Executive Program – AI Strategy for Leaders
Design & Lead AI Strategy in 2026
AI Landscape – GenAI, LLMs & Agentic AI- AI Market Trends, Technology Stack & Ecosystem
- AI Business Impact & Value Creation
- AI Strategy Frameworks & Opportunity Prioritization
- AI Operating Models, Roles & Capabilities
- Data Strategy, Architecture & Readiness
- AI Governance, Risk, Ethics & Regulation
- AI Transformation Leadership & Roadmap Design
Build an executive‑level understanding of AI, GenAI and agentic AI to design practical strategies, operating
models and roadmaps for your organization.
This program is designed for CXOs, business leaders and senior
product and technology leaders.
(500)
Ratings
AI Strategy for Leaders Complete Curriculum
Key Topics:
Evolution from rules‑based systems to Generative and Agentic AI
Large Language Models, multimodal AI and foundation models
Enterprise AI agents, autonomous systems and platforms
Global and India AI market trends, adoption patterns and maturity models
AI technology landscape – infra, platforms, tools, open‑source vs proprietary
Key Topics:
AI value creation framework – cost reduction, revenue growth, CX and innovation
Industry‑specific AI use cases – BFSI, healthcare, retail, manufacturing, services
Mapping AI use cases to processes and customer journeys
Measuring impact – financial, operational, CX and innovation metrics
Key Topics:
AI strategy framework – vision, ambition, pillars and principles
Identifying and prioritizing AI opportunities – journeys, processes, competitive analysis
Feasibility, viability and readiness assessments
Build vs buy vs partner decisions and hybrid approaches
Key Topics:
Operating models – centralized, federated, hub‑and‑spoke and AI CoE
Leadership, product, technical, domain and governance roles in AI
Capability design – who does what, where and how in AI initiatives
Key Topics:
Role of data in AI – quality, volume and readiness
Data strategy – collection, management, integration, governance and monetization
Data and AI architecture – lakes, lakehouses, feature stores, vector DBs and cloud platforms
MLOps and AI infra for training and inference
Key Topics:
AI talent landscape and demand–supply gaps
AI literacy, fluency and proficiency for different audiences
Upskilling strategy and organizational AI capability roadmap
Key Topics:
AI risks – technical, ethical, operational, business and regulatory
AI governance – structures, policies, review mechanisms and accountability
Responsible AI – fairness, bias, privacy, explainability, oversight and robustness
Key Topics:
Overview of EU AI Act, India DPDP Act and other key regulations
Industry‑specific requirements and risk categories
Compliance readiness, documentation and audit practices
Key Topics:
Common AI transformation pitfalls and success factors
Change management for AI – resistance, communication and stakeholder engagement
Building an AI‑ready culture and leadership behaviors
Key Topics:
Designing foundation, pilot, scaling and transformation phases
Defining input, activity, output and outcome metrics for AI
Action planning – immediate next steps, 90‑day and 12‑month AI strategy plans
