Review an AI-supported analysis before it informs a management decision. Check its claims against source data, examine assumptions and counterarguments, and prepare your own recommendation with outstanding questions.
In the course case, a persuasive investment analysis claims demand increased by 20 percent, while quarterly data show a five percent decline. A second analysis reports the decline but leaves costs open. Review both and explain what information is still needed for the investment decision.
Prepare a decision paper documenting AI use, responsibilities and your recommendation for the decision meeting. A personal AI work card keeps your task, sources, review criteria, improved instructions and worked examples for later use.
What you will work on
Review an AI-assisted analysis for data sources, assumptions, uncertainty and limitations, and separate supported findings from open points.
Define and justify decision rights, approval limits and control requirements for a specific AI-assisted decision.
Correct a persuasive analysis based on a false assumption.
Who it is for
Managers with decision-making responsibility
Risk and crisis managers
Specialists responsible for AI governance and data
Topics
Data sources and quality in AI-generated analyses
Assumptions, uncertainty and limitations of AI outputs
Decision rights, approval limits and accountability
Benefit, cost, risk and control requirements
Counter-check and handling of dissenting views
Documentation and first use in a decision forum
Your result
Your review guide and corrected work sample help you check sources, assumptions and calculations in an AI analysis before the next management decision.
Prerequisites
Experience with management decisions and access to a decision case in your own area of responsibility. If confidential information is unavailable, you work on an anonymised example case. You need access to an AI tool for the analysis; alternatively, you can use a provided example analysis. A basic understanding of risk and control questions is helpful; programming knowledge is not required.
Format and approach
Planned as about 6–8 hours of digital self-study with templates, an annotated example and self-checks. Work asynchronously on your own case or an example case; personal feedback, coaching and group work are not included.
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