Programme Content
S1: CTO/CAIO mandate in the AI era; aligning technology, AI, and business strategy; stakeholder map
S2: Technology vision & roadmap: prioritization, value creation, and ESG considerations
S3: CTO–CIO–CDO–CISO collaboration; communication & influence with the C‑suite and board
S4: Technology investment prioritization: portfolio thinking, business case evaluation, risk–return trade‑offs
S5: Business model innovation, market & competitive analysis; digital product and go‑to‑market alignment; Data monetization models
S6: Analytics for decision-making: dashboard and KPI design, interpretation pitfalls; Data governance: ownership, data sources, and data platform choices
S7: Introduction to Machine Learning: demystification with use cases
S8: Types of learning and tasks, training vs inference, performance evaluation
S9: Machine Learning algorithms and models
S10: AI and deep learning concepts
S11: Deep learning architectures and algorithms
S12: SOTA AI systems across industries and applications
S13: GenAI architectures and algorithms
S14: Large Language Models (LLMs): prompting, hallucination, and bias
S15: GenAI systems and Agentic AI: RAG vs fine‑tuning vs Agents
S16: Digital transformation concepts and case studies
S17: AI Capability and Change management: infrastructure, talent, culture, and stakeholder management
S18: Future readiness: technology forecasting, trends, and long‑term tech/AI strategy
S19: Tech/data/AI teams and organization: infrastructure challenges and governance
S20: Nurturing digital and AI innovation: experimentation culture; internal venture/PoC governance
S21: Mentoring for the future: leadership, ethics, crisis, and sustainability in technology‑led initiatives
S23: Enterprise architecture in practice: modular, composable, and API‑first platforms; vendor management
S24: Cloud strategy: IaaS/PaaS/SaaS choices, target architecture, and cost/performance considerations
S25: Cloud migration playbooks: re‑host vs re‑platform vs re‑architect; SRE and operational excellence
S26: DevOps and platform engineering: CI/CD, platform teams, golden paths, and observability
S27: From PoC to production: enterprise GenAI delivery lifecycle; common success/failure patterns
S28: LLM/ML operations (MLOps/LLMOps): deployment, monitoring, drift detection, cost optimisation
S29: Build vs buy vs partner: evaluating GenAI vendors, integration architectures, security and legal aspects
S30: Cybersecurity strategy for CTOs: Zero Trust, IAM, data security; CTO–CISO collaboration model
S31: Threat landscape: current attack patterns, sectoral risks; prioritising defences as a tech leader
S32: Incident response and business continuity: breach tabletop, BCP/DR planning, communication in a crisis
S33: Compliance & assurance: SOC 2, ISO 27001, sector‑specific norms; board‑level cyber risk metrics
S34: Evaluating emerging tech: hype vs value, innovation funnels, PoC governance and kill criteria
S35: IoT & edge computing architectures: industrial deployments, data pipelines, lifecycle & security basics
S36: Blockchain/DLT in the enterprise: where it works and where it doesn’t; patterns and constraints
S37: Tech‑scanning and future‑proofing decisions: quantum, new compute paradigms, and long‑term bets
S38: Final capstone presentations and feedback: evaluated by IITR faculty (industry experts may join as panelists)
