AI in Automation
What AI Can Do, What Testers Must Do
Partnering with AI in Automation
Ronak Ray
Vice President of QA & AI Strategy — Forbes
Speaker Notes: "AI is transforming how we test software — but transformation doesn't mean replacement. Today, I'll talk about where AI truly adds value and where testers remain irreplaceable."
Why This Talk, Why Now?
The New Reality:
- AI is flooding software engineering.
- Testing is under immense disruption.
- 66% of DevOps teams are using or plan to use AI within 12 months.
Myth: AI replaces testers.
Truth: AI empowers testers who evolve.
Speaker Notes: "This isn't a future trend—it's happening right now. The central question isn't 'Will AI take my job?' The real question is 'How do I partner with AI to do my job better than ever before?' We've seen this before — recorders didn't replace musicians, spreadsheets didn't replace accountants. AI won't replace testers; it will amplify them."
The Grand Shift in Software Testing
⚙️ Manual
⚙️ Automation
🧠 AI-Augmented QA
Deterministic
Probabilistic
Core Message:
We're moving from brittle scripts to intelligent, adaptive systems. As building gets faster, validation gets harder, and testing becomes the deterministic checkpoint in a non-deterministic world.
Speaker Notes: "For years, we've dealt with rigid, fragile scripts that demand constant maintenance. That era is ending. When AI writes code instantly, our bottleneck becomes validation. We're in the middle of a paradigm shift toward intelligent systems."
Part 1
What AI Can Do Well Today
The AI Arsenal: Augmenting the Testing Lifecycle
Intelligent Test Generation
Parse requirements from Jira/Figma to auto-generate test cases.
Self-Healing Automation
Adapts to UI changes in real-time, reducing script maintenance.
Predictive Defect Analysis
Analyzes historical data to predict bug-prone areas.
Advanced Visual Validation
Computer vision detects meaningful UI regressions.
Synthetic Test Data Generation
Creates realistic, privacy-safe data at scale.
Test Optimization
Identifies redundancies and optimizes scheduling.
Speaker Notes: "Think of AI as an arsenal of specialized tools. It can read our user stories and draft test cases. It tackles the nightmare of script maintenance. It acts like a fortune teller, pointing to risky code areas. It validates UIs with human-like nuance and generates complex test data without exposing sensitive information."
Deep Dive: Why Self-Healing is Your Best First Step
The Problem
Traditional scripts are fragile. A minor UI change breaks them, creating a huge maintenance burden.
The AI Solution
AI watches for changes during test runs. When an element's locator is modified, AI finds the new one and allows tests to continue without manual intervention.
The Business Case
- Measurable reduction in engineering hours.
- Immediate and undeniable ROI.
- Builds crucial trust in AI.
- Funds further investment.
Speaker Notes: "If you're wondering where to start, start here. The constant pain of flaky tests is universal. Self-healing provides a clear, quantifiable win. You can tell your manager, 'We saved 50 hours this month on test maintenance.' That success story is your gateway to exploring everything else AI offers."
What AI Can't (Yet) Do
Understand business intent
Judge what matters vs what just passes
Detect UX friction or emotional quality
Handle ambiguity, bias, or ethics
Provide strategic direction
Speaker Notes: "AI doesn't understand why something matters. It predicts patterns — it doesn't assess impact or risk. An AI can confirm a button works, but only a human can determine if the overall experience is intuitive and delightful."
Part 2
What Testers Must Own
The Tester's Evolving Mandate: From Executor to Strategist
Repetitive
Exploratory
Execution
Strategy
Functionality
User Experience
Your New Role: The AI "Manager" & "Trainer"
Speaker Notes: "Our value proposition changes dramatically. We're no longer just executors. We are quality strategists. We're the explorers who go off-script. We're the guardians of user experience. Think of yourself as managing a highly efficient AI assistant — you provide strategic direction and domain knowledge; AI provides the speed."
What Testers Must Still Own
Critical thinking & risk assessment
Contextual prioritization
Validating results against business goals
Ethical and governance oversight
Domain expertise & business context
Speaker Notes: "Testers are still the conscience of quality — the ones ensuring that what passes tests truly serves users and business needs. Your domain expertise is your superpower in the age of AI."
The High-Demand Skillset for the AI Era
Technical Skills
- Prompt Engineering
- Data Literacy
Strategic Skills
- Domain Expertise
- Critical Thinking
Essential Mindset
- Adaptability
- Continuous Learning
Speaker Notes: "To thrive, evolve your skills. You don't need to become a data scientist. Learn to 'talk' to AI through prompt engineering. Become comfortable analyzing data. But most importantly, amplify what you already have: deep business knowledge and critical thinking."
Part 3
How to Partner Effectively
The "Paired Testing" Model: Human + AI
AI is the "Driver"
- Rapidly generates test scripts & data
- Executes thousands of tests in parallel
- Handles low-level technical details
Human is the "Navigator"
- Sets strategic direction & testing goals
- Observes AI execution & interprets results
- Uses expertise to investigate anomalies
Speaker Notes: "How does this partnership work in practice? Let's call it 'Paired Testing.' The AI is the driver, with hands on the keyboard, executing at incredible speed. We are the navigators, providing strategic direction, watching results, and making the final call on quality. This is continuous co-evolution."
Partnership Framework
AI Can Do
Tester Must Do
Generate tests
Validate relevance
Write automation
Ensure maintainability
Find anomalies
Interpret impact
Suggest next steps
Choose what matters
Key Insight: AI is like a junior tester — fast but inexperienced. It needs human mentorship and validation.
Speaker Notes: "Think of AI as a co-pilot, not an autopilot. It can optimize routes, but you still need someone steering. AI provides speed; humans provide wisdom."
Case Study: Parallelization at Scale (Forbes)
The Challenge:
A growing regression suite was becoming a major bottleneck in our CI/CD pipeline, slowing down releases.
The AI-Assisted Strategy:
We paired human strategy (Navigator) with AI execution (Driver) to optimize, prioritize, and run tests at massive scale.
🚀 60%Faster Runs
📈 5.7%Higher Velocity
✅ 100%Coverage Kept
Speaker Notes: "Let me make this concrete with Forbes. Our test suite was becoming a monster. We applied the Paired Testing model. As navigators, we set the strategy. The AI did the heavy lifting—optimizing, prioritizing, and running tests at a scale we couldn't manage manually. The result? Higher-quality software, delivered faster."
Part 4
Your Actionable Framework
Getting Started on Your AI Journey
1
Assess & Identify
Find your biggest bottleneck.
2
Start a Pilot
Pick one high-impact area for a quick win.
3
Build Data Foundation
Centralize test results, defects, and logs.
4
Train & Scale
Upskill your team and expand.
Speaker Notes: "You can start this journey next week. Find your single biggest pain point. For many, that's test maintenance. Run a pilot on self-healing. Prove the value. That success gives you momentum to build your data foundation, train your teams, and scale from there."
AI Maturity Model
AI-Assisted
Tool-level (Copilot, Gemini)
AI-Augmented
Integrated into pipelines (CI/CD Anomaly Detection)
AI-Integrated
Autonomous systems (Self-healing orchestration)
Recommendation: Start at assisted, progress toward integrated — don't jump straight to autonomy.
Speaker Notes: "The most successful teams start small. Pick a repetitive workflow, experiment, measure, and expand. Don't aim for full autonomy on day one."
Metrics That Matter
Key Point: Success isn't just about test pass rate — it's about measuring AI-human synergy.
Speaker Notes: "Track the right things. Don't just measure pass/fail rates. Measure the partnership's effectiveness."
Pitfalls to Avoid
Using AI as a shortcut for thinking
Speaker Notes: "The fastest way to fail with AI is to stop thinking critically. Use it as an accelerator, not an excuse. Clean data is essential. Start small and prove value before scaling."
Redefining the Tester
From Bug Hunter Quality Strategist
Coach AI, don't compete
Focus on system quality
Elevate judgment & ethics
Become an AI orchestrator
Speaker Notes: "The modern tester becomes an AI orchestrator — someone who ensures quality at scale, responsibly. We're moving up the value chain."
The New QA Formula
(AI Speed
Tester Judgment)
Quality at Scale
Your Path Forward:
🧭 Embrace augmentation
🔍 Redefine metrics
🤝 Build literacy & guardrails
🏁 Lead AI adoption
Speaker Notes: "This is the new partnership model — speed meets sense. That's the future of testing. We're not being replaced; we're being elevated."
Key Takeaways
AI is an Augment, Not a Replacement. The goal is a human-AI partnership.
Your Role Becomes More Strategic. Move from execution to strategy and user experience.
Partner Using "Paired Testing". Be the strategic "navigator" to AI's "driver."
Start Small, Prove Value, Scale. Begin with a focused pilot to build momentum.
Speaker Notes: "To wrap up: First, stop thinking about replacement and start thinking about augmentation. Second, recognize your value is shifting up the chain. Third, embrace the Paired Testing model. Finally, don't get overwhelmed. Start small, get a win, and build from there. The future of our profession is incredibly exciting."
"AI won't replace testers. But testers who embrace AI will replace those who don't."
✨ Thank You
Questions?
/in/ronakray
Speaker Notes: "Testing isn't disappearing — it's evolving. The ones who thrive will be those who partner intelligently with AI. Thank you, and I'm happy to take your questions."