From data to profit how businesses leverage data to grow their top & bottom lines
Transform your company's AI and data frameworks to unlock the true power of disruptive new tech In From Data to Profit: How Businesses Leverage Data to Grow Their Top and Bottom Lines, accomplished entrepreneur and AI strategist Vineet Vashishta delivers an engaging and insightful new take on m...
| Otros Autores: | |
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| Formato: | Libro electrónico |
| Idioma: | Inglés |
| Publicado: |
Hoboken, New Jersey :
John Wiley & Sons, Inc
[2023]
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| Materias: | |
| Ver en Biblioteca Universitat Ramon Llull: | https://discovery.url.edu/permalink/34CSUC_URL/1im36ta/alma991009752720506719 |
Tabla de Contenidos:
- Cover
- Title Page
- Copyright Page
- Contents
- Introduction
- A Novel Asset Class with a Greenfield of Opportunities
- The Road from Laggard to Industry Leadership
- Technical Strategy as a New Top-Level Construct
- Playbook for the Enterprise
- Systems, Models, and Frameworks
- Introducing Data to the Enterprise
- Chapter 1 Overview of the Frameworks
- Continuous Transformation
- Three Sources of Business Debt
- Evolutionary Decision Culture
- The Disruptor's Mindset
- The Innovation Mix
- Meet the Business Where It Is
- The Technology Model
- The Core-Rim Model
- Transparency and Opacity
- The Maturity Models
- The Four Platforms
- Top-Down and Bottom-Up Opportunity Discovery
- Large Model Monetization
- The Business Assessment Framework
- The Data and AI Strategy Document
- Data Organizational Development Framework
- More to Come
- Chapter 2 There Is No Finish Line
- Where Do We Begin? With Reality
- Defining a Transformation Vision and Strategy
- Paying Off the Business's Digital Debt
- Managing the Value Creation vs. the Technology
- A Master Class in Continuous Transformation Strategy
- Evaluating Trade-Offs
- What Happens When the Business Loses Faith in Data and AI?
- What's Next?
- Chapter 3 Why Is Transformation So Hard?
- Cautionary Tales
- Data-Driven Transparency
- The Nature of Technology and FUD
- The Business Has Been Lied to Before
- Is It Sci-Fi or Reality?
- The Coming Storms
- Time Travel
- Time Travel in the Real World
- Data-Driven, Adaptive Strategy
- What's Next?
- Chapter 4 Final vs. Evolutionary Decision Culture
- Implementing Change and Taking Back Control
- Paying Off Cultural and Strategic Debt
- Playing Better Poker Means Folding Bad Hands
- Fixing the Culture to Reward Data-Driven Decision-Making Behaviors
- A Changing Incentivization Structure
- What's Next?.
- Chapter 5 The Disruptor's Mindset
- The Innovation Mix
- Exploration vs. Exploitation
- What Happens with Too Much or Too Little Innovation?
- Innovate Before It's Too Late
- EVs and Innovation Cycles
- Putting the Structure in Place for Innovation
- Building the Culture for Innovation
- An Innovator's Way of Thinking
- Managing Constant Change and Disruption
- Preventing Data-Driven and Innovation from Spiraling Out of Control
- What's Next?
- Chapter 6 A Data-Driven Definition of Strategy
- How Quickly the Innovators Became Laggards
- Using Strategy to Balance the Scales
- Redefining Strategy
- Resistance and Autonomy
- The Cost of Resisting Change
- What's Next?
- Chapter 7 The Monolith-Technical Strategy
- The Business Model
- A Few Examples of Business Models
- The Need for Technical Strategists
- The Operating Model
- Scale to Infinity and Super Platforms
- The Implications of an Automated Operating Model
- The Technology Model
- The Best Tool for the Job
- Making the Connection to Value from the Start
- What's Next?
- Chapter 8 Who Survives Disruption?
- Using Frameworks to Maintain Autonomy
- Reducing Complexity While Maintaining Autonomy
- Technology Cannot Solve All Our Problems
- Making Decisions with Core-Rim and the Technology Model
- Defining the Value Proposition
- How Technology First-Businesses Scale
- Can We Be Confident That Business Units Won't Be Completely Erased?
- What's Next?
- Chapter 9 Data-The Business's Hidden Giant
- Does the Business Really Understand Itself?
- Moving from Opaque to Transparent
- Getting Deeper into Workflows and Experiments
- Data Gathering and Business Transparency
- Understanding the Workflow
- Improving Workflows with Data
- Designing a Better Framework
- What's Next?
- Chapter 10 The AI Maturity Model
- Capabilities Maturity Model.
- Data Gathering, Serving, and Experimentation
- Starting with Experts
- A Race Against Complexity and Rising Costs
- The Product Maturity Model
- The Data Generation Maturity Model
- What's Next?
- Chapter 11 The Human-Machine Maturity Model
- What Happens When Technology Adapts to Us?
- The Human Machine Maturity Model
- Hidden Changes as Models Take Over
- Human-Machine Collaboration Is a New Paradigm
- Holding Machines and Models to a Higher Standard
- Understanding Reliability Requirements
- What's Next?
- Chapter 12 A Vision for AI Opportunities
- The Zero-Sum Game: Winners and Losers
- Near- and Mid-Term Opportunities
- Best-in-Breed Solutions
- Preparing Products for Transformation
- Opportunity Discovery Gets the Business Off the Sidelines
- Top-Down Opportunity Discovery
- Monetization Assessment
- Just Because It Can Be Built. . .
- What's Next?
- Chapter 13 Discovering AI Treasure
- Bottom-Up Opportunity Discovery
- Giving Frontline Teams a Framework to Leverage Data and AI
- The AI Product Governance Framework
- What Happens if No One Brings Opportunities Forward?
- It May Be Bottom-Up, But It Still Starts at the Top
- What's Next?
- Chapter 14 Large Model Monetization Strategies-Quick Wins
- AI Operating System Models
- AI App Store
- Quick-Win Opportunities
- The Digital Monetization Paradigm
- Understanding the Risks
- What's Next?
- Chapter 15 Large Model Monetization Strategies-The Bigger Picture
- What Are the Costs?
- How the Models Work
- Flaws Are Opportunities
- Disrupting College
- Advanced Content Curation
- How Microsoft Successfully Monetized Their 10 Billion Investment
- Large Models Enabling Leapfrogging
- Workflow Mapping Becomes Even More Critical
- What's Next?
- Chapter 16 Assessing the Business's AI Maturity
- Starting the Assessment
- Culture
- Leadership Commitment.
- Operations and Structure
- Skills and Competencies
- Analytics-Strategy Alignment
- Proactive Market Orientation
- Employee Empowerment
- The Data Monetization Catalog
- What's Next?
- Chapter 17 Building the Data and AI Strategy
- Defining the Data and AI Strategy
- The Executive Summary
- The Introduction
- Strategy Implementation
- Introducing the Data Organization
- Next Steps
- Needs, Budget, and Risks
- What's Next?
- Chapter 18 Building the Center of Excellence
- The Need for an Executive or C-level Data Leader
- Navigating Early Maturity Phases
- The Data Organizational Arc
- Benefits of the Center of Excellence Model
- Connecting Hiring to the Infrastructure and Product Roadmaps
- Getting Access to Talent
- Common Roles for Each Maturity Phase
- What's Next?
- Chapter 19 Data and AI Product Strategy
- The Need for a Single Vision
- Defining Data and AI Products
- The Business's Four Main Platforms
- Leveraging Data and AI Strategy Frameworks
- Workflow Mapping and Tracking
- Assessing Product and Initiative Feasibility
- Pricing Strategies for Data and AI Products
- Problem, Data, and Solution Space Mapping
- Managing the Research Process
- The AI Evangelist: Community Building for Platform Success
- What's Next?
- Index
- EULA.
