Su Belagodu

AI Native CPO, Creator of HITL proprietary model; Ethical AI advisor

Who is Really in the Loop AI or Human

AI Governance Strategist · Creator of the HITL Maturity Model™

Stop assuming your oversight works. See a proven maturity framework to see exactly where human judgment is not optional.

Diagnose Your AI Oversight With Brutal Honesty

Every oversight gap has a name. Learn to identify whether you're suffering from burnout design or silent failures.

Name the Failure Mode Costing You Right Now

Design routing, escalation, and feedback systems that route the right cases to humans, close the learning loop.

Architect Oversight That Doesn't Break People

How to build a system that handles the work you shouldn't be doing and sharpens the work only you can do.

Head of Product | AI for non-tech users

Booking.com

DeepL

ABOUT YOU

Zalando

TikTok

Myles Sutholt

Head of Product who uses the system he teaches every day with his team.

Cohort 2

Cohort 3

You already use AI. You probably think it is going well.When researchers measured experienced professionals using AI on real tasks, they were 19% slower. They believed they were 20% faster. (METR, 2025)"I thought I was already using AI well. Turns out I was just using it often." — Wole, Product DesignerThe gap is not your skills. It is what AI knows about your work before you start. Your projects, priorities, stakeholders, what "good" looks like. Without that, every session starts from zero.This course builds two things. First: a system that handles the recurring work and frees up roughly half a day a week. Second: AI advisors modeled on your real stakeholders that challenge your documents, prepare you for meetings, and catch what you missed."A system I use every single day. One that has increased the quality of my work immensely." — Renata, Product OwnerLimited to 20 students. Personal feedback on every submission.

Stop doing work your AI should do. Delegate, systematize, and scale yourself.

Your AI Already Knows Your Work

Automate the Work That Drains Your Week

AI Advisors That Challenge Your Thinking

Output That Is Visibly Better

A System That Compounds

No Coding Required

You already use ChatGPT, Claude, or similar tools regularly. Not just tried once. You have AI habits you want to build on.

Use AI (like ChatGPT or Cursor) regularly (not just tried once)

You need to install tools like Claude Code or Cursor. Some company laptops block this. If you are unsure, check with your IT team.

Can install software 

You will see code you did not write. AI writes it. You describe what you want in plain English. We walk you through every step.

Willing to try things that feel unfamiliar at first

You get decent output but re-explain context every session. You want AI that knows your work and challenges your thinking.

PMs, designers, engineers, data professionals. The title matters less than the work: multiple projects, documents, decisions across teams.

You want to walk into meetings having already stress-tested your arguments. You want documents that survive the first round of review.

Most AI governance programs fail because they stop at theory & principles.

AI Governance Advisor & Ph.D. Scholar

Centene

ISACA

Trenton Health Team

Cyber Pros Training

Francois B. Arthanas

AI Governance Advisor & Ph.D. Scholar (CISSP, CISA, AAIA) | Founder, Cyber Pros Training

Cohort 1

In this 1-Day Workshop you will NOT spend the day reading the framework line by line.You will build.By the end of the workshop, you will have a practical NIST AI RMF implementation package you can reuse for internal AI systems, vendor AI tools, GenAI copilots, RAG systems, chatbots, predictive models, automated decision systems, and AI-enabled workflows. That's because your organization probably already knows the right words:Responsible AI. Trustworthy AI. Human oversight. Fairness. Transparency. Privacy. Safety. Accountability.But when an actual AI system needs to be approved, the hard questions start:Which AI systems do we have? Who owns them? Who could be harmed? Who accepts residual risk? What happens when the system fails?That is where many AI governance efforts break down. AND that's the gap this workshop closes. You will answer four essential questions:<h3>1. Govern</h3>Who owns AI risk, what policies apply, and how do we make decisions?<h3>2. Map</h3>What is the AI system, where is it used, who is affected, and what could go wrong?<h3>3. Measure</h3>How do we test, evaluate, verify, validate, and document the risks?<h3>4. Manage</h3>What controls, approvals, monitoring, escalation, and incident response are needed?

For teams responsible for approving, governing, or deploying AI

Risk, compliance, legal, privacy, security and internal audit teams

Consultants, advisors, procurement and vendor-risk teams

The playbook to evaluate enterprise AI risk against NIST RMF & ISO 42001

Principal AI Strategist, ISO 42001 AIMS

The Home Depot

WCTC

Northwestern Mutual

Omnicom Media

SC Johnson

Eric Dabols

AI Product Leader driving governed, enterprise-scale ML infrastructure.

Q2 2026 Enterprise Sprint: AIMS & NIST AI RMF

The Reality: The board demands rapid AI ROI, but a regulatory tsunami—led by the EU and Colorado AI Acts—has changed the rules overnight. System liability now rests squarely on your shoulders.The Trap: You cannot outsource this liability to cloud providers. Relying on ad-hoc AI deployments or hoping siloed InfoSec, Data, and Product teams organically align is a massive business risk. Scaling without a unified framework invites security vulnerabilities and compliance failures.The Transformation: This workshop cuts through the hype to deliver a pragmatic executive toolkit. We aren't training you to be an auditor. Using the Applied AI Framework (AAIF™), you will learn to navigate the core standards governing enterprise AI (ISO/IEC 42001 and the NIST AI RMF). You will discover how to evaluate if your current architectures are truly ready to support compliant scaling.The Outcome: You will leave equipped with the exact diagnostic questions to assess your teams. You will gain the strategic clarity to identify operational gaps, force cross-functional alignment, and confidently evaluate your organization's readiness to deploy AI safely.

Translate the NIST AI RMF into scalable AIMS. Shift from managing risky pilots to driving governed, enterprise-scale AI product velocity.

Operationalize the NIST AI RMF

Architect Compliance Velocity with AAIF™

Execute Discovery & Validation Protocols

Enforce Responsible Usage Governance

Product Managers &amp; AI Strategists: You own the roadmap and need to bridge raw ML capabilities with governed, enterprise-scale execution.

AI Data Specialists &amp; ML Architects: You build the models but need robust AIMS infrastructure to safely deploy them into production.

AI Governance &amp; Risk Leaders: You must translate static regulatory frameworks into agile protocols that accelerate product deployment.

Shift from ad hoc AI efforts to disciplined, structured, workflow-driven collaboration before significant investment.

Workshop Lead: Netflix, Google, LinkedIn

Google

Netflix

Microsoft

LinkedIn

Paul Bryan

I help organizations design and develop AI products that deliver business value.

<blockquote>AI applied to the wrong workflows leads to chaos. This course helps Enterprise teams launch successful AI pilots by aligning on workflows, roles &amp; responsibilities, risks, guardrails, and AI functional requirements before major investments.Industry reports suggest that over half of enterprise AI pilots never scale to production. Many pilots stall because the workflows were not ready for AI.This course addresses the real bottleneck: cross-functional alignment on key details before committing to technical debt.You will leave with:<ul><li>An AI Workflow Canvas: A structured way to identify where AI can move the needle in your organization</li><li>An AI Workflow Scorecard: A shared evaluation framework your cross-functional team can use to assess value, feasibility, data readiness, and risk</li><li>A Thin-Slice Prototype: Storyboards, scene cards, and an action flow diagram that aligns stakeholders across PM, DS, UX, Eng, and Biz on exactly what the AI system will do, feasibility and risk, and where humans are needed in the loop.</li></ul></blockquote>

Identify High-ROI Workflows Ready for AI

Identify high-friction workflows suitable for Enterprise AI

Prioritize AI initiatives using structured evaluation criteria

Clarify roles and decision rights before pilot launch

Surface feasibility and governance risks early

Make AI behavior visible to cross-functional stakeholders

Define readiness criteria before committing to a pilot

AI product team members (PM, UX, DS, ENG) who need a shared framework to align on workflows, feasibility, and responsibility before building

Product and business leaders accountable for Enterprise AI outcomes who must prioritize workflows and reduce risk before committing budget

AI governance, legal, and risk stakeholders who need structured ways to surface constraints, guardrails, &amp; ownership early in AI initiatives