• define what AI is and the technologies behind it
• describe the historical development of AI
• identify the current applications of AI in finance and accounting, and recognise their significance for the profession
• explain the five dimensions that determine AI capability and its limitations
• recognise why professional judgement and accountability continue to be vital in an AI-enabled profession.

• distinguish between key frameworks for classifying AI use in finance and explain what they mean for professional oversight
• identify the core AI capabilities relevant to finance and explain what each involves in practice
• explain how workflow and process mapping enables effective AI deployment in finance
• recognise how AI adoption changes skills requirements, roles and operating models within the finance function.

• explain how AI can support different types of business strategy and identify how finance professionals can facilitate that support within their organisation
• evaluate and prioritise AI opportunities within the context of a financial organisation
• describe the ways in which finance professionals can contribute to AI strategy
• identify the key elements of a strong business case for an AI initiative and assess whether a proposed case is robust.

• understand the importance of data quality, data access and data governance prior to implementing an AI adoption in a financial organisation
• evaluate specific AI tools and solutions proposed to deliver agreed priorities, and recognise the importance of model transparency and explainability when evaluating an AI solution
• identify the governance and ethical requirements for responsible AI deployment, including human oversight and accountability
• apply a structured framework to assess whether an AI deployment meets the requirements for responsible use
• identify the skills and role changes required for effective AI deployment and assess what organisational readiness looks like in practice.

• evaluate the specific risks associated with predictive AI used for consequential decisions
• design and assess governance and mitigation approaches for managing AI risk
• apply a structured framework to assess whether an AI deployment meets the requirements for responsible use
• assess the impact of AI adoption on professional judgment
• identify the organisational conditions and individual practices needed to maintain the human capabilities that AI cannot replicate.

6. AI and data governance

• assess the implications of the expanding data landscape on finance's role in data governance
• apply a lifecycle governance framework to AI deployments in finance
• identify the privacy, security, and third-party risk obligations that apply when AI systems process personal data
• evaluate the key regulatory frameworks and professional standards governing AI and data use in finance (including the EU AI Act, ISO 42001, GDPR, and IESBA's Technology Provisions).

• explain how predictive and generative AI models produce their outputs and distinguish prediction from understanding when assessing the reliability of AI-generated outputs in finance
• identify the risks linked to generative AI, including sycophancy, unintended personalisation, across long inputs, and explain why professional judgement alone cannot eliminate them
• analyse how cognitive biases, including those specific to generative AI's fluency, shape the way finance professionals interpret generative AI outputs
• apply a practical framework to interpret generative AI outputs responsibly, and evaluate the copyright, confidentiality and regulatory obligations that arise when using AI-generated work.

• recognise how your existing communication skills impact the quality of generative AI (GenAI) output and the value it creates
• apply a structured framework to brief GenAI clearly and give specific feedback to successfully iterate and improve outputs
• use AI effectively to support the tasks of analysis, summarising, drafting and classification and extraction
• identify the questions finance professionals should ask IT or vendors when specifying and reviewing prompting logic, including what documentation is needed.

• recognise common methodological traps in data analysis, including overfitting, mistaking correlation for causation and drawing conclusions from insufficient data
• identify key questions to ask to mitigate the risks that come with AI-enabled data analytics
• evaluate how AI-enabled business intelligence tools translate raw data into dashboards and reports and assess whether that translation is sound and visible
• apply ACCA and IESBA’s fundamental ethical principles to the evolving role of finance professionals in an AI-enabled world, emphasising accountability, adaptability and ongoing interpretation.

• explain AI agents and agentic AI and how they differ from other forms of automation and generative AI
• apply a structured test to judge where agentic AI genuinely fits in finance processes
• identify common signs that claims about its capabilities are overstated
• evaluate the governance and regulatory responsibilities involved in using agentic AI, including the three lines model and relevant international standards
• assess how AI changes both the finance function and individual careers, including the conditions for successful transformation and a framework for prioritising personal skill development.