AI LITERACY • IT & BUSINESS LEADERS

AI Literacy Self-Assessment for Banking & Enterprise Leaders

Rate your current capability across four pillars. Answer honestly— this is a tool for reflection and learning, not a compliance exam.

Progress0 / 16 questions answered

Pillar 1: Conceptual Understanding

How well you understand core AI concepts, limits, and implications.

Q1.1

Fraud Detection Rules vs. Behavioral AI

How confident are you in explaining the difference between rule-based fraud systems and AI-driven behavioural models to your board or regulator?

1 — No real understanding / not confident at all2 — Basic awareness, but cannot explain clearly3 — Working understanding; can discuss at high level4 — Strong understanding; can challenge and guide decisions5 — Expert; can lead strategy and advise others

Q1.2

Data Quality vs. Algorithm Sophistication

When reviewing AI proposals, how clearly can you distinguish between data quality issues and algorithmic limitations?

Q1.3

Explaining AI Decisions to Regulators

How comfortable are you in explaining model behaviour, limitations, and documentation requirements to internal audit or regulators?

Q1.4

ML vs. GenAI vs. Predictive Analytics

How clearly can you articulate when to use traditional ML, generative AI, or simpler predictive analytics for a business problem?

Pillar 2: Strategic Assessment

Your ability to evaluate AI use cases, value, and build vs. buy decisions.

Q2.1

Evaluating AI Use Case Worth

How confident are you in assessing which AI use cases are commercially attractive versus ‘shiny demos’?

Q2.2

Build vs. Buy for AI

How strong is your ability to decide when to build in-house vs. buy platforms / models from vendors or cloud providers?

Q2.3

Total Cost of Ownership (TCO)

When approving AI budgets, how well do you account for end-to-end TCO (data, infra, people, change, risk) rather than just licenses?

Q2.4

Back-Office Automation Proposal

If a team brings an automation proposal using AI, how effectively can you challenge scope, risk, data needs, and ROI?

Pillar 3: Organizational Readiness

How ready your organization is in terms of skills, structure, and change.

Q3.1

AI Implementation Positioning

How clearly is AI positioned in your organisation’s strategy (experiments vs. core capability vs. differentiator)?

Q3.2

Workforce Preparation

How prepared is your workforce (leaders + frontline) for AI-driven change in terms of skills, communication, and incentives?

Q3.3

Scaling AI Pilots

How strong is your track record of taking AI pilots into production with adoption, not just POCs?

Q3.4

AI Governance Structure

How clear are the roles, accountabilities, and escalation paths for AI across business, tech, risk, and compliance?

Pillar 4: Ethical & Regulatory Governance

Your readiness to manage risk, regulation, and responsible AI use.

Q4.1

Identifying Bias in Credit AI

How confident are you that you can identify and challenge potential bias in credit / risk AI models used in your organisation?

Q4.2

Explaining Decisions to Regulators

How ready are you to explain AI decisions, evidence, and model limitations to regulators or consumer protection bodies?

Q4.3

Third-Party Vendor Risk

How robust is your oversight of AI vendors, model providers, and cloud partners from a risk and compliance perspective?

Q4.4

Ethical AI Framework

How mature is your ethical AI framework (principles, policies, review forums, and escalation mechanisms)?