At my previous company we wanted to launch an AI‑powered recommendation engine for our e‑commerce platform, but there was no clear product vision.

I was tasked with creating a compelling vision that aligned business goals, user needs, and technical feasibility.

I conducted market research, analyzed user behavior data, and ran workshops with engineering, data science, and sales teams. I drafted a vision statement focused on personalized shopping experiences that increase average order value by 15%. I presented the vision to senior leadership, secured budget, and created a roadmap with phased AI feature rollouts.

The vision was approved, the project launched on schedule, and within six months the recommendation engine boosted conversion rates by 12%, meeting our target and earning recognition in the company’s quarterly review.

Our AI platform had a backlog of feature ideas ranging from predictive analytics to natural language search.

I needed to prioritize features that delivered the highest ROI while staying technically feasible.

I built a scoring framework that weighed business impact, user demand, data availability, and implementation effort. I gathered input from sales, support, and engineering, scored each feature, and presented a prioritized roadmap to the product council.

The top‑ranked features generated a 20% increase in user engagement within three months, and the transparent process improved cross‑team trust.

During a pitch for a new computer‑vision feature, the executive team was unfamiliar with deep‑learning terminology.

My goal was to convey the technology’s value and risks in plain language to secure funding.

I created an analogy comparing the model to a photographer learning to recognize objects over time. I used visual aids showing before‑and‑after images, highlighted business outcomes (e.g., reduced manual inspection time), and addressed concerns about data privacy with simple compliance charts.

The executives approved a $1.2 M budget, and the feature launched three quarters later, cutting manual inspection costs by 30%.

We were developing an AI‑driven hiring tool that screened resumes automatically.

Ensure the product was fair, unbiased, and compliant with regulations before launch.

I led a cross‑functional ethics review, implemented bias detection metrics, sourced diverse training data, and added an explainability layer so recruiters could see why candidates were ranked. I also consulted legal to align with EEOC guidelines and drafted a transparency policy for candidates.

The tool passed internal audits, reduced time‑to‑hire by 40%, and received positive feedback from HR partners for its fairness and transparency.

Our competitor announced a new AI chatbot, and we needed to release a comparable feature within eight weeks.

Lead a team of engineers, data scientists, designers, and marketers to ship the MVP on schedule.

I set clear sprint goals, established a daily stand‑up, created a shared Kanban board, and prioritized critical path tasks. I facilitated rapid prototyping sessions, removed blockers by negotiating scope with marketing, and kept leadership updated with concise status reports.

We delivered the MVP in seven weeks, achieving 85% of the target functionality. The feature generated a 10% increase in user engagement in the first month.

After launching an AI‑based fraud detection system, senior leadership asked for measurable impact.

Define and track success metrics that reflect both technical performance and business value.

I established a KPI dashboard tracking detection precision, false‑positive rate, reduction in manual review hours, and cost savings. I set up A/B testing to compare against the legacy system and scheduled monthly reviews with finance and ops teams.

Within three months, the system improved detection precision by 18%, cut manual reviews by 40%, and saved $500 K annually, exceeding our targets.