NextGen CEO: AI Strategies for Leadership

Week 3 - Generative AI and Agentic AI: What Senior Leaders Need to Know

• Distinguish predictive, generative and agentic AI by strategic capability, limitation and use.

• Explain how probabilistic outputs affect reliability, consistency, explainability and decision quality.

• Assess material risks arising from hallucination, overconfidence and model variability.

• Determine when executive decisions require verification, human judgement or exclusion of AI.

Week 4 - Strategic AI Technology and Sourcing Choices for Executives

• Assess the strategic dependencies among enterprise data, models, compute, cloud and applications.

• Compare open and closed models and build–buy–partner options.

• Evaluate alternatives across performance, control, speed, lifecycle cost, integration, resilience and vendor dependence.

• Recommend a sourcing strategy aligned with enterprise priorities and risk appetite.

Week 5 - From Use Cases to AI Portfolio Strategy

• Translate enterprise strategy into a coherent AI investment portfolio.

• Prioritise initiatives by strategic fit, value, feasibility, risk and time to impact.

• Balance core improvement, transformation and experimentation.

• Sequence investments according to dependencies, capital constraints and organisational capacity.

Week 6 - Geopolitics, Compute Access, and AI Strategy for Asia

• Explain how chips, cloud infrastructure, data centres and energy affect AI access, cost and resilience

• Analyse export controls, industrial policy and sovereign-AI initiatives across Asia and major global markets.

• Assess enterprise exposure across sourcing, data residency, concentration and business continuity.

• Define resilience options for critical AI infrastructure and model access.

Week 7 - AI-Led Products, Services and Business Models

• Distinguish AI-enhanced offerings from AI-native products, services and business models.

• Evaluate AI-driven opportunities for growth, differentiation and new customer value.

• Assess monetisation, pricing, data-feedback and ecosystem economics.

• Determine the capabilities, partnerships and governance required to launch and scale selected models.

Week 8 - Competitive Strategy in an AI-Driven Market

• Analyse how AI changes industry structures, value chains and competitive boundaries.

• Identify defensible sources of AI-enabled competitive advantage.

• Evaluate threats from incumbents, AI-native entrants and platform providers.

• Decide where the enterprise should lead, match, partner, acquire or exit.

Week 9 - AI Operating Model: Centralised, Federated and Hybrid Structures

• Compare centralised, federated and hybrid enterprise operating models for AI.

• Determine ownership across enterprise teams, business units and control functions.

• Allocate lifecycle decision rights, resources, handoffs and escalation paths.

• Select an operating model aligned with strategy, scale, capability and risk.

Week 10 - AI Governance Model and Responsible AI Oversight

• Define enterprise AI governance principles, risk appetite and accountability.

• Determine the roles of the board, executive leadership, business owners and assurance functions.

• Evaluate AI inventory, risk-tiering, review, exception and escalation mechanisms.

• Formulate a governance model that supports responsible innovation and effective oversight.

Week 11 - AI Safety and Regulatory Readiness

• Distinguish AI safety, alignment, security, operational and compliance risks.

• Assess material failure modes and enterprise exposure across the AI lifecycle.

• Compare relevant regulatory approaches across the EU, US, India, Singapore and ASEAN.

• Evaluate readiness across testing, documentation, monitoring, incident response and executive accountability.

Week 12 - Human-AI Decision Rights and Trust

• Classify AI-supported decisions by impact, reversibility, uncertainty and stakeholder consequence.

• Select appropriate human-in-, human-on- or human-out-of-the-loop arrangements.

• Establish authority thresholds, overrides, escalation and recourse.

• Evaluate trust risks arising from automation bias, opacity, overreliance and weak accountability.

Week 13 - AI Scale-Up Mechanisms: From Pilots to Business-as-Usual

• Diagnose strategic, technological, operational and organisational barriers to scaling AI.

• Evaluate production readiness across data, integration, reliability, security and service operations.

• Determine enterprise enablers including reusable platforms, standards, funding and product ownership.

• Develop a phased scale-up roadmap with decision gates, accountabilities and stop-orscale criteria

Week 14 - Leading AI Adoption: Workforce, Workflows and Change

• Assess AI’s impact on tasks, workflows, roles and workforce composition.

• Determine workflow redesign, reskilling, hiring and redeployment priorities.

• Develop an enterprise change agenda covering leadership, culture, incentives, communication and learning.

• Define workforce and adoption outcomes for executive oversight.

Week 15 - Agentic AI and Autonomous Workflows

• Distinguish assistants, copilots and agentic systems by autonomy, control and accountability.

• Evaluate enterprise workflows for agentic redesign.

• Assess the strategic value and material risks of autonomous workflows.

• Set executive guardrails for permissions, human checkpoints, monitoring and fallback.

Week 16 - AI Strategy Under Uncertainty

• Identify technological, economic, competitive, regulatory and geopolitical uncertainties affecting AI strategy.

• Construct alternative scenarios and observable strategic signposts.

• Apply real-options logic to staged, reversible and scalable commitments.

• Establish review triggers and contingency choices for adapting enterprise strategy.

Week 17 - AI-Led Customer Growth, Marketing and Sales Transformation

• Analyse how AI can reshape customer journeys and commercial processes across marketing, sales and service.

• Redesign end-to-end workflows and human–AI roles across acquisition, personalisation, conversion, retention and service.

• Evaluate customer-data, consent, fairness, transparency and brand-trust implications.

• Define commercial outcomes, performance measures and governance requirements for AI-led transformation.

Week 18 - AI-Led Transformation of Operations, Supply Chain, Finance and Risk

• Analyse AI-led transformation opportunities across operations, supply chain, finance and risk.

• Evaluate interdependencies across planning, execution, financial decision-making, controls and risk oversight.

• Redesign cross-functional decision flows, human roles, controls and exception-management processes.

• Develop an integrated transformation blueprint specifying expected value, dependencies, accountabilities and performance measures.

Week 19 - AI Funding, Cost-to-Serve and ROI Discipline

• Estimate the full lifecycle economics of AI, including data, compute, integration, talent, operations and risk.

• Compare central, business-unit and co-investment funding models.

• Evaluate risk-adjusted business cases using baselines, attribution, time horizons and scenario sensitivity.

• Make continue, pivot, scale or stop capital-allocation decisions.

Week 20 - AI Performance Management and Board Reporting

• Define balanced indicators across value, adoption, operations, technology and risk.

• Establish performance targets, ownership and executive review cadence.

• Diagnose value leakage and material deviations requiring intervention.

• Use board-level reporting to inform capital reallocation, risk response and strategic decisions.

Week 21 - Integrating the Enterprise AI Agenda

• Integrate AI strategy, portfolio, technology, operating model, governance, talent, funding and performance.

• Resolve cross-functional dependencies, sequencing choices and enterprise trade-offs.

• Align business-unit priorities with enterprise standards and risk appetite.

• Develop an enterprise AI roadmap with milestones, executive sponsorship and accountabilities.

Week 22 - Board and C-Suite Leadership of the Enterprise AI Agenda

• Frame AI choices for the board and C-suite in terms of strategy, capital, performance, risk, workforce and trust.

• Communicate options, uncertainty and trade-offs without unnecessary technical detail.

• Formulate clear recommendations, decision asks and accountabilities.

• Build alignment among the board, executive team and critical stakeholders around the enterprise AI agenda.

2-Day India Immersion: Anchor the immersion in India’s sovereign-AI positioning and its place in the global AI order, linked to boardroom and war-room exercises

• Analyse India’s AI compute, data, talent, policy and innovation ecosystem.

• Evaluate the implications of sovereign AI, geopolitics and compute access for enterprise strategy.

• Make time-bound capital, governance, safety and scaling decisions in executive simulations.

• Defend recommendations using evidence, stakeholder impacts and risk–return trade-offs.

• Convert simulation insights into enterprise action priorities.