self-paced

AI Best Practices

Learn how organisations and teams can deploy AI safely, practically and in ways that support measurable business value. This course focuses on use case selection, governance, security, privacy, evaluation and evidence-based decision-making so learners can avoid overpromising AI capabilities.

Duration
Self-paced, approximately 60–90 minutes
Price
£1.00
  • Self-paced lessons
  • Assessment
  • Credential

Certificate

AI Best Practices Certificate

Awarded on successful completion of all required course steps.

Course syllabus

Lessons are grouped into clear steps with visual cues, so each course is easier to scan before you start.

Lessons unlock after purchase. Use the entry code from your confirmation email to register, or choose “Already purchased?” above.

  1. Step 1 · Lesson

    Treat AI as a Core Business Capability

    This lesson explains why AI should be managed as a strategic business capability rather than an experimental side project. Learners explore how successful AI adoption depends on clear ownership, operational readiness, risk management and alignment with organisational priorities.

  2. Step 2 · Lesson

    Start with Business Value

    This lesson teaches learners how to link AI initiatives to clear outcomes such as efficiency, decision quality, customer experience, innovation and risk reduction. It also introduces practical ways to define value before investing time, data or budget in an AI project.

  3. Step 3 · Lesson

    Define Use Cases Before Choosing Tools

    This lesson shows how to avoid chasing AI tools for their own sake by starting with the problem, users, data, risks and success criteria. Learners practise thinking through the review process and evidence needed to justify an AI use case.

  4. Step 4 · Lesson

    Choose the Right AI Entry Point

    This lesson compares common AI adoption routes: ChatGPT for fast employee productivity, APIs for product or workflow integrations, and open-weight models for customer-controlled infrastructure. Learners assess when each option is appropriate, including the operational responsibilities that come with more controlled deployments.

  5. Step 5 · Lesson

    Governance, Ownership and Accountability

    This lesson explains how to clarify responsibility for AI-enabled workflows, data, permissions, monitoring, review, escalation and compliance. Learners examine why unclear ownership increases operational, legal and reputational risk.

  6. Step 6 · Lesson

    Security and Privacy Basics for AI Deployment

    This lesson covers the security and privacy controls organisations should consider when deploying AI, including access controls, data boundaries, retention controls, auditability, monitoring, encryption and secure transport. Learners learn how these controls support safe adoption without blocking practical business use.

  7. Step 7 · Lesson

    Evaluate Claims and Avoid Overpromising

    This lesson focuses on evidence-based claims and the importance of being clear about what AI can and cannot reliably do. Learners explore how to challenge assumptions, identify unsupported promises and communicate AI capability responsibly to stakeholders.

  8. Step 8 · Lesson

    Prototype, Test and Improve Continuously

    This lesson teaches learners to treat model choice as a hypothesis rather than a fixed decision. It covers prototyping, testing on representative examples, evaluating quality and then optimising for cost, latency and risk as part of continuous improvement.

  9. Step 9 · Assessment

    AI Best Practices Assessment

    Final assessment for the AI Best Practices course, covering business value, use case definition, governance, operational readiness, risk, data considerations and appropriate AI entry points.

  10. Step 10 · Credential · Optional

    AI Best Practices Completion Badge

    Awarded when the learner completes all lessons and passes the AI Best Practices Assessment.