#EngineeringIntelligence: How Women at BrowserStack are Redefining the AI-Augmented Engineering Era | INWED 2026

My favorite AI use case right now as a product leader is using it as a strategy sparring partner grounded in real data.

The hardest part of the AI shift is discerning what’s actually true and figuring out where to place our bets. AI has compressed that strategic loop for me from weeks to a single afternoon.

I use it day to day to pressure-test strategies — turning a rough thesis into a defensible, evidence-backed point of view, with counter-arguments surfaced rather than hidden. It helps ground decisions in data, moving from a sharp question to an answer the same afternoon instead of the next sprint. It also keeps me current, synthesising what practitioners are genuinely learning about AI in the software lifecycle, so strategy reflects reality, not hype. It also helps turn research into artifacts — diagrams, one-pagers, and prioritization work ready to be put in front of leadership.

Outside of work, I use AI to buy back time—minimizing the hours spent planning and researching so I can focus on making memories with my family.

AI has made judgement faster and better-evidenced. And that is exactly what #EngineeringIntelligence means to me: using intelligence to make sharper engineering-product decisions, and building that same execution intelligence into the product.

This International Women In Engineering Day, the theme #EngineeringIntelligence made me reflect on how my relationship with AI has evolved. Most conversations about AI in engineering focus on writing code faster. For me, the real shift has been somewhere else entirely.

I'm an engineering manager leading 18 engineers across multiple projects at BrowserStack; two of them critical, parallel migration programs. On any given day regression cutoffs, blocked developers, shifting milestones and leadership updates, all compete for the same few hours. AI is what helps me hold all of it in view at once.

Instead of drowning in context-switching, I use it to keep a bird's-eye view across the work: surfacing regression readiness before every cutoff, flagging which developers are blocked or drifting, tracking milestone progress across the migrations, and turning scattered signals across Jira, Slack and our dashboards into clear decisions and crisp status updates.

It doesn't make the work easy — nothing does. What it does is buy back my attention from the busywork, so I can spend it on the parts that actually need a human: coaching my team, unblocking people, and making the harder calls.

That's what Engineering Intelligence means to me — not replacing engineering judgment, but clearing enough noise that there's room to use it well.

To every woman building in tech: the judgment is the part that's yours. The tools just give you more room to use it.

My favourite AI use case is using it as a thought partner that helps me navigate the many hats I wear as an engineering manager and beyond.

At work, I rely on AI for everything from architecture discussions and communication to accelerating workflows and reducing repetitive effort. Seeing how AI can free up engineers to focus on higher-value problems is something I find incredibly exciting.

In my personal life, I enjoy using AI to bring more structure to everyday routines. One simple but surprisingly impactful example has been building recurring reminders and meal-planning workflows for my son's tiffin. It takes away the constant mental overhead of figuring out "what's for tomorrow?" and helps me stay organized.

What I appreciate most about AI is that it gives me back time and headspace. Whether it's enabling teams to move faster or helping me manage daily life more efficiently, I see AI as a powerful enabler—one that amplifies human creativity and productivity.

As a Quality Engineering Manager, AI is deeply embedded in my day-to-day work to help my team move faster, make better decisions, and focus on what matters most—shipping high-quality products.

It has drastically condensed our timelines—reducing production defect analysis from days to mere hours and cutting test automation efforts down to a half-day.

Beyond the metrics, it’s a brilliant thought partner that I use throughout the day. It helps in brainstorming test scenarios, outlining project plans, structuring stakeholder communication and more.

I also apply this structured optimization outside of work; during a recent home construction project, I used AI to analyze material quality, estimate costs, and build negotiation strategies with vendors.

While AI has become everyone's new friend, I believe in using it thoughtfully and purposefully. The more experience you have, the more powerful AI becomes as a strategic partner. It isn't about replacing expertise—it's about amplifying it.

As an engineer, one of the most exciting changes I've witnessed is how AI has transformed the way we learn, connect ideas, and solve problems.

My favorite aspect of using AI is its ability to provide instant context and accelerate understanding.

Whether I'm onboarding to a new architecture, exploring an unfamiliar technology, or connecting complex concepts across systems, AI helps bridge knowledge gaps and enables me to contribute more effectively and confidently. Tools like GitHub Copilot and Claude have helped immensely in this regard.

As a working mother, AI has also become incredibly valuable beyond the workplace. It serves as a learning companion in my parenting journey, helping me better understand my son, discover insightful books and videos, and explore evidence-based techniques that support his growth and development. More importantly, it helps me reflect on how I can grow alongside him—balancing the demands of career and family while remaining intentional in both roles. My absolute favorite find in this journey has been the "The Power to Choose" book series for kids.

My favourite AI use case isn't one tool — it's getting an entire team (and beyond) to actually use AI well.

As someone helping lead the AI adoption on the Test Management team while also being a Full-Stack Engineer, I get to weave AI across our day-to-day engineering, from writing frontend optimized code and tests to drastically reducing API turnaround times and speeding up how we resolve issues. The part I'm proudest of is leading a company-wide Developers Round Table (DRT), where I walked engineers through the AI tools and MCPs (Model Context Protocol integrations) we can plug into our workflows — and showed, with real examples, how we can cut down the tedious parts of our process so we can focus on the actual engineering.

A lot of this instinct comes from the hackathon circuit. Winning 16 hackathons over the years, including BrowserStack's own AI-First Initiative Hackathon, has instilled in me a "build fast, use every tool available" mindset. And that is exactly what I try to bring to the team. During BrowserStack's hackathon, we built and released an end-to-end workflow for production tickets in 24 hours, that handled initial triage, team assignment, and even provided a fix in the form of a PR without a single manual intervention, cutting down human effort by 70–85%.

Demystifying AI for other developers has easily been the most rewarding part of this journey.

When I started out in QA automation, the part of the job I dreaded wasn't writing tests — it was everything around it. Our Live and App Live regression work is scattered across several repositories: the test framework in one, the app code in another, shared helpers in a third (and my sanity somewhere in a fourth that I never found).

Before I could automate anything, I'd spend hours hopping between repos like a detective with too many tabs open, trying to reconstruct how a feature actually behaved versus how it claimed to behave. The real engineering was buried under a mountain of "wait, where does this function even live?"

My favorite AI use case rescued me from that mountain. And honestly, calling it a "pair-programmer" undersells it — it's more like an autonomous teammate who has read the entire codebase and, unlike me, remembers all of it. I hand it a whole task and it does the unglamorous legwork — reading across all those repos, tracing how a feature works, pulling in the right helpers — and comes back with working automation for me to review. What's left for me is the fun part that actually needs a human: deciding what's worth testing, hunting edge cases, and the judgment calls. The work feels lighter — and a lot more like thinking again (and a lot less like crying into a stack trace).

It doesn't stop at work, either. At home I've leaned into the same idea — small automations to offload the mental clutter. My favorite is using AI to plan my week: I dump in my chaotic to-do list, deadlines, and the seven reminders I set and ignore, and it hands back a calm, prioritized plan — like a personal assistant who is more organized than I am (and judges me only slightly).

So, if I could tell one woman thinking about engineering one thing, it'd be this: AI isn't here to replace the curiosity that got you into this field. It just clears the noise — and the 47 browser tabs — so you can spend more time on the parts you actually fell in love with.

My favorite way to leverage AI is actually as a challenger rather than an assistant.

I frequently ask AI to critique my thinking, disagree with my proposals, find gaps in my code, push back against my technical reasoning, surface missed edge cases and more. It forces me to think more critically and refine my engineering and problem solving approach.

This knack for optimization translates into my personal life, too. I recently returned from maternity leave and my younger daughter has just started solids. I use AI to map out meal plans and double-check that we are covering the right nutritional needs through the week.

It’s incredible how the same technology that sharpens my engineering logic can also seamlessly make everyday parenting decisions a little easier.

AI has become an essential multiplier across both my professional and personal life.

At work, I leverage GitHub Copilot to quickly build sample repositories for customer demos, event showcases, and debugging complex technical issues. I’ve built custom Gemini Gems for account intelligence gathering, Mutual Success Plan creation, research, and other recurring workflows. I also use AI regularly to analyze customer feedback, identify trends, and accelerate problem-solving.

NotebookLM has become my go-to companion for continuous learning. The Audio Overview feature helps me convert lengthy documents, reports, and research material into bite-sized audio summaries that I can consume while walking, commuting, or doing household chores.

I'm currently experimenting with Claude workflows to automate day-to-day tasks and improve productivity as well. I am still early in the journey, but excited about the possibilities.

Beyond productivity, AI has given me a fun and creative outlet too. I used ChatGPT, DALL·E, and Canva to create personalized invites, games, and creative content for a friend's wedding. It turned out to be a huge hit and reminded me how creative AI can be.

What I love most is that AI hasn't replaced any part of my job—it has helped me become more efficient, learn faster, and spend more time on work that truly requires human judgment, creativity, and deep collaboration.

One of my favourite AI use cases is how it helps me be a better problem-solver and communicator in my role as a Customer Engineer.

Navigating a vast landscape of testing frameworks, automation tools and customer environments means my days are packed with complex issues, log analysis, escalations, and cross-functional meetings. AI acts as a collaborative assistant that optimizes my entire workflow.

Managing back-to-back conversations while ensuring that key details, decisions, and action items are captured can be challenging. Post-meeting, I feed my raw notes into AI to instantly summarise issues, extract action items, identify blockers, suggest logical next troubleshooting steps, and draft polished customer follow-ups. It helps to refine customer communications, create documentation, summarize discussions, and even prioritize work effectively. It’s also incredibly effective for digesting historical contexts like long email threads, previous issues or support tickets, and account handovers. It helps me quickly understand the background, identify important details, and get up to speed faster.

What excites me most is how AI amplifies engineering thinking. By stripping away the administrative overhead of customer engineering, AI allows me to dedicate my energy to deep collaboration, problem-solving and driving better outcomes for our users.

In my role, AI serves as both a troubleshooting partner and a technical mentor.

When debugging complex customer issues, I treat it as a sounding board to explore hypotheses, validate my thought process, and uncover troubleshooting paths I might have missed. It helps me look at problems from different perspectives while still relying on my own technical judgment to reach a solution.

It is particularly powerful when dealing with massive log volumes, like Appium or device execution logs. Instead of manually scanning thousands of lines, I use Gemini Pro to rapidly isolate failure points, allowing me to focus entirely on root-cause investigation. I’ve even built custom Gemini Gems based on historical resolutions to fast-track solutioning. The gems help me quickly ground with past learnings, eliminate known possibilities, and speed up troubleshooting when similar issues arise.

Ultimately, AI doesn’t replace my technical judgment; it gives me the space to think deeper and solve problems faster.