Market intelligence · strategy
I turn ambiguous executive problems into research-backed product, GTM, and investment decisions, with an AI-augmented method most analysts don't have.
What I do
AI augmentation
Output
Stage 1 of 5
What I do
I run point with stakeholders directly. They bring a problem, often with an answer already in mind; I reframe it into the real, answerable question and pressure the assumption that their preferred solution is the right one.
AI augmentation
I use AI to map the problem space and surface alternative framings and hidden assumptions before anything gets scoped.
Output
A scoped mandate leadership signs off on, and clarity on whether we are validating their hypothesis or finding a better one.
Stage 2 of 5
What I do
I design the approach, the hypotheses, sources, and analytical model, and set the standard my team of five analysts delivers to.
AI augmentation
AI drafts competing hypotheses and source maps, so coverage gaps surface on day one instead of in the final review.
Output
A research plan and a quality bar the team executes against.
Stage 3 of 5
What I do
I direct the team and our AI-enabled workflows to gather and synthesize evidence at scale across primary and secondary sources, staying the quality gate and upskilling analysts as we go.
AI augmentation
AI accelerates first-pass retrieval and synthesis across large, messy corpora, turning a pile of documents into structured evidence the team can interrogate.
Output
A synthesized evidence base, not a stack of links.
Stage 4 of 5
What I do
I personally red-team the findings, and the stakeholder's original solution, against counter-evidence and edge cases, so what reaches the client survives scrutiny.
AI augmentation
I use AI as an adversary, generating the strongest counter-argument and flagging where we are over-reaching.
Output
A view that holds up, whether it confirms the client's instinct or challenges it.
Stage 5 of 5
What I do
I translate the analysis into a clear recommendation, validate the path or put a research-backed alternative on the table, with the rationale, risks, and implication for roadmap, GTM, or investment.
AI augmentation
AI helps shape tight narratives and decision-ready artifacts, so the thinking lands in the room instead of in an appendix.
Output
A decision the client can act on, and a relationship that grows into the next mandate.
Three sectors, one through-line: nine years embedded, sitting inside the client's problem rather than lobbing reports over a wall. Telecom taught me to defend a forecast to the people betting on it. Emerging tech taught me to build AI into how research gets done. Industrial technology is where it comes together: running engagements end-to-end, leading a team, and turning ambiguity into decisions leadership acts on.
Across APAC and MEA operators, I owned the regional forecasts and became the client's first call on why the market was moving. Over time I took on the team's escalations, quality reviews, and bi-monthly readouts to senior leadership. It taught me the number matters less than the judgment you can defend behind it.
Read case study →02Leading Startup Series research on generative AI and synthetic data, I mapped fast-moving startup landscapes into market-ready insight; then I started building AI into the work, designing workflows that cut per-report turnaround by 20% and tuning the firm's internal AI retrieval. The method that now runs through my work started here.
Read case study →03Embedded with a global industrial leader, I run point on the engagements behind product and go-to-market decisions across industrial and data-center markets. I've shaped a sub-brand's product roadmap, defined GTM priorities for a new product, scoped proposals that grew the engagement 50%, and I lead and upskill a team of five, now building a new market-intelligence offering from the ground up.
Read case study →Short analysis on the markets I track. One piece at a time, building.
I'm a market intelligence and strategy professional with nine years spent embedded inside client problems rather than observing them from the outside. I started in telecom, forecasting operator movements across APAC and MEA and learning to defend a number to the people making bets on it. I moved to the frontier of emerging technology, leading research on generative AI and synthetic data, and there I began rebuilding research itself around AI, designing workflows that made my team faster and our thinking wider. Today I'm embedded with a global industrial leader as a manager: I run the engagements behind product and go-to-market decisions, lead and upskill a team of five, and have grown the account by half over two years. The through-line is judgment under ambiguity. Stakeholders rarely arrive with a clean question, so my job is to reframe the real one, direct a team and an AI-augmented method against it, and come back with a decision they can act on, their instinct validated or a better path found. I'm now looking for a manager-level role in consulting or corporate strategy where deep sector range, an AI-enabled method, and a record of leading people and growing relationships turn ambiguity into decisions that matter.