What kinds of AI tools are you using daily in your software development? And how are those AI tools changing your daily work?

Accelq with Gen AI, Playwright with MCP, GitHub Copilot → Its helping me to speed up my work significantly. 

I’m using Claude and chat gpt. I would use ChatGPT to make jira tickets that where formatted to my company template. And for Claude I would use it to make test apps to practice cypress and playwright automation.

I’m using Copilot, Claude code 

Github copilot, Claude 

1. GitHub Copilot - For writing Automation scripts using Playwright MCP server.
2. ChatGPT/Claude/Gemini/Perplexity - For content creation and messages refinement.
3. Atlassian Rovo - Summarize the requirements that are stored in confluence page and also converting testing requirements to Test cases.
4. Google Antigravity - Exploring the new IDE apart from VS code that I am using.

What I use it for:

Writing test automation scripts (Playwright, Selenium)
Generating test data
Creating API test cases
Writing utility functions

Also I am using below AI tool specific to Salesforce applications which comes out of the box.

Salesforce Einstein / Agentforce

What I use it for:

Testing AI-powered features in Salesforce

Understanding Data Cloud insights

Validating Einstein predictions

Testing Agentforce agent behaviors

Daily, I interact with multiple classes of AI—each serving a different cognitive role rather than a task:

• Generative AI for acceleration
  Used for drafting test scenarios, release notes, risk narratives, and architectural options. This saves time—but time saving is the least interesting benefit.
• Analytical / reasoning models for decision pressure
  I use AI to challenge assumptions:
  “What breaks if this release slips?”
  “What hidden coupling are we ignoring?”
  “Where is quality being assumed instead of engineered?”
  This has reduced opinion-based decisions and exposed blind spots earlier in the cycle.
• AI-assisted QA & release intelligence
  Instead of testing features, I increasingly test behaviors, signals, and system resilience.
  AI helps surface risk patterns across logs, test failures, and historical release data—changing QA from gatekeeping to early warning systems.

But the real shift isn’t what AI I use.
It’s how my daily work has changed.

Before AI:

• I spent time producing artifacts
• I moved linearly: design → build → test → release
• Quality was something we verified late

Now:

• I spend time orchestrating outcomes
• Design, validation, and release thinking happen simultaneously
• Quality is a continuously adapting property of the system

AI has collapsed phases.

What used to be:

“Think → document → hand over → fix later”

Is now:

“Model → simulate → adapt → ship with confidence”

So my day is no longer about:

• Writing more
• Testing faster
• Using more tools

It’s about:

• Making better decisions earlier
• Designing systems that self-correct
• Shifting teams from execution to intentional engineering

AI didn’t make me faster.
It made my thinking compound.

And that’s the difference between using AI
and working in an AI-native operating model.

— Umanga Buddhini

I use Claude 

GitHub Copilot and the Continue plugin for VS Code with different MCPs on a daily basis. Copilot helps me quickly generate boilerplate code, suggest implementations, and speed up repetitive tasks. 

As an SAP QA specialist focused on Enterprise Continuous Testing (ECT), my perspective on AI goes beyond simple code generation. My daily "stack" is a fusion of Google Gemini Advanced (as my cognitive partner) and the embedded AI capabilities within the Tricentis suite.

​Here is how these tools have fundamentally shifted my workflow:

​1. From Scripting to Predictive Quality Architecture:

I don't just use LLMs to write automation scripts; I leverage them for "Preventive Risk Analysis." In complex SAP environments, I feed intricate logs and transport change manifests into AI models to identify potential integration failures within our Jenkins pipelines before execution. This has shifted my focus from fixing broken tests to preventing them.

​2. Project Astra (My Digital Twin):

I am actively developing a concept called "Testing with a Digital Twin" (Project Astra). Rather than manually executing repetitive scenarios, I’m training an AI agent on my own behavioral patterns and domain knowledge to conduct autonomous Exploratory Testing across our ERP modules.

​The Result: AI has transformed my role from a "Test Executor" to a "Quality Orchestrator." I no longer just hunt for simple bugs; my digital twin and I hunt for systemic bottlenecks at an enterprise scale.

Copilot for day-to-day activity (which can plug in Playwright,VSCode,GitHub)