Phani Prasad Thimmapuram

Enterprise Architect, GenAI Architecture and Strategy

Governance and Observability in AI Systems

Bank of America

J.P. Morgan

DBS Bank

Virtusa

Societe Generale

Governance and observability are the foundation of enterprise AI, ensuring systems remain transparent, accountable.

Students will gain practical insights into building, governing, monitoring, and scaling enterprise AI systems.

Students will learn how to design, govern,monitor AI systems

Students will learn how to establish governance frameworks, implement AI observability practices

Establish governance frameworks, implement AI observability.

In 2 weeks, you'll learn how to implement an AI Governance Framework in practice and gain the confidence to make AI Governance operational.

Pioneering Trustworthy & Responsible AI

Shell

University of Warwick

KT Secure

AI You Can Trust

Dr Georgiana Marsic

Experienced Data & AI Governance Leader. ISO 42001 Lead Implementer.

NatWest Group

Lloyds Bank

Fujitsu Global

HSBC

Credit Suisse

Dr Paul Dongha

Paul is the Head of Responsible AI and AI Strategy at one of UKs largest banks.

Cohort 1

June 2026

Learn how to implement your organisation’s AI Governance Framework in 2 weeks. Leave with practical knowledge, frameworks and ready-to-deploy templates, but more importantly with the clarity of knowing how to embed Responsible AI practices in your organisation to build trust with your stakeholders and prepare for compliance with the EU AI Act and other AI regulations.

Practical course showing how to make AI Governance operational

The case for AI Governance and the architecture of an AI Governance Framework

Establish your AI Governance Purpose, Principles and Direction

Authority, Accountability and Governance Structures

Risk, Control Environment and Assurance

Establish AI Lifecycle Governance to Govern AI Solutions Including Agentic AI

Capability, Implementation and Evolution: Operationalising AI Governance 

Anyone interested to learn about how AI Governance is implemented in an organisation

Leaders / practitioners who directly oversee or contribute to establishing Responsible AI practices

Compliance guardians (e.g., Legal Counsel, Privacy Officer, Chief Risk Officer) or AI product leaders (e.g., Head of AI, Product Managers)

Most AI governance courses teach ideal conditions. This one doesn't. 

US Top 100 Most Influential AI Leaders

Thomson Reuters

Concordia

Ciena

Deloitte

Temi Odesanya

AI Governance Practitioner & Educator

A hands-on workshop for professionals expected to govern AI with limited authority, incomplete visibility, and real institutional resistance. It's built on real AI governance challenges, not framework walkthroughsYou have accountability for AI governance. You did not get the authority, budget, or timing required to prevent every bad outcome. The business moves fast. Deployments go live before governance reviews them. Vendors say the model is proprietary. Senior leaders want answers you're still trying to piece together. And you're expected to have defensible governance posture regardless.This workshop is built for the Monday morning problem - the deployment that's three weeks from go-live, the shadow AI discovered through a customer complaint, the escalation memo you need to send without ending your career.Temi teaches from active practitioner experience — operating simultaneously under institutional pressure at a large enterprise and building the actual deliverables this course is designed around. The structural spine of this course — high accountability, limited authority — is not a concept drawn from research. It is the operating condition this course was built from.

You leave as a practitioner who knows how to govern AI under the pressure and build a record that is defensible regardless of the company

You will leave with AI governance decision templates, questionnaires

Embedding AI Governance Into Delivery Without Disrupting Innovation 

Bonus included: AI Governance Prompt Book  

Make AI risk concrete enough that it cannot be dismissed.

Govern generative AI and AI Agent specifically

This course skips the orientation. We build on existing foundational knowledge, frameworks — not from scratch 

Exposure to AI Governance, Risk or Compliance in a professional context

 If you've never had exposure to AI projects, this course will feel advanced. If you have, it will feel like finally having the right words

Exposure to AI/ ML projects in a professional context

Risk and compliance professionals whose portfolio now includes AI — applying old frameworks and finding the fit is off.

AI governance leads and responsible AI program managers with a mandate but limited authority — writing policies teams route around.

Legal counsel, CPOs, and digital ethics leads making AI architecture decisions that will shape governance for years, without a playbook.

Design AI governance, manage risk & compliance—become a trusted enterprise leader

Ex-JPMorgan Data Leader | AI Governance

JPMorgan Chase & Co.

NatWest

Centrica

Barclays

Prajesh Jha

Founder-GravitasETRM | Ex - JPMC | ADNOC, BP, ENI, Centrica, NatWest, Barclays

Most AI courses teach you how to build models. Almost none teach you how to control them once they’re in production—and that’s where careers are being made (or broken).Today, organizations are deploying AI faster than they can govern it. That creates real risk: regulatory exposure (EU AI Act, model audits), reputational damage from biased or opaque decisions, and financial loss from poorly controlled models. Leaders are under pressure to answer questions like: Who owns this model? How is it monitored? Can we explain it to regulators? Most teams don’t have clear answers.This program focuses on that gap. You’ll learn how to design and implement practical AI governance frameworks—policies, controls, risk models, and oversight structures that actually work in enterprise environments. Not theory, but how to operationalize governance across the AI lifecycle.If you’re already working with data or AI, this is the layer that elevates you—from building systems to owning accountability, influencing decisions, and operating at leadership level.

Lead AI Governance: Control Risk, Meet Regulation, Drive Decisions

Design Enterprise AI Governance Frameworks

Control AI Risk, Compliance & Regulatory Exposure

Operationalize AI Governance End-to-End

Influence Stakeholders & Lead Governance Decisions

Build Career Path in AI Governance Leadership

Deliver Real-World AI Governance Implementation

Mid–senior data/AI professionals aiming to lead governance, risk &amp; strategy roles

Risk, compliance &amp; audit professionals expanding into AI governance &amp; regulation

Tech leaders &amp; consultants driving enterprise AI adoption with accountability &amp; control

From Experiments to Scale: How Leaders Build High-Performing, Trusted AI Systems

Fractional Chief Product and AI Officer

Fanatics

eBay Inc.

Walmart

Upwork

Subha Shetty

AI-first product leader, transformation coach, and advisor to F100 and start-ups

Evals are having a moment - and for good reason. As AI systems move from pilots to production, leaders are confronting a hard truth: performance without trust doesn’t scale and AI drift is real.But evals alone are not the system.This workshop reframes evals within a broader, executive lens: performance + governance = trust at scale. You’ll learn how leading organizations are moving beyond isolated testing toward building AI systems that are measurable, reliable, and continuously improving.Grounded in insights from leading institutions and practitioners such as the WEF, McKinsey, Deloitte, Stanford HAI, and voices such as Dario Amodei, Andrew Ng and more - this session connects cutting-edge thinking to practical execution. This is a highly interactive session. You will:<ul><li>​Work hands-on to design and build your own eval (for an AI or agent use case)</li><li>​Explore how Guardian Agents, KYA (Know Your Agent), and feedback loops enable trust at scale Why This Matters Now The conversation is shifting from What can AI do? to Can this system scale and be trusted? Winning leaders will go beyond the best pilots - they will build high-performance governed AI systems that are continuously improving.</li></ul>

You will walk away with your first (or improved) eval. 
A trust framework with evals, governance, HITL, Guardian Agents and more...

Diagnose AI trust issues more precisely  

Build your first eval or improve an eval 

Map 20+ trust techniques to solving the issue  

Write your Governance policy 

For product/AI leaders scaling systems<ul><li>You run evals but unsure they're measuring the right things</li><li>You need a system to catch AI drift early</li></ul>

For leaders dealing with org trust<ul><li>Stakeholders are skeptical of AI and you can't articulate why</li><li>You're asked to "make AI more reliable" </li></ul>

For leaders moving from pilots to production<ul><li>You're responsible for AI quality and inherited a system with fuzzy measurement infrastructure</li></ul>

Agentic AI

Coding with AI

AI Workflows

Claude Code

OpenClaw

Vibe Coding

AI Evals

AI Transformation

RAG & Search

AI for PMs

AI for Engineers

AI for Designers

AI for Marketers

AI for Founders

Product Sense

Product Discovery

User Research

Prototyping

Growth

Analytics

Tech Foundations

Strategy

Influence

Leadership

Career Growth

Machine Learning

LLM Ops

Context Eng

Security

System Design

Figma

Design Systems

Visual Design

Design Strategy

Automation

Content Marketing

Demand Gen

Go-to-Market

Product Marketing

Positioning

Social Media

Brand

B2B Marketing

SEO & AEO

AI for Leaders

AI Governance

Communication

Management

People Operations

Exec Presence

Storytelling

Goal-setting

Personal Brand

Fundraising

Everyone

Operators

Data Scientists

Business Analysts

User Researchers

Customer Success

Project Managers

HR Professionals

Sales People

Lawyers

Finance

Investors

Real Estate

Educators

Creators

Executives and senior leaders who set strategy, lead teams, and drive organizational outcomes.

Engineers and engineering leaders who build, deploy, and scale software systems.

Establishing policies, ethics frameworks, and oversight for responsible AI use in organizations.

Applying AI tools and techniques to professional workflows — assign alongside more specific AI topics.

Operating and deploying large language models in production — fine-tuning, inference, monitoring, and cost optimization.

Governance and Observability in AI Systems

What you'll learn

Why this topic matters

You'll learn from

Phani Prasad Thimmapuram