AI Readiness & Responsible Use Program

(This program is only for organizations)

AI is no longer optional. It’s becoming foundational to how work gets done. But the risks of improper use: unintended data leaks, ethical breaches, non-compliance are just as significant as the opportunities.

IAAIR’s AI Readiness & Responsible Use Program is designed to close this capability gap. We equip your entire organization, not just your tech teams to adopt, adapt, and apply AI tools safely, smartly, and strategically.

This is not an off-the-shelf course. It’s a custom-built transformation program to future-proof your enterprise through knowledge, policy, and practical skill.

Program Vision

To make AI a secure, ethical, and productivity-enhancing asset across your organisation—by empowering every employee with awareness, proficiency, and confidence in using AI responsibly.

Why this program is critical now?

  • 92% of Fortune 500 companies are already experimenting with generative AI, but most lack a company-wide strategy for secure use.

  • Shadow AI (unauthorised use of AI tools) is increasing risk exposure in core departments of organizations.

  • Compliance regulations (e.g., GDPR, HIPAA, AI Act) are evolving rapidly, and most organizations are unprepared for audit-readiness.

  • AI’s productivity potential is real, but it’s only unlocked when staff understand what tools are appropriate, what data can be used, and when human oversight is required.

Who is this course for?

  • Organizations with 500+ employees across diverse departments

  • Enterprises actively using AI tools such as ChatGPT, Copilot, Claude, Gemini, Grok, etc.

  • CIOs, CHROs, and L&D leaders building future-proof capability

  • Risk, compliance, and governance heads seeking to reduce liability

  • Government agencies and NGOs adopting digital transformation at scale

Program Structure

(Four Strategic Phases)

  • Objective: Establish a common understanding of AI concepts, capabilities, and boundaries.

    Topics:

    • What is AI, machine learning, and generative AI (in simple terms)

    • Understanding LLMs and how they generate responses

    • How AI tools collect, store, and learn from input

    • Limitations of AI: hallucinations, bias, and model drift

    • Recognising when AI should not be used

    Deliverables:

    • Department-agnostic learning modules

    • Interactive demos of popular AI tools

    • Quick-start guides for safe AI experimentation

  • Objective: Train employees to apply AI within their specific roles while upholding privacy, compliance, and security standards.

    Topics by Department:

    • HR: Recruitment automation, resume screening, internal communications

    • Marketing: Campaign generation, SEO content, audience segmentation

    • Legal & Compliance: Contract analysis, policy summarisation, legal chatbots

    • Finance: Forecasting, reporting automation, budget modelling

    • Operations: Workflow optimisation, documentation, decision support

    • IT & Security: Policy enforcement, usage monitoring, risk detection

    Key Concepts Covered:

    • What data is safe vs. sensitive (PII, client data, trade secrets)

    • Avoiding "data leakage" via public AI tools

    • Aligning AI use with internal governance frameworks

    • Case studies of real-world breaches and their consequences

    Deliverables:

    • Role-specific playbooks and guidelines

    • Policy alignment workshops

    • Customised risk checklists

  • Objective: Apply concepts through practical, scenario-driven exercises.

    Activities:

    • Crafting effective, secure prompts for different use cases

    • Validating AI-generated responses for accuracy and bias

    • Collaborative simulations using enterprise-relevant challenges

    • Failure scenario reviews: what went wrong, how to fix it

    Example Exercises:

    • Marketing team drafts a campaign using generative AI—legal and HR review for risk

    • Finance team generates budget forecast—analyses AI's assumptions

    • HR team builds an AI-based interview tool—reviews for fairness and bias

    Deliverables:

    • Live workshops (onsite/virtual)

    • Sector-specific AI taskboards

    • Performance feedback on prompt quality and decision-making

  • Objective: Ensure sustainable adoption, internal ownership, and measurable impact.

    Components:

    • AI Literacy Certification (Level 1-3 based on completion & performance)

    • Policy Development Toolkit:

      • Templates for acceptable AI use

      • Departmental checklists

      • Escalation protocols for risky outputs

    • Train-the-Trainer Modules:

      • Empower internal champions to maintain and scale the program

      • Build capacity for continuous AI governance and upskilling

    Deliverables:

    • Official certification badges (HR-verifiable)

    • Customised internal policy drafts

    • AI Champions Network Setup Guide