NXGN-060／AI Risk Management

Corporate Seminar

Next-Generation Risk Management

AI Risk Lessons from Energy, Defence and Finance

You do not need to prepare for unknown AI risks from scratch. Transplant the wisdom of three fields that have long treated risk management as the core of the business into AI governance.

Format: Lecture + Discussion
Audience: Executives, directors, risk & AI teams
Schedule: Contact us for details
Lecturer: Ariki Ono

Enquire about this seminar

Does any of this sound familiar?

Worried about AI risk?

Building the governance and risk management needed to prevent AI incidents seems to require specialist expertise. Has that led you to put the brakes on using AI?

Fear of data leakage, copyright infringement and hallucinations is stalling your use of generative AI

You want to put AI governance in place but do not know where to begin

When AI causes a problem, it is treated as an individual's mistake and the matter ends there

You follow regulatory developments, but they have not translated into internal mechanisms or day-to-day operations

This seminar does not stop at explaining regulation.
You will take the methods that leading fields have refined over decades and turn them into AI risk management you can start tomorrow.

What You'll Gain

Skills and insights you will walk away with

Explain four risk management methods from leading fields and apply them to AI

You will be able to apply the risk management methods of Shell, BP, the US Defense Logistics Agency and Goldman Sachs to AI risk management.

Classify and inventory your organisation's AI risks

Using security, quality and ethics as categories, together with real incidents at the Chicago Sun-Times and Samsung Electronics, you will identify the risks your organisation is likely to face.

Sketch the backbone of AI governance from national guidance and peer examples

Overlay the work of Walmart, Sony, IBM and Mondelez on the three pillars of AI governance set out by Japan's Ministry of Economy, Trade and Industry and the NIST AI RMF to sketch a structure that fits your organisation.

Take home small first steps you can start tomorrow

Communicating a usage policy, a risk checklist and extending existing rules: three first steps that do not wait for a heavyweight framework.

Program

How the programme is structured

Participants read cases from other fields and real AI incidents through the lens of their own organisation. The programme is designed for you to leave with what your organisation will start tomorrow, in your own words.

Poll

Which AI incident is most likely to happen in your organisation? A live poll reveals how participants see the risks.

Case Study

Read Shell, BP, the US Defense Logistics Agency, Goldman Sachs and real AI incidents through the lens of your own organisation.

Discussion

What must change so that AI failures are not written off as an individual's mistake? Discuss in groups.

Chapter 01

Learning from Leading Fields: Risk Management in Energy, Defence and Finance

Energy: Shell's scenario planning (seeing the range of uncertainty rather than making forecasts)
Energy: BP's rule-responsibility model (lessons from the 2010 Gulf of Mexico oil spill)
Defence: strategic radar and weak-signal detection at the US Defense Logistics Agency (DLA)
Finance: Goldman Sachs's collaborative culture (why its losses stayed small in the subprime crisis)
Mapping the four methods onto an AI risk management framework and the NIST AI RMF

Chapter 02

How to Manage AI Risk

Three categories of AI risk: security, quality and ethics
Incidents: AI-generated content at the Chicago Sun-Times and the data leak at Samsung Electronics
Mapping AI risks to the existing laws that apply to them
The three pillars of AI governance (METI AI Guidelines for Business)
Peer examples: Walmart, Sony, IBM and Mondelez
Managing risk across the AI lifecycle, and the challenges of global roll-out

Chapter 03

Three First Steps: Start Without Waiting for a Full Framework

Communicating a usage policy, a risk checklist and extending existing rules
AI project logs and in-team reviews
AI risk management is not a brake but a guardrail

Voices

What past participants say

“

Lessons from other industries

Hearing about the BP accident made me want to revisit how we review incidents, which we had been closing as individual mistakes.

Participant (industry and role undisclosed)

“

Now we know where to start

I had assumed building governance would take a long time. The seminar led us to start by adding AI to our existing information security rules.

Participant (industry and role undisclosed)

“

Seeing it as a guardrail

I realised that what had been holding back our use of AI was not the risk itself, but our lack of preparation.

Participant (industry and role undisclosed)

FAQ

Frequently asked questions

What is AI risk management?

It is the ongoing work of identifying and assessing the security, quality and ethical risks of using AI, and running countermeasures and monitoring. The AI RMF from the US National Institute of Standards and Technology (NIST) organises this into four functions: Govern, Map, Measure and Manage.

Why learn from energy, defence and finance?

Because these fields have long treated risk management as the core of the business and refined their methods over decades. Preparing for multiple futures (scenario planning), fixing rules rather than blaming individuals, detecting early signals and a culture of escalating risks quickly all apply to fast-moving, unknown AI risks.

Where should we start with AI governance?

There is no need to wait for a full framework. You can start with three steps: communicate guidance on AI use across the organisation, apply a risk checklist to a small AI project, and add AI to your existing information security rules.

Will stronger risk management not slow down AI adoption?

Quite the opposite. In McKinsey's 2025 survey, 44% of organisations had experienced an incident from using generative AI. It is the lack of preparation that holds adoption back. Risk management is not a brake but a guardrail that lets people take on new challenges with confidence.

How does this differ from the other AI seminars?

NXGN-040 "Responsible Management and AI" covers executive responsibility and global regulation, and NXGN-050 "The AI Steward: A New Role" covers the front-line role of stopping AI. This seminar focuses on building the organisational mechanisms to prepare for AI risk.

Request

Interested in hosting this seminar at your organisation?

We are happy to tailor the schedule, capacity and format (in-person or online) to suit your needs. Enquiries from HR and corporate planning departments are most welcome.

Enquire about this seminar