Jason Lim:
I help organisations turn complex operational challenges into data-driven decisions.
I combine customer discovery, product thinking and technical implementation to move enterprise AI initiatives from ambiguous problem to deployed, measurable business impact.
Enterprise experience across IBM, KPMG, and early-stage AI ventures
How I Work
How I solve problems
Not a résumé of tools. A repeatable way of moving from an ambiguous operational problem to a deployed system that changes a business metric.
Business Problem
Start with the outcome the organisation actually needs, not the technology they think they want.
Customer Discovery
Talk to the people who live in the workflow today. Watch, don't just ask.
Workflow Analysis
Map the process as it actually runs, exceptions and workarounds included.
Data Understanding
Find out what data really exists, where it lives, and how fragmented it is.
Solution Design
Design against the real constraints: existing systems, approval structures, and risk tolerance.
Deployment
Ship into production with governance, observability, and a real maintenance owner.
Business Impact
Measure against the outcome defined in step one, and iterate.
I don't start with a technology and look for a place to apply it. I start with the operational problem, spend real time with the people affected by it, and let the data and the workflow tell me what the right solution looks like. Enterprise software succeeds or fails on adoption, not architecture elegance, so every design decision gets checked against whether it actually gets used and whether it moves the business metric that mattered in the first place.
Case Studies
Real world examples of translating complex operational challenges into data driven solutions.
Each case study explores the problem context, stakeholder needs, data requirements, technical approach, implementation decisions and business impact, demonstrating how I bridge the gap between users, technology and outcomes.
Enterprise Experience
Where this thinking was built
Select an organisation to see the focus areas and the kind of work involved.
Government customers · AI adoption
IBM
Worked directly with government and enterprise customers on AI adoption, running discovery workshops to surface operational requirements, building and delivering technical demonstrations and proofs of concept, and coordinating across engineering, sales, and delivery teams to move AI initiatives from workshop to pilot.
Technical Expertise
Organised by business capability
Not a technology inventory. Each capability is what it lets me do for a customer, backed by the tools underneath it.
Enterprise Skills Matrix
The emphasis is on evidence, not technology: where each capability was actually exercised.
| Capability | Evidence |
|---|---|
| Customer Discovery | IBM Government Workshops |
| Workflow Analysis | IBM + KPMG |
| Product Strategy | Sherlocked.ai |
| Operational Analytics | KPMG |
| AI Engineering | IBM + OpenOnion |
| Cloud Infrastructure | Docker, OpenShift, Terraform |
| Executive Communication | IBM, Strategy Consulting, OpenSummit.ai |
| Solution Demonstrations | IBM |
| Cross-functional Collaboration | IBM + KPMG |
Decision Journal
Structured thinking, in writing
Practical notes on enterprise AI adoption, customer discovery, governance, and shipping enterprise products, instead of a blog about frameworks.
2 June 2026 · 2 min read
Enterprise AI Adoption Starts With Workflows, Not Models
Most AI adoption programs fail before the model is ever evaluated, because the workflow underneath it was never mapped.
14 May 2026 · 2 min read
Customer Discovery Is a Technical Skill, Not a Soft One
Engineers who treat discovery as 'the PM's job' end up building technically correct solutions to the wrong problem.
22 Apr 2026 · 2 min read
Data Fragmentation Is the Default State, Not the Exception
Plan for fifteen disconnected systems from day one. The organisations with clean, unified data are the exception, not the baseline.
Interviews About AI
Practical insights from founders, executives and engineers
Conversations with people building and deploying AI in the real world, condensed into one takeaway each.
AI Community Engagement
Where I stay close to how the field is actually moving
Regular engagement with builder and solution-engineering communities, not just conference attendance.
How I Learn
A learning ecosystem, not a reading list
I deliberately combine enterprise consulting, customer conversations, startup ecosystems and experimentation so each one keeps the others honest.
I intentionally combine enterprise consulting, customer conversations, startup ecosystems and experimentation to continuously improve how I solve operational problems. None of these on their own is enough: enterprise projects teach scale and governance, the startup ecosystem teaches speed and validation, and interviews and industry events keep both grounded in what's actually changing. Writing it down in the Decision Journal is what turns a conversation into something I can reuse next time.
Mentors & Industry Advisors
Guidance from people who've done this before
These mentors provided guidance throughout Sherlocked.ai's customer discovery, product strategy and go-to-market development.
Recognition
Awards & recognition
- Innovator Pro Winner
- UNSW Employability Top Performer
- Global Consulting Group Pitch Winner
- Enactus Nationals
- Society of Actuaries Challenge Semi-Finalist