Succeeding with AI How to Make AI Work for Your Business (Veljko Krunic) (Z-Library)

【One-Line Pitch】 A practical, technology-neutral playbook for turning AI from a costly experiment into measurable business value—written for executives, project leaders, and data scientists who need to ask the right questions before writing any code. No prior AI knowledge required. 【Book Arc】 - **Opening (~0%–10%)**: Frames the core problem—well-funded AI projects fail not from lack of talent or tools but from misalignment between business and technology. Introduces the book's philosophy: effectiveness (doing the right things) over efficiency. - **Early (~10%–35%)**: Establishes who the book is for and the foundational skill of asking actionable questions. Teaches readers to start from business actions they can take, then work backward to analyses—never the reverse. Covers defining measurable business metrics and triaging projects by value and ease. - **Middle (~35%–55%)**: Explores what project leaders actually need to know about AI (less than they fear), how AI projects differ from ordinary software projects, and how to link AI capabilities to concrete business actions using cross-industry examples like medical imaging and recommendation engines. - **Late (~55%–80%)**: Moves into the ML pipeline—what it is, how it shapes project risk, and how to analyze it. Covers guiding a project to success with effectiveness metrics, early warning signals, and course correction before calcification sets in. - **Ending (~80%–100%)**: Surveys AI trends that may affect the reader's business, closing the loop on how to keep the framework relevant as technology infrastructure evolves. 【Key Takeaways】 - **Business and technology must co-own the project** (Early): The fastest path to failure is executives saying "hire tech people and let them loose on our data" or data scientists saying "business people handle business, we handle technology." Both sides must engage. - **Start from actions, not data** (Early): Don't ask a question if you can't imagine what you'd do with the answer. The number of possible analyses always exceeds the number of actionable ones—let business actions drive analysis. - **Define measurable business metrics before technical ones** (Early): A good metric directly relates to business success and gives both AI and business teams a shared quantitative language. If your department lacks one, define it yourself. - **Technical AI knowledge is not the primary skill for project leaders** (Middle): Managing an AI project is an application of management science. Leaders need to define metrics and processes, not master statistics or algorithm internals. - **AI projects differ from ordinary software projects** (Middle): They combine business, computer science, mathematics, statistics, and ML—and AI cannot rescue a poor business case. Analysis alone never makes money. - **The ML pipeline is a first-class management concern** (Late): Understanding and analyzing the pipeline lets leaders spot risks and intervene early, keeping projects from drifting into expensive irrelevance. - **Effectiveness metrics keep projects on track** (Late): Build in metrics that resist calcification—measures that stay meaningful as the project evolves, not just at launch. - **The framework is technology-neutral and process-neutral** (Opening): It applies to any organization size, cloud or on-premise, any ML framework. The principles outlast the infrastructure. 【Reading Tips】 - **Skim the technical infrastructure debates** (cloud vs. on-premise, Spark vs. Hadoop): the author explicitly stays technology-neutral and focuses on business-technology linkage. - **Deep-read Chapters 1–4** if you're a project or business leader—this is where the actionable-question framework and metric definition live. - **Data scientists should focus on the ML pipeline chapters (5–6)** and the effectiveness-metrics discussion in Chapter 7, which address how to keep technical work aligned with business goals. - **Treat unfamiliar business-domain examples as deliberate practice**: the author chose them so you can rehearse adapting AI to new contexts, which is the core skill. - **Take away one habit**: before any analysis, ask "What action will this inform?" If you can't answer, don't fund it. 【Coverage Limits】 The excerpts cover the book's framing, early framework chapters, and portions of the middle sections on AI use cases and business-technology linking. Later chapters on ML pipeline analysis, project guidance, and AI trends are referenced but not detailed in the available excerpts.