Leadership with AI

Preparatory Session: IS/IT 101 (optional)

No. of Sessions: 1

Description

This session offers a practical introduction to Information Systems and Information Technology for non-IT practitioners. It helps participants build familiarity with the core concepts, technologies, and terminology that underpin modern digital organizations. Covering areas such as infrastructure, data, software, enterprise systems, analytics, security, emerging technologies, technology stacks, and IT investment, the session prepares participants to engage more meaningfully with the technology and AI-focused components of the program.

Objective

To build foundational IS/IT literacy among non-IT participants so that they can better understand technology-enabled organizations, engage with digital and AI concepts more confidently, and participate effectively in discussions throughout the Leadership with AI program.

Topics

• Introduction Information Systems/Technology

• Computing Infrastructures and architectures

• Data modelling, data management and data management systems/technologies

• Understanding software and software development

• Enterprise systems, eCommerce and payment systems

• Business Intelligence/Analytics and AI/ML

• IoT, AR/VR, Digital twins

• IT/IS Security and Security Technologies

• Technology/Solution Stacks

• Investing in IT

Outcomes

Participants will leave the session with:

• a working familiarity with key IS/IT terms and concepts

• a clearer understanding of how major technology components support business processes and digital transformation

• improved confidence in engaging with technology-oriented discussions in the rest of the program

• a stronger appreciation of the managerial relevance of IT, data, software, analytics, security, and emerging digital technologies

Module 1: Digital Foundations for AI-Ready Organizations

No. of Sessions: 2

Description

AI cannot deliver value in isolation. For AI to work at scale, organizations need the right digital foundations: data, systems, integration layers, infrastructure, and security. This module helps participants understand the broader technology context within which AI initiatives succeed or fail. It introduces the enterprise technology stack and explains how AI, IoT, cloud, and data come together in modern digital transformation.

Objective

To help participants understand the digital building blocks required to make organizations AI-ready, and to appreciate how AI must be integrated with enterprise systems, workflows, data, and security architecture.

Session 1: The Digital Enterprise Context for AI View session PDF ↗

Topics

• Enterprise information systems and digital technologies

• Evolution of digital technologies and the changing landscape

• The role of AI, IoT, Cloud, and Data in digital transformation

• Why AI must be connected to business processes and enterprise systems

Learning Outcomes
By the end of this session, participants will be able to:

• explain why AI cannot be implemented effectively in silos

• describe the broader digital context within which AI operates

• identify the role of data, cloud, and connected systems in AI-led transformation

• recognize what makes an organization ready for AI adoption

Session 2: Integration, Automation and Security FoundationsView session PDF ↗

Topics

• Computing infrastructure and data management systems

• APIs, microservices, orchestration, and interoperability

• Foundations of end-to-end automation

• Securing digital systems for AI-enabled organizations

Learning Outcomes
By the end of this session, participants will be able to:

• understand the role of infrastructure and integration in enterprise AI

• distinguish key components of AI-enabling technology architecture

• appreciate the importance of automation readiness

• identify major security, privacy, and resilience considerations in AI adoption

Module 2: The AI Spectrum — From Classical AI to Agentic AI

No. of Sessions: 4

Description

This module provides a structured and holistic understanding of AI. It moves from the foundations of intelligence and problem solving to classical AI, machine learning, deep learning, Generative AI, and Agentic AI. A distinctive feature of this module is its emphasis on understanding the differences between AI paradigms, when each is appropriate, and how they can be combined in practice.

Objective

To help participants build a strong conceptual foundation in AI and understand the practical differences, complementarities, and use-cases of classical AI, machine learning, Generative AI, and Agentic AI.

Session 1: Understanding AI, Intelligence and Problem SolvingView session PDF ↗

Topics

• What is AI and how it evolved

• Defining intelligence in organizational and business settings

• Role of data, data modelling, metadata, modelling generic entities, structuring knowledge using knowledge graphs, ontologies and similarities, data modelling for highly personalized and contextual intelligence

• Data-driven, knowledge-driven, operational, and common-sense intelligence

• Machine learning: supervised, unsupervised, reinforcement, and evolutionary computing approaches

• Understanding semantic AI, data driven AI/ML, Generative AI and Agentic AI.

Learning Outcomes
By the end of this session, participants will be able to:

• explain the evolution and meaning of AI in business terms

• distinguish different forms of intelligence relevant to organizations

• understand how AI supports problem solving and decision making

• understand role of data, metadata, semantic AI in building intelligence

Session 2: Generic problems and applications, core AI/ML techniques/algorithmsView session PDF ↗

Topics

• Role of AI/ML in building intelligent systems

• Generic problem types AI/ML solves: classification, clustering, regression, intelligent matching, recommender systems, association rule mining, optimization etc.

• N=1 analysis for highly personalized experiences

• How generic applications can be built using AI/ML such as forecasting, sentiment analysis, segmentation, market-basket analysis, personalization and recommendation etc.

• Understanding techniques behind: ML techniques, case-based reasoning, expert systems, genetic algorithms, model-based reasoning, collaborative and content filtering.

• Integrating various systems to solve problems more effectively with 360-degree view.

• Business applications of AI/ML and how AI/ML can be used to address use-cases in different domains.

Learning Outcomes
By the end of this session, participants will be able to:

• explain the basic families of machine learning methods and how they differ

• understand different generic problems AI/ML models and solves and connect to the real-world use-cases in respective domains.

• how semantic (knowledge) based intelligence and data driven intelligence can be integrated

• assess when rule-based, semantic, and ML approaches are more suitable

Session 3: Deep Learning and Generative AIView session PDF ↗

Topics

• Problem complexity and neural networks

• Deep learning fundamentals

• Foundations and capabilities of Generative AI

• Multimodal AI

• RAG, fine-tuning, prompt engineering, and context engineering

• Leveraging classical AI using Generative AI

• Limitations, risks, and mitigation

Learning Outcomes
By the end of this session, participants will be able to:

• understand where deep learning becomes relevant

• explain the business significance of Generative AI

• distinguish prompt engineering, context engineering, RAG, and fine-tuning

• identify key risks such as hallucinations and inappropriate outputs

Session 4: Agentic AI and Combining AI ApproachesView session PDF ↗

Topics

• Leveraging classical AI, ML, and GenAI together

• Operationalising AI in business workflows

• AI agents and Agentic AI

• End-to-end automation and human-in-the-loop considerations

Learning Outcomes
By the end of this session, participants will be able to:

• explain what Agentic AI is and how it differs from other AI approaches

• understand how multiple AI paradigms can work together

• identify where agents can add value in enterprise contexts

• appreciate the importance of oversight, orchestration, and human judgment

Module 3: Tools, Platforms and Practical AI Enablement

No. of Sessions: 3

Description

This module focuses on how AI concepts can be translated into practical applications, workflows, and organizational capabilities. Designed for experienced practitioners and leaders, it introduces representative AI tools, languages, frameworks, platforms, and implementation approaches that can improve personal productivity, support leadership activities, and enable the successful execution of AI initiatives.

The module moves progressively from using AI tools effectively, to applying AI in managerial and leadership work, and finally to understanding how AI-enabled solutions are designed, integrated, automated, grounded, orchestrated, evaluated, and deployed. Participants will gain practical exposure through demonstrations of representative tools and technologies without being expected to become software developers.

Objective

To enable participants to use AI tools effectively in their own work, apply AI to leadership and managerial activities, and develop sufficient practical and technical understanding to lead, sponsor, evaluate, and guide AI initiatives within their organizations.

The progression should be: Get ready → How to use AI to use/lead AI → for → Use AI personally → Lead with AI → Lead AI initiatives

Session 1: AI Tools for Personal and Professional ProductivityView session PDF ↗

Topics

• Understanding the AI Productivity Tool Landscape

• Prompt Engineering Fundamentals

• Context Engineering

• AI for Everyday Knowledge Work

• Working with Multimodal AI

• Responsible and Effective Use

Learning Outcomes
By the end of this session, participants will be able to:

• identify suitable AI tools for different productivity tasks

• write clearer and more structured prompts

• use context, examples, constraints, and output formats effectively

• apply AI to research, writing, analysis, presentations, and meeting preparation

• use multimodal AI tools for working with documents, images, audio, and data

• critically review and verify AI-generated outputs

• use AI tools responsibly while protecting sensitive information

Session 2: AI for Leadership and Managerial EffectivenessView session PDF ↗

Topics

• AI as a Leadership Copilot

• AI for Decision Support

• AI for Strategic and Operational Planning

• AI for Team and People Leadership

• AI for Customer and Stakeholder Engagement

• AI for Knowledge Management

• Moving from Individual Use to Team Productivity

Learning Outcomes
By the end of this session, participants will be able to:

• use AI to support planning, analysis, decision preparation, and communication

• apply AI to strategic and operational leadership activities

• use AI for stakeholder analysis, customer engagement, and team coordination

• convert meetings and discussions into decisions, actions, and follow-up plans

• apply AI to organizational learning and knowledge management

• create reusable AI-enabled practices for teams

• recognize where human judgment, accountability, and sensitivity remain essential

Session 3: Tools and Technologies for Leading AI Initiatives

Topics

• From Business Problem to AI-Enabled Solution

• Selecting the Appropriate AI Approach

• Components of an AI-Enabled Solution

• Model Platforms and APIs

• Enterprise Data and RAG

• Workflow Automation and Integration

• Low-Code and Enterprise Application Platforms

• Agent and Orchestration Frameworks

• Classical AI and Machine-Learning Technologies

• AI-Assisted Software Development

• Evaluation, Monitoring, and Observability

• Build, Buy, Configure, or Integrate

• Guiding In-House AI Development

Learning Outcomes
By the end of this session, participants will be able to:

• explain how AI solutions move from business problems to working applications

• identify the major components of an AI-enabled solution

• understand the roles of Python, APIs, SQL, JSON, databases, workflows, RAG, and agents

• distinguish between rules, ML, GenAI, RAG, automation, and Agentic AI implementations

• recognize representative platforms, frameworks, and technologies used by AI engineering teams

• evaluate no-code, low-code, platform-based, and custom-development approaches

• understand how AI solutions connect with enterprise systems and data

• review prototypes and ask informed technical and business questions

• guide internal AI or computer-science teams through prototype, pilot, evaluation, and scale-up

• assess whether an organization should build, buy, configure, or integrate an AI solution

Module 4: AI Strategy, Leadership and Transformation

No. of Sessions: 3

Description

This module focuses on the leadership challenge of AI: identifying high-value opportunities, shaping strategy, building capabilities, driving adoption, managing change, choosing implementation pathways, and scaling responsibly. It helps participants move from understanding AI to leading AI-led transformation in their organizations.

Objective

To equip participants with the strategic, organizational, and leadership perspectives needed to identify, prioritize, implement, govern, and scale AI initiatives in real business settings.

Session 1: Identifying AI Opportunities and Designing Strategy

Topics

• Understanding AI use-cases

• Integrating AI into existing systems and processes

• AI-ready data foundations

• Building systems as part of digitalization initiatives

• Responsible, ethical, and governed use of AI

• Security, regulatory and compliance considerations

Learning Outcomes
By the end of this session, participants will be able to:

• identify and prioritize suitable AI use-cases

• understand how AI fits into business processes and digital initiatives

• appreciate the role of data readiness in successful implementation

• incorporate ethics, governance, and compliance into AI strategy

Session 2: Leading AI-Driven Organizational Change

Topics

• What it means to be an AI-ready organization

• AI-first leadership

• Technical understanding required for leaders

• Talent, capability building, and cross-functional collaboration

• Change management and scaling AI across the organization

Learning Outcomes
By the end of this session, participants will be able to:

• explain the organizational capabilities needed for AI readiness

• understand the role of leadership in driving AI-led change

• identify talent and capability requirements for AI initiatives

• recognize the main change management challenges in AI adoption

Session 3: Scaling AI — Business Models, Build-vs-Buy and Execution

Topics

• Agentic AI and implications for business models

• Strategic positioning and differentiation in the AI era

• Prototyping, rapid proof of concept, scoping, and success gates

• Build-vs-buy playbook

• Token economy and AI economics

• Industry frameworks for implementation and scale-up

Learning Outcomes
By the end of this session, participants will be able to:

• understand how AI may reshape business models and strategic choices

• evaluate build-vs-buy decisions more effectively

• use prototyping and success gates to reduce implementation risk

• appreciate the economics and scaling considerations of AI initiatives

Module 5: Business Case Studies* and Use-cases on AI

No. of Sessions: 4

Description

This module brings the program to life through real-world business cases on AI adoption, transformation, innovation, and strategic execution. It helps participants examine how organizations across sectors have approached AI, what strategies and roadmaps they followed, what outcomes they achieved, what challenges they faced, and why some initiatives succeeded while others struggled. The module is designed to connect leadership frameworks with actual implementation journeys and business results.

*Mostly these case studies would be from Harvard Business Impact Education. Cases will be selected based on concentration of verticals participants belong to and the choices based on the survey.

Objective

To help participants understand how organizations across the world are leveraging AI in practice, including the strategic choices they made, the implementation pathways they followed, the value they realized, the risks and barriers they encountered, and the lessons leaders can draw for their own organizations.

Overall Learning Outcomes for Module

By the end of this module, participants will be able to:

• interpret AI case studies using a leadership and strategy lens

• connect AI concepts, tools, and frameworks from earlier modules to real business situations

• assess AI initiatives in terms of strategy, implementation, governance, ROI, and scalability

• identify best practices for AI adoption and transformation

• recognize warning signs, pitfalls, and failure patterns in AI programs

• derive practical lessons for designing and leading AI initiatives in their own context

Module 6: Capstone Project — AI Leadership in Action

Presentations: 8-10 hours

Parallel project across the program with final presentation at the end. 1 final presentation session + guided milestone reviews during the program. Approximate hours for all presentations: 8-10 hours (based on selected projects).

Description

The capstone project is the culminating component of the program. It is designed to help participants apply the concepts, frameworks, tools, and leadership perspectives covered across the earlier modules to a real organizational problem or opportunity. Participants will identify a business challenge, evaluate how AI can create value, choose an appropriate AI approach, and develop a practical implementation roadmap. The capstone extends the program’s focus on AI-ready organizations, AI use-cases, tools and platforms, build-vs-buy decisions, governance, and business transformation.

Objective

To enable participants to synthesize learning from the program and translate it into a practical AI initiative, transformation proposal, or solution blueprint relevant to their organization, industry, or chosen domain.

Capstone Purpose

The capstone is intended to move participants from understanding AI concepts to applying them in a structured, leadership-oriented manner. It should help them demonstrate not only knowledge of AI, but also judgment on where AI fits, which approach is suitable, what organizational readiness is required, and how implementation can be governed and scaled. This fits naturally with program’s emphasis on digital foundations, the AI spectrum, practical enablement, and AI-led transformation.

Capstone Format

Participants may work individually or in small teams. Each participant or team should select one real or realistic problem statement from their organization, function, sector, or an assigned industry context. The capstone may be developed progressively across the program in the following way:

• after the early modules, define the business problem and current context

• after the AI foundations module, identify the suitable AI approach or mix of approaches

• after the tools/platforms module, outline the solution pathway and implementation choices

• after the leadership module, complete the strategy, governance, change, and scaling plan

• after the case-study module, refine the proposal using lessons from real-world implementations

Capstone Themes

The project may focus on one of the following:

1. AI-led process improvement

2. Productivity enhancement using AI

3. Customer experience transformation

4. AI-enabled decision support

5. Domain-specific AI solution design

6. AI agent or workflow automation concept

7. Enterprise AI adoption roadmap

8. Responsible AI and governance framework for a business function

Deliverables

Each participant or team may be asked to submit the following (through google form/app):

1. Problem Statement: A clear articulation of the business problem, pain point, or opportunity.

2. Current-State Assessment: A brief description of the current process, system, decision flow, or business context.

3. AI Opportunity Identification: Explanation of where AI can help and why the problem is appropriate for AI.

4. Recommended AI Approach: Selection of the most suitable approach, such as classical AI, machine learning, Generative AI, Agentic AI, or a hybrid model.

5. Data and Technology Readiness View: Key requirements related to data, systems, integration, platforms, and enterprise readiness.

6. Solution Blueprint: A conceptual design of the proposed AI-enabled solution, workflow, assistant, or transformation initiative.

7. Implementation Roadmap: Suggested phases, milestones, pilots, proof-of-concept stages, and scale-up approach.

8. Governance and Risk Considerations: Key ethical, regulatory, privacy, security, and oversight considerations.

9. Value Realization Framework: Expected benefits, KPIs, ROI logic, or success metrics.

10. Final Presentation: A concise presentation summarizing the capstone proposal and recommendations.

Expected Outcome

At the end of the capstone, participants should be able to present a practical and leadership-oriented answer to these questions:

1. What business problem are we solving?

2. Why is AI relevant here?

3. Which type of AI is most appropriate?

4. What digital and data foundations are needed?

5. Should we build, buy, or partner?

6. What are the implementation risks?

7. How will we measure success?

8. What is the roadmap for execution and scale?

Evaluation Criteria

1. Relevance of the Business Problem: How clearly the participant identifies a meaningful and realistic business challenge.

2. Appropriateness of the AI Approach: How well the chosen AI paradigm matches the nature of the problem.

3. Practicality of the Solution Design: Whether the proposal is realistic in terms of data, systems, integration, tools, and organizational context.

4. Strategic and Leadership Quality: How well the participant addresses adoption, change management, governance, talent, and implementation decisions.

5. Governance and Risk Awareness: Whether the proposal adequately considers ethics, regulation, privacy, security, and human oversight.

6. Value Creation Logic: How clearly the participant defines expected impact, metrics, business value, and success criteria.

7. Clarity of Communication: How effectively the participant presents the problem, solution, roadmap, and expected outcomes.

Group formation

1. Group of 5-12 participants aligned based on interest, job profile and current role or role to be played.

2. Choosing the project

   • Vertical: Banking, finance, insurance, manufacturing, agriculture, healthcare, education, government etc.

   • Function: HR, Marketing, Finance and Accounting, Logistics etc.

   • Specific use case: e.g. insurance underwriting, end-to-end recruitment automation using agentic AI, marketing automation in specific vertical or in general.

3. Have a mix of business leaders, domain & technical experts (with CS/IT background) etc.