Ten years turning customer data into decisions businesses commit to — across financial services, enterprise technology, and AI-native products.
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"Customer transformation is won in the room where people decide — not in the dashboard."
I'm a customer strategy and transformation leader with ten years across Dell Technologies and Citibank. My work lives in the gap between customer insight and the decisions a business actually commits to.
I diagnose where the experience is breaking, build the case in language each function can act on, and drive the change through to a measurable outcome — across financial services, enterprise technology, and now AI-native products.
Most companies don't have an insight problem. They have an action problem.
Customer data is everywhere — dashboards, surveys, journey maps, NPS scores. What's rare is the translation: turning all of it into a decision a leadership team will commit to and fund. Insight that doesn't change what a company does isn't strategy. It's just research.
And as AI accelerates how fast companies can generate insight, that gap only widens — because understanding why customers behave the way they do is still a human job.
Client names withheld for confidentiality. The outcomes are real.
At a major card issuer's retail-services arm, I owned customer journey strategy across co-branded card portfolios. Right before launch, journey mapping surfaced a problem no one had spotted: customers would receive cards that didn't work for weeks, with no explanation. We fixed it cross-functionally — and the practice became standard for the next conversion.
$1.5B portfolio · ~1M customers migrating
I led a global technology company's medium-business buyer-profile program — a ten-country research effort to define who the medium-business buyer was and how sales should engage them. I aligned a ~30-person cross-functional coalition and turned dense technical research into insight every function could act on.
10-country research · ~30-person coalition
I relaunched a global technology company's North America esports program from the ground up — building the intake-to-delivery process and securing sponsor funding when the company committed zero. It grew to 59 events reaching more than 57,000 participants, and drove $152M in attributed client pipeline.
59 events · 57,000+ participants
Client details withheld per confidentiality agreements
Customer data is everywhere — dashboards, surveys, journey maps, NPS scores. What's rare is the translation: turning all of it into a decision a leadership team will commit to and fund.
Insight that doesn't change what a company does isn't strategy. It's just research.
That gap is where I work. I diagnose where the customer experience is actually breaking, build the case in language each function can act on, and drive the change through to a measurable outcome.
And as AI accelerates how fast companies can generate insight, that gap only widens — because understanding why customers behave the way they do is still a human job.
A five-step method run end to end across financial services and enterprise technology. Every step anchors back to what the customer actually needs.
Where AI fits
AI sharpens the diagnosis, the translation, and the instrumentation. The room where people align and commit — step two — is the part no tool replaces.
Diagnose
Map the broken state with the people actually in it — not from a desk. Combine qualitative depth with quantitative signal to pinpoint where the experience is failing.
Co-create
Bring customers and cross-functional teams into the same room to design the fix. Structured sessions that push past the obvious answer to one the group commits to and pilots.
Translate
Turn raw, technical, or siloed input into something every function can act on. The insight has to land differently for a CMO than for a product team — and differently again for a sales floor.
Drive
Move it to execution and scale through a real pilot or relaunch. This is where the cross-functional alignment built in steps 2 and 3 converts into action on the ground.
Instrument
Build in measurement so it keeps improving after I've moved on. A transformation without a measurement system is a project, not a capability.
Insight only becomes a decision when the right people build it together. These are the methods I use to make that happen.
Structured co-creation sessions that move a cross-functional group from empathy to a tested prototype — pushing past the obvious answer to one the group commits to and pilots.
One-on-one conversations that go past surface feedback — using curiosity and the "5 Whys" to reach root causes behind behavior, and the motivations data alone can't show.
Mapping the customer's real experience step by step, with the people in it — surfacing friction and handoff gaps, turning scattered signal into a picture leadership can act on.
"AI can mirror emotions. Truly understanding customers is still a human job."
— Michelle Fernandes
A worked example, then three more places the method applies. The firm is invented. The benchmarks are sourced.
Worked example · Diagnose → Translate
A regional wealth-management firm with 200 advisors and a 40,000-client book. A client named Ana, 62, eight months from retiring, with a $700K 401(k) to roll over. Both invented. Everything underneath them is sourced.
I mapped Ana's journey twice. Once as it runs today, with the arithmetic behind every leak visible on the page: about $11.1M a year in the model. Then again with AI agents in the loop and a target metric at every step, worth roughly $5.6M back in year one and compounding toward an $8M run-rate as the fixes mature.
The recovery rates are conservative on purpose. A plan that promises the whole leak back in year one is a plan to distrust.
Built with an agentic AI setup I run: standing instructions plus reusable skills that carry out the research, the synthesis, and the journey math on their own, while I direct the strategy and check the output.
Fictitious firm, illustrative model. Swap the journey and the method runs the same for a bank, an electronics brand, or a software company.
Step 01 · Diagnose
Support tickets, call transcripts, survey verbatims, and reviews usually sit in separate systems. AI can synthesize all of it into a ranked map of where the experience is breaking — turning the slowest part of a transformation into a starting point a team can react to in the first week.
I validate, prioritize, and decide what's worth fixing — AI ranks, the room chooses.
Step 03 · Translate
Buyer research usually dies as a slide deck nobody opens. AI lets that research live as a model a salesperson can query in plain language — and get an answer grounded in the real study. The insight stops being a document and becomes something the front line uses in the moment.
I build the buyer model and the guardrails — AI serves it, I make sure it's honest to the research.
Step 05 · Instrument
Most customer health scores are a quarterly look backward. AI can read engagement signals continuously and map them to retention and expansion indicators — so a weakening account shows up as an early warning, not a renewal-quarter surprise.
I define which signals matter and what each one should trigger — AI watches, the team acts.
Advisory
As an advisor to an AI-powered career-acceleration startup, I designed a customer journey mapping approach and a CX health scoring framework — a model that maps early engagement signals to retention indicators, so the team could see which behaviors pointed to churn before it happened.
Practice
I build small AI tools for my own work — a research synthesizer that turns 10-Ks and earnings calls into a one-page brief, a networking-prep system, and a self-coaching loop. The leverage isn't the AI itself; it's knowing what to ask and what to do with the answer.
I'm exploring Senior Manager and Director roles in Customer Strategy & Transformation across tech, fintech, and payments — and open to advisory and fractional engagements alongside. If that sounds like a fit for your team, I'd love to hear from you.