #QA #SoftwareTesting #Automation #AIinTesting #QualityAssurance

🔍 GenAI Is Redefining the Future of QA — and It’s Time We Evolve With It Quality Assurance has always been at the heart of product excellence. But as software complexity grows and release cycles shrink, the expectations from QA engineers are evolving — and Generative AI (GenAI) is accelerating that evolution faster than ever. The role of QA is no longer limited to executing test cases or finding bugs. With GenAI, we now have the tools to amplify our capabilities, speed up workflows, and focus on what truly matters — delivering value and quality at scale. Here’s how GenAI is transforming the QA landscape: ⚙️ From Manual Creation to Intelligent Generation: AI can convert requirements or user stories into comprehensive test cases in seconds — covering not just the obvious but also potential edge cases we might miss. 🔧 From Flaky Automation to Self-Healing Tests: AI-driven frameworks can understand application context, automatically update broken locators, and adapt to UI changes — drastically reducing maintenance overhead. 📊 From Data Overload to Actionable Insights: GenAI can analyze logs, summarize complex issues, and even predict potential quality risks — enabling QA teams to focus their efforts where the impact is highest. 🧠 From Reactive Testing to Predictive Quality: With historical data and user patterns, AI models can highlight high-risk areas before issues occur — shifting QA from detection to prevention. But here’s the key insight: AI won’t replace QA engineers — those who embrace AI will replace those who don’t. To stay ahead, QA professionals must evolve: ✅ Become Quality Strategists, not just Test Executors. Focus on understanding user impact, business value, and risk prioritization. ✅ Invest in AI Literacy and Prompt Engineering. The better we communicate with AI, the more powerful it becomes in our workflows. ✅ Embed AI into the QA lifecycle. From planning and automation to exploratory testing and reporting, use AI to augment every step. The future of QA belongs to those who combine human intuition with AI intelligence — delivering quality faster, smarter, and with more impact than ever before. Are you already integrating GenAI into your QA strategy? I’d love to hear how it’s transforming your approach. 👇 #GenAI #QualityEngineering #AIinQA #FutureOfQA #Automation #PromptEngineering #SoftwareTesting

🚨 “Thanks to AI, we’re laying off our QA team this year.” That’s what I told my team last week… 🙈 But here’s the truth 👇 AI is now: ✅ Generating test cases ✅ Writing automation scripts ✅ Suggesting fixes So… do we still need QA professionals? Or is AI the new tester? Here’s what the latest research & industry voices reveal: 1️⃣ AI boosts speed, not quality Yes, companies ship faster. But nearly half lose $1M+ from outages when humans aren’t in the loop. 2️⃣ More AI = more testing, not less Tech keeps expanding → more scenarios to test. AI can handle boilerplate, but only humans define what “quality” really means. 3️⃣ AI lacks context & judgment It can create 100 test cases in seconds… for the wrong system (true story: Gmail tests in a product with no Gmail). Business logic, critical thinking, empathy → still human-only. 4️⃣ The future is collaboration, not replacement By 2025, 40% of IT budgets will include AI testing. That means more automation of repetitive tasks AND more demand for QA engineers to guide, review & validate AI output. 💡 Reality check: we didn’t fire anyone. AI won’t kill QA — it will elevate it. The QA engineers who thrive will be those who treat AI as a co-pilot, not a competitor. 👉 Our new QA Automation cohort starts soon. Drop “one on one” in the comments, and I’ll personally give you a free consultation. Now I’m curious — would you trust AI alone to sign off on a production release? #QA #Automation #AI #SoftwareTesting #QualityEngineering

🚀 AI is reshaping how we think about production releases in QA. Traditionally, QA has been the final gatekeeper—focused on defect detection and regression cycles. But with AI, we’re moving into a new era: predictive, proactive, and production-aware QA. Here’s how AI is changing the game: 🔹 Predictive Risk Analysis – AI models analyze historical defects, code churn, and commit history to flag high-risk areas before release. 🔹 Smart Test Optimization – Instead of running every regression test, AI selects the most impactful cases based on change impact and past failures. 🔹 Real-Time Anomaly Detection – During rollout, AI tools monitor logs, performance metrics, and error spikes to catch issues before users notice. 🔹 Release Confidence Scoring – Teams now use AI-driven dashboards that assign a “confidence score” to each release candidate, helping leaders make data-backed go/no-go decisions. This shift means QA is no longer just about finding bugs—it’s about building trust in every release. 👉 Imagine a world where production releases are not stressful “big bang” events, but predictable, data-driven milestones. That’s the future AI is enabling. How is your team leveraging AI in QA today? #AIinQA #ProductionReady #QualityEngineering #PredictiveQA #DevOps #Automation #TechLeadership #TechLeadership #Innovation #FutureOfQA #DigitalTransformation #SoftwareQuality.

🤖 Benefits of AI in QA 1. Smarter Test Coverage - AI analyzes code changes and user behavior to identify high-risk areas. - Ensures broader, more relevant test coverage with fewer blind spots. 2. Predictive Defect Detection - Learns from historical bugs and test results. - Anticipates where issues are most likely to occur before they surface. 3. Self-Healing Automation - Test scripts adapt automatically when UI elements or workflows change. - Reduces maintenance effort and false negatives. 4. Faster Root Cause Analysis - AI clusters failures, traces logs, and pinpoints probable causes. - Cuts down debugging time significantly. 5. Intelligent Prioritization - Not all bugs are equal—AI ranks issues by severity, user impact, and release criticality. - Helps teams focus on what matters most. 6. Continuous Learning & Adaptation - Every test cycle improves the AI’s accuracy. - QA evolves alongside the product, becoming more efficient over time. 7. Efficiency & Cost Savings - Less manual intervention, faster cycles, and reduced rework. - Frees QA teams to focus on exploratory and strategic testing. #AIinQA #AutomationTesting #QualityEngineering #DevOps #PredictiveQA #TechLeadership #QAInnovation #LinkedInTech

“QA is becoming irrelevant.” I keep hearing this. GenAI writes code, automates tests, and executes faster than ever. But here’s the truth no one talks about: QA isn’t dying. It’s evolving. It’s leveling up. ✅GenAI can create. ❌ But it can’t care. ✅It can automate. ❌But it can’t own accountability. ✅It can optimize. ❌But it can’t guarantee trust. ✅AI can hallucinate. ❌QA can’t. We’ve moved far beyond ticking boxes. QA today is about validating trust, safety, and ethics. It’s about building guardrails for intelligent systems — not just testing features. We’re not here to compete with AI. 👉 We’re here to lead with it. We’re becoming 'AI Quality Strategists' — shaping how intelligent systems behave in the real world. If you’re in QA, this isn’t the end. This is your moment to lead. Step up. Redefine quality. Shape the future. 💬 What’s your take — is QA evolving fast enough to keep up with GenAI? #QA #GenAI #QualityEngineering #SDET #ResponsibleAI #TechLeadership #Automation #AIagents #FutureOfTesting #Innovation #Leadership

🤖 AI Testing Roadmap: The Future of QA 🚀 The world of Software Testing is rapidly evolving, and AI is at the heart of this transformation. Here's how testers can level up step by step: 🔹 Foundation: Learn AI Basics 🧠 Understand Data Fundamentals 📊 Get hands-on with Setup Tools 🛠️ 🔹 Automation, Integration & Optimization: Use Predictive Analytics to identify bugs before they happen Create Synthetic Data for smarter, faster testing Enable Automated Execution for seamless workflows 🔹 Advanced AI in QA: Build Self-Healing Tests 🩹 that fix themselves Integrate with CI/CD Pipelines for continuous delivery Aim for Fully Autonomous QA – where AI runs the show! 🌟 The journey is clear: start small, grow smart, and let AI take your QA to the next level. 💡 #AITesting #QualityAssurance #TestAutomation #SoftwareTesting #FutureOfQA #DevOps #ContinuousTesting