Founder & Principal
Karen Joyce
AI leader. Published researcher. Founder, Resonant Scale.
“Real AI impact is specific. It demands genuine understanding of the technology, the business, and the human conditions that determine whether anything actually works.”
Background
Resonant Scale was built on more than a decade of experience inside some of healthcare's most complex organizations.
We have delivered AI products that perform under clinical, operational, and financial scrutiny. Predictive models embedded directly into clinical workflows. LLM-powered tools that recovered tens of millions in denied revenue. Decision support systems built for high-stakes domains where getting it wrong has real consequences. Work that touched over seven million patients' lives, and was recognized for it.
We have established AI organizations from scratch, building the prioritization frameworks, operating models, and leadership structures needed to turn ambition into a defensible roadmap.
The experience that matters most, though, is not what was built. It is what was learned about why AI initiatives succeed or fail, and what it actually takes to make them stick.
13+
years
Enterprise AI leadership across regulated industries
7M+
lives
Patients reached through clinical AI products
Proven
Eight-figure financial impact to organizations
Research
Before moving into enterprise leadership, the work here started in computational neuroscience and complex systems, with peer-reviewed research in brain-inspired agent-based models published more than a decade before agentic AI became an industry term. That foundation shapes how we think about emergence, feedback, and what it really means for a system to be intelligent.
● Published in JAMA Open, PLOS Computational Biology, Neural Networks & more
Why Resonant Scale
Resonant Scale is built on a simple observation from years inside large, complex organizations: the ones achieving the most from AI are rarely the ones adopting it most broadly. They are the ones going deepest in the right places, with solutions designed around their specific conditions.
Most technology approaches try to solve AI at the enterprise level, spreading capability thin and expecting impact to follow. That rarely works. What works is understanding the specific problem, the specific team, and the specific constraints before a single line of code is written.
That is how we work. Discovery first, always. Then iterative delivery, so impact is visible early and compounds on every cycle. Because the change management and the organizational conditions that determine whether AI actually takes root tend to be where initiatives live or die, we treat it as the work itself, not an afterthought.
Every client situation is different. We are here to understand yours.