Financial services and banking
AI and technology training for financial services
Banks, insurers, asset managers and the firms that serve them are using these tools already, usually ahead of the oversight that was meant to govern them. The work is not slowing that down. It is training people to verify what comes back, teaching managers what to review and how often, and giving the executive layer the questions to ask before the next proposal is signed off.
For those who set expectations and review the output.
Built around financial services and banking work
3 courses · 20 facilitated hours
AI Governance and Oversight for Managers
The oversight duties that sit with line management: approving use cases, handling incidents, and keeping a record of what the team does with AI.
Addresses: Responsible Use, Culture & Support
Manager, Director / Head of
Course outlineMeasuring AI Adoption and Value
Licence counts and login stats say nothing about whether work has changed. This course builds a measurement approach you can defend: a baseline, a small set of honest metrics, and a report your leadership will trust.
Addresses: Culture & Support, Tools & Access
Manager, Director / Head of
Course outlineRunning a Rollout That Sticks
Most technology failures are adoption failures. This is about the twelve weeks after go-live: who champions it, what you measure, and how to tell early that it is not landing.
Addresses: Culture & Support, Daily Practice
Team Lead, Manager, Director / Head of
Course outlineWhat this sector brings into the room
- Second line asks what the first line is actually doing
- Risk and internal audit start asking teams what they use AI for, and the honest answer is usually that nobody has written it down. Managers train on applying the firm's own policy to live requests, setting review thresholds and keeping a register that stands up to being asked for.
- Client and customer material cannot go anywhere
- Sessions are run on the constraint rather than around it: what may enter a tool, what may not, where the line sits for client-identifiable and market-sensitive material, and what the approved route looks like when someone needs the answer anyway.
- Analysis nobody can reproduce is worse than no analysis
- The analysis and reporting work is built around a trail: how the figure was reached, what was checked, and what a reviewer needs in order to sign it. Speed that cannot be defended is not a saving in this sector.
Also runs for any sector
These work the same way whoever is in the room. They are still delivered on your own material, so they are no less specific to you - the sector simply is not what changes them.
Leading an AI-Ready Team
For the managers who decide whether adoption sticks. Focuses on setting expectations, removing blockers, and building the team habits that survive after the training ends.
Addresses: Culture & Support, Daily Practice
Team Lead, Manager, Director / Head of
Course outlineRunning an AI Champions Network
Champions programmes fail politely: volunteers are named, nothing changes. This is for the manager who owns the network - choosing the right people, giving them time and permission, and keeping it alive after month two.
Addresses: Culture & Support, Daily Practice
Team Lead, Manager, Director / Head of
Course outlineWhich of these does your organisation actually need?
The readiness assessment scores each person across five dimensions and maps the gaps onto this catalogue, by department and by role. Five minutes per person, and it answers the question with a measurement rather than a guess.