I work upstream of solutions.
I find the problem worth solving, build the evidence around it, and test what deserves to move forward, across process, tooling, automation and AI.
Currently working across Salesforce's Prospect to Cash Innovation & Customer Zero Transformation, where I use field evidence, workflow diagnosis and opportunity prioritization to help make investment decisions more defensible.
Three problems I keep coming back to: where AI is worth pursuing, what makes it trustworthy, and what has to change around it for the operating model to scale.
Finding the work worth automating
When low AI adoption isn't an enablement problem
When skilled people compensate for broken systems
Should this AI use case earn its place?
I kept coming back to the same question: when does an AI idea actually deserve to move forward? So I turned the decision logic I was using in my work into something tangible and testable.
I built this lightweight prototype to pressure-test an opportunity across six dimensions, before enthusiasm turns into a scale decision.
Try the prototype → Describe an AI use case. See what it challenges.It doesn't average the six inputs. It applies rules, the way judgment does: a weak problem sends you back to discovery no matter how good the AI looks, and shaky data or workflow fit means it can't be a scale bet yet.
The prototype is intentionally simple. The point wasn't to automate judgment, it was to make the judgment visible enough to test.
Strategy and transformation, grounded in what the field actually does.
I'm Emma Lancrin, a strategy and transformation leader with 10+ years across consulting, GTM and seller experience. I turn ambiguous business problems into evidence-based decisions, combining field research, workflow diagnosis and prioritization to distinguish what calls for process, tooling, automation or AI.