Become An Ai Strategist

[mitsumi]

Become An Ai Strategist
Published 10/2023
MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz
Language: English | Size: 698.88 MB | Duration: 1h 51m

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

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