Introduction
The programme “Data-Driven Leadership: Making Decisions Using Artificial Intelligence” develops professional practice in Leadership. It connects the subject with workplace challenges by examining existing evidence and practices and choosing methods appropriate to the organization and its available resources.
The learning progresses from artificial intelligence applications and controls to leading change and building engagement, with practical work on data analysis and decision support, leadership and team direction, strategy formulation and prioritization. Participants prepare outputs to support workplace application and review the quality and results of implementation.
Learning objectives
- Design an AI use case with verification criteria and human review in an application related to “Data-Driven Leadership: Making Decisions Using Artificial Intelligence”.
- Turn available data into documented analysis that supports a specific professional decision.
- Identify leadership practices suited to the situation and allocate responsibilities clearly.
- Translate strategic direction into actionable objectives and initiatives.
- Prepare a change plan that addresses the effects of transformation on people, procedures, and skills.
Learning areas
1. Artificial Intelligence Applications and Controls
- Identify a task that AI can support, and separate inputs and human decisions from outputs produced by the tool.
- Prepare suitable test data and examples while respecting confidentiality, privacy, and permissions, and use de-identified training exercise data.
- Write clear instructions covering the task, context, constraints, and output format, and compare multiple formulations using a fixed set of examples.
- Check outputs for errors, bias, and unsupported information, and identify cases that require specialist review.
- Assess the feasibility of use in terms of quality, time, cost, and risk, and document controls for approval, monitoring, and stopping when failures occur. The application examines a case related to “Data-Driven Leadership: Making Decisions Using Artificial Intelligence”.
2. Data Analysis and Decision Support
- Frame an analytical question linked to a specific decision, and define the unit of measurement, time period, and data needed to answer it.
- Check data quality for completeness, accuracy, consistency, and duplication, and document cleaning steps and limitations on reliance.
- Select appropriate comparisons, indicators, and charts, and distinguish statistical correlation from causal explanations of results.
- Interpret results in the work context, test assumptions and unusual values, and present alternatives and their associated uncertainty.
- Prepare an analytical report containing the finding, recommendation, data source and date, review owner, and next follow-up step.
3. Leadership and Team Direction
- Analyze the leadership situation in terms of the task, the team's experience, and the constraints affecting the decision, and select an appropriate approach to direction.
- Distinguish the leader's responsibility for results from team members' responsibility for delivery, and define performance expectations that can be monitored.
- Apply delegation by specifying the task, authority, resources, and review checkpoints, and address overlapping team responsibilities.
- Conduct a leadership conversation that includes listening, questioning, and specific feedback linked to behavior and results.
- Address a practical situation requiring a choice between leadership approaches, and document the reasons for the choice and how its effects will be monitored.
4. Strategy Formulation and Prioritization
- Analyze the internal and external environment using available data, and distinguish opportunities from assumptions that require verification.
- Define a specific strategic problem and connect it to the organization's mission and the value sought by beneficiaries and stakeholders.
- Set measurable objectives with a baseline, target, and time horizon, and assign a clear owner to each objective.
- Prioritize initiatives according to impact, feasibility, resources, risks, and interdependencies, explaining the reasons for the trade-offs.
- Build an implementation roadmap covering phases, owners, resources, review gates, and a mechanism for adjusting priorities when circumstances change.
5. Leading Change and Building Engagement
- Identify the gap between the current and target states, and analyze the effects of change on tasks, relationships, and required skills.
- Map stakeholders by influence and interest, and define the roles of sponsors, managers, and team representatives.
- Design communication messages explaining the reasons for change, its benefits, and the required steps, with channels for listening and responding to feedback.
- Diagnose the causes of resistance and match them to appropriate responses, such as clarifying roles, providing training, or redistributing workload.
- Select indicators of adoption of new behaviors and actual use, and review pilot results before expanding implementation.
Practical outputs
- A use case brief and a test template for the quality of AI outputs.
- A dashboard and decision memo explaining the data, assumptions, and alternatives.
- A map of leadership roles and a mechanism for team delegation and follow-up.
- A strategy map and a priority matrix for proposed initiatives.
- A change plan covering stakeholders, communication, and capacity building.
Who should attend
- Practitioners, specialists and supervisors whose work relates to the programme subject.
- Managers and teams working in leadership, according to their practical responsibilities.
- Professionals responsible for applying the outputs of “Data-Driven Leadership: Making Decisions Using Artificial Intelligence” within their organizations.
Prerequisites
- Working familiarity with the programme field and the ability to discuss a professional case or challenge.
- Confirm the suitability of your experience and any specialist prerequisites with the Union before participation.
Conclusion
The programme “Data-Driven Leadership: Making Decisions Using Artificial Intelligence” brings together concepts, case review and a practical application. Participants prepare steps that can be adapted to their responsibilities and organizational resources, with an indicator to follow application and review results after returning to work.
Participation arrangements and any special requests are specified in the confirmation issued by the Union.