Bespoke training programmes for boards, ExCos and senior leadership in financial services — from half-day AI literacy sessions to multi-week immersive workshops that build lasting AI fluency across your organisation.
3
Training tiers
10+
Modular workshops
6
FS sectors covered
78% of FS boards say AI is a top-3 priority, yet fewer than 20% feel equipped to govern it. AI-literate leadership teams adopt 3x faster.
Why executive AI training matters now
AI is no longer a technology project — it is a board-level strategic lever. Yet most executive teams lack the fluency to challenge AI business cases, stress-test vendor claims, or govern AI risk effectively. The gap between what boards need to know and what they currently understand is widening every quarter.
78%
of FS boards say AI is a top-3 priority, yet fewer than 20% feel equipped to govern it
3×
faster AI adoption in firms with AI-literate leadership teams
$2.4M
average cost of a misguided AI investment that a 3-hour board session could prevent
Three training tiers
Each programme is tailored to audience seniority, industry context and organisational maturity. No generic slides — every session uses your institution's real data, use cases and regulatory context.
FoundationHalf-day · Board & ExCo
AI Fluency for the Boardroom
3 hours · Interactive
A concise, jargon-free session designed to give board members and ExCo the vocabulary, frameworks and confidence to govern AI effectively.
What AI actually is — and isn't — in the context of banking and capital markets
GenAI, LLMs and agentic AI: cutting through the hype with live demonstrations
The AI value chain: where revenue, cost and risk sit
Five questions every board member should ask their CTO and CRO
Regulatory obligations: SR 11-7, SS1/23, EU AI Act, CBUAE — what boards must know
BoardExCo
Advanced1–2 days · Senior leadership & heads of function
Strategic AI for Senior Leaders
1–2 days · Workshop format
An intensive workshop for C-minus-one leaders, heads of technology, risk, operations and data. Builds the strategic thinking and practical fluency needed to commission, challenge and govern AI programmes.
Building an AI strategy anchored in P&L impact — not technology hype
AI operating models: centralised, federated and hub-and-spoke — trade-offs for FS
A multi-week programme that embeds AI fluency across the organisation. Combines executive workshops, hands-on labs, use-case sprints and governance design sessions into a cohesive transformation journey.
Capstone: Board presentation of AI strategy, prioritised use cases, governance model and 12-month roadmap
BoardExCoTechnologyRisk & Compliance
Modular workshop menu
Each module can be delivered standalone or combined into a bespoke programme. All modules are adapted to your institution's sector, size, regulatory context and AI maturity.
Module
Duration
Audience
Key outcomes
AI for the boardroom
3 hours
Board, ExCo
AI literacy, governance vocabulary, 5 challenge questions
GenAI demystified
Half day
All leaders
Hands-on with LLMs, prompt engineering, evaluating AI outputs
AI strategy design
1 day
ExCo, Strategy
Prioritised AI roadmap anchored in P&L and competitive position
AI risk & governance
1 day
CRO, Compliance, Audit
Risk taxonomy, three lines model, regulatory preparedness
AI operating model
Half day
CTO, CDO, COO
Centre of excellence design, team structure, vendor governance
Each programme is adapted to your institution type. The use cases, regulatory context, risk examples and case studies differ significantly across sectors.
Sector
Focus areas
Regulatory lens
Commercial banking
Credit decisioning, AML/KYC, customer intelligence, financial close
SR 11-7, SS1/23, CBUAE
Capital markets
Market surveillance, trading, post-trade, data products
National AI strategy, data sovereignty, AI governance frameworks
NDMO, PDPL, Vision 2030
Every session is built from your institution's real data, use cases and regulatory context. No generic slides — no recycled content.
What makes this different
Practitioner-led
Delivered by someone who has built and governed AI programmes at scale in FS — not by trainers reading from a manual.
FS-native
Every example, case study and framework is drawn from banking, capital markets, insurance and regulation. No generic cross-industry filler.
Outcome-anchored
Every session ends with a concrete deliverable — an action plan, a prioritised roadmap, a governance model, or a board-ready output. Never just "awareness."
Board-level AI literacy framework — what every director should know
The knowledge architecture that enables board members to ask the right questions and challenge
AI claims with confidence. Built to match how boards actually operate.
Tier 1: Vocabulary (know these terms)
LLM — Large Language Model. Learns patterns from text, generates plausible text.
Token — Fragment of text. ~4 characters per token. Cost drivers.
Hallucination — AI generates false information confidently.
Inference — Running a model to get a prediction or output. The expensive part.
Training — Teaching a model from data. Usually one-time or periodic.
RAG — Retrieval-Augmented Generation. Feed external data into a model to ground it.
Fine-tuning — Specialising a model for your domain by training on your data.
Agents — AI systems that can break down tasks, use tools, and act autonomously.
Bias — Model learns to discriminate along protected dimensions (race, gender, age).
Explainability — Ability to articulate why the model made a decision.
Tier 2: Business context (know the playbook)
The four levers of AI value: revenue uplift, cost reduction, risk mitigation, capital efficiency.
Why most AI use cases take longer and cost more than forecast. (Last-mile integration, governance, data quality.)
The vendor landscape: frontier models (OpenAI, Anthropic, Google) vs platforms (Salesforce, SAP) vs custom.
Why cost-per-inference matters more than total cost. (Token usage scales with volume.)
The trade-offs between privacy (self-hosted), speed (API), and cost (batch vs real-time).
Why governance, not just technology, determines AI success. (Model risk, audit trails, oversight.)
Tier 3: Challenge questions (know what to ask)
On value: "How did you model the cost-per-decision? What's the comparison to the human workflow?"
On risk: "What happens when the model is wrong? How is that error detected and remediated?"
On governance: "Who is accountable for this AI decision — the model owner or the business owner?"
On compliance: "If regulators asked us to explain this decision, could we? In plain English?"
On cost: "What are you paying per inference? How does that scale with volume?"
On vendors: "What happens if [vendor] doubles their pricing or goes bankrupt? What's our exit cost?"
AI glossary for financial services — 50+ essential terms
A reference dictionary covering concepts boards and executives encounter. Not exhaustive, but covers
the essentials for governing AI in FS.
Core ML concepts
Accuracy: What % of predictions are correct? Not the same as value.
Baseline: Performance of the simplest possible model. Anything below baseline is suspicious.
Confusion matrix: Breaks down correct/incorrect across classes. Reveals if model is biased toward one class.
Feature engineering: Crafting inputs to the model. 80% of ML work, often ignored by vendors.
Overfitting: Model learns noise in training data, performs worse on new data.
Precision vs Recall: Precision = how many positives are correct. Recall = how many true positives did you find? Trade-off.
Validation set: Data held back to test the model. If you test on the data you trained on, the number is fake.
GenAI-specific
Chain-of-thought: Asking the model to explain its reasoning step-by-step. Often improves accuracy.
Context window: How much text can the model "see" at once? Larger is better but more expensive.
Few-shot learning: Showing the model examples of what you want it to do, without formal training.
Prompt injection: Adversarial input that makes the model ignore its guardrails.
System prompt: Hidden instructions that guide model behaviour. Usually not visible to users.
Temperature: How creative is the model? Low = predictable, high = creative but less reliable.
Tokens consumed: How much did this request cost? Usually tokens-in + tokens-out.
Risk & governance
Audit trail: Complete log of every input, decision, and output. Non-negotiable for FS.
Bias: Model systematically discriminates. Can be in training data, feature engineering, or optimization.
Concentration risk: Relying on one vendor or model type. Dangerous. Diversify.
Data drift: Model was trained on old data; new data has shifted. Performance decays.
Explainability: Can you articulate why? Regulators increasingly demand this.
Model risk: Structural risk that the model's assumptions are wrong. Usually larger than we admit.
Three lines of defence: Business owner (1st), risk/compliance (2nd), audit (3rd).
Operations & cost
Batch processing: Run many predictions at once, cheaper but slower.
Inference: Running the model to make a prediction. This is the expensive, ongoing cost.
Latency: How fast is the response? Real-time (milliseconds) vs batch (hours)?
Model drift: Model performance degrades over time as the world changes.
Observability: Ability to monitor model performance in production in real-time.
Throughput: How many requests per second can the system handle?
Vendor lock-in: Moving to a different vendor is expensive or impossible.
Interactive knowledge check — test your AI fluency
After the training, participants take a 10-question assessment to validate learning. Sample questions below:
Q: Why does hallucinationetin matter for a fraud detection system? A: Because false positives at scale create operational burden and customer frustration. B: Because the model will make completely false claims. C: Because regulators forbid it.
Best answer: A (in FS, false positives are operationally expensive)
Q: You're told your credit model has 92% accuracy. What's the next question you should ask? A: What's the baseline accuracy? B: Is 92% good? C: Both A and B.
Best answer: C (you need both context)
Q: An AI vendor quotes you $2/1M tokens for inference. How do you compare that? A: It's the cost. B: You need to know token volume, model tier, and whether it's input or output tokens. C: It's expensive.
Best answer: B (pricing only matters at scale)
Q: Your AI system is deployed and performing well. The team wants to remove the human approval step. What's your concern? A: Cost savings aren't real. B: Model risk gets worse as the environment changes. C: Regulators won't allow it.
Best answer: B (drift and unforeseen failure modes are always risks)
Q: You discover your bias testing was only run on 1% of transactions. What does that tell you? A: Testing is inadequate. B: You probably have undetected bias. C: Both A and B.
Best answer: C (you haven't proven the system is fair)
Recommended reading list — where to go deeper
Resources curated for financial services executives and boards. Skip the hype, focus on substance.
Governance & risk frameworks
Federal Reserve SR 11-7: Supervisory Guidance on Model Risk Management (2011, updated 2020)
OCC Bulletin 2023-35: Bank Use of Artificial Intelligence
DFSA / DFA Guidance on Algorithmic Trading and Use of AI in Authorised Firms
IOSCO Artificial Intelligence and Machine Learning in Capital Markets (2023)
Strategy & business models
McKinsey: The State of AI in 2024 (annual)
Goldman Sachs: Harnessing Productivity Potential of AI (FS-specific)
Accenture: Banking on AI (use cases and ROI models)
Oliver Wyman: AI in Capital Markets (market surveillance, trading, post-trade)
Technical deep-dives
Andrej Karpathy: A Recipe for Training Neural Networks (substrate for understanding model risk)
Papers with Code (arxiv.org/list/stat.ML) — cutting-edge research
Hugging Face Documentation — understanding model architectures and limits
NIST AI Risk Management Framework (practical risk categories)