AI-Enabled Strategic Decision Making Training Course

Participants can create a prioritised portfolio of AI-supported strategic decisions.

• Link corporate objectives to decision themes, capabilities and measurable outcomes
• Use a decision inventory to expose bottlenecks, delays and inconsistency
• Segment opportunities into assistive, augmentative, automated and autonomous decision modes
• Score initiatives using weighted impact, readiness, complexity and risk criteria
• Sequence portfolio investments through dependency mapping and benefit-realisation milestones
• Set stop, pivot and scale criteria for AI decision initiatives

Participants can define an AI decision problem with measurable business value and boundaries.

• Write decision charters specifying owner, users, trigger, action and constraints
• Translate strategic questions into prediction, ranking, classification or optimisation problems
• Build benefits logic models from intervention to measurable business result
• Calculate expected value using base rates, decision costs and outcome probabilities
• Separate model accuracy measures from operational and financial value measures
• Document assumptions, exclusions and counterfactuals within the initial value case

Participants can assess whether available data can support a reliable AI-enabled decision.

• Profile source data for completeness, timeliness, consistency, lineage and representativeness
• Define minimum viable datasets against the decision charter and target outcome
• Identify label quality problems using sampling, adjudication and error taxonomies
• Assess data drift risks from policy changes, customer behaviour and process redesign
• Create data lineage maps from source system to executive recommendation
• Apply data quality thresholds before model training or operational deployment

Participants can select suitable analytical methods for different decision questions.

• Match regression models to continuous forecasts such as demand and revenue
• Use classification models for eligibility, fraud, churn and risk decisions
• Apply clustering to segment customers, cases, suppliers and service demand
• Choose optimisation methods for allocation, scheduling, pricing and resource deployment
• Position large language models for synthesis, retrieval and recommendation drafting
• Compare rules, statistical models and machine learning using decision requirements

Participants can distinguish predictive insight from evidence that supports intervention choices.

• Separate correlation, prediction and causation within strategic decision claims
• Construct causal diagrams using directed acyclic graphs and confounding variables
• Define treatment, control, outcome and eligibility for intervention evaluation
• Use randomised controlled trials where operational conditions permit experimentation
• Apply difference-in-differences to estimate policy and programme effects
• Recognise selection bias, survivorship bias and post-treatment variable errors

Participants can combine AI forecasts with scenarios to inform strategic planning.

• Build baseline forecasts using time-series decomposition, trend and seasonality
• Evaluate forecast accuracy with MAE, RMSE, MAPE and calibration measures
• Create driver-based scenarios for inflation, demand, regulation and capacity shifts
• Use Monte Carlo simulation to quantify uncertainty around strategic outcomes
• Stress-test forecasts against structural breaks, outliers and regime changes
• Present prediction intervals rather than single-point forecasts to decision makers

Participants can structure constrained allocation decisions using optimisation principles.

• Formulate objective functions for cost, service, revenue and risk trade-offs
• Specify decision variables, constraints and feasible operating ranges clearly
• Apply linear programming to budget allocation and capacity planning decisions
• Compare optimal recommendations with feasible policies and operational constraints
• Analyse sensitivity using shadow prices, scenario ranges and constraint changes
• Set override rules when optimisation conflicts with policy or ethics

Participants can use generative AI safely for structured strategic analysis and drafting.

• Design prompts using role, task, evidence, constraints and output format
• Ground responses through retrieval-augmented generation and approved source collections
• Test generated analysis for factuality, citation quality and unsupported claims
• Use structured outputs for option comparison, risk registers and briefing notes
• Establish human review checkpoints for consequential recommendations and external communications
• Identify prompt injection, data leakage and confidential information risks

Participants can evaluate AI models against decision performance, fairness and operational requirements.

• Define acceptance criteria for accuracy, recall, precision, latency and cost
• Interpret confusion matrices against business costs of false decisions
• Assess calibration before using predicted probabilities in decision thresholds
• Set thresholds using expected utility, risk appetite and capacity constraints
• Measure subgroup performance to identify disparate error rates and outcomes
• Compare model performance against human judgement, rules and current baselines

Participants can explain AI recommendations, uncertainty and limitations to decision makers.

• Use global feature importance to explain overall model behaviour
• Apply SHAP values to examine individual recommendations and contributing factors
• Distinguish explanation, justification, causation and accountability in executive briefings
• Communicate uncertainty through confidence intervals, ranges and scenario narratives
• Prepare challenge questions for assumptions, data gaps and unintended consequences
• Document explanation requirements according to stakeholder and regulatory needs

Participants can identify and control major risks associated with material AI decisions.

• Apply NIST AI RMF functions: Govern, Map, Measure and Manage
• Identify harms affecting fairness, privacy, safety, security and human rights
• Conduct AI impact assessments for high-consequence decisions and affected groups
• Define proportional controls based on decision materiality and model autonomy
• Establish incident reporting, redress processes and accountable remediation ownership
• Align control evidence with ISO/IEC 42001 AI management system requirements

Participants can design governance arrangements for AI-supported strategic decisions.

• Assign business ownership, technical ownership, risk ownership and approval authority
• Design human-in-the-loop workflows for review, override and exception handling
• Set decision thresholds requiring escalation to senior accountable officers
• Create model cards documenting purpose, limitations, performance and intended users
• Define approval gates from discovery through deployment and material change
• Establish audit trails for inputs, recommendations, actions and overrides

Participants can prepare an operating model that embeds AI recommendations into daily decisions.

• Map current-state and future-state workflows using swimlane process diagrams
• Identify role changes, capability gaps and decision handoffs before deployment
• Design adoption measures covering usage, override rates and decision cycle time
• Plan communication for affected teams, customers, regulators and governing bodies
• Create training requirements for users, reviewers, approvers and support teams
• Apply change impact assessment to policies, processes and performance measures

Participants can establish monitoring and review arrangements for deployed AI decision products.

• Specify monitoring metrics for data drift, model drift and outcome deterioration
• Set alert thresholds for performance degradation and unacceptable subgroup disparities
• Schedule challenger models, back-testing and periodic independent model review
• Track overrides to identify operational friction and hidden model failure
• Maintain decision logs linking recommendations, actions, outcomes and subsequent learning
• Define retraining triggers, approval requirements and rollback procedures

Participants can present AI-supported recommendations clearly to executive and governance audiences.

• Structure executive recommendations around decision, evidence, options, risks and ask
• Build decision memos using situation-complication-question-answer narrative logic
• Compare options through financial value, risk exposure and implementation feasibility
• Visualise uncertainty with fan charts, tornado diagrams and scenario matrices
• Anticipate board challenge on accountability, controls, assumptions and downside risk
• Write decision records capturing rationale, dissent, conditions and approvals

Participants can adapt AI decision methods to common high-value sector use cases.

• Credit portfolio actions combining default prediction, affordability and policy constraints
• Fraud triage balancing detection rates, investigator capacity and customer friction
• Public-service case prioritisation using need indicators, safeguards and review pathways
• Supply allocation under disruption using demand forecasts and constrained optimisation
• Customer retention interventions tested through uplift modelling and controlled experiments
• Capital investment choices using scenario analysis, risk-adjusted returns and sensitivities

Participants can plan an implementable AI decision initiative from discovery through scale.

• Break delivery into discovery, prototype, validation, deployment and monitoring stages
• Estimate costs for data preparation, integration, governance, support and change
• Build milestone plans with owners, dependencies, evidence and approval gates
• Prioritise technical architecture decisions around interoperability, security and maintainability
• Define benefits tracking from baseline measurement through realised value review
• Prepare procurement questions for model providers, platforms and implementation partners

Participants can build, review and present a complete AI-enabled strategic decision blueprint for a material organisational decision.

• Select a material decision with clear owner, trigger and strategic consequence
• Develop the decision charter, value case and measurable outcome hierarchy
• Specify data requirements, analytical approach and model evaluation criteria
• Design human oversight, governance controls and evidence for approval
• Create implementation roadmap, benefits measures and monitoring arrangements
• Present executive recommendation and defend assumptions, trade-offs and risk controls