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Introduction
Course Introduction
- Senior leaders increasingly recognise that business analytics and artificial intelligence must be central to corporate strategy rather than peripheral operational activities. This Mini MBA business analytics programme equips executives with strategic frameworks and practical tools to formulate integrated analytics and AI strategies, design governance structures and lead transformation initiatives that deliver sustainable value. It balances high-level strategic perspective with functional depth in analytics, decision intelligence and responsible AI to support effective board-level decision-making. This course can be facilitated in London, Dubai, Kuala Lumpur, Nairobi, and other major business centres on client-preferred dates.
Why Choose This Course?
- • Develop the strategic capability to position analytics and AI as core drivers of corporate strategy, competitive positioning and long-term value creation rather than isolated technical projects
• Master the art of constructing compelling, board-ready business cases that secure investment, align stakeholders and demonstrate clear pathways to measurable returns
• Acquire practical frameworks for designing governance, risk management and ethical oversight structures that enable responsible scaling of AI across the enterprise
• Gain the ability to integrate advanced analytics, generative AI and emerging autonomous systems into strategic planning, innovation and operational decision-making
• Build personal leadership presence and influence to drive cultural change, build organisational capability and communicate complex analytical insights to non-technical audiences
• Create a personalised strategic roadmap for analytics and AI that can be implemented immediately within the participant’s organisation
Who Should Attend ?
- • Chief Analytics Officers, Chief Digital Officers and Heads of Strategy accountable for enterprise analytics, AI and digital transformation agendas at board level
• Directors of Innovation, Analytics Directors and Heads of Data Science responsible for translating strategic intent into actionable programmes and measurable outcomes
• AI Strategy Managers, Digital Transformation Leads and Business Intelligence Managers tasked with developing and executing integrated analytics and AI initiatives
• Strategy Managers, Planning Managers and Operations Directors integrating data-driven insights and AI capabilities into core business planning and execution
• Senior Business Analysts, Analytics Managers and Decision Support Leads responsible for delivering high-quality insights that inform strategic choices
• Strategy Analysts and Analytics Specialists supporting executive teams with evidence-based analysis, scenario modelling and performance tracking
5 Days
06 Jul – 10 Jul 2026
Seoul
£4,515
Choose the date and location that suits you:
Learning Objectives
- By the end of this programme, participants will be able to:
• Formulate integrated business analytics and AI strategies that align with corporate objectives and secure board-level sponsorship and resource allocation
• Design enterprise governance frameworks that balance innovation acceleration with robust risk management, ethical standards and regulatory alignment
• Develop comprehensive business cases for analytics and AI investments that quantify value, risks and return pathways in terms meaningful to executive and investor audiences
• Lead cross-functional teams in translating strategic analytics and AI priorities into operational roadmaps, capability-building plans and phased implementation programmes
• Establish performance measurement and benefits realisation systems that link analytics and AI initiatives directly to strategic, financial and operational outcomes
• Evaluate emerging technologies, including generative and agentic AI, for their strategic relevance and design responsible adoption approaches tailored to organisational context
• Drive cultural and organisational change that embeds data-driven decision-making and AI-augmented processes at every level of the enterprise
• Communicate complex analytical concepts, strategic implications and governance requirements with clarity and influence to boards, regulators and diverse stakeholder groups
Course Delivery Approach
- • Executive strategy workshops and board-level simulation exercises focused on real organisational challenges and strategic decision scenarios
• In-depth analysis of global case studies illustrating successful and unsuccessful analytics and AI strategy execution across industries
• Collaborative group projects in which participants develop complete analytics and AI strategy proposals, governance designs and implementation roadmaps
• Masterclasses on specialised topics including AI ethics and governance design, value measurement methodologies and leading AI-driven transformation
• Personal action learning projects applying programme frameworks directly to participants’ current organisational contexts with structured feedback
• Strategic coaching conversations and peer learning circles to strengthen executive presence, influence and personal leadership in analytics and AI strategy
Course Syllabus
- MODULE 01
Strategic Foundations of Business Analytics and AI
• Examining the evolving relationship between data, analytics, artificial intelligence and corporate strategy in dynamic competitive environments
• Distinguishing operational, tactical and strategic applications of analytics and AI with clear implications for resource allocation and governance
• Identifying the critical organisational capabilities, data assets and cultural conditions required for analytics and AI to deliver strategic impact
• Mapping the maturity journey from ad-hoc analytics use to enterprise-wide, strategy-integrated AI capability
• Establishing principles of evidence-based leadership that combine human judgement with advanced analytical insight
• Recognising common failure patterns in analytics and AI initiatives and the strategic lessons they provide for leaders
MODULE 02
Formulating and Aligning Analytics and AI Strategy
• Translating corporate vision, strategic priorities and market realities into coherent analytics and AI strategic objectives
• Conducting strategic diagnostics to assess current analytical maturity, capability gaps and competitive positioning
• Prioritising analytics and AI initiatives based on strategic impact, feasibility, risk profile and organisational readiness
• Aligning analytics and AI strategy with corporate planning cycles, capital allocation processes and performance management systems
• Creating strategic narratives that articulate the role of analytics and AI in future business models and competitive advantage
• Establishing governance mechanisms to maintain strategic coherence across multiple concurrent analytics and AI programmes
MODULE 03
Enterprise Data Strategy and Decision-Grade Information Assets
• Defining enterprise data strategies that simultaneously support operational excellence, regulatory compliance and strategic insight generation
• Evaluating data architecture options and their implications for analytical flexibility, scalability, security and cost
• Implementing data quality, master data and metadata management practices that ensure reliable inputs for high-stakes decisions
• Designing data governance frameworks that balance accessibility, security, privacy and ethical use across the organisation
• Building data literacy and analytical confidence among senior leaders and strategic decision-makers
• Creating mechanisms for continuous data asset improvement and strategic data monetisation opportunities
MODULE 04
Advanced Analytics, Predictive Modelling and Decision Intelligence
• Applying advanced analytical techniques to generate foresight, simulate strategic scenarios and support complex decision-making
• Integrating predictive, prescriptive and decision intelligence capabilities into strategic planning and risk management processes
• Interpreting sophisticated analytical outputs and communicating their strategic implications to non-technical executive audiences
• Embedding model governance, validation and continuous learning processes to maintain analytical reliability over time
• Designing human-AI collaboration models that augment rather than replace executive judgement in high-stakes contexts
• Assessing the limitations, uncertainties and potential biases inherent in analytical models used for strategic purposes
MODULE 05
Generative AI, Agentic Systems and Strategic Innovation
• Identifying high-value strategic and operational use cases for generative AI in knowledge work, innovation and decision support
• Evaluating the transformative potential of agentic AI systems for process automation, exception handling and autonomous execution
• Developing responsible adoption frameworks that address hallucination risks, intellectual property concerns and workforce implications
• Integrating generative and agentic capabilities into existing analytics ecosystems and strategic planning processes
• Anticipating second-order effects of widespread generative and agentic adoption on industry structures and competitive dynamics
• Building organisational experimentation capacity while maintaining appropriate strategic oversight and risk controls
MODULE 06
AI Ethics, Governance and Responsible Innovation Leadership
• Establishing ethical principles, values frameworks and governance structures for analytics and AI across the enterprise
• Identifying, assessing and mitigating bias, fairness, transparency and accountability risks in AI-enabled strategic decisions
• Designing oversight mechanisms, audit trails and escalation processes that maintain human accountability for AI outcomes
• Integrating AI risk management into enterprise risk frameworks, internal audit and board reporting structures
• Creating cultures of responsible innovation that encourage disciplined experimentation within clearly defined boundaries
• Preparing the organisation for evolving regulatory expectations and societal scrutiny regarding AI use
MODULE 07
Building Organisational Analytics and AI Capabilities
• Conducting rigorous capability assessments across people, process, technology and data dimensions against strategic ambitions
• Designing talent strategies, organisational structures and development programmes that build sustainable analytics and AI expertise
• Establishing effective operating models, whether centralised, federated or hybrid, for analytics and AI delivery
• Leading cultural transformation that normalises data-driven dialogue and AI-augmented decision-making at all levels
• Managing strategic partnerships with technology providers, specialist firms and academic institutions to accelerate capability development
• Measuring and improving analytics and AI maturity through defined frameworks and regular organisational assessment
MODULE 08
Value Measurement, ROI and Strategic Benefits Realisation
• Defining comprehensive value frameworks that capture financial, operational, strategic, risk and reputational benefits of analytics and AI
• Developing robust methodologies for measuring, attributing and reporting return on analytics and AI investments
• Creating balanced scorecards and strategic KPIs that link analytics and AI performance directly to corporate objectives
• Implementing benefits realisation processes that ensure intended value is captured, sustained and continuously optimised
• Communicating value, impact and lessons learned to boards, investors and external stakeholders with clarity and credibility
• Establishing continuous improvement disciplines that maximise the long-term value contribution of analytics and AI
MODULE 09
Leading AI-Driven Transformation and Digital Strategy Execution
• Developing integrated digital and AI transformation roadmaps that align with corporate strategy and business model evolution
• Leading large-scale change programmes that embed analytics and AI into core processes, decision rights and organisational culture
• Managing the human, structural and cultural dimensions of transformation while maintaining operational performance
• Identifying, prioritising and sequencing innovation opportunities enabled by analytics, generative AI and autonomous systems
• Building agile yet governed delivery models that accelerate value capture while managing execution and reputational risk
• Positioning the organisation as a credible analytics and AI leader within its industry, ecosystem and talent market
MODULE 10
Corporate Strategy Integration, Board Oversight and Strategic Foresight
• Embedding analytics and AI considerations into corporate strategy formulation, annual planning and resource allocation processes
• Establishing effective board-level oversight mechanisms for AI strategy, performance, risk appetite and ethical boundaries
• Integrating AI-related risks into enterprise risk management frameworks and strategic decision-making protocols
• Conducting horizon scanning, scenario planning and strategic foresight exercises focused on emerging technologies and disruptions
• Building organisational resilience and adaptive capacity through the strategic application of analytics and AI capabilities
• Developing personal leadership practices and succession approaches that sustain excellence in analytics and AI strategy over time
Organisational Impact
- • Stronger alignment between analytics and AI investments and corporate strategic priorities with improved capital allocation discipline
• Enhanced board and executive confidence in governing AI initiatives through clear frameworks, risk visibility and value measurement
• Accelerated realisation of efficiency, innovation and competitive benefits from analytics and AI through disciplined execution
• Reduced strategic and reputational risk through proactive governance, ethical oversight and robust accountability structures
• Sustainable organisational capability that reduces dependence on external expertise while building internal strategic depth
• Clear demonstration of analytics and AI maturity to investors, regulators, partners and talent markets
Personal Impact
- • Advanced strategic leadership capability to operate confidently at the intersection of technology, business strategy and organisational change
• Enhanced ability to influence boards and senior stakeholders through compelling narratives, rigorous analysis and credible governance proposals
• Clearer professional pathway towards Chief Analytics Officer, Chief Digital Officer, Head of Strategy and other C-suite analytics and digital roles
• Practical skills in business case development, governance design and transformation leadership immediately applicable to current and future roles
• Expanded perspective on emerging technologies and their strategic implications that supports long-term career relevance and adaptability
• Greater personal confidence and executive presence when leading complex, cross-functional analytics and AI initiatives
• 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 environment where the difference between market leaders and followers increasingly hinges on the quality of analytics-enabled strategy and the discipline of AI governance, this programme prepares leaders to move from awareness to mastery. Participants leave equipped not only with frameworks and tools but with the strategic judgement and leadership capability to turn data and artificial intelligence into enduring sources of competitive advantage and responsible organisational progress.
• Enrol now in the Mini MBA in Business Analytics & AI Strategy to develop the board-level strategic acumen, governance expertise and transformation leadership skills required to drive high-impact analytics and AI strategies.
• Disclaimer: This is a professional development programme and does not confer an academic degree or MBA qualification from any university.
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