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AI Leader: Agentic AI - Maturity, Scaling & ROI
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11 students
AI Leader: Agentic AI - Maturity, Scaling & ROI
Lead Enterprise AI Transformation - Assess Agentic AI maturity, build roadmaps and drive ROI
Created byPranab Das
Last updated 6/2026
English
What you'll learn
- Understand how to move from AI pilots to enterprise-scale AI adoption
- Learn why most AI initiatives fail to scale despite heavy investments
- Assess AI maturity across business, data, technology, and governance
- Build a practical AI maturity heatmap for your organization
- Design a scalable AI transformation strategy aligned with business goals
- Gain a clear, non-technical understanding of Agentic AI to make informed leadership decisions
- Learn how to prioritize AI initiatives based on readiness and business impact
- Structure AI investments based on readiness and expected ROI
- Diagnose enterprise AI readiness gaps using practical frameworks and case studies
- Understand the Agentic AI inflection point and why maturity now matters
- Apply leading AI maturity models in real-world business contexts
- Transition from experimentation to governed AI at scale
- Learn how to manage risks introduced by autonomous AI systems
- Understand how governance acts as a scaling accelerator—not a constraint
- Design governance approaches for enterprise AI systems
- Identify and mitigate AI sprawl across teams, tools, and systems
- Understand the role of AI Control Planes in enterprise-scale deployments
- Understand how enterprise AI architectures enable scalability and governance
- Learn how to design a 24–36 month AI roadmap aligned with business goals
- Learn how to present AI strategy and maturity to CXOs and board members
- Align AI initiatives with measurable business outcomes and KPIs
- Develop a leadership mindset for driving AI-led organizational change
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Course content
9 sections • 36 lectures • 10h 23m total length
Requirements
- No technical prerequisites are required for this course
- Basic understanding of how enterprises operate (IT, operations, sales, etc.) is helpful
- Interest in AI from a strategic or business perspective is recommended
- No prior experience in AI, machine learning, or data science is needed
- No programming or coding skills are required
- No mathematical or statistical background is required
- Sections 1 and 2 provide a clear, non-technical foundation in Agentic AI concepts
- The course focuses on strategy, governance, scaling, and ROI—not implementation
- Willingness to think in terms of systems, trade-offs, and enterprise architecture is beneficial
Description
In today’s rapidly evolving AI landscape, building models is no longer enough. The real challenge is scaling AI across the enterprise—safely, systematically, and with measurable business impact.
This course, “AI Leader: Agentic AI - Maturity, Scaling & ROI”, is designed to help you move beyond experimentation and develop the skills needed to lead AI transformation at an enterprise level.
A key differentiator of this course is that it first builds a clear conceptual foundation of Agentic AI. Through the first two sections, you will develop a non-technical understanding of how AI agents work—covering concepts like memory, RAG, orchestration, and control planes—so you can confidently engage in architectural and strategic discussions.
Building on this foundation, the course then shifts into leadership, strategy, and execution.
You will learn how to assess AI maturity, identify readiness gaps, and design a structured roadmap that aligns business strategy, technology, governance, and investment decisions. The course introduces proven maturity models and shows how to translate them into actionable insights using tools like heatmaps, diagnostic frameworks, and structured investment approaches.
A central part of this course is the Orion case study, a realistic enterprise scenario that demonstrates both failure and success patterns in AI adoption. Through Orion, you will learn how poor sequencing, weak governance, and fragmented investments lead to failure—and how to correct them using structured frameworks.
In addition, the course introduces critical modern challenges such as AI Sprawl and explains how enterprises can address them using Control Plane architectures, enabling governed, scalable, and autonomous AI systems.
By the end of this course, you will be able to design a 24–36 month AI roadmap, structure AI investments based on business impact, implement governance as a scaling accelerator, and create board-ready AI business cases that clearly articulate ROI, risk, and strategic outcomes.
This course is not about coding models—it is about leading AI transformation in the Agentic AI era.
What You Will Learn
Assess AI maturity using industry frameworks (Gartner, IBM, MIT, Accenture, AIMAA)
Build AI readiness heatmaps and identify critical gaps
Diagnose enterprise AI failures using structured frameworks
Understand risk amplification in Agentic AI systems
Design phased AI roadmaps (Foundation → Enablement → Acceleration)
Structure AI investments based on business value and readiness
Implement governance models that enable safe and scalable AI
Build board-ready AI business cases with ROI and risk clarity
Translate AI strategy into executable enterprise programs
Who This Course Is For
CXOs and decision-makers evaluating AI investments and strategy
Aspiring and current AI Leaders, Product Leaders, and Technology Leaders
Consultants working in AI, Digital Transformation, or Strategy
Enterprise and Solution Architects
Senior engineers looking to transition into leadership roles
Business leaders responsible for AI-driven transformation initiatives
Prerequisites
No prior knowledge of AI or machine learning is required
The first two sections provide a clear, non-technical primer on Agentic AI concepts and architecture
Interest in enterprise strategy, transformation, or technology leadership is helpful
Why This Course Is Different
Focuses on enterprise AI execution, not just theory
Combines multiple maturity models into a unified framework
Includes a real-world case study (Orion) for practical learning
Covers modern enterprise challenges like AI Sprawl and Control Plane architecture
Provides downloadable tools (heatmaps, roadmaps, governance templates, business case models)
Designed to help you think and operate like a CAIO (Chief AI Officer)
Outcome
By the end of this course, you will be able to:
Assess where an organization stands in its AI journey
Define a clear and scalable AI strategy
Design execution roadmaps and structured investment approaches
Manage risk and governance effectively
Present AI transformation plans confidently to leadership and boards
Final Thought
AI is no longer an experimental capability—it is a strategic imperative.
The organizations that succeed will not be the ones that build the most AI,
but the ones that scale it intelligently, govern it effectively, and deliver measurable business outcomes.
This course will show you how.
Who this course is for:
- CXOs, business leaders, and senior executives looking to understand and scale AI within their organizations
- Technology leaders (CTOs, CIOs, Heads of Engineering, Heads of Data/AI) driving AI strategy and implementation
- Aspiring leaders who want to transition into AI strategy, product, or transformation roles
- Consultants and advisors working on digital transformation, AI adoption, or enterprise modernization
- Product leaders and program managers responsible for AI-driven initiatives and roadmaps
- Business leaders across functions (operations, sales, finance, customer experience) exploring AI-led transformation
- Professionals involved in enterprise decision-making, strategy, or innovation initiatives
- Leaders struggling to move AI initiatives from pilot stage to production at scale
- Organizations looking to build structured AI maturity, readiness, and governance frameworks
- Teams evaluating how to prioritize AI investments and drive measurable ROI
- Professionals seeking to understand AI from a system, architecture, and business perspective—not just tools
- Individuals who want to engage confidently in AI strategy discussions with technical teams
- Leaders responsible for managing risks, governance, and compliance in AI deployments
- Anyone looking to understand how Agentic AI is reshaping enterprise systems and workflows