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Custom AI Governance Document
The Global Workforce Imperative
We need a common language for AI fluency.
Across industries, roles, and geographies — there is no shared standard for what it means to work effectively with AI. That gap is costing organisations productivity, resilience, and competitive edge. It is time for a new shift. Until everyone in the organisation has an understanding of how and when to use AI, it remains an enigma with low returns.
92%
of executives say AI fluency will be critical within 2 years — yet fewer than 1 in 3 have a training framework in place.
$4.4T
estimated annual productivity gain possible from effective AI adoption across the global workforce.
40%
of all working hours globally are exposed to automation or augmentation by large language models.
7 in 10
employees say they need better AI training but don't know where to begin or what standard to meet.
What AI Fluency Index Does
From AI ambiguity to an AI-ready workforce — a complete system for every stage of the journey.
01
Create AI Competencies
Define exactly what AI fluency looks like for every role, level, and function in your organisation — not a generic framework, but one built for your context.
AI Fluency Framework
02
Redesign Roles for AI
Embed AI proficiency requirements into job architectures — alongside the core human skills that must be preserved. Not AI instead of thinking. AI with thinking.
Role Redesign Builder
03
Assess on What You Need
Evaluate candidates and employees against the specific AI competencies their role requires — not a generic AI quiz, but a role-matched fluency assessment with real diagnostic power.
AI Fluency Assessment
04
Train on What They Need
Deliver targeted AI upskilling mapped to each role's actual AI fluency gaps — not generic AI awareness, but applied, role-specific capability development that changes how people work.
Training Architecture
=
The Result
An AI-ready workforce — the future of work.
Professionals who use AI deliberately, evaluate it critically, and take accountability for the outcomes. Who bring more to AI — and get more from it — because they never stopped thinking.
✕
Not this: AI-natives who cannot think, write, decide, or act without feeding the problem into a language model — and who, without it, produce work that is slower, shallower, and less defensible than before AI existed. Dependency is not fluency. Activity is not capability. Token counts are not a measure of anything that matters.
AI Fluency Index is the only platform that takes an organisation from AI ambiguity to a structured, measurable, continuously evolving AI-ready workforce — built around what your roles actually need, not a generic standard.
The AI Fluency Paradox
AI-natives are the new unskilled labour — unless we redesign the work itself.
Growing up with AI tools does not make someone AI-fluent. A generation that has never written a research brief without AI assistance, never drafted a contract without autocomplete, and never made a judgment call without a chatbot suggestion is not more capable — it is more dependent. The risk is not that AI replaces jobs. It is that humans who cannot work without AI become unable to work with it critically.
MIT / Harvard, 2024
Heavy AI users scored 23% lower on original ideation tasks than light users — even when AI tools were removed. The more fluent in AI, the less fluent in independent thought.
Microsoft Research, 2025
Knowledge workers who relied on AI for synthesis tasks showed measurable cognitive offloading — reduced ability to evaluate the quality of the AI's output over time.
Stanford HAI, 2025
Employees trained on when and why to use AI — not just how — demonstrated 40% higher output quality scores than those trained on tool proficiency alone.
✕ The AI-Native Trap
What happens when AI use is unreflective
Uses AI for every task without asking whether AI is the right tool
Cannot identify when AI output is wrong, biased, or hallucinated
Presents AI-generated work as their own without critical review
Loses the ability to form independent judgment over time
Cannot explain decisions to colleagues, clients, or regulators
Productivity gains plateau as cognitive atrophy reduces output quality
Creates compounding organisational risk: AI errors undetected, unchallenged, and amplified
✓ The AI-Ready Professional
What genuinely AI-fluent work looks like
Chooses AI deliberately — knowing when it adds value and when it does not
Critically evaluates every AI output before acting on it
Uses AI to accelerate work that still requires human judgment at the end
Retains and exercises domain expertise independently of AI support
Can explain, defend, and take responsibility for AI-assisted decisions
Gets more from AI because they bring more to the interaction
Builds organisational capability — not dependency — through AI use
The human skills AI cannot replace — and must not erode
⟁
Critical Thinking
The ability to evaluate information — including AI output — for accuracy, relevance, and bias. AI can surface information; only humans can judge whether it is trustworthy, contextually appropriate, and worth acting on. This skill atrophies fastest under unreflective AI use.
◈
Domain Expertise
Deep knowledge of a field, industry, or function that enables meaningful judgment — not just pattern recognition. AI is trained on the past; domain expertise enables professionals to see where AI's training data fails the present problem. The expert and the AI are most powerful together.
◳
Ethical Judgment
The capacity to recognise the ethical dimensions of a decision and act accordingly — even when under time pressure, organisational incentive, or when AI outputs suggest a different course. No AI system can hold professional responsibility. Only humans can.
⬡
Communication & Accountability
The ability to explain, defend, and take ownership of decisions — to clients, colleagues, regulators, and the public. AI can draft the words; only humans can stand behind them. Organisations where AI makes decisions nobody can explain are organisations that have outsourced accountability.
"The capacity of humans to work with AI — not just alongside it — will define the economic winners and losers of the next decade."
World Economic Forum — Future of Jobs Report 2025
The Measurement Problem
AI Effectiveness: The Measurement Paradox
AI adoption is being measured by the wrong things everywhere — and the gap between AI activity and AI capability is widening. Understanding why, and what to measure instead, is the first step to building a workforce that is genuinely AI-ready.
AI capability is advancing faster than our collective ability to harness it. Most organisations treat AI training as a technology question — when in reality it is a human capability question.
The missing piece is a universal, industry-agnostic benchmark for AI fluency: knowing how to evaluate outputs, integrate AI into workflows, manage risks, and continuously adapt. Without that benchmark, no consistent upskilling is possible. This platform is built to change that.
"The capacity of humans to work with AI — not just alongside it — will define the economic winners and losers of the next decade."
World Economic Forum — Future of Jobs Report 2025
The Measurement Crisis
Organisations are mistaking usage for fluency.
Token counts. Prompt volumes. Licences deployed. Workshop attendance. These metrics tell you nothing about whether your workforce can actually use AI well — they measure activity, not capability. The result: a workforce that looks AI-active but isn't AI-fluent, and leadership making investment decisions on misleading data.
01
The Vanity Metric Trap
"Token Maxxing"
Employees generate more prompts because that's what gets measured. Volume is not value. Ten thousand tokens of poor-quality AI output is not more capable than three precise prompts producing a better decision.
02
The Checkbox Problem
Training Without Transfer
A completed module from 12 months ago tells you almost nothing about what an employee can do with AI today. AI fluency must evolve as tools change — and most one-time training programmes don't.
03
The Generalist Fallacy
One-Size-Fits-No-One
A lawyer's AI fluency needs are fundamentally different from a finance analyst's. When training is context-free, so are the results — and so is any measurement of whether it worked.
04
The Invisible Return
No Link to Outcomes
Without a competency framework linking AI capability to job performance, leaders cannot answer: Did our AI upskilling improve decision quality? Reduce time-to-output? AI training budgets are defended on faith, not evidence.
What organisations measure today
How much AI is being used
→
What actually drives returns
How well people use AI for what their job requires
The AI Fluency Index Approach
Upskill for the job. Measure what matters.
Our approach replaces vanity metrics with a competency architecture that tells organisations exactly where AI capability is strong, weak, and what closing that gap is worth in real performance terms. Every role has a different AI fluency profile — we map the right capabilities to the right roles, then build assessment and development tools around that mapping.
Role-Specific AI Fluency
Legal
Contract Review & Legal Research
AI fluency means accelerating contract analysis and legal research while applying expert judgment to validate outputs and manage liability. Token volume is irrelevant. Output quality and risk identification are everything.
Accuracy of AI-assisted contract risk identification vs. manual review
Time saved on legal research without increase in error rate
Ability to spot AI hallucinations in legal citations
Compliance with AI use policy and client disclosure rules
Finance
Analysis, Forecasting & Reporting
Finance professionals need the critical ability to interrogate AI-generated forecasts rather than accept them uncritically. The measure is whether outputs are more accurate and faster — not how often AI was used.
Reduction in reporting time with maintained or improved accuracy
Quality of AI-assisted variance analysis and anomaly identification
Ability to challenge AI forecasting assumptions with domain knowledge
Audit trail quality for AI-assisted financial decisions
HR & People
Talent, Performance & Culture
The critical competency is not using AI tools — it is evaluating whether AI-assisted people decisions are fair, legally defensible, and aligned to organisational values, while actively managing algorithmic bias.
Bias audit scores for AI-assisted shortlisting and performance assessments
HR team confidence in explaining AI-assisted decisions to employees
Compliance with automated decision-making disclosure requirements
Quality and consistency of AI-assisted job design and levelling
Operations
Process Design & Workflow Automation
Operations leaders need to identify where AI adds genuine speed and accuracy, and where human judgment remains essential. The measure is not AI adoption rate, but whether AI-integrated processes deliver better throughput and resilience.
Process cycle time reduction attributable to AI integration
Error rate before and after AI-assisted process changes
Quality of AI risk assessment in operational change management
Team AI fluency uplift as a leading indicator of process improvement
Marketing & Comms
Content, Insights & Customer Intelligence
Marketing teams must maintain brand authenticity and manage risks of synthetic content while using AI for creation, analysis, and optimisation. The measure is campaign effectiveness and content quality — not volume of AI-generated outputs.
Quality scores for AI-assisted content vs. baseline (brand, accuracy, tone)
Campaign performance improvement attributable to AI-driven insight
Compliance with AI content disclosure requirements by jurisdiction
Team ability to detect and correct AI-generated factual errors
Executive Leadership
Strategy, Governance & Decisions
For executives, AI fluency means understanding AI well enough to set strategy, govern risk, and ask the right questions of their teams. The measure is the quality of AI investment decisions and whether the organisation builds genuine AI capability.
Quality of AI governance framework and risk oversight
ROI clarity on AI investments with measurable business outcomes
Leadership confidence in engaging regulators on AI policy
Organisation-wide AI fluency uplift as a strategic KPI
What Gets Measured vs. What Should
✕ Token Maxxing Metrics
What most organisations measure today
Number of AI prompts sent per employee per month
Number of AI licences activated
Volume of AI-generated content produced
Number of employees who completed an AI awareness module
AI tools piloted or launched by IT
AI mentioned in performance review comments
Hours of AI training consumed (not assessed)
Token usage across enterprise AI subscriptions
✓ AI Fluency Index Metrics
What drives genuine productivity and resilience
Role-specific AI competency score by level and function
Quality of AI-assisted outputs vs. pre-AI baseline
Ability to critically evaluate AI outputs before acting
Demonstrated understanding of AI risk, bias, and ethics
Fluency progression over time — not just one-time certification
AI capability linked to job performance outcomes
Organisational AI fluency gap map by role and geography
ROI on AI upskilling tied to measurable business improvements
3.5×
higher productivity gains when measuring AI competency rather than usage (McKinsey, 2025)
68%
of AI initiatives fail to deliver expected ROI when workforce capability is not assessed at the outset (Gartner, 2025)
4 in 5
employees say AI training they received was too generic to change how they actually work (Deloitte, 2025)
2.2×
more likely to sustain AI gains when fluency is integrated into performance management (WEF, 2025)
Ready to measure fluency, not just usage?
The AI Fluency Framework and Assessment map capability gaps by role, track genuine fluency progression over time, and link AI upskilling directly to the business outcomes that matter.
01 — Global Landscape
Global AI Frameworks
Select a region to explore the AI governance frameworks most relevant to your context — then use the builder to synthesise a custom governance document.
⬡
Governance Document Builder
Enable builder mode, click any framework card to select it, and generate a tailored governance blueprint.
OECD
OECD Principles on AI (Updated 2024)
Updated May 2024 and endorsed by 47 jurisdictions, these principles form the source language for the EU AI Act's values clauses and most national AI strategies. They cover values-based design, transparency, robustness, accountability, and human oversight.
Policy Guidance
UNESCO
Recommendation on the Ethics of AI
Signed by 193 member states, the world's first global AI ethics instrument. In 2025 UNESCO launched AI Competency Frameworks for Students and Teachers — the first intergovernmental attempt to define AI literacy as an educational standard.
Ethics Standard
Council of Europe
Framework Convention on AI (CETS 225)
The world's first binding international AI treaty, opened for signature September 2024. Signed by the EU, US, UK, and 45 nations, it applies human rights, democracy, and rule of law principles to the full AI lifecycle — covering both public and private sector AI.
Binding Treaty
G7
Hiroshima AI Process & Code of Conduct
The 2023 Code of Conduct, adopted by 50+ AI organisations, was extended in 2025 to address agentic AI — the first intergovernmental framework to tackle AI that acts autonomously on behalf of users.
Intergovernmental
ISO/IEC
ISO/IEC 42001:2023 — AI Management Systems
The first certifiable AI governance standard. The companion BS ISO/IEC 42006:2025 defines AI auditor qualifications. An official crosswalk to the NIST AI RMF enables dual-framework compliance; increasingly required in regulatory procurement.
Certifiable Standard
EU AI Office
GPAI Code of Practice (August 2025)
Applicable from August 2, 2025, the GPAI Code sets transparency, safety evaluation, and incident reporting requirements for foundation model providers. Adopted by OpenAI, Google, Anthropic, Meta, and Mistral as their primary EU compliance instrument.
Compliance Code
NIST
AI RMF 1.0 + GenAI Profile (2024/25)
Updated March 2025 to address generative AI risks and supply chain vulnerabilities. NIST AI 600-1 (July 2024) adds 12 GenAI-specific risk categories. NIST IR 8596 (December 2025) bridges the AI RMF with the Cybersecurity Framework 2.0.
Risk Framework
WEF
AI Governance Alliance (2025)
Now 300+ member organisations. The 2025 AI Readiness Toolkit for boards and Responsible AI Playbook for SMEs explicitly embed workforce AI fluency as a measurable governance dimension.
Industry Alliance
IEEE
Ethically Aligned Design + P2863
The third edition addresses AI human-value embedding. The 2025 P2863 recommended practice for organisational AI governance provides a structured implementation companion for enterprises.
Technical Standard
NIST
AI RMF + GenAI Profile (March 2025 update)
The de facto US enterprise AI standard, updated March 2025 for GenAI risks. Referenced by the FTC, FDA, SEC, and EEOC in enforcement guidance — making it effectively mandatory for federal contractors and highly adopted in the private sector.
Risk Framework
White House
America's AI Action Plan (July 2025)
Released July 23, 2025, the Trump Administration's AI Action Plan targets US global AI dominance through innovation-first policy, NSF/DOE workforce AI education programmes, and federal AI infrastructure investment — steering away from prescriptive enterprise mandates.
National Strategy
White House
National Policy Framework for AI (March 2026)
Pursuant to EO 14365, this legislative blueprint recommends Congress adopt a unified national AI law preempting state regulations — the most concrete federal AI governance proposal in US history, shaping enterprise compliance expectations for 2026-27.
Legislative Blueprint
Congress
Great American AI Act (Discussion Draft, June 2026)
Released June 4, 2026, by Reps. Obernolte and Trahan — a bipartisan 270-page draft creating the first comprehensive federal AI framework. Four titles: Frontier AI Governance, Workforce, Cybersecurity, and R&D. Codifies the Center for AI Standards and Innovation (CAISI) within the Commerce Department.
Federal Legislation
DHS / CISA
AI Safety & Security Guidelines (Updated 2025)
Updated in 2025 to cover agentic AI systems and AI-assisted cyberattacks. Includes workforce competency requirements for AI security personnel in critical infrastructure — reflecting that AI fluency is now a national security prerequisite.
Security Guidance
FTC
AI & Algorithmic Accountability Enforcement
The FTC's AI enforcement actions have accelerated through 2025-26. Its 2025 commerce report identified opacity, unfair personalisation, and discriminatory outputs as primary enforcement priorities — placing pressure on organisations to demonstrate staff AI literacy.
Enforcement Guidance
European Commission
EU AI Act Conformity Framework
The Digital Omnibus (signed July 8, 2026) modified the enforcement timeline but not the framework's scope. Transparency obligations (Article 50) now live from August 2, 2026. High-risk Annex III systems deferred to December 2027; Annex I to August 2028.
Regulatory
EU AI Office
GPAI Model Supervision (Active August 2026)
The EU AI Office holds full enforcement powers over GPAI models from August 2, 2026, with powers to investigate, order corrective measures, and impose fines. The AI Office published implementing rules in early 2026 on accessing model weights and infrastructure during investigations.
Enforcement
EU HLEG
Ethics Guidelines for Trustworthy AI
Seven trustworthiness requirements — human agency, robustness, privacy, transparency, diversity, societal wellbeing, and accountability — underpin the EU's entire AI policy architecture and are referenced by all member state AI regulators.
Ethics Guidelines
EDPB
GDPR + GenAI Guidance (2025)
The EDPB's 2025 GenAI guidance clarified that LLM outputs used in hiring, credit, or content moderation trigger Article 22 obligations. Organisations must ensure staff can meaningfully explain AI-assisted decisions to affected individuals.
Data Protection
European Parliament
AI Liability Directive (Under Revision)
The November 2025 Digital Omnibus introduced simplification amendments, but the core principle — inadequate AI governance creates legal exposure — remains. Legislative progression expected through 2026-27.
Liability Framework
EC
Cybersecurity & AI Action Plan (July 2026)
The Commission's July 7, 2026 Cybersecurity and AI Action Plan sets out coordinated measures to make EU AI systems more secure and resilient — including competency requirements for personnel responsible for AI system security across critical sectors.
Cybersecurity
UK DSIT
AI Regulation and Safety Bill (Lords, July 2026)
The AI Regulation and Safety Bill passed its Lords second reading July 3, 2026 — a significant shift toward statutory foundations from the UK's previously fully voluntary approach. It proposes a legal duty of care for frontier AI developers, mandatory pre-deployment safety evaluations, and statutory authority for the AI Security Institute. Not yet law; committee stage ahead.
Emerging Legislation
AI Security Institute
Frontier AI Trends Report (December 2025)
The AI Security Institute (rebranded February 2025) published its first Frontier AI Trends Report in December 2025, assessing capability trajectories across reasoning, autonomy, and misuse potential. The UK coordinates the 11-nation International AI Security Institute network.
Safety Evaluation
UK DSIT
AI Growth Lab (October 2025)
A cross-economy regulatory sandbox enabling organisations to pilot AI innovations under modified regulatory conditions. Successful pilots can trigger permanent regulatory reform — one of the most responsive AI governance mechanisms globally.
Regulatory Sandbox
ICO
ICO AI & Data Protection Guidance Suite
Expanded through 2025-26 to cover AI in the workplace, agentic AI, and explainability. The ICO works jointly with the FCA on financial services AI and with Ofcom on media AI — a multi-regulator model in place of a single AI Act.
Data Governance
FCA
Consumer Duty as AI Accountability (2025)
The FCA's Consumer Duty has become the primary AI accountability mechanism for UK financial services, requiring firms to demonstrate AI-driven products deliver good consumer outcomes. AI-specific Consumer Duty guidance expected from the FCA in 2026.
Financial Services
NHS England
AI Framework for Health and Care (Updated 2025)
Updated in 2025 to address generative AI in clinical settings — including LLM use in documentation, diagnosis support, and patient communication. Introduces explicit AI literacy requirements at individual, team, and organisational levels.
Healthcare
MIIT / MOST
New Generation AI Development Plan
China's foundational AI strategy targets AI leadership by 2030 with mandatory AI education integration at all curriculum levels, a 500,000-strong AI specialist workforce target, and sector-specific deployment guidance across banking, healthcare, and manufacturing.
National Strategy
CAC
Generative AI Service Measures (2023, Updated 2025)
Revised 2025 guidance tightened content labelling requirements and extended obligations to enterprises deploying third-party GenAI APIs. Among the most operationally active AI compliance regimes globally, with regular CAC enforcement actions published.
Platform Regulation
CAC
AI-Generated Synthetic Content Rules (2025)
China's 2025 synthetic content rules require all AI-generated images, audio, and video to carry both visible and invisible digital watermarks — a global first in technically mandated AI provenance. Providers must train staff on watermarking compliance processes.
Content Standard
CAC
Companion AI Rules (In Force July 15, 2026)
Emotional support and companion AI rules entered enforcement on July 15, 2026 — covering user protection, disclosure requirements, and operator responsibilities for AI systems designed for ongoing personal relationships. Significant for consumer-facing AI products.
Consumer AI
Ministry of Education
AI Literacy Curriculum Standards
Mandatory AI literacy at all curriculum levels, with defined competency outcomes by graduation. One of the most institutionalised AI fluency pipelines globally — creating a structured national talent pipeline aligned to strategic AI goals.
Education Standard
PBOC / CBIRC
Financial AI Governance Guidelines
Staff in AI-enabled financial roles must meet defined competency standards and undergo regular AI ethics training. Updated 2025 guidance addressed GenAI use in credit decisions and algorithmic trading.
Financial Services
Japan
AI Promotion Act (Effective June 4, 2025)
Japan's first AI law, approved May 28, 2025 and effective June 4. Establishes the AI Strategy Headquarters under the Prime Minister's Office. Compliance remains guideline-driven under the 2024 AI Guidelines for Business — an innovation-first approach deliberately designed to avoid EU-style prescriptiveness.
National Legislation
South Korea
AI Basic Act (Effective January 22, 2026)
The first Asian nation with comprehensive AI framework legislation. Establishes governance institutions, trustworthiness requirements for high-impact AI, and mandatory risk assessments. Extra-territorial effects apply to some cross-border activities affecting Korean users.
National Legislation
Singapore IMDA
AI Verify + Model AI Governance (Updated May 2025)
AI Verify updated May 29, 2025 to cover generative AI — adding testing for hallucination, attribution, and adversarial robustness. Singapore's voluntary governance model is the most widely adopted AI governance reference across ASEAN.
Governance Toolkit
India MeitY
AI Mission + Responsible AI Guidelines (2025)
India's AI Mission (March 2024, INR 10,371 crore) funds infrastructure and skills development. Updated 2025 Responsible AI guidelines add sector-specific supplements. A standalone AI Act is in public consultation as of 2026, with statutory drafts expected.
National Strategy
ASEAN
ASEAN Guide on AI Governance (Updated 2025)
Updated in 2025 to address generative AI — covering content authenticity, automated decision-making, and cross-border AI data flows. ASEAN is developing regional AI standards interoperability enabling mutual recognition across 10 member states by 2027.
Regional Framework
MAS Singapore
FEAT Principles + Project MindForge (2025)
MAS's Project MindForge (2025) developed GenAI risk assessment standards for financial services — including a GenAI risk taxonomy adopted by banks across Singapore, Hong Kong, and Malaysia as the de facto regional standard for AI risk governance in finance.
Financial Services
DISR
Australia's AI Ethics Principles + Mandatory Guardrails
Eight voluntary ethics principles (2019) are now complemented by 10 proposed mandatory guardrails for high-risk AI. Voluntary application underway since 2024; legislative backing expected through sector-specific frameworks rather than a standalone AI Act.
National Framework
CSIRO
Responsible AI Network & AI Assurance Framework (2026)
CSIRO is developing an AI Assurance Framework (2026) intended as Australia's national AI governance reference standard — aligned to ISO 42001 and designed for organisations of all sizes.
Research Standards
APSC / DTA
APS Mandatory AI Policy (Updated 2025)
Updated in 2025 with role-specific competency expectations for Senior Executives and Secretaries — making Australia's federal public sector among the world's most AI-literate government workforces. A benchmark widely referenced by state governments and private sector.
Government Policy
APRA
CPG 220 AI & Model Risk Supplement (2024)
Sets specific expectations for AI model validation, explainability, and ongoing monitoring in financial services. Places AI governance accountability explicitly at board and senior management level — a de facto executive AI fluency mandate for banking, insurance, and superannuation.
Financial Regulation
OAIC
Privacy Act Reform + GenAI Guidance (2025)
OAIC published GenAI and privacy guidance in 2025, covering training data, output accuracy, and data minimisation. Privacy Act amendments introducing automated decision-making rights are in exposure draft, with OAIC enforcement of AI-related privacy breaches expected to accelerate in 2026.
Privacy
Safe Work Australia
AI in the Workplace — WHS Guidance (2025)
Published 2025 guidance on AI as an occupational health and safety risk — covering algorithmic management, AI performance monitoring, and psychosocial hazards. Australia is among the first jurisdictions to formally address AI as a workplace safety issue.
Workplace Safety
UAE MOCAI
UAE National AI Strategy 2031 + AI Regulatory Ecosystem
Extended in 2025 with a world-first: an AI-powered regulatory intelligence ecosystem that uses AI to draft, update, and monitor laws in near-real-time. Mandatory AI literacy for all federal employees; AI fluency positioned as a national development priority.
National Strategy
UAE MOCAI
UAE AI Ethics Guidelines (Updated 2025)
Updated in 2025 to address generative AI — adding principles on content authenticity and AI-human collaboration boundaries. All entities operating in the UAE are expected to align, with sector regulators developing implementation guidance.
Ethics Guidelines
Saudi SDAIA
Saudi National AI Strategy + Governance Regulations
Targets 20,000 AI specialists by 2025 and 100,000 by 2030. SDAIA compliance audits underway across Vision 2030 priority sectors (finance, health, education, tourism) from 2025. Sector-specific AI governance supplements in development.
National Strategy
Qatar MCIT
Qatar National AI Governance Framework (In Development)
Expected for publication in 2026, targeting high-risk AI in finance, healthcare, and government. Early drafts indicate a mandatory AI governance certification scheme aligned to ISO 42001 — Qatar positioned to become the first Gulf state with enterprise AI certification requirements.
National Strategy
DIFC / ADGM
Financial Free Zone AI Governance (Updated 2025)
DIFC extended senior manager accountability to AI governance under the FSMR; ADGM published GenAI-specific risk guidance for asset managers in 2025. Making the UAE's financial free zones among the most advanced AI governance environments in MEA.
Financial Services
Israel
AI Innovation Authority Framework (Updated 2025)
Updated in 2025 with sector-specific guidance for healthcare and financial services. Referenced in defence procurement, health technology certification, and public sector AI deployment standards — reflecting Israel's globally significant AI R&D intensity per capita.
National Framework
02 — Regulatory Landscape
Global AI Regulations
Select a region to explore the laws, acts, and bills most relevant to your organisation — then use the Framework Builder to create a customised compliance framework.
◈
Custom Framework Builder
Enable builder mode, then click + on any regulation to add it to your personalised compliance framework.
2025
ISO/IEC 42001 — AI Management System Standard
Rapidly adopted as the universal benchmark for AI governance certification. The companion BS ISO/IEC 42006:2025 defines AI auditor qualifications; an official NIST crosswalk enables dual-framework compliance. Many regulators globally now treat ISO 42001 certification as prima facie evidence of adequate AI governance.
Active
2024
Council of Europe AI Convention (CETS 225)
The world's first binding international AI treaty, signed September 2024 by the EU, US, UK, and 45 nations. Applies human rights and rule of law principles to the full AI lifecycle for both public and private sector AI — creating enforceable international accountability obligations.
Active
2025
Paris AI Action Summit — International AI Safety Report
The February 2025 Paris summit produced the International AI Safety Report — endorsed by 97 countries, the first government-commissioned scientific assessment of frontier AI risk. The 11-nation AI Security Institute network coordinates global frontier AI evaluations, sharing evaluation methodologies.
Active
2025
EU GPAI Code of Practice (Active August 2025)
Applicable from August 2, 2025. Sets transparency, safety evaluation, and incident reporting requirements for GPAI model providers. Adopted by OpenAI, Google, Anthropic, Meta, and Mistral as their primary EU compliance instrument and the de facto global standard for frontier AI governance.
Active
2025
EO 14179 — Removing Barriers to American Leadership in AI (January 2025)
Signed January 23, 2025, revoked Biden's EO 14110. Establishes a deregulatory, innovation-first AI policy. Directed development of the AI Action Plan (July 2025) and shifted federal AI governance from oversight-first to competitiveness-first.
Active
2025
EO 14365 + National Policy Framework (December 2025 / March 2026)
EO 14365 (December 11, 2025) directs a minimally burdensome national AI standard and created an AI Litigation Task Force to challenge state AI laws. The White House National Policy Framework (March 20, 2026) recommends Congress adopt legislation broadly preempting state AI laws — the most significant federal AI governance move in US history.
Active
2026
Great American AI Act — Discussion Draft (June 4, 2026)
Bipartisan legislation (Reps. Obernolte / Trahan) for the first comprehensive federal AI framework. Four titles: Frontier AI Governance, Workforce, Cybersecurity, and R&D. Would codify CAISI within Commerce ($100M/yr), require frontier model audits by Independent Verification Organisations, and preempt state AI model development laws.
Draft
2026
Colorado AI Act (SB 205) — Effective June 30, 2026
The first comprehensive US state AI law requires impact assessments, AI disclosures, and staff training on bias detection for high-risk AI systems. Under scrutiny from EO 14365's AI Litigation Task Force; its status may change if federal preemption legislation passes.
Active
2026
California AI Transparency Act (SB 53) — Effective January 1, 2026
Requires developers of covered generative AI systems to implement AI detection tools and disclose AI-generated content. Accompanied by AB 2013, requiring public disclosure of AI training data.
Active
2026
Texas TRAIGA — Effective January 1, 2026
Texas Responsible AI Governance Act covers high-impact AI in employment, healthcare, finance, and housing. Imposes impact assessment, transparency, and human oversight requirements, making Texas one of the two US states with comprehensive active AI compliance obligations.
Active
2026
NYDFS Binding Guidance on AI in Financial Services (2026)
The New York Department of Financial Services published binding AI governance guidance for DFS-regulated entities, covering model risk management, algorithmic fairness, and board-level accountability — the most prescriptive US financial services AI compliance requirement as of mid-2026.
Active
2026
EU AI Act — Transparency Obligations Live August 2, 2026
Article 50 transparency obligations now in force from August 2, 2026: chatbot disclosure, synthetic content marking, and deepfake labelling. Fines up to €15M or 3% of global turnover. Commission guidelines published July 20, 2026. A new prohibition on AI-generated non-consensual intimate imagery added to Article 5.
LIVE NOW
2026
EU AI Act — Digital Omnibus (July 8, 2026)
The Digital Omnibus, signed July 8, 2026, deferred Annex III high-risk systems (recruitment, credit scoring, law enforcement, education) from August 2, 2026 to December 2, 2027 — a 16-month extension. Annex I systems (medical devices, machinery) deferred to August 2, 2028. SME compliance simplified for firms up to 750 employees and €150M revenue.
Active
2025
EU GPAI Enforcement — EU AI Office Active August 2026
The EU AI Office holds full enforcement powers over GPAI models from August 2, 2026: powers to investigate, demand access to model weights and code, order corrective measures, and impose fines. The GPAI Code of Practice is the primary compliance instrument for frontier AI labs.
Active
2018
GDPR Article 22 + EDPB GenAI Guidance (2025)
The EDPB's 2025 GenAI guidance clarified that LLM outputs in hiring, credit, or content moderation trigger Article 22 obligations. Staff capable of explaining AI-assisted decisions to affected individuals is now a GDPR compliance expectation, not just a best practice.
Active
2025
EU Data Act — Effective September 12, 2025
Creates new rights and obligations around data generated by connected devices and AI systems. Works in tandem with the EU AI Act's data quality requirements, with direct implications for staff data governance competencies in AI-intensive roles.
Active
2026
AI Regulation and Safety Bill — Lords Second Reading July 3, 2026
Passed its Lords second reading on July 3, 2026. Proposes a legal duty of care for frontier AI developers above a compute threshold, mandatory safety evaluations before deployment, and statutory authority for the AI Security Institute. Not yet law — committee stage and third reading ahead. Statutory obligations expected no earlier than 2027.
In Lords
2026
Data (Use and Access) Act 2025 — In Force February 5, 2026
Replaced GDPR Article 22 with new Articles 22A–22D, making solely automated decisions lawful in more circumstances but only where transparency, human review, and contestability safeguards are documented. In force February 5, 2026 — the most significant UK AI-adjacent legislation as of mid-2026.
Active
2025
ICO Guidance on AI + GenAI in the Workplace (2025)
Expanded through 2025-26 to cover AI in employment, agentic AI systems, and AI explainability. The ICO works jointly with the FCA on financial services AI — signalling its next enforcement priorities and the multi-regulator approach the UK is pursuing in place of a single AI Act.
Active
2025
FCA Consumer Duty + AI Governance Guidance (2025)
Consumer Duty has become the primary AI accountability mechanism for UK financial services. Updated AI guidance in 2025 and anticipated AI-specific Consumer Duty implementation guidance in 2026 create a de facto AI competency requirement for all FCA-regulated firms.
Active
2026
Companion AI and Emotional Support AI Rules — In Force July 15, 2026
China's companion AI rules entered enforcement July 15, 2026, covering user protection, mandatory disclosure, and operator responsibilities for AI systems designed for ongoing personal relationships. Significant for all consumer-facing AI products targeting Chinese users.
Active
2023
Interim Measures for Generative AI Services (Updated 2025)
2025 revisions tightened content labelling requirements and extended obligations to enterprises deploying third-party GenAI APIs. One of the most operationally active AI compliance regimes globally, with regular CAC enforcement actions providing real-world guidance on compliance expectations.
Mandates both visible and invisible digital watermarks on all AI-generated images, audio, and video — a global first in technically mandated AI provenance. Providers must implement content authenticity infrastructure and train staff on compliance processes.
Updated 2025 guidance extended obligations to recommender systems in enterprise productivity tools and internal HR platforms — creating one of the most operationally detailed AI workforce competency mandates globally.
Active
2026
Comprehensive AI Law — Under Active Legislative Development
In active legislative development as of mid-2026, expected to consolidate sectoral regulations into a unified risk-tiered framework. Early drafts indicate provisions for mandatory AI literacy certification in regulated industries and national AI safety review for high-capability systems.
Draft
2025
Japan AI Promotion Act — Effective June 4, 2025
Japan's first AI law. Establishes the AI Strategy Headquarters under the Prime Minister. Compliance remains guideline-driven under the 2024 AI Guidelines for Business — innovation-first, avoiding EU-style prescriptiveness.
Active
2026
South Korea AI Basic Act — Effective January 22, 2026
The first Asian nation with comprehensive AI framework legislation. Establishes governance institutions, trustworthiness requirements, and mandatory risk assessments. Sector-specific AI competency requirements for financial services, healthcare, and employment are in development.
Active
2025
Singapore — AI Verify Updated for GenAI (May 2025)
AI Verify updated May 29, 2025 to cover GenAI — adding testing for hallucination, attribution, and adversarial robustness. Singapore's voluntary governance model remains the most widely adopted AI governance reference across ASEAN.
Active
2026
India — Draft Digital India Act (July 1, 2026)
Published July 1, 2026 — contains India's first statutory AI liability framework for Indian operators. Alongside the existing DPDP Act implementing rules (2025), it creates a dual compliance obligation for enterprises processing personal data with AI in India.
Draft
2024
Canada — Directive on Automated Decision-Making (Updated June 2025)
Canada's AIDA (Bill C-27) lapsed January 6, 2025 when Parliament prorogued. The existing Directive on Automated Decision-Making (updated June 24, 2025) requires government AI systems to meet compliance obligations by June 2026. Ontario's Bill 194 (November 2024) regulates public sector AI provincially.
Active
2025
Government Response to Safe & Responsible AI Consultation (2025)
Confirmed mandatory guardrails for high-risk AI with a phased regulatory approach — embedding AI-specific obligations into existing sector-by-sector frameworks rather than a standalone AI Act. A National AI Strategy refresh is expected in 2026.
Active
2024
10 Mandatory Guardrails for High-Risk AI (Voluntary Application 2024-26)
Human oversight, transparency, accountability, and contestability — currently applied voluntarily pending legislative backing. Organisations in regulated sectors are encouraged to self-assess; mandatory legislative backing expected through sector-specific legislation.
Voluntary
2023
APS Mandatory AI Policy — Commonwealth Literacy Requirements (Updated 2025)
Updated in 2025 with role-specific competency expectations for Senior Executives and Secretaries. One of the world's first government-wide mandatory AI fluency requirements, now a benchmark widely referenced by state governments and private sector organisations.
Introduces rights around automated decision-making and AI transparency. When enacted, organisations must notify individuals of AI involvement in significant decisions and provide human review pathways — directly impacting staff AI competency in HR, credit, and healthcare roles.
Draft Legislation
2024
APRA CPG 220 AI & Model Risk Supplement (2024)
Sets specific expectations for AI model validation, explainability, and monitoring. Places AI governance accountability explicitly at board level — a de facto executive AI fluency mandate for banking, insurance, and superannuation sectors.
The UAE approved a world-first: an AI-powered ecosystem that uses AI to draft, update, and monitor laws in near-real-time. Positions the UAE as the global frontier of AI-native governance — enabling near-real-time regulatory updates across all UAE government sectors.
Active
2025
UAE PDPL — Updated Executive Regulations (2025)
Updated Executive Regulations (2025) added provisions on automated decision-making transparency and human oversight. Organisations must document AI involvement in significant personal data decisions and ensure relevant staff hold defined AI governance competencies.
Active
2024
DIFC Data Protection Law — AI Amendment (2024, Updated 2025)
DIFC Commissioner guidance (2025) extended senior manager accountability to AI governance under the FSMR. DIFC-regulated entities must have compliance staff capable of auditing AI-driven financial decisions — the most prescriptive AI governance requirement in the Gulf.
Active
2024
Saudi Arabia PDPL Amendments + SDAIA Compliance Audits (2025)
SDAIA compliance audits underway from 2025 for enterprises in Vision 2030 priority sectors. Updated PDPL regulations address automated decision-making and AI processing of personal data — creating audit-based enforcement pressure for the first time.
Active
2026
Qatar National AI Governance Framework (In Development 2026)
Expected for publication in 2026, covering high-risk AI in finance, healthcare, and government services. Early consultation documents indicate a mandatory ISO 42001-aligned certification scheme — potentially the first Gulf state with enterprise AI certification requirements.
In Development
2025
Israel AI Innovation Authority Framework (Updated 2025)
Updated 2025 with sector-specific guidance for healthcare and financial services. Referenced in defence procurement and public sector AI deployment standards — reflecting Israel's globally significant AI R&D intensity.
Active
03 — In Practice
Companies Doing It Right
A growing body of organisations are moving beyond AI experimentation to embed AI fluency as an operational capability — with measurable outcomes.
i. Companies Adapting AI — News & Analysis
Financial Services · 2024–26
JPMorgan Chase: AI-First at Enterprise Scale
Deployed LLM Suite to 60,000+ employees across investment banking and operations. AI fluency modules are mandatory before tool access; outputs subject to structured review. The bank reports measurable gains in research synthesis, document review, and client reporting speed — and has since extended the programme to wealth management and compliance teams.
Sector: Finance | Scale: 60,000+ users
Healthcare · 2024–26
Mayo Clinic: Role-Stratified AI Governance
Established an AI governance board that vets all tools before clinical deployment. Radiologists, nurses, and administrators each hold differentiated AI competency requirements — a role-stratified model cited by the AMA as a global best practice. In 2025 Mayo extended the framework to cover GenAI in clinical documentation and patient communications.
Sector: Healthcare | Model: Role-stratified
Professional Services · 2024–26
Deloitte: AI Academy at Global Scale
A $1.4B investment in AI capabilities including a global AI Academy that has trained 100,000+ professionals globally. AI training completion is linked to performance reviews and career progression — and Deloitte's AI fluency model for client organisations is now commercially available as a standalone consulting product.
Sector: Consulting | Trained: 100,000+
Retail · 2024–26
Walmart: AI Fluency as Operational Literacy
Mandatory AI literacy training for store managers and supply chain leads. Associates are assessed on their ability to interpret AI-generated recommendations rather than passively follow them — a distinction that reflects genuine AI fluency over AI activity. Walmart's AI fluency model is now embedded in its vendor qualification process.
Sector: Retail | Scope: Global ops
ii. Companies Training Their Employees
01
Amazon
AI Ready: 2M People Trained
Amazon's AI Ready programme committed to training 2 million people globally and has exceeded that target as of 2025. Internally, all employees access an AI learning passport linked to role-specific competency paths via AWS Skill Builder.
02
Microsoft
AI Skills Initiative + Copilot Champions
Pledged 2.5M people trained across EMEA and APAC via LinkedIn Learning and GitHub. Internally, every division has designated Copilot champions who track competency uplift — making AI fluency a line-management accountability, not just an L&D metric.
03
PwC
AI Learning Exchange — Scenario-Based Assessment
$1B invested in AI upskilling across 75,000 US employees. PwC's distinctive approach: scenario-based assessments that test judgment and application rather than recall — setting a benchmark for outcome-linked AI fluency measurement.
04
Accenture
AI School — 250,000 Trained
Accenture's AI School has trained 250,000 employees with a role-stratified curriculum from frontline to C-suite. Formal competency certification is tied to LearnVantage; the curriculum is published as a reference for client organisations.
05
IBM
SkillsBuild + watsonx Badges
IBM's SkillsBuild offers job-family-specific AI pathways for HR, finance, legal, and IT. The watsonx badge programme provides industry-recognised credentials at three levels — Explorer to Expert — integrated with IBM's talent marketplace.
06
Unilever
AI Fluency as a People Metric
Unilever integrated AI fluency into its global performance review cycle — every employee has a documented AI learning goal and managers are assessed on team capability-building. This treats fluency as a people metric rather than an IT outcome, and is among the most cited examples of HR-led AI adoption globally.
04 — The Framework
AI-Ready Workforce
Building an AI-ready workforce requires structured, measurable, and continuously updated approaches to human capability — spanning how we assess fluency, how we train, and how AI integrates into the rhythms of work.
A shared language for human-AI capability — built for every role, every industry, every level of the organisation.
The AI Fluency Competency Framework defines what it means to work effectively with AI — not just technically, but strategically, ethically, and operationally. Four progressive levels mapped across five core competency dimensions give organisations a consistent benchmark to assess, develop, and recognise AI capability across the full workforce.
4
Progressive fluency levels — Foundational to Strategic
5
Core competency dimensions spanning understanding to leadership
20
Defined competency statements across the full framework
∞
Industries and roles the framework serves without modification
The Four Levels of AI Fluency
Level 01
Foundational
All employees · Every function
Understands what AI is and can engage with AI-assisted tools responsibly. Can identify when AI is in use and apply basic judgment about outputs.
AI AwarenessSafe UseBasic Evaluation
Level 02
Practitioner
Knowledge workers · Team leads
Applies AI tools effectively within their domain, crafts purposeful prompts, critically evaluates outputs, and knows when to challenge or override AI recommendations.
Prompt CraftCritical EvaluationRisk Awareness
Level 03
Advanced
Specialists · Managers · Domain leads
Designs and oversees AI-integrated workflows, assesses AI tools for organisational fit, manages AI-related risks within their function, and builds team AI capability systematically.
Workflow DesignRisk ManagementTeam Development
Level 04
Strategic
Senior leaders · Executives · Board
Sets AI strategy, governs AI risk at an organisational level, engages with regulators and ethics bodies, and creates the conditions for an AI-ready workforce.
AI StrategyGovernanceCultural Leadership
Competency Dimensions × Fluency Levels
Dimension
Foundational
Practitioner
Advanced
Strategic
AI Conceptual Understanding
Knows what AI is and how common tools work conceptually
Understands model types and limitations relevant to their domain
Compares AI approaches and assesses technical fit for business problems
Engages credibly with AI developers, researchers, and external experts
Applied AI Use
Uses AI tools safely and appropriately in daily tasks
Designs effective prompts and applies AI to accelerate domain-specific work
Integrates AI into team workflows and measures impact on output quality
Champions AI adoption across the organisation and drives capability investment
Critical Evaluation
Spots obvious errors or inappropriate AI outputs before acting
Systematically evaluates AI outputs for accuracy, bias, and fitness for purpose
Establishes team-level quality standards for AI-generated work
Sets organisational standards for AI output quality and human oversight
Ethics, Risk & Responsibility
Understands basic ethical obligations and when to flag AI concerns
Identifies bias, privacy risks, and ethical issues in their AI use
Manages AI risk within their function and escalates appropriately
Governs AI risk at an organisational level and engages with regulators
Adaptive & Continuous Learning
Open to learning new AI tools; updates personal AI practices
Actively keeps pace with AI developments in their domain
Builds team learning culture and keeps AI workflows current
Commits organisational resources to continuous AI capability development
Full Framework — Available on Request
The complete AI Fluency Framework goes deeper.
The detailed version includes 20 fully defined competency statements with behavioural indicators, role-family mapping across 12 industry sectors, assessment rubrics, performance management integration guides, and benchmark data from early adopter organisations.
20 fully defined competency statements with behavioural indicators
Role-family mapping across 12 industry sectors
Assessment rubric and scoring guide for each competency
Integration guide for performance management and career frameworks
Responded within 2 business days
02 — Role Redesign
Redesigning roles for an AI-embedded future of work
Every entry-level job description written before the AI era was written without AI in mind. Roles that don't include AI proficiency as a core requirement are already behind. Use the builder to see how a role can be reimagined — preserving core function while embedding the AI fluency the role now demands.
⬡ Interactive Builder — Load an example or paste your own
Step 1 — Define the Role
Paste a job description or load an example
Job Title
Industry
Role Level
Job Description (paste or type)
or load an example
Redesigned Role Profile
AI-embedded job architecture
◳
Select an industry, paste or load a job description, and click Redesign to generate an AI-embedded role profile.
⬡ AI-Ready Role Profile
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Industry
—
Level
—
AI Fluency Required
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Ready to redesign your organisation's roles for an AI-ready workforce?
The full AI Fluency Index role redesign service maps AI proficiency requirements across your entire job architecture — from entry level to executive — tailored to your industry, operating model, and strategic AI priorities.
Effective AI training is contextual, continuous, and competency-linked. Successful programmes move beyond awareness sessions toward embedded, role-specific learning that changes how people actually work.
Foundational AI Literacy
Every employee needs a baseline understanding of what AI is, how LLMs work conceptually, and what responsible use looks like — knowing how to evaluate information before acting on it.
Role-Specific AI Application
Leading organisations design AI pathways mapped to specific job families. HR professionals learn AI in talent acquisition; legal teams learn AI in contract review; finance professionals in forecasting. Contextual relevance is the key to learning transfer.
Critical Evaluation & Prompt Craft
Core AI fluency: critically assessing AI outputs — identifying hallucinations, bias, and gaps — before acting. Complemented by prompt engineering: designing queries that yield reliable outputs. Organisations investing here see the largest productivity gains.
Ethical Judgment & Risk Awareness
AI fluency is incomplete without understanding the ethical dimensions — privacy, bias, accountability. Employees need a working framework for recognising when AI-assisted decisions require additional scrutiny, escalation, or override. Increasingly a regulatory requirement.
Continuous Learning & Adaptation
AI evolves at a pace that makes any one-time training obsolete within months. Effective architectures build in continuous learning: quarterly refreshers, model release briefings, and peer learning communities — treating fluency as an ongoing operating condition.
Leadership & Strategic AI Fluency
Executives need the ability to make sound AI investment decisions, interpret audit results, engage regulators, and set the right cultural tone. Business schools are increasingly developing executive AI literacy as a standalone discipline.
AI fluency delivers its greatest value when embedded in the core processes that govern how work gets done — not treated as a standalone learning initiative.
05 — Operational Embedding
Process Integrations
AI fluency delivers its greatest value when embedded in the organisational processes that govern how work gets done — not treated as a standalone learning initiative.
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Performance Management
Integrating AI fluency into performance management signals that AI capability is a professional expectation. Leading organisations set AI learning goals in annual review cycles and recognise AI-augmented productivity in compensation frameworks.
AI fluency goals embedded in annual KPIs and OKRs
Manager certification as AI adoption leads
Competency-linked progression frameworks
AI-augmented output quality as a performance metric
360-degree feedback on AI judgment and ethical use
Quarterly AI fluency check-ins in review cycles
◫
Risk & Compliance
Risk and compliance functions must evolve to include AI-specific oversight — understanding model outputs as audit-relevant artefacts and ensuring employees can identify and report AI risk.
AI risk registers mapped to ISO/IEC 42001
Mandatory AI impact assessments for high-risk use cases
AI-specific incident reporting pathways
Compliance training on EU AI Act and sector regulations
Ongoing monitoring of AI outputs in regulated workflows
AI fluency requirements for compliance officers and auditors
◳
Legal
Legal teams face a dual challenge: advising on AI-related legal risk while using AI tools in their own work. AI fluency spans IP implications, liability in AI-assisted decisions, and the rapidly evolving regulatory landscape.
AI in contract analysis: risk and quality controls
Intellectual property and AI-generated content policies
Liability frameworks for AI-assisted legal decisions
Data privacy compliance in AI tool deployment
AI-specific clauses in vendor and supplier contracts
Legal team AI fluency certification and CPD requirements
⬡
Ethics
Ethical AI use requires operational mechanisms. Leading organisations establish AI ethics review boards, build ethics checkpoints into deployment pipelines, and train employees to apply ethical judgment at the point of AI use.
AI ethics review board with cross-functional representation
Ethics-by-design checkpoints in AI deployment pipelines
Bias audit requirements for customer-facing AI systems
Whistleblower-equivalent pathways for AI misuse reporting
Ethics training embedded in all AI fluency programmes
Alignment with UNESCO AI Ethics Recommendations and ISO 42001
Our Mission
Building the global standard for human-AI capability.
AI Fluency Index was founded on a single conviction: that the gap between AI capability and human readiness is not a technology problem — it is a people problem. And like all people problems, it is solvable with the right frameworks, the right measurement, and the right commitment to genuine capability-building.
We believe every organisation deserves a clear, credible, and actionable answer to: "Are our people ready to work with AI?" That answer requires a common language. Building that language is our work.
Rigour
Evidence-based frameworks grounded in real workforce research, not vendor-driven narratives.
Universality
Designed for all roles, industries, and geographies — not just technical teams.
Practicality
Tools organisations can use in performance reviews, hiring, training, and governance.
Independence
No vendor relationships. No platform bias. Our only interest is in what actually builds genuine AI fluency.
Why we built this
"AI readiness is not a technology problem. It is a people problem — and like all people problems, it is solvable."
Built by practitioners who have sat at the intersection of people strategy, organisational learning, and emerging technology.
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Founder
Shuchi Singh
MBA (HR) — XLRI Jamshedpur · B.Tech · SHRM-SCP · Microsoft Certified AI Transformation Leader
Shuchi founded AI Fluency Index on a conviction that the greatest bottleneck to meaningful AI adoption is not the technology itself — it is the absence of a shared standard for human AI capability. With a career spanning HR strategy, people transformation, and organisational design, she bridges the technical and the human: translating AI complexity into workforce strategy that organisations can actually implement. Her grounding in human resources from XLRI, SHRM-SCP certification, and engineering background gives her a rare ability to design frameworks that are simultaneously rigorous and operationally useful.
AI Workforce StrategyHR TransformationOrganisational DesignAI Fluency FrameworksPeople Analytics
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Co-Founder
Coming Soon
We are building this together. Our co-founder brings complementary expertise across every dimension of what AI Fluency Index is building — more to be shared shortly.
To be announced
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