Kristina Grcic

About Kristina Grcic

Making AI trustworthy isn’t just a modeling challenge - it’s an information one.I come to AI from an information science background (MLIS), which means I focus on designing the information layer AI depends on—how information is structured, labeled, governed, and understood. For me, trustworthy and explainable AI doesn’t start with algorithms; it starts with clear, contextualized, and well‑managed information that can be traced, explained, and trusted.As an Information Scientist at MGIC, I work at the intersection of information science, AI readiness, and governance. I help the organization thoughtfully experiment with and scale AI capabilities by designing and aligning the underlying information foundations—always with an eye toward responsible use, transparency, and long‑term sustainability. My experience organizing, classifying, and curating information shapes how I approach AI challenges: not as purely technical problems, but as information problems that require clarity, traceability, and shared understanding.My work typically spans a few key areas:AI Business Enablement: Supporting AI proofs of concept and use cases by clarifying information needs, defining success measures, and assessing readiness so efforts stay grounded in real business value.Information & AI Readiness: Applying information science principles—like semantics, metadata, and stewardship—to help both structured and unstructured information work effectively with AI.AI Governance: Bringing an information‑focused perspective to governance frameworks that balance innovation with risk management, compliance, and responsible information practices.Enterprise Capability & Opportunity Mapping: Maintaining visibility into AI capabilities and opportunities across the organization, and evaluating them using consistent criteria for value, feasibility, and information risk.I’m especially passionate about turning emerging AI capabilities into practical, trusted outcomes built on intentionally designed information foundations. As my work increasingly spans teams and initiatives, I focus on creating shared standards, reusable patterns, and enterprise‑level clarity—so others can deliver consistent, explainable AI results. Along the way, I’m continuing to expand my impact through enablement, influence, and thought leadership in the evolving AI and information space.

Experience

1. Information Scientist

   MGIC

   Jul 2025 — Present

   As an Information Scientist, I work at the intersection of Information Science, AI readiness, and governance, helping the organization responsibly experiment with, evaluate, and scale AI capabilities.Grounded in information science, my role focuses on how information must be structured, contextualized, governed, and made explainable so that AI systems can produce trusted, defensible, and reusable outcomes.Key areas of focus include:Supporting AI proof‑of‑concepts (POCs) and use cases by clarifying information requirements, success measures, explainability considerations, and alignment with measurable business outcomes.Applying and evolving standardized frameworks to evaluate AI initiatives and platform capabilities based on benefits, risks, feasibility, and information readiness.Contributing to AI‑ready information and data practices, including semantic standards, metadata patterns, and governance‑aligned guidance that support responsible and sustainable adoption.Maintaining visibility into enterprise AI capabilities and opportunities, enabling reuse, prioritization, and coordination across business and technology teams.Providing information‑centric advisory input to AI governance, including considerations for information provenance, traceability, third‑party data usage, and responsible handling of AI prompts and outputs.Establishing and promoting information traceability and explainability patterns that connect source information through transformations to AI outputs, supporting auditability, transparency, and business understanding.Partnering cross‑functionally with data, analytics, AI, legal, risk, and compliance stakeholders to align information practices with regulatory, ethical, and consumer protection expectations.Enabling adoption of shared practices through guidance, collaboration, and education, rather than enforcement, to help teams deliver consistent and trusted AI outcomes.

Education

• University of Wisconsin-Milwaukee

  Master of Library & Information Science - MLIS, Library and Information Science

• University of Wisconsin-Milwaukee

  Bachelor’s Degree, Information Technology Management

  2016 — 2019

• Purdue University

  Economics, Business Management

  2006 — 2010