CBA Certified AI-Driven Management Professional

AI Capabilities and Limits20%

• Distinguish generative AI, predictive analytics and rule-based automation by the type of problem each is suited to
• Interpret the probabilistic nature of large language model output and its consequences for reliability, consistency and factual accuracy
• Identify task characteristics that predict strong AI performance, such as pattern-rich, language-heavy and tolerance-for-review tasks, versus characteristics that predict failure
• Evaluate claims made about an AI tool by asking what evidence, evaluation method or benchmark would substantiate them
• Select an appropriate response to hallucinated, biased or outdated AI output in a work context

Use Case Selection and Scoping22%

• Evaluate candidate use cases using a value, feasibility, data and risk screen, and rank them for a pilot portfolio
• Distinguish between tasks to automate, tasks to assist and tasks to leave fully human, based on error cost and review capacity
• Calculate simple expected benefit figures, such as hours saved per week and cost per output, using spreadsheet-level arithmetic
• Select success criteria and baseline measures for a pilot before it starts, including quality as well as speed measures
• Identify scoping failures such as unbounded ambition, missing data, no owner and no exit criteria, and choose the correction
• Decide among build, buy, use-existing-tools and wait options for a given scenario

Leading Adoption and Change18%

• Select communication and framing approaches that reduce fear and build realistic expectations when introducing AI to a team
• Distinguish causes of resistance, such as job insecurity, quality distrust and skill anxiety, and match each to an effective response
• Evaluate skill-building options for a team, from structured practice sessions to champion networks and shared prompt libraries
• Interpret adoption signals, including usage patterns, shadow use of unapproved tools and quiet abandonment, and choose the correct intervention
• Select what to report to senior stakeholders about an AI initiative, balancing candour about limits with credible progress

AI-Assisted Teams and Workflows20%

• Select the appropriate human review point for an AI-assisted workflow given the error cost and volume of the task
• Distinguish accountability for AI-assisted work: who owns the output, who owns the error, and what the manager must make explicit
• Evaluate quality control approaches for AI-assisted output, including sampling, second review and checklist verification
• Interpret changes in team workload, skill mix and role content as AI absorbs routine tasks, and select fair responses
• Calculate simple throughput and review-capacity figures to judge whether a proposed AI-assisted workflow is sustainable
• Select delegation practices for AI-assisted work, including when to require disclosure that AI was used

Governance, Risk and Responsible Use20%

• Distinguish AI-related risks a manager owns, such as data leakage, confidentiality breaches and unreviewed errors, from risks owned at organisational level
• Select the correct handling of personal, confidential or client data when staff use AI tools, applying data protection principles at decision level
• Evaluate an acceptable-use rule set for a team, identifying gaps, overreach and unenforceable provisions
• Identify situations that require escalation beyond the manager, including high-stakes automated decisions, regulated-sector constraints and suspected harmful output
• Interpret the manager-level implications of emerging AI regulation and risk-tiered approaches, without reliance on any single jurisdiction's text
• Select proportionate responses to an AI-related incident, including containment, disclosure and process correction