Positioning life sciences AI for buyers who don’t stop at the abstract
Shannon Hall, PhD · Scientific GTM Strategist
Built from interviews with the people who make the call, with every claim traced to what’s shipped. Because AI has to earn an expert’s trust.
What I bring
Projects that focused on customer value, defining the buyer, and product positioning
Read them in order, or start with the problem closest to yours.
Three teams built on one definition of customer value
One segment, two sales motions
One script review a week became three to five, same headcount
The question I ask has changed. The rigour hasn’t.
- The Bench
“Is this finding true and defensible?”
Trained as a scientist, then learned to translate it
A research doctorate and 8 years at the bench taught me how to design defensible systems to solve unanswered questions; early experience in communications built my ability to translate for a non-scientific audience.
- The Interface
“Can the customer explain and adopt this?”
Entered industry at the customer interface
Scientific credibility and the ability to explain it became a commercial job: supporting sales, driving adoption, and building education strategy across global enterprise accounts in a regulated market.
- The Strategic Layer
“Does our positioning shape what the market believes?”
Grew into the strategic layer
I moved from customer-facing execution into product and marketing strategy, owning infrastructure that go-to-market teams depend on and making complex products sellable to sophisticated buyers.
How I use AI in my work
AI lets me move faster, but it hasn’t changed my standard of proof. That’s the baseline I won’t go below, no matter how many new tools I try. I keep exploring ways to get more done without cutting corners. Here’s how that plays out in practice.
Compress evidence review
Synthesize large volumes of research, calls, and competitive material in hours instead of weeks, so decisions start from a complete picture.
Autonomous market intelligence systems
Standing workflows that monitor competitors, buyers, and category language, and surface shifts before they show up in pipeline.
Dynamic data visualization
Turn raw datasets into charts and cuts on demand, so I can see the patterns from several angles. The interpretation stays mine.
Audit positioning at scale
Check messaging consistency across every asset and segment, catching drift a manual review would miss.
How this portfolio was made
This site was built with AI. Every strategic claim, and the judgment behind it, is mine.
Where AI did the work
Site code and interactivity
AI wrote the code for every page, including responsive layouts and interactive case-study elements. For the Friday Slide, it worked out the game mechanics, built the synthetic dataset, and wrote the Lovable build prompts. I designed the player experience, then reviewed and ran every build.
Graphics, palette and type
AI turned my visual direction into a reusable design system with shared tokens for colour, type, and spacing and a common set of components. It then applied this across every page so the site reads as one piece. The references and aesthetic calls were mine: a warm editorial look on an ink and cream base, with pink and coral for emphasis.
First-pass browser QA
AI ran the first round of checks: layouts at different screen sizes, text overflow, interaction states, reduced-motion settings, and obvious visual breaks. I did the final review.
Where I intervened
Every strategic claim
I defined the positioning, chose the evidence, and approved every claim about customers, markets, outcomes, and my work. I signed off on all copy in plain text before it went into the build.
Confidential details generalized
No former employer, product, customer, or colleague is named. I abstracted client details while keeping each case study’s strategic problem and decision logic intact so the reasoning still holds up.
Editorial judgment and critique
I set the narrative for every page and pushed back on generic language. I refined the hierarchy and interactions and decided what stayed, changed, or was cut until each piece held together. On the Friday Slide, that meant rejecting AI’s first honest slide as accurate but dull and throwing out a playtest fix that made the honest path passive.