How has AI transformed your daily work compared to 1 year ago?

A year ago, I was focused on traditional test automation, but today AI is at the core of my workflow. It has completely transformed how I handle large-scale tasks, like using AI-driven workflows to migrate a legacy Cucumber/JS test framework to Playwright/TS. It has also pushed me beyond testing into building autonomous AI agents for complex tasks, like automated job matching using portfolio reasoning. Mastering prompt engineering has been the ultimate game-changer for my daily productivity!

A year ago, I was executing tasks. Today, I’m directing outcomes.

AI hasn’t just improved my speed it has removed the invisible friction in my workflow.
As a QA professional, what once took hours test creation, debugging, and analysis now begins in minutes.

But the real transformation isn’t automation.
It’s elevation.

I’ve moved from writing tests to designing quality.
From chasing bugs to anticipating risks.
From manual effort to strategic thinking.

AI didn’t replace my role it refined it.
It pushed me to focus on what truly matters: judgment, clarity, and impact.

The biggest shift?
I no longer spend most of my time doing the work.
I spend it deciding what work is worth doing.

Thanks,

Ramanan

Earlier testing, writing test cases, reviwing was taking the time. Understanding the domain knowlege was taking time. 

After different AI came in market  following things help speed up. It helped 50-60% operation on day to day basis. The task which was taking 3-6 hours for basic things now same task can be done in 1-2 hours. 

1. Writing down test cases, reviewing test case
2. Understanding the PRD, BRD document. If anything is not understand some part can check with AI. 
3. Writing business emails, Client communication
4. Takiing meeting notes and creating the MOM 
5. Github copiolet and chatGPT for writing the automation faster way. Writing the codes for frontend UI like PLaywright script, REstassured framework for API testing. 
6. Generating the PPT for business / client meeting or demo its easy and faster way.
7. Created the custom GPT for domain knowlege agent. Where I dont need to connect with project manager or scrum master for questions. Can simply ask to custom GPT and get all answers with example. 
8. Review PR of collegue made easy. If there are improvement of security related concerns using AI can be done faster. Also important code quality for writing the better code.
9. Huge help in coding, automation done faster way

Keeping AI as my Assistant. He always works what I instruct.

AI helps generate smarter test scenarios, prioritize high-risk areas, detect anomalies earlier, and improve defect prediction accuracy. In testing, this means less effort spent on repetitive regression cycles and more focus on exploratory validation, edge-case analysis, and quality strategy. Overall, AI has increased productivity, reduced turnaround time, and enabled more proactive rather than reactive testing.

AI has shifted daily work from manual, repetitive execution toward faster, insight-driven decision-making. Tasks that once required significant time—such as drafting test cases, analyzing logs, identifying defect patterns, and maintaining automation scripts—are now accelerated through AI-assisted tools.

A year ago, most of my work as a QA engineer was execution-heavy and manual testing, writing test cases from scratch, coordinating with devs for clarifications, and spending a lot of time just understanding requirements.

Today, AI has shifted my role from execution →  thinking.

Here’s what has actually changed for me:

1. Test design is faster, but deeper

Earlier, writing test scenarios was time-consuming and often limited by how much I could think manually. Now, I use AI to generate first-level scenarios quickly, and I focus more on refining edge cases, business risks, and real user behavior.

2. Requirement understanding improved significantly

Instead of going back and forth with PMs/devs repeatedly, I now break down requirements with AI identifying gaps, unclear logic, and missing validations upfront. This has reduced back-and-forth and improved my confidence before testing even begins.

3. Faster debugging and root cause thinking

When I hit a bug now, I don’t just report it, I analyze it better. AI helps me validate assumptions, explore possible causes, and even simulate scenarios I might have missed earlier.

4. Documentation and communication became stronger

From writing bug reports to drafting BRDs and even LinkedIn posts—AI has improved how I communicate. I spend less time framing sentences and more time focusing on clarity and impact.

5. Shift toward system-level thinking

I’ve started thinking beyond “feature testing” into flows, integrations, and architecture (APIs, AI pipelines, validation layers, etc.). AI has helped me understand areas like Node.js systems, data flow, and AI integrations—even without a hardcore dev background.

6. Productivity increased, but expectations also increased

AI didn’t just make things easier, it raised the bar. Now the expectation is faster delivery, better coverage, and more ownership.

7. Increased visibility and cross-functional trust

With AI improving both my speed and quality of output, my work became more visible to leadership. This led to being considered for PM-level responsibilities, where I’m now contributing not just in testing, but also in requirement thinking, prioritization, and overall product direction.

In short:
AI hasn’t replaced my work, it has changed where I add value.
Less time on repetitive tasks, more time on thinking, analysis, and decision-making.

A year ago, my day was largely defined by the "grind"—the repetitive, manual tasks that are necessary for quality but leave little room for high-level thinking. Today, AI has effectively become my "Chief of Staff," allowing me to shift my focus from simple execution to strategic quality engineering.

Here are the four key ways my daily work has evolved:

• From Blank Pages to Refined Strategy Last year, starting a new test plan or requirement analysis meant staring at a blank screen for an hour. Now, I use AI to generate "Version 0.1" of test scenarios, bug reports, and even Gherkin scripts. I no longer spend time on the boilerplate; instead, I spend my energy on edge-case analysis and identifying business risks that a model might miss.

• Bridging the "Domain Gap" Faster I used to spend hours (and many Slack messages) chasing down Product Managers to clarify complex PRDs. Now, I feed those documents into a custom GPT or LLM to identify logical gaps and contradictions before the first meeting. It has made me much more proactive—I’m showing up to meetings with "probing questions" rather than just asking "what does this mean?"

• The 10x Developer/Tester Mindset Coding automation scripts used to be a 4-hour task. With AI Code Assistant, that same task takes few minutes. More importantly, it has lowered the barrier for me to explore new areas like Playwright or complex API frameworks that I previously didn't have the "bandwidth" to learn. I’m no longer just a tester; I’m building custom utilities and interactive data dashboards that provide real visibility to leadership.

• CAAS (Communication as a Superpower) Documentation used to be the "least exciting" part of the job. Now, AI helps me turn messy meeting notes into polished MOMs (Minutes of Meeting) and converts technical jargon into clear, influential bug reports that tell a story. This has significantly increased the trust and visibility I have with stakeholders—they see the impact of my work much more clearly now.

The Bottom Line: Compared to a year ago, I’m working smarter, not harder. AI hasn't replaced my judgment; it has cleared the "noise" so my judgment is the only thing I have to focus on. I’ve regained about 20-30% of my day, which I now reinvest into exploratory testing and system-level thinking.