Bob Pulver welcomes Andrew Gadomski, Founder and Managing Director of Aspen Analytics, to talk about how AI and data science are reshaping the future of HR, talent, and organizational decision-making.
Andrew is a veteran workforce data strategist who shares candid, practical insights on what it really takes for companies to evolve their data maturity, why LLMs can’t be treated like magic wands or oracles, and how to make AI work with your people, not instead of them. From “decision gravity” to the fallacy of talent pipeline management, this episode is a masterclass in balancing technological possibility with human nuance.
Keywords
Andrew Gadomski, Aspen Analytics, workforce analytics, decision intelligence, data maturity, talent strategy, HR transformation, responsible AI, talent pipeline, future of work
Key Takeaways
The difference between using AI as a prediction tool vs. a decision-making tool—and why that matters
“Decision gravity” and how influence travels through an organization
Why most organizations aren’t “data mature” and how to assess where you really are
LLMs (like ChatGPT) aren’t ready to make decisions—they need guardrails, oversight, and smart humans
The myth of a linear talent pipeline and how hiring should actually work
Data-informed != data-driven: what smart decision-making really looks like
How to frame AI adoption around people, not just tools
Sound Bites
“Data is a tool for influence—not control.”
“If you don't trust the decision, you won't trust the data.”
“AI will tell you what it would do. It won't tell you what you should do.”
Chapters
00:00 – Welcome and Guest Intro Overview of Andrew’s role at Aspen Analytics and his approach to data-driven transformation.
05:10 – What “Data Maturity” Really Means Why most organizations overestimate their data capabilities—and what a mature approach actually involves.
12:40 – Decision Gravity and Influence Mapping How organizational decisions really get made and why influence—not hierarchy—is what drives outcomes.
21:25 – Prediction vs. Decision: The Role of AI Understanding how AI fits into human workflows, and why relying on LLMs for decisions is risky.
31:00 – The Limits of Large Language Models (LLMs) Where LLMs can be helpful, where they hallucinate, and how to set trust boundaries around their output.
40:30 – Hiring Myths and the Talent Pipeline Fallacy Why treating hiring like a “pipeline” misses the mark, and what a better model could look like.
52:15 – Building Trust Through Responsible AI How trust, transparency, and cultural readiness shape whether AI is embraced—or ignored.
63:00 – Reframing Success: Learning, Not Just Automation Closing reflections on how organizations can prioritize adaptability, curiosity, and practical value in the AI era.
72:30 – Final Takeaways and Where to Learn More Andrew’s parting thoughts on decision support, ethical data use, and leading with intentionality.
Andrew Gadomski: https://www.linkedin.com/in/andrewgadomski
Aspen Analytics: https://www.aspenanalytics.io/
For advisory work and marketing inquiries:
Bob Pulver: https://linkedin.com/in/bobpulver
Elevate Your AIQ: https://elevateyouraiq.com
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