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
10 Days
29 Jun – 10 Jul 2026
London
£7,675
Choose the date and location that suits you:
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
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
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
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
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
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
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
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
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
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
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
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.
