In this phase the results of the decision analysis are translated into concrete, actionable recommendations to support implementation.
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Objectives of the phase
- Formulate clear recommendations based on the results.
- Link the analytical outcomes to practical decisions and policy actions.
- Consider feasibility, constraints and real-world implications.
- Support decision-makers in selecting and implementing the preferred alternative(s)
Tasks involved in the phase
This phase is typically led by the facilitator or decision analyst and builds directly on the results that have been communicated and the feedback provided by stakeholders. The phase involves the following tasks:
- Identifying the recommended alternative(s) or policy options.
- Outlining implementation steps and required resources for possible options.
- Considering institutional, legal and practical constraints.
- Integrating stakeholder perspectives and acceptance considerations.
The recommendations may include several options, recognising that different stakeholders or perspectives may favour different alternatives. The aim is not to identify a single "best" option, but rather to support decision-makers and policy-makers by providing informed recommendations and explaining the implications of the available alternatives.
Outcome of the phase: Clear and justified decision or policy recommendation(s) for the decision makers and practical guidance for implementation.
Follow up Questions for the AI
Ask the following questions to get more information from AI:
- What are the key principles for translating analysis results into operational action recommendations?
- What additional factors beyond the results of decision analysis model should be considered in decision-making?
- How should uncertainty and sensitivity results influence recommendations?
- What are good practices for presenting actionable policy advice?
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Benefits and Risks of Using AI
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Benefits (++)
- Supporting the formulation of structured and evidence-based recommendations.
- Translating analytical results into actionable steps.
- Identifying implementation considerations (e.g. risks, constraints, stakeholders).
- Generating alternative policy options or scenarios.
- Structuring justification and argumentation for the recommendations
Benefit Scoring: ++ Medium
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Risks (!!)
- Providing normative or prescriptive recommendations without understanding full context.
- Ignoring important real-world constraints (political, institutional, legal).
- Overstepping the role of decision-makers by suggesting final decisions leading to loss of accountability and ownership in decision-making.
- False sense of objectivity in recommendations derived from AI.
- Misalignment between AI-generated recommendations and stakeholder values.
Risk Scoring: !! Medium
Questions to make use of the benefits
Prerequisite for each of these questions is that the results of the analysis as well as sensitivity analysis are available.
- Based on the results, can you formulate a set of decision or policy recommendations that take different stakeholder views into account and describe the logic behind the recommendations?
- Can you identify key assumptions and conditions for successful implementation of the recommended alternatives and what risks and barriers might affect implementation?
Note on all the above questions: The stakeholders and/or facilitator should always validate the results, and AI can make misinterpretations of the provided information.
Questions to avoid the risks
- Why is it risky to rely on AI for final decision or policy recommendations? Understanding of the risks is essential in terms of successfully carrying out the DA process.
- How can responsibility and accountability be maintained when interpreting the recommendations?
Questions NOT to ask
- What decision should be made in this case?
Final decisions must remain with decision-makers and stakeholders