Christy Carroll | AI Strategy + Product Design
Can AI be trustworthy?
After a decade of hands-on work with AI, starting with IBM Watson in 2015, I’ve watched the technology get dramatically better while adoption stays static. The pattern is almost always the same: teams optimize for accuracy, ship something that technically works, and then wonder why nobody uses it.
The problem isn’t the model. It’s the gap between what the system can do and whether people believe it.
I specialize in closing that gap.
The frameworks
My work is built on Human-First AI — an approach rooted in user research, systems design, and a decade of hands-on product work, adapted for the specific challenges AI introduces. It includes diagnostic tools that help teams understand why technically accurate AI still fails adoption, and what to do about it.
These include trust calibration, the GPS-not-chauffeur model (and god forbid, the unhinged cabbie), and for agentic systems, an agent trust hierarchy.
I’m happy to walk through these in conversation.
What I’ve worked on
Document intelligence: DocAI
UX audit, research, and strategy for an AI system interpreting policy documents for financial underwriters. Research revealed the trust calibration gap: 80% model accuracy, but only 30-50% user confidence, with professionals bypassing AI summaries altogether in favor of manual verification (or using a different AI tool). I designed evidence-surfacing patterns so users could verify results and build appropriate trust.
Education: Personalized tutoring assistant
Designed conversational guidance that helped college students ask better questions, stay engaged with material, and build confidence in their own reasoning instead of relying on answers alone.
Enterprise assistants: Visa, IBM, & ADP
Conversation patterns, tone frameworks, and error-handling for internal AI that supports complex workflows while respecting employee expertise and autonomy.
Healthcare: Patient appointment scheduling
Shaped conversational flows for sensitive, high-stakes interactions where clarity, accessibility, and graceful recovery from errors were as important as speed.
Human-AI collaboration: Real-time agent assist
Research with live agents testing prototypes for concurrent customer handling — up to three at once. Observed how they interpret AI suggestions in the moment, where cognitive load accumulates, and what support actually helps instead of interrupts.
Channel strategy: IVR self-service playbooks
Playbooks and decision frameworks for when voice interaction can resolve an issue, when to escalate to humans, and when traditional UI is the better choice.
Evaluation frameworks: LLM quality beyond accuracy
Developed evaluation approaches measuring tone, frustration handling, condescension, and trust impact—the dimensions that predict adoption, not just task completion.
How I work
Research that reveals trust dynamics
I watch actual users interact with AI systems and pay attention to where trust breaks down—not just whether tasks technically complete. The gap between “it worked… kinda” and “I’d willingly use this tool again” is where most AI products fail.
Cross-functional fluency
I can talk to engineers about token limits, context windows, hallucination patterns, and evaluation signals, translate for product teams about user needs and adoption risks, and help design teams adapt their existing skills for conversational systems.
Systems thinking for AI
I’ve adapted foundational UX principles (like Nielsen’s heuristics) for conversational AI—accounting for probabilistic behavior, turn-taking dynamics, and trust calibration that don’t exist in traditional interfaces.
Capability-building
I don’t just design—I help teams build their own conversational design capability through frameworks, workshops, and knowledge transfer. The goal is for you to keep improving your conversational tools long after I’m gone.
Experience
Senior AI Design Consultant — Slalom / Capital One
Conversational and AI UX for enterprise document intelligence, including research, heuristics, and trust calibration frameworks addressing the gap between model accuracy and user adoption.
Conversational Architect — Amelia.ai (now SoundHound AI)
Text and voice agents in healthcare and service contexts, focusing on multi-turn flows, error recovery, and safe escalation paths.
Lead Product Designer — IBM Watson & Cloud Garage (2015–2020)
Led design for early Watson AI products including education and customer intelligence applications—full product design, not just conversation flows. Later, I ran AI-focused design engagements through IBM Garage, facilitating enterprise design thinking workshops and helping Fortune 100 clients define conversational patterns, evaluation frameworks, and AI integration strategies. This is where I developed the foundational thinking about human-AI collaboration that became my trust calibration work.