Description

Most teams I work with know they need AI. The harder question is where to actually start — and what will stick beyond a proof of concept.

I help enterprise and mid-size teams figure that out and then build it. Workflows, automation, decision tools, and the training to make it land with real teams.

What I do

Design AI workflows for sales, ops, marketing, customer, support, and decision-making
Help leadership teams cut through 20 use cases and pick the right one to start with
Build AI products and MVPs
Train corporate teams to understand, adopt, and lead AI — hands-on, not theory

What I've delivered

Built AI pre-sales intelligence for a B2B sales org — one view of deals before proposals go out
Designed an AI decision layer for a 1000+ SKU wholesale business — teams stopped arguing about data and started making calls
Helped a BFSI leadership team sequence 20 AI use cases by risk and readiness — started with retention, not fraud
Built conversational AI for customer support across global digital platforms
Trained teams across industries

Background

20+ years in enterprise tech and digital platforms
Director at VMware, ran a 200+ person engineering org
BFSI, SaaS, MarTech, Customer, Data and enterprise platforms

Languages

English
Native or bilingual

Workplace preferences

Remote only

Primarily works remotely

BFSI Exchange Dubai
AI Strategy, Product & Automation Consultant
TECH
October 2025 - December 2025 (2 months)
Shifted Leadership Thinking from “What AI Can Do” to “Where AI Should Start”

In one BFSI engagement, the leadership team initially identified nearly 20 potential AI use cases and was keen to start with fraud detection, given the nature of the organization. Before committing, we stepped back and evaluated the options more holistically — not just from a data availability standpoint, but also considering trust, risk tolerance, change impact, and how AI-driven decisions would be perceived by customers and internal teams.
Given the high cost of false positives, low tolerance for error, and the reputational risk of early AI missteps in fraud scenarios, we collectively agreed not to begin there. Instead, we decided to first productionize a customer retention use case, where data was already well-structured, AI outputs could remain advisory, and there was greater room for learning and risk tolerance.
Fraud detection was done as a parallel proof-of-concept, with results monitored over time and models refined iteratively based on false positives.
Following this, we laid out a medium-term plan for the remaining use cases and revisited them gradually as confidence and governance matured.
AI Decision Automation (Dashboards, Insights, Agents) AI Product & MVP Development Product Strategy
Wholesale Business (1000+ SKUs)
AI-Powered Decision Intelligence for Product & Category Teams
DIGITAL AND IT
August 2025 - December 2025 (4 months)
Description

Used AI to simplify decision-making across a large product portfolio (~1000+ SKUs) where teams had data but struggled to align on actions.

What I did
Designed AI-supported decision layer across product categories
Applied consistent logic across data to avoid fragmented analysis
Enabled explainable insights (pricing, positioning, demand signals)
Outcome
Shifted discussions from “data reconciliation” → “decision-making”
Reduced noise across teams and aligned leadership view
Enabled faster decisions on discontinue / reposition / prioritize
AI Process Automation (Sales, Ops, Hiring) AI Product & MVP Development AI Workflow & Use Case Design AI Decision Automation (Dashboards, Insights, Agents) Leadership Development
Global SaaS Company
Conversational AI for Customer Support & Self-Service
July 2025 - October 2025 (3 months)
Description

Transformed rule-based chatbot systems into conversational AI experiences across digital platforms to improve customer support and self-service.

What I did
Designed conversational AI workflows based on real user queries
Prioritized rollout using traffic and query patterns
Built governance model with human-in-the-loop validation
Outcome
Improved customer self-service experience
Reduced dependency on manual support and external vendors
Scaled support across global digital platforms
Digital Transformation AI Decision Automation (Dashboards, Insights, Agents)