REACHOS

READOS / Technology

AI in Business Practice

Choosing, building, testing, securing, governing and paying for AI that does useful work.

18 chapters · About 198 minutes · Free to read

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KEVOS® READOSTECHNOLOGYAI in BusinessPracticeREADOS COLLECTION18 CHAPTERS

Contents

  1. 01Technology is a means, not a strategy: start with the outcome, then test the ecosystemA technology proposal can gather momentum before anyone asks what it is for. How to define the outcome first, compare real alternatives and check whether an option can be adopted.
  2. 02Adopting AI in a small business: start with the work, choose bets deliberately, redesign tasksAI creates value when it changes a workflow or decision, not when a tool is installed. How to pick use cases as bets, redesign tasks before changing roles and keep people accountable.
  3. 03Finding and designing AI use cases that pay: from business problem to a scored, specified opportunityLong lists of AI ideas rarely become value. A disciplined discovery method: frame the problem, map the work, set requirements, check data readiness and score value, feasibility and risk.
  4. 04Is your AI a feature you sell or a tool you use? Different owners, economics and risksAI inside what you sell and AI inside how you work are different assets. How to give each the right owner, cost model and measures, and what changes when a tool becomes a product.
  5. 05How large language models work, for business decision-makers: tokens, training, context and limitsYou do not need to build an AI model to use one well, but you do need to know how it works. Tokens, training, context windows, settings and why models invent things, in plain terms.
  6. 06Prompting, retrieval or fine-tuning: choosing how to adapt an AI model to your businessWhen an AI model gets things wrong, the fix is often not fine-tuning. How to choose between better prompts, retrieval, tools and fine-tuning, and what each costs to build and maintain.
  7. 07Putting company knowledge to work with retrieval-augmented generation: how it works and how to build it wellAn AI assistant that answers from your own documents can save hours or confidently quote the wrong version. How retrieval-augmented generation works, where it fails and how to build it.
  8. 08Vision, documents and speech: practical multimodal AI in operationsAI that reads documents, understands photos and transcribes speech can remove hours of data entry and note taking. Where it works, how to measure accuracy and how to keep people in control.
  9. 09AI agents and automated workflows: what they can safely do and how to keep them under controlAI agents can plan and act across systems, but each step adds risk. When to use a fixed workflow instead, and how to bound agents with permissions, approvals, limits and audit trails.
  10. 10Data readiness is a business habit, not an IT projectMessy data is usually a symptom of how work is done. How to define good-enough data for each use, give information an owner, control access and fix errors where they start.
  11. 11Machine learning in a small factory: inspection, maintenance and forecasting, and what each needs to workMachine learning can inspect parts, flag failing machines and forecast demand, given the right data and checks. How to match the method to the problem, test it honestly and keep it working.
  12. 12Evaluating generative AI: gold datasets, rubrics, grounding checks and regression testsGenerative AI changes silently when prompts, models or documents change. How to build gold datasets and rubrics, use judge models carefully, test safety and gate every release.
  13. 13Is the AI good enough to rely on? Benchmarks, test scenarios and the decision to deploy, support or shelveA strong demo is not evidence. How to compare AI with your current process, test it on unseen and difficult cases, set acceptance rules first and decide to deploy, support or shelve.
  14. 14Securing AI systems: prompt injection, data leakage, poisoned documents and least privilegeAI systems can be talked into misbehaving by the content they read. How prompt injection, poisoned documents, data leakage and excessive agency work, and the controls that contain them.
  15. 15Fair use of AI in hiring and people decisions: bias, testing and human reviewAI tools can screen applications and rate staff quickly, and can repeat past unfairness at scale. How to choose, test and govern them so people decisions stay fair and explainable.
  16. 16Governing AI decisions: what you have let a machine decide, and oversight that actually worksAutomated decisions often slip from advice to action without anyone authorising it. How to record what machines may decide, set limits, make human review real and plan how to switch it off.
  17. 17The real cost of running AI: tokens, latency, hardware and the choice between local and cloudAI bills grow quietly as usage and prompts grow. How to calculate cost per request and per outcome, manage speed, size hardware for local models and decide between cloud and local.
  18. 18Taking AI from pilot to production: integration, operations, adoption and benefitsMany AI pilots impress, then stall. How to deliver in thin slices, meet production standards for security, monitoring and support, drive real adoption and show benefits against a baseline.

These topic-based reading collections are organised alphabetically; chapters may be read in any order.

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