Pi Mapper
AI agents need to understand your processes to work reliably — the systems, the workflows, the policies, the exceptions and the approval needs. I help operations teams build the operating model, the process knowledge layer and evals for that.
About
Malika Bhatia
Founder, Pi Mapper
London, UK
I started in Six Sigma and lean — learning to map how work actually flows, not how it's supposed to. Then outsourcing, where the discipline of handing off processes to third parties forced a rigour about documentation and process clarity that most organisations hadn't needed before. Then large ERP implementations, where the gap between what a system assumes and what an organisation actually does becomes very expensive, very fast.
After that came process automation and RPA — which taught me that automating a broken process just breaks it faster. The underlying process intelligence has to come first. That lesson turns out to apply even more forcefully to AI agents, which are considerably less forgiving than a well-configured RPA bot.
Each wave has raised the bar on how well you need to understand your processes before you embed technology into them. AI is the highest bar yet — and process intelligence, encoded as agent skills, is the foundation that makes it crossable. Get that right, and AI agents become a genuine operational capability. Pi Mapper is my attempt to help organisations build it.
Writing
I write for process owners, operations managers, and the people responsible for making AI investments actually land. No hype, no vendor positioning — just honest thinking on what works and why.
What I do
I take on engagements where I think I can genuinely help — and try to be honest when I'm not the right fit. The work is always practical and grounded in what your teams actually need, not what looks good in a deck.
01
Using event log data to surface how your operations actually run — not how they're documented. The essential foundation before any AI can reason reliably about your processes.
02
Designing the division of labour between your people and AI agents: what agents handle, where humans stay in the loop, and how you avoid the failure modes that come from getting that boundary wrong.
03
Building the empirical grounding layer that AI systems need to reason correctly about your operations — rather than performing confidently against documented fiction.
04
Structured evaluation of AI systems before and after go-live. Are your agents actually doing what you think? Where do they fail, and under what conditions? Honest answers before the stakes get high.
Get in touch
Whether you're in the middle of an AI rollout that's not landing, or you're earlier in the journey and want to think it through — feel free to get in touch. No pitch, no pressure.
malika@pimapper.com"The problem with most AI in operations isn't the AI. It's that nobody bothered to find out what the operations actually look like before they started."
Select a post to edit, or create a new one.