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Introduction
Course Introduction
- In complex, fast-moving business environments, leaders face mounting pressure to make high-quality decisions under uncertainty while navigating vast information flows and competing priorities. This AI decision-making training programme equips managers, strategy professionals and decision-makers with practical frameworks to integrate artificial intelligence into decision processes, improving speed, consistency and outcome quality. Participants explore decision intelligence methodologies, human-AI collaboration models and AI-augmented workflows that transform raw data and analytical outputs into confident, defensible choices. Emphasis is placed on responsible adoption, bias mitigation and measurable decision improvement through this AI-driven decision masterclass. This course can be facilitated in London, Dubai, Kuala Lumpur, Nairobi, and other major business centres on client-preferred dates.
Why Choose This Course?
- • Accelerate decision quality and speed by applying structured decision intelligence frameworks that combine AI-generated insights with human judgement and contextual understanding
• Reduce cognitive biases and decision errors through systematic approaches that surface assumptions, challenge mental models and incorporate diverse perspectives alongside AI outputs
• Improve strategic and operational outcomes by integrating predictive, prescriptive and generative AI capabilities into planning, prioritisation and resource allocation processes
• Strengthen organisational agility by building real-time decision support systems, scenario simulation capabilities and adaptive response mechanisms
• Establish clear governance and accountability for AI-supported decisions, ensuring transparency, explainability and appropriate human oversight at every stage
• Build sustainable internal capability to scale AI-augmented decision-making across teams and functions while maintaining ethical standards and stakeholder trust
Who Should Attend ?
- • Director of Strategy and Strategic Planning accountable for enterprise decision quality and AI-enabled strategy execution
• Head of Performance Management and Decision Support responsible for designing decision frameworks and performance improvement initiatives
• Strategy Managers and Business Planning Leads translating analytical insight into actionable strategic and operational choices
• Decision Support Managers and Analytics Leads in business units embedding data-driven and AI-augmented practices into daily operations
• Performance and Operations Managers responsible for resource allocation, prioritisation and outcome delivery under uncertainty
• Senior Business Analysts and Decision Support Specialists conducting analysis, preparing decision materials and supporting management choices
10 Days
29 Jun – 10 Jul 2026
London
£7,675
Choose the date and location that suits you:
Learning Objectives
- By the end of this programme, participants will be able to:
• Design decision intelligence frameworks that integrate AI-generated insights, business rules and expert judgement to improve the quality and speed of strategic and operational decisions
• Apply AI-augmented decision-making processes to complex, uncertain business problems by combining predictive modelling, scenario analysis and prescriptive recommendations
• Develop human-AI collaboration workflows that leverage generative AI and prompt engineering to accelerate insight synthesis, option generation and decision documentation
• Establish governance structures, accountability mechanisms and ethical guidelines that ensure responsible, transparent and auditable AI-supported decision processes
• Evaluate and mitigate cognitive biases, algorithmic bias and decision traps through structured techniques that combine behavioural science with AI transparency tools
• Design real-time decision support systems, dashboards and alerting mechanisms that deliver timely, relevant insight without overwhelming decision-makers
• Communicating complex AI outputs, assumptions and uncertainty ranges clearly to senior stakeholders to support confident, informed and defensible choices
• Lead organisational adoption of AI-driven decision practices by building capability, managing change and measuring improvements in decision outcomes and organisational performance
Course Delivery Approach
- • Intensive practitioner workshops combining decision framework design, AI use-case development and bias mitigation exercises with realistic business scenarios
• Hands-on laboratory sessions focused on building decision intelligence prototypes, prompt engineering for decision workflows and scenario simulation models
• Detailed examination of organisational case studies demonstrating successful AI-augmented decision transformation and common implementation pitfalls
• Collaborative group projects developing decision frameworks, governance protocols and adoption roadmaps under expert facilitation and peer challenge
• Expert-led discussions on emerging practices in decision intelligence, agentic AI for autonomous decisions and evolving expectations for human oversight
• Personal and team action planning with structured support to translate learning into immediate improvements in participants’ decision-making practice and organisational impact
Course Syllabus
- MODULE 01
Foundations of Decision Intelligence and AI-Augmented Choice
• Understanding the evolution from traditional decision-making to decision intelligence that systematically combines data, analytics, AI and human judgement
• Defining the core components of high-quality decisions including clarity of objectives, comprehensive options, robust analysis and effective implementation
• Recognising the limitations of unaided human decision-making and the specific ways AI can augment rather than replace judgement
• Identifying high-impact decision domains where AI integration delivers measurable improvement in speed, consistency or outcome quality
• Establishing principles for responsible AI application in decision contexts including transparency, accountability and proportionality of automation
• Mapping the end-to-end decision intelligence lifecycle from problem framing through insight generation, choice and outcome evaluation
MODULE 02
Structuring Decisions: Frameworks, Objectives and Option Generation
• Applying structured decision frameworks that decompose complex problems into clear objectives, alternatives and evaluation criteria
• Utilising AI capabilities to generate, expand and refine decision options beyond traditional brainstorming and linear thinking
• Defining decision quality criteria that balance analytical rigour with practical feasibility, stakeholder alignment and implementation constraints
• Designing decision charters and scoping documents that prevent scope creep and focus analytical effort on material choices
• Integrating multiple stakeholder perspectives and conflicting objectives into coherent decision structures
• Establishing processes for revisiting and refining decision frames as new information emerges or circumstances change
MODULE 03
AI Capabilities for Prediction, Optimisation and Prescriptive Support
• Applying predictive analytics and machine learning outputs to inform probability estimates, trend projections and outcome forecasts relevant to decisions
• Using optimisation techniques and prescriptive analytics to identify preferred courses of action under defined constraints and objectives
• Integrating scenario modelling and simulation to evaluate the potential consequences of different choices before commitment
• Designing decision support tools that present AI-generated recommendations alongside confidence levels, assumptions and sensitivity analysis
• Balancing the power of AI recommendations with appropriate scepticism, contextual judgement and accountability for final choices
• Measuring the realised value of AI-supported decisions through outcome tracking, counterfactual analysis and benefit realisation reviews
MODULE 04
Human-AI Collaboration and Augmented Decision Workflows
• Designing effective human-AI collaboration models that allocate tasks according to comparative advantage while maintaining human accountability
• Applying prompt engineering techniques to elicit high-quality, relevant and actionable outputs from generative AI for decision preparation and documentation
• Building AI-augmented workflows that accelerate research synthesis, option evaluation, risk assessment and communication of recommendations
• Establishing clear protocols for when AI outputs are accepted, challenged, overridden or escalated in decision processes
• Managing cognitive load and automation bias to ensure decision-makers remain engaged and critically evaluate AI contributions
• Creating feedback mechanisms that capture decision outcomes and continuously improve the quality of AI support over time
MODULE 05
Cognitive Biases, Decision Traps and Mitigation Strategies
• Identifying common cognitive biases and decision traps that affect individual and group choices in business contexts
• Applying structured debiasing techniques that combine behavioural science with AI transparency and challenge mechanisms
• Designing decision processes that deliberately surface assumptions, encourage dissent and incorporate diverse perspectives
• Using AI tools to detect potential bias in data, analysis and recommendations while maintaining human oversight of ethical implications
• Establishing pre-mortem and post-mortem practices that improve future decision quality through systematic learning from outcomes
• Building organisational decision hygiene that reduces the incidence and impact of bias across recurring and high-stakes choices
MODULE 06
Real-Time Decision Support, Dashboards and Alerting Systems
• Designing decision support systems that deliver timely, relevant and actionable insight without creating information overload or alert fatigue
• Establishing key decision indicators and threshold frameworks that trigger appropriate management attention and intervention
• Integrating real-time data feeds, predictive signals and AI-generated alerts into operational and strategic decision workflows
• Creating escalation protocols and decision rights that clarify when automated recommendations require human review or approval
• Balancing automation benefits with the need for human context, exception handling and accountability in high-stakes situations
• Measuring the effectiveness of decision support systems through usage patterns, decision speed and outcome quality metrics
MODULE 07
Scenario Planning, Simulation and Adaptive Decision-Making
• Developing scenario planning capabilities that explore plausible futures and test the robustness of decisions under different conditions
• Applying simulation and modelling techniques to understand system dynamics, feedback loops and unintended consequences of choices
• Designing adaptive decision frameworks that enable timely adjustment as new information emerges or circumstances evolve
• Integrating AI-driven scenario generation and stress testing into strategic planning and risk-informed decision processes
• Building organisational capability to make decisions under deep uncertainty while maintaining strategic coherence and stakeholder confidence
• Establishing learning mechanisms that capture insights from implemented decisions and feed them into future scenario and strategy work
MODULE 08
Governance, Ethics and Accountability in AI-Supported Decisions
• Establishing governance frameworks that define roles, responsibilities and approval processes for AI-augmented decision-making
• Developing ethical guidelines and impact assessment processes that address fairness, transparency and potential unintended consequences
• Creating accountability structures that assign clear responsibility for decisions regardless of the degree of AI involvement or automation
• Integrating AI decision support into existing risk management, compliance and audit frameworks to ensure comprehensive oversight
• Designing documentation and audit trail standards that support internal review, regulatory expectations and post-decision evaluation
• Building capability for ongoing ethical review and continuous improvement in AI-supported decision practices
MODULE 09
Change Management and Adoption of AI Decision Practices
• Developing adoption strategies that address cultural, skill and process barriers to AI-augmented decision-making
• Designing training and capability-building programmes that enable decision-makers to use AI tools effectively and responsibly
• Managing organisational change through clear communication, demonstration of value and structured support for new decision behaviours
• Establishing centres of excellence or communities of practice that sustain momentum and share lessons across functions
• Measuring adoption progress and decision improvement through defined metrics, feedback loops and benefit realisation tracking
• Creating sustainable internal capability that reduces reliance on external consultants and accelerates continuous enhancement of decision practices
MODULE 10
Building Organisational Decision Intelligence Capability
• Developing comprehensive decision intelligence strategies that align technological capabilities with business priorities and risk appetite
• Designing operating models that integrate decision support functions with strategy, analytics, risk and operational teams
• Building talent strategies that develop the diverse skills required for high-quality, AI-augmented decision-making across the organisation
• Establishing technology and data architectures that support scalable, governed and accessible decision intelligence capabilities
• Creating measurement frameworks that track decision quality, speed, consistency and business impact over time
• Positioning decision intelligence as a core organisational capability that drives competitive advantage and strategic agility
Organisational Impact
- • Improved quality and speed of strategic and operational decisions that enhance competitive positioning and value creation
• Reduced incidence and impact of decision errors, biases and missed opportunities through structured, AI-augmented processes
• Stronger alignment between analytical insight, AI capabilities and actual decision outcomes across the organisation
• Greater organisational agility in responding to changing conditions through real-time decision support and adaptive frameworks
• Sustainable decision intelligence capability that becomes a source of enduring competitive advantage rather than a one-off initiative
• Clear demonstration of decision maturity to boards, investors and stakeholders that enhances institutional confidence and credibility
Personal Impact
- • Advanced practical expertise in decision intelligence, AI-augmented workflows and bias mitigation directly applicable to strategy, performance and leadership roles
• Enhanced ability to design, govern and communicate decision frameworks that deliver better outcomes while managing associated risks and ethical considerations
• Stronger skills in integrating predictive, prescriptive and generative AI into existing decision processes and strategic workflows
• Clearer professional pathway towards Director of Strategy, Head of Decision Intelligence and senior business leadership positions
• Improved capacity to influence decision culture and embed data-driven, AI-enabled practices across teams and functions
• Expanded professional perspective on the interplay between technology, human judgement and organisational decision quality that supports long-term career advancement
• General Notes
• Sector customisation available on request
• Training material provided
• Elevoris Certificate of Training issued to all participants
• Optional post-programme advisory coaching available
• In an era where the quality of decisions determines organisational success more than ever before, mastery of AI-driven decision-making distinguishes leaders who merely react to information from those who systematically convert complexity into confident, high-quality choices. By combining structured frameworks, responsible AI integration and disciplined human oversight, professionals transform decision-making from an art reliant on intuition alone into a repeatable capability that delivers superior outcomes under uncertainty.
• Enrol now in the AI-Driven Decision-Making Masterclass to develop the frameworks, practical skills and leadership capability required to master AI-augmented decision intelligence and drive measurable improvement in your organisation’s most consequential choices.
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