Built For Technical & Nontechnical backgrounds

Week 1: The Big Picture. Introducing Holistic Data & AI Strategy

The Big Picture is a blueprint for Data and AI Strategy that connects technology to value. I explain the frameworks that help business leaders decide how the business will achieve its goals with data and AI. I cover 5 critical frameworks to support critical decisions.



Week 2: Culture Change & Preparing The Business For Implementation

Strategy must be actionable, and we quickly move from theory to implementation. Businesses are built in operational silos that must be broken down for data and AI to succeed. Culture change is necessary, and I cover 3 frameworks to get it done.



Week 3: Implementation Frameworks & Data Monetization

Implementation begins with the initial assessment, data monetization, and opportunity discovery. Data and AI Strategy are extensions of the business strategy. I cover frameworks that establish data as an asset with quantifiable value and support C-level leaders in opportunity selection.



Week 4: The Maturity Models & AI Innovation Strategy

The maturity models take another step toward execution. Along with opportunity discovery, they help C-level leaders make critical decisions about how far and fast the business should mature. Innovation must be part of the mix, and I provide frameworks to keep initiatives aligned with value.



Week 5: The Data & AI Strategy Artifacts. Setting Up For Execution

It’s time to deliver the Data and AI strategy artifacts. These set the business up for success by aligning decision-making across the firm. Execution and maturity progression are incremental processes, so alignment frameworks keep multiple initiatives moving in the same direction.



Week 6: Case Studies On AI Strategy Implementation & Overcoming Barriers

There’s no such thing as perfect. Throughout the course, I explain the frameworks from 2 angles: how it should be and how to implement frameworks for our messy reality. Implementation cases draw from my experience and case studies to explain how to succeed, even if you start with a worst-case scenario.

1. Introduction

1. Course Benefits & Outcomes
2. Course Outline & Overview
3. What Is Strategy?
   

3. Integrating Data And AI Strategy Into Business Strategy

1. The Technology Model: Where To Begin?
2. Why Does The Business Need Data Analytics & AI Strategies?
3. Data Strategy: Definition & Objectives
4. AI Strategy: Definition & Objectives
5. The Data Monetization Catalog
6. Case Study: What Happens When Technical Strategy Doesn’t Align With Business Strategy?
7. Case Study: Apple Using Data As A Competitive Advantage
   

5. Strategy Implementation 

1. Case Study: The Cautionary Tale Of Zillow
2. Why Is Transformation So Hard?
3. Case Study: Data And AI Maturity Drivers
4. Getting Buy In From Senior Leadership
5. Collaborating With External Teams
6. Case Study: What Happens When Data Science Initiatives Fail?
   

7. Data Science And Strategy

1. Becoming A Strategic Partner
2. Decision Support Systems And KPIs
3. The Data Science Value Stream
4. Case Study: Microsoft’s Initial AI Product Strategy
5. Capabilities Based Competition
6. Case Study: TikTok vs Google. Competing On Best In Class Capabilities
7. Case Study: Exploring Partnerships For Capabilities Acquisition

9. Assessing the Business

1. Introduction To The Assessment Framework
2. Culture
3. Leadership Commitment
4. Operations And Structure
5. Skills And Competencies
6. Analytics-Strategy Alignment
7. Proactive Market Orientation
8. Employee Empowerment
9. Opaque VS Transparent And Justifying Early Initiatives
10. AI Strategy KPIs

11. Data Product Strategy

1. Introduction To Data Product Strategy
2. Monetization Phase
3. Productization Phase
4. Commercialization Phase
5. Key Implementation Points
6. Messaging And Executive Communications Framework
7. Implementation Cases And Examples 

13. Causal KPIs And Communications Frameworks

1. Introduction To Causal KPIs
2. The Implications Of Causal KPIs
3. The Microsoft Causal Communications Framework
4. The Amazon Causal Communications Framework
5. Core-Rim And Path To Causal Methods

Limited Skills, Expertise, & Knowledge

Access to talent and building a successful internal training program requires alignment across the business. The AI product roadmap or project plan and value stream define the business’s need for capabilities holistically. They provide detailed guidance for hiring and upskilling programs.

Implementing Data & AI Is Too Expensive

AI Governance gives CxOs a framework to manage the value creation side of AI initiatives. The 1st phase estimates initiative value using a frontline approach. The 2nd phase assesses the solution’s feasibility and costs. Each iteration is a low cost lesson on identifying high value opportunities and minimizing solution costs. Nothing moves forward until there is a plan for returning value.

Project Complexity: Difficulty With Integration & Scale

Complexity is resolved by the AI Governance framework. Senior leaders manage the value creation without being pulled into managing the technology or workflows. That removes the complexity of oversight. Coordination across the business is a key driver for a holistic AI Strategy vs. a strategy limited to a few groups and use cases.

Lack Of Tools Or Platforms

Once business needs are defined by the AI Strategy artifacts, most firms find the tools and platforms they need to support data, development, and deployment.

Too Much Data Complexity

The Data Monetization Catalog is another artifact of the AI Strategy planning process. The data valuation process reveals which datasets can be used for model training and leveraged to address business challenges. Focusing on the highest value datasets reduces complexity. Data maturity progresses in phases.