About Prateek Bawa
Technical depth, product judgement and a commercial lens.
My background spans data science, software development and product management. So I tend to approach AI opportunities from three directions at once: what the evidence supports, what can actually be built, and whether the change is worth making.
Raised in India, shaped by twelve years in the UK, now working between both.
One career, four perspectives
Each stage of the career changed the question being asked.
Data science
What does the evidence support?
Understanding the information, assumptions and signals behind a problem — and being honest about what the data can and cannot tell you.
Software development
What can actually be built?
Understanding how systems really work: integrations, edge cases, failure modes and the distance between a demo and something a business can depend on.
Product management
What is worth building — and will people use it?
Understanding users, prioritisation, trade-offs and adoption. Enterprise product work at Accenture and Jaguar Land Rover, focused on cost savings, operational efficiency and revenue growth.
AI change — Underpin Works
Where is AI genuinely worth deploying?
Using all three perspectives at once: finding where AI can move a business, testing whether the change is worth making, and turning the opportunity into a working system.
Where the judgement was formed
Experience working with global enterprises and major infrastructure organisations — environments where systems matter, mistakes are expensive and change has to earn its case.
Point of view
We don't start with tools. We start with where AI can move the business.
AI should be deployed where the foreseeable business value justifies the cost and complexity of changing the workflow. If the business case isn't strong enough, I'll say so.
Start from first principles
Understand the outcome, constraints and real work before choosing the technology.
Simplify before you automate
Remove unnecessary steps, approvals and handoffs before turning the process into software.
Automate the repetition, preserve the judgement
Let the system handle routine work while people remain responsible for decisions, context and approval.
Remove before you add
A better workflow usually has fewer steps. If automation is only being added without friction being deleted, the process isn't finished.
ROI first. Technology second.
From conversation to system
The same person who has the commercial conversation designs and builds the system — no handoff between strategy and implementation.
Start with the business
Have an AI opportunity in mind?
Tell me what you're trying to improve. We can work backwards from the business outcome to determine whether AI belongs in the solution — and whether the opportunity is worth pursuing.