Programme Parameters
Advanced Postgrad
5 Weeks (50 Hrs)
Online (e-LMS)
• Prior working knowledge of the field and comfort with core tools and quantitative reasoning.
• A laptop or desktop with a stable internet connection.
• Willingness to complete assignments and the capstone project.
About This Course
AI Leadership and Strategy for Business Leaders is a non-technical program for executives and managers steering their organisations into the AI era. You learn to cut through hype and identify where AI genuinely creates value, how to prioritise and fund use cases, and how to build the data, talent and infrastructure capability to deliver them. The program covers the leadership dimensions that decide success: managing risk, ethics and governance; leading change and culture; and measuring returns. You finish able to author and drive an AI strategy for your organisation. A verified e-Certificate of competency and e-Marksheet from the Deep Science & Technology Consortium.
🎯 Program Aim
This program equips business leaders to set AI strategy — identifying high-value use cases, building capability, managing risk and leading change — without needing to code.
📋 Course Objectives
1. Identify and prioritise high-value AI use cases.
2. Build data, talent and infrastructure capability.
3. Manage AI risk, ethics and governance.
4. Lead organisational and cultural change.
5. Measure the return on AI investment.
👥 Who Should Enroll?
• Executives, directors and senior managers
• Product and transformation leaders
• Founders and strategy professionals
• Anyone leading AI adoption
🚀 Key Learning Outcomes
• The ability to set and drive an AI strategy.
• A prioritised AI roadmap for your organisation.
• The judgement to separate AI value from hype.
• A verified e-Certificate of competency and e-Marksheet from the Deep Science & Technology Consortium.
💎 What You'll Gain
Live & Recorded Sessions
Lifetime access to class recordingse-Certificate on Completion
Cryptographically verified credentialPost-Programme Support
Direct access to mentors & councilHands-On Experience
Notebooks, real-world code & datasetsCurriculum Outline
Where AI Creates Value, and Where It Does Not
• Distinguishing automation, augmentation and new-product opportunities
• Assessing feasibility against data reality rather than vendor demonstrations
• Build, buy or partner, and the total cost each choice actually carries
Prioritisation and Investment
• Value-versus-feasibility screening and killing projects early
• Unit economics of inference and the cost curve leaders routinely underestimate
• Setting success measures before the pilot, not after
Data, Talent and Operating Model
• The data foundations that must exist before models are worth funding
• Team topologies: central platform, embedded practitioners, or both
• Change management and why adoption, not accuracy, determines returns
Governance a Board Can Defend
• Regulatory exposure by jurisdiction and sector
• Reputational and concentration risk from third-party model dependence
• Escalation paths and the decisions that must never be fully delegated
From Pilot to Production
• Why pilots stall: integration, ownership and unclear accountability
• Sequencing a portfolio so early wins fund harder work
• Communicating progress to boards, staff and customers honestly
Technical Specifications
| Parameter | Requirement |
|---|---|
| Covered Tool / Platform | RStudio |
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