Become An Ai Strategist

Ensure value delivery from data and AI

What you'll learn

The skills and experience required for the role of an AI Strategist

Obtain a framework for doing an AI strategy at your organization

The importance and wider context of AI strategy

Consulting and technical skills for the role

Requirements

Basic understanding of technical areas related to data science, engineering and analytics

Basic understanding of business areas, such as budgeting, project and product management

Description

Data is everywhere around us. Generative AI and deep learning ensured that the AI winter remains firmly behind us. But why do we still struggle to get value from those technologies? Except for a few tech giants, most companies - even those at the forefront of innovation in other fields, such as the automotive and energy sectors - don't achieve the desired impact. The answer to this is a lack of a new role that is needed - that of a data and AI strategist. In this course, we'll define the key terms of the AI strategy field. Then, we'll dive deeper into the concrete skills and paths you can take to become one. Finally, the bulk of the course is focused on the 3D model of doing data and AI strategy - a framework you can apply daily as a strategist.Many of the concepts and ideas are based on my conversations with worldwide leaders in the data and AI strategy field, such as Tom Davenport, Nicolas Averseng, and Doug Laney. Those became the foundation of the Elements of Data Strategy book and serve as a companion to this course (participants get a free copy).Data and AI strategy is a rapidly maturing field, and there's a first movers' advantage to all of us who dive in head first into this challenging but gratifying new career.

Overview

Section 1: Introduction

Lecture 1 Introduction

Lecture 2 Start with the Why

Lecture 3 Current State

Lecture 4 Motivating Factors

Lecture 5 Housekeeping

Section 2: Definitions

Lecture 6 Definitions Introduction

Lecture 7 Strategy and Tactics

Lecture 8 The Hierarchy of AI Needs

Lecture 9 The When and Where of AI Strategy

Lecture 10 StratOps

Lecture 11 Products vs. Projects

Lecture 12 Data Products

Lecture 13 Who is a Data and AI Strategist?

Lecture 14 Exercise

Section 3: Skills and Methods Deep Dive

Lecture 15 Skills and Methods Deep Dive Introduction

Lecture 16 The Data and AI Strategist Venn Diagram

Lecture 17 Technical Skills

Lecture 18 Types of Understanding

Lecture 19 Business Skills

Lecture 20 Systems Thinking

Lecture 21 Systems Thinking Skills

Lecture 22 Boundary Setting and Black Boxes

Lecture 23 Abstraction Levels

Lecture 24 Method Spotlight: MECE

Lecture 25 Method Spotlight: Value Stream Mapping (VSM)

Lecture 26 Feedback Loops

Lecture 27 Communication Skills

Section 4: The 3D Model of Data and AI Strategy: Due Diligence

Lecture 28 The 3D Model: Overview

Lecture 29 Due Diligence: Overview

Lecture 30 Alignment with the Business Strategy

Lecture 31 Current State Analysis (CSA): Overview

Lecture 32 Use Case Audit

Lecture 33 Data Audit

Lecture 34 Architecture and Technology Audit

Lecture 35 Data Maturity Assessment

Lecture 36 Ambition Setting and Gap Analysis

Section 5: The 3D Model of Data and AI Strategy: Design

Lecture 37 Design: Overview

Lecture 38 Analogies

Lecture 39 Influence Cascade

Lecture 40 Use Cases

Lecture 41 Lighthouse Projects and Pilotitis

Lecture 42 Method Spotlight: Double Diamond

Lecture 43 Use Cases: Ideation

Lecture 44 Use Cases: Feasibility

Lecture 45 Use Cases: Prioritisation

Lecture 46 Target Data Architecture

Lecture 47 Target Technology

Lecture 48 Data Governance

Lecture 49 Method Spotlight: Data Mesh

Lecture 50 Operating Model

Lecture 51 Method Spotlight: Team Topologies

Lecture 52 Budgeting

Lecture 53 Timeline and Roadmap

Lecture 54 Final Deliverables

Section 6: The 3D Model of Data and AI Strategy: Delivery

Lecture 55 Delivery: Overview

Lecture 56 Cargo Cults

Lecture 57 Implementation Maze

Lecture 58 Soft Agile and Lean Definitions

Lecture 59 Example of Waste

Lecture 60 Combining Lean and Agile

Lecture 61 Method Spotlight: Team Data Science Process

Lecture 62 Data Platform and Sandbox

Lecture 63 MLOps

Lecture 64 Templating and Documentation

Lecture 65 Method Spotlight: Closing the Loop

Lecture 66 Impact Assessment

Lecture 67 Portfolio Management

Lecture 68 Final Exercises

Section 7: Conclusion

Lecture 69 Conclusion

Head of Data and AI who wants to prepare an operational strategy for their team,Chief Data Officer who wants to make their organization more innovative in data and AI,Senior Data Scientist, or Engineer who wants to pursue new use cases in data and AI,Management Consultants who work in the field of data and AI