I learned this the hard way: building integrations at Coinbase, then putting guardrails around the lead-to-quote agent at AAA. This holds up as technology evolves from workflows to agents.
Three products from three eras of technology. Pick one to see the thesis at work.
Branch sellers built every quote across 20 screens. I built an Agentforce agent that takes a lead to a finished quote in a single prompt. It now has 511 weekly active users.
I could easily have copied the lead creation form and the lead conversion experience into a chatbot. Instead I started from what the seller was actually trying to do: get a quote in front of their customer as fast as possible, so they'd have something concrete to react to.
Customers were waiting over three weeks for an initial response to their support case, and our reputation was taking a hit. The CEO declared a code red and assembled a dedicated response team from every impacted group. I joined with my integrations team and scoped us to case creation while other teams took case resolution. We worked around the clock, and no change was too small if it moved the backlog.
I had the team inspect the creation flow: what data was sent on the case creation call, and what the validation step actually needed. The bottleneck was a user validation calling our master data management system, the source of truth for customer data. Calling into our internal database to validate core customer information was taking forever, so I decoupled the two. Cases were created first and the verification ran in parallel.
That became our architecture pattern. File uploads needed an Opswat virus scan before entering a Coinbase system, so I had the team run the scan in parallel too instead of letting it block case creation.
When the team started load-testing Salesforce to find its upper limit, I redirected them. Chasing the theoretical max wouldn't answer the real question. We tested against 10x our peak volume and our forecasted growth instead.
I owned the Campaigns product line in B2B Marketing. Salesforce had just acquired a team of machine learning engineers and data scientists in Tel Aviv, and I flew there to roadmap with them directly. We started from one question: what data do we actually have, and what could we build with it?
We built Pardot's first machine-learned capability: Campaign Insights. While a campaign was running, it surfaced what was resonating: which titles were engaging, which send times worked, how it compared to similar campaigns. No dashboards to build.
I ran the beta on real customer data and sat in feedback sessions on whether the insights and the UX resonated, then iterated from there. It launched in September 2018 as a new license. In its first year, my feature was attributed tens of thousands in additional revenue.