TAYLLORCOX brings a practical perspective on the topic "M_o_R® 4: managing risks in digital and AI projects". How to update risk register for machine learning and vendor models.

This article stems from current topics in the training and certification field (inspired by, for example, the TAYLLORCOX blog on tx.cz) and connects them with project and service management.

New risks

First, clarify the context: what outputs must your team deliver, what regulations or strategic priorities apply, and where are the greatest risks. Without this, the methodology becomes just a template.

We recommend combining the official syllabus with internal lessons learned. Brief retrospectives after each wave of change significantly increase the chance that new habits will take hold.

1. Data and bias in models.

2. Vendor and regulatory dependencies.

3. Service continuity during incident.

Procedure

Below we outline three concrete steps that you can implement even without a large budget.

• Add qualitative scenarios to numbers.

• Connect risk with change advisory board.

• Practice tabletop exercises regularly.

Summary

If you want a deeper guide or certified training, contact the TAYLLORCOX team – we will prepare a course, workshop, or combination of e-learning and in-person training according to your goals.

Quick comparison

The table contains indicative information; the final offer always depends on the course date and format.

Risks change faster than annual plan.

Do not forget to involve HR and security roles early – the sooner they sit at the table, the less rework at the end of the project or audit.

Want to continue? Check out the dates for PRINCE2®, ITIL® and other standards courses at prince2.cz or write to a consultant – we will be happy to recommend the right combination of modules.