A practical micro-course that helps organisations anticipate and prepare for the next wave of AI governance. Learn how to evaluate autonomous decision-making, navigate cross-border rules, address emerging AGI ethics, and convert evolving standards into actionable controls—culminating in a clear 30–60-day governance action plan.
A fast, practical introduction to ethical generative AI in the workplace. Learn to spot deepfakes and voice clones, respect ownership and attribution, apply model guardrails, and design sensible moderation workflows—so your organisation can adopt AI responsibly while protecting brand, people, and compliance.
A concise, hands-on guide to keeping humans in control of AI. Learn how to choose between in-loop and on-loop oversight, implement effective overrides and failsafes, assign accountability for high-stakes decisions, and build continuous monitoring with clear escalation paths so your organisation can deploy AI safely and compliantly.
Learn practical, sector-specific guardrails for ethical AI in healthcare, finance, the public sector, and education. Spot risks early, apply governance tools (e.g., DPIAs, model cards), and align with safety and compliance expectations so your organisation can deploy AI responsibly and preserve trust.
A practical micro-course that gives teams the core skills to audit AI systems with confidence: risk-based methodologies, internal vs. third-party audit practices, robust audit trails and logging, and alignment to recognised certification schemes like ISO/IEC 42001—so your organisation can demonstrate responsible, compliant AI.
A compact, hands-on primer for teams who need to govern data responsibly in AI projects—covering provenance and lineage, consent and data rights, synthetic data and minimisation, and data sovereignty with cross-border considerations—so initiatives remain compliant, ethical, and trustworthy.
Give your team a fast, practical foundation in building trustworthy AI—focusing on explainability, privacy, security, and inclusive design—so they can reduce risk, meet governance expectations, and deliver AI outcomes that stakeholders understand and trust.
A practical micro-course for teams who need to spot and reduce AI bias, understand fairness metrics and trade-offs, and set up clear redress and accountability mechanisms—no prior experience required.
A concise, business-focused introduction to enterprise AI governance. Learn how to set up oversight committees, clarify roles and responsibilities, embed controls across MLOps and the AI lifecycle, and create effective internal policies that keep AI projects compliant, auditable, and aligned to organisational goals.
Get a clear, practical overview of today’s AI regulatory landscape—covering the EU’s approach, the NIST AI Risk Management Framework, China’s directives, and soft-law/industry self-regulation—so your organisation can reduce risk, build trust, and operationalise responsible AI without getting lost in legal detail.
A concise, hands-on micro-course that teaches teams to identify and classify AI risks, run AI Risk Impact Assessments (AIRIA), put effective guardrails and controls in place, and respond swiftly to incidents—making everyday AI use safer, compliant, and auditable across the organisation.
A concise, business-friendly introduction to applying AI for market intelligence in agribusiness. Learn how ML improves price forecasting, how to test product–market fit with AI, how LLMs speed trend and valuation analysis, and where fintech/insurtech models can unlock new value—without needing a technical background.
A concise, business-friendly introduction to AI governance that equips teams with a shared vocabulary, clear roles, and a practical checklist for responsible AI. Learn the principles of transparency, accountability, and fairness, understand global vs local challenges, and leave ready to assess real use cases with confidence.
A concise, hands-on introduction to the ethical foundations of responsible AI. Learn to apply utilitarian, rights-based, and justice/equity perspectives to real workplace scenarios, use quick bias/impact tools, and set guardrails—especially where autonomous decisions require human oversight.
A concise, non-technical overview of how AI transforms food production and processing—covering predictive maintenance, recipe optimization, robotics in packaging, and sensory analysis—so teams can cut waste, increase yield and consistency, and launch safe, scalable pilots aligned to quality and compliance goals.
A concise, non-technical introduction to using AI for food security: learn how models forecast risk, simulate policy choices, predict yields at scale, and track climate and geopolitical stressors—so governments, NGOs, and supply chains can act earlier and target aid more effectively.
A concise, non-technical primer on using AI to improve agricultural supply chains—covering demand/supply forecasting, route optimisation, cold-chain monitoring, post-harvest loss reduction, and real-time visibility—so teams can find quick wins, lower costs, and boost on-time, in-full delivery.
A fast-paced introduction to applying AI across food production—from vision-based grading and predictive microbiology to AI-enabled traceability and early spoilage detection. Learn how to scope pilots, interpret model outputs, and embed controls that meet compliance needs while improving quality and reducing waste.
A concise, non-technical introduction to using AI for regenerative and sustainable farming. You’ll explore practical use cases—crop rotation and cover crops, biodiversity and soil regeneration monitoring, input-reduction modeling, and precision carbon farming—while learning how to source the right data, set KPIs, and communicate ROI and ESG impact across your organization.
A concise, hands-on introduction showing farm and agribusiness teams how to use AI for hyperlocal weather forecasting, risk and adaptation planning, seasonal crop optimisation, and carbon footprint reduction—delivered in a non-technical format for rapid staff upskilling.
A rapid, business-focused primer on turning farm data into better decisions. Learn how unified data platforms, predictive and prescriptive analytics, and mobile/edge AI work together to optimise planning, allocate resources, and empower field teams—without needing a technical background.
A fast, practical introduction to AI automation in agriculture—covering autonomous machinery, robotic weed and pest control, greenhouse automation, and smart irrigation—so teams can assess value, requirements, and risks and confidently plan pilots and scale-up.
A concise, non-technical introduction to applying AI in livestock operations. Learn how sensors, computer vision, and predictive analytics enable early health detection, behaviour insights, and feed optimisation—plus practical steps to evaluate ROI, data needs, and responsible rollout for your organisation.
A concise, hands-on introduction to applying AI in crop monitoring—covering image analysis, early disease and pest detection, predictive yield modelling, and soil health/moisture assessment—so agriculture teams can act faster, cut waste, and improve sustainable outcomes.
This course discusses the integration of AI into change governance frameworks. Students will learn how to manage change risk in AI projects, establish metrics and KPIs, and understand how governance in AI-driven change initiatives can ensure alignment with organizational objectives and regulatory requirements.
This course provides an overview of AI-driven change management platforms, covering tools and vendors that support the integration of AI in change processes. Students will learn how to evaluate platform features, consider integration challenges, and assess ROI and adoption metrics to maximize the impact of AI in change management.
