The Future Of Enterprise AI
A practical roadmap to operational intelligenceBy Jasper Livingstone
Length5h 41m
About this audiobook
The Future of Enterprise AI: A Practical Roadmap to Operational Intelligence is a forward-thinking guide for business leaders, managers, and decision-makers seeking to turn artificial intelligence from a promising technology into measurable business value.
As AI reshapes how organizations operate, compete, and innovate, success depends on more than adopting new tools. It requires a clear strategy, reliable data, capable people, responsible governance, and the ability to embed intelligence into everyday workflows. This book provides a practical roadmap for moving beyond isolated AI experiments toward scalable, connected, and outcome-driven operations.
Explore how enterprise AI can improve decision-making, automate complex processes, strengthen operational efficiency, enhance customer experiences, and create new opportunities for growth. From building an AI-ready organization to managing risk, governance, workforce transformation, and long-term adoption, this guide translates complex concepts into practical insights for real-world implementation.
Whether you are beginning your enterprise AI journey or expanding existing initiatives, The Future of Enterprise AI will help you develop the strategic clarity and operational intelligence needed to build a smarter, more adaptive, and future-ready organization.
Audiobook details
GenreTechnology, Business and Economics
Length5 hrs 41 mins
Narrated byListen with 1,000+ voices
FormateBook with Audio
LanguageEnglish
Table of contents
1Introduction
2From AI Hype to Business Outcomes
3Choosing the Right Enterprise Use Cases
4Designing the AI Value Stream
5Building an AI-Ready Data Foundation
Show all chaptersShow less
6Creating a Single Source of Truth
7Data Labeling and Ground-Truth Strategy
8Selecting Models for Operational Fit
9Prompting for Enterprise-Grade Reliability
10Designing Human-in-the-Loop Workflows
11Automating Decisions with Policy Rules
12Integrating AI into Existing Systems
13Operationalizing with MLOps and LLMOps
14Measuring Model Performance in Production
15Managing Drift and Continuous Improvement
16Governance for Responsible Enterprise AI
17Risk Controls for Bias, Privacy, and Safety
18Building an AI-Enabled Workforce
19Change Management for Long-Term Adoption
20Running a Portfolio of AI Experiments
21Creating an Enterprise AI Operating Model
22Scaling to Operational Intelligence at Scale
23Final Thoughts
24About the Author
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