Shalyn Nystrom
Principal Consultant: Data Science & Analytics @ Source Allies
Des Moines, Iowa, United States
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Shalyn Nystrom is a Principal Consultant in Data Science & Analytics at Source Allies. She is focused on enabling teams to build products with data in mind, empowering leaders to make data-driven decisions, and elevating clients' AI capabilities from experimentation to operationalized solutions. In addition to her breadth of experience across the data gamut, she is a culture shaper, always looking to help others find their passion and grow.
Shalyn holds a M.S. in Computational Biology from Carnegie Mellon University, and a B.S. in Bioinformatics and Computational Biology from Iowa State University. In her free time, she enjoys family life with her husband and toddler, along with crafting and traveling the world.
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Close the GenAI “Learning Gap”: Self‑Improving AI Without Fine‑Tuning
Close the GenAI “learning gap” using self‑improving feedback loops and observability. Continuously improve AI systems without costly fine‑tuning.
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The MIT State of AI report surfaced a brutal truth: most GenAI systems do not retain feedback, adapt to context, or improve over time. While frontier models get better with every release, enterprises rarely gain a durable advantage, because their systems don’t actually learn.
The default answer is fine‑tuning. In practice, it’s often expensive, brittle, slow to iterate, and tightly coupled to a specific model version. Worse, it can lock teams out of rapidly improving frontier models.
This session presents an alternative: learning‑loop architectures that allow enterprise GenAI systems to improve continuously, without fine‑tuning, while remaining flexible enough to adopt new models as they emerge.
You’ll see how feedback from real usage can be captured, measured, and reintegrated safely into production systems. We’ll demonstrate how observability, evaluation, and automated optimization work together to turn GenAI from a static capability into a learning system.
We’ll explore:
* Automated Prompt Optimization: enabling systems to evolve their own instructions using Genetic‑Pareto (GEPA) techniques based on measurable feedback
* Observability‑Driven Learning: detecting failure patterns and routing targeted corrections back into the system
* Trust & Auditability: fitting learning loops into existing governance, compliance, and risk frameworks rather than fighting them
If your GenAI initiative is stuck in pilot, or producing inconsistent or stagnant results, this session shows the missing half: the learning loop that makes improvement routine instead of exceptional.
The Human Premium: Reclaiming Connection in the Era of AI
As we integrate AI into our workflows, we face a new paradox: the more efficient we become technically, the more we risk losing the micro-interactions that build team trust. When we automate the mundane, we often inadvertently automate the human connections that sustain innovation. This "automated silence" can lead to intellectual silos, workplace loneliness, and a hidden accumulation of Social Debt.
Just like Technical Debt, Social Debt is the cost of prioritizing speed over structural integrity—specifically, the social fabric of our teams. Drawing on my experience as a data and AI practitioner and culture-shaper, this session explores the "Meaningless Gap": the fundamental barrier where AI lacks the lived experience humans use to build culture.
Whether you lead by title or by influence, you will walk away with practical strategies to identify where your team’s connection is thinning and how to reclaim the Human Premium—the intentional, high-stakes interactions that ensure our humans, not just our tools, remain the primary engine for the solutions we build.
Shalyn Nystrom
Principal Consultant: Data Science & Analytics @ Source Allies
Des Moines, Iowa, United States
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