Business Analysis

How long can you keep collecting a good salary inside a company that is obviously too inefficient to survive what’s coming? This book shows you how to turn the broken workflows you already understand into income you control - before your paycheck reminds you it was never really safe.

Eighteen practitioner-oriented technical notes covering the full arc of early-stage company finance, from the financing gap through to exit planning.

If a humanoid robot carries about $28,000 a year in fixed cost, then at $60 contribution per billable hour it breaks even at roughly 467 billable hours per year, or just under 9 hours per week. But if supervision is heavier and contribution drops to $28 per hour, break-even jumps to 1,000 hours per year, or about 19.2 hours per week.

Palantir: Spy-tech, enterprise AI, or the architect of Operational Sovereignty? This unparalleled analysis (current as of Oct'26) cuts through the hype and controversy to reveal Palantir's core technology, its path from meme stock to S&P 500, and its unique strategy for a complex age. Essential insights await.

Stay informed about the technology that matters.IT & AI Digest brings together concise analysis and insights on artificial intelligence, software, cybersecurity, cloud computing, robotics, digital business, and emerging technologies.No endless scrolling. No unnecessary noise.Just important ideas, explained clearly.Read less. Understand more.

The most advanced (show me!) systems engineering writing on the 'constraints' subject. For technical leaders.

Unlock the full potential of Microsoft Dynamics 365 Business Central with "Beyond the Ledgers," your hands-on guide to mastering data analytics and AI-driven insights. Discover practical techniques to transform data into strategic decisions, boost efficiency, and stay ahead in the digital age. Get ready to revolutionize how you see and use data in your organization!

Unlock the potential of data to transform government and public policy! 

名单不等于市场，订单也不等于客户价值。本书从跨境B2B的询盘、需求确认和资源分配出发，帮助读者区分事实、推断与尚待验证的经营判断。

A practical eight-stage method for making better decisions when information is incomplete. Learn to separate facts from assumptions, compare realistic options, test key uncertainties, record your reasoning and review the result.

Estimate honestly. Model fearlessly. This book turns T-shirt sizing into fast, team-friendly estimation and hands you a complete e-commerce warehouse build on BigQuery—star schemas, ETL pipelines, performance tuning and all. From decision frameworks to real-world trade-offs, you'll gain the confidence to plan realistically and architect data systems that scale.

A detailed plan does not always lead to a better decision. Learn how to separate facts from assumptions, identify the unknowns that matter, and test your beliefs before taking action. This short, practical book includes a decision-review worksheet you can use in your own life or work.

AI can analyze the data. But who decides what the data means—and what to do next?Modern organizations have more information than ever, yet making good decisions remains difficult.AI-Powered Smart Decision Making shows how to combine AI, data, analytics, and human judgment to turn information into insight and insight into action.Discover a practical framework for making better decisions across strategy, marketing, sales, operations, finance, risk, and beyond.AI is not the decision. AI improves the decision process. Table of ContentsIntroduction—The New Era of AI-Powered Decision MakingChapter 1 — The Foundations of Better Business DecisionsChapter 2 — From Intuition to Data-Driven Decision MakingChapter 3 — Understanding Data for Better DecisionsChapter 4 — Business Analytics: Turning Data into InsightChapter 5 — How AI Changes Business Decision MakingChapter 6 — Building an AI-Powered Decision FrameworkChapter 7 — Asking the Right Questions: AI, Data & Decision ProblemsChapter 8 — Predictive Decision MakingChapter 9 — Prescriptive Analytics and Decision OptimizationChapter 10 — AI for Strategic Business DecisionsChapter 11 — AI for Marketing and Customer DecisionsChapter 12 — AI for Sales, Operations and Supply ChainChapter 13 — AI for Financial and Risk DecisionsChapter 14 — Human + AI: The New Decision-Making PartnershipChapter 15 — Bias, Uncertainty and the Limits of AI DecisionsChapter 16 — Trustworthy, Responsible and Explainable AI DecisionsChapter 17 — Measuring Decision Quality and Business ImpactChapter 18 — Building an AI-Powered Decision CultureChapter 19 — Designing an AI-Powered Decision SystemChapter 20 — The Future of Smart Decision MakingConclusion — From Data to Better DecisionsDisclaimer

AI is no longer just a technology experiment.It is becoming part of how businesses communicate, process information, serve customers, manage workflows, analyze data, and make decisions.But where should AI actually be used?AI-Driven Operations provides a practical roadmap for answering that question.Discover how to:Find the right processes to automateTurn manual workflows into intelligent workflowsUse AI agents responsiblyConnect AI with business systemsReduce operational costsMeasure real business valueProtect data and maintain human oversightScale successful AI initiativesThe future of business is not about using the most AI.It is about using AI intelligently.

Your manager calls you into a meeting. Everyone is cheerful. The Head of Sales is there. “We heard AI is really useful for boosting productivity. We’d love to explore how it can help us in Sales.” Your manager agrees. “This is the perfect pilot. Please give us a concept until next week so we can see what we can do.”The follow-up arrives quickly. Sales is overloaded. Everyone is busy serving customers. Nobody has time for a workshop, but they do have Sales.xlsx. That is what you get. The clock is ticking.And then you open Sales.xlsx.You have nothing. No requirements, no workflows, not even a proper data model. What you have is a snapshot of the messy reality Sales is dealing with every day. Customer names, contacts, opportunity values, dates, products, notes, colors that apparently mean something, and a field called Status_Final_2, which tells you enough about its history that asking about Status_Final_1 would probably ruin the afternoon.And then it strikes you: they are not even using the CRM. You do not know why. Nothing you know from IT seems to apply in Sales, and they do not particularly care. They have customers to serve. Whatever happened between the shiny CRM rollout and this spreadsheet happened years ago. Today, Sales.xlsx is the truth.And now, just because AI promises productivity, they are asking for the benefits.How is that even supposed to work?