Inside New York’s AI Vision with Shreya Amin — A Conversation with Sanjay Puri on the CAIO Connect Podcast

Shreya Amin explains how she built New York’s AI foundations—turning policy into action, upskilling workers, & creating a model for responsible public-sector AI

WASHINGTON, DC, UNITED STATES, November 28, 2025 /EINPresswire.com/ -- The public sector is racing to understand, regulate, and responsibly deploy AI—but few people have been at the center of this transformation like Shreya Amin, New York State’s former and first Chief AI Officer. In a recent episode of the CAIO Connect Podcast, hosted by Sanjay Puri, Amin offers a rare, behind-the-scenes look at what it takes to bring AI into one of the largest and most complex government ecosystems in the world.

Who is Shreya Amin?
Amin’s career is anything but linear—and that’s her superpower. With roots in theoretical math and physics, she transitioned into data science roles in finance, software, edtech, and civic tech. This unique blend of analytical rigor and real-world experience eventually positioned her to become New York State’s first CAIO, where she led statewide strategy, governed responsible AI use, and spearheaded major data transformation efforts.

Building the Foundations of Statewide AI
When Amin stepped into the role, New York already had an AI acceptable-use policy—but it was just a high-level document. Her job was to translate it into action.

Turning Policy Into Practice
Amin worked directly with agency leaders to help them understand how to implement the guidelines, not just read them.

Upskilling a Massive Workforce
She led a statewide AI training rollout for employees with vastly different levels of AI familiarity. The mission wasn’t just education—it was reassurance. Many employees feared job displacement, so the program emphasized safety, new opportunities, and hands-on practice through an enterprise-safe LLM tool.

Bringing a Product Mindset to Government
Amin introduced something the government doesn’t often see: a structured product vision. She focused on high-volume pain points—like document backlogs—and created a sprint-based playbook for evaluating and launching new AI tools. The result? The very first AI product went live in just four weeks, giving agencies a template and a confidence boost.

Challenges, Lessons, and Hard-Won Insights

Change Management Is Everything
Amin spent her first months meeting with stakeholders across agencies. With long-tenured staff and union environments, she learned that reskilling can’t be rushed—it must unfold in phases.

Choose High-ROI Use Cases
In the public sector, value matters even more than in the private sector. Amin prioritized tasks that were:
* High volume
* Measuring-friendly
* Repetitive and time-consuming
If a task takes 15 minutes and AI can bring it down to 2, that’s real impact.

Governance Must Evolve
Instead of endless paperwork, Amin pushed for embedded guardrails—like logging and privacy checks—so AI projects could move faster without increasing risk.

Talent Strategy: Think Creatively
Since hiring AI experts is notoriously slow in government, she built small R&D groups with graduate students and forged advisory networks with prominent AI leaders.

AI Agents, Autonomy, and the Data Problem
Amin believes autonomy should match the cost of error. Agents are great for coordination or suggestions, but not for regulated, high-stakes decisions—at least not yet. And while data quality is a challenge, the real issue is coordinating data across legacy systems, not a shortage of data itself.

Advice for Future CAIOs
Amin’s closing wisdom is powerful:
* Know the details and tie AI directly to business outcomes.
* Think like a product leader—understand the user’s world.
* Share playbooks so the next wave of CAIOs can build faster.
Her journey is a blueprint for how government can embrace AI responsibly, boldly, and with a human-centered focus.

Upasana Das
Knowledge Networks
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