Paul Sherman

Most research functions produce reports. I build organizational capabilities that enable insight pipelines.

The distinction matters. A report is a point-in-time artifact. A pipeline is an organizational capability: a set of processes, relationships, and communication channels that continuously convert user evidence into product decisions. When I join a team, I'm not just running the next study. I'm wiring the insight-to-action loop so it keeps running after any single study ends.

That means I think about research at three levels simultaneously:

• Project: At the research or discovery project level, I design and execute mixed-methods research that triangulates behavioral data, attitudinal data, and generative qualitative data.
• Operations: At the operations level, I build the processes, tools, and stakeholder relationships that make research a recurring input to product decisions rather than a one-off event.
• Strategy: At the strategic level, I help leadership make decisions under uncertainty by assessing the weight of available evidence and communicating confidence levels so the organization can act rather than wait for perfect information.

I also bring something most UX researchers don't: over 25 years of experience researching how people work with computers and automation. My PhD work investigated how pilots and air traffic controllers calibrate trust in and build mental models of automated systems.

That same problem is now the central design challenge of every AI-augmented product. I've researched it from both sides: designing AI features that users can understand and trust, and integrating AI into research operations so humans stay in the interpretive loop. This isn't a recent interest. It's the thread that connects everything I've done.

My methods span the full research spectrum:

• Discovery and design sprints
• Contextual inquiry
• In-depth interviews
• Jobs-to-be-done analysis
• Journey mapping & service blueprinting
• Persona definition
• Usability testing
• Survey research
• Behavioral analytics
• and more

I pick the method that fits the question, not the other way around.

I've worked across healthcare, fintech, legal tech, insurance, telecommunications, procurement, and enterprise SaaS. The domain matters less than the complexity. I do my best work in products where the problem space is tangled, the stakeholders are many, and the path from evidence to action requires translation across teams.

On the tools side, I work in Figma, Mixpanel, FullStory, Qualtrics, Maze, and the usual research stack. And I use AI tools (ChatGPT, Claude Cowork, Gemini, NotebookLM) as supervised research accelerators. They handle pattern identification across large qualitative datasets while I maintain interpretive control over what the patterns mean and what to do about them.