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Table of Contents: Define which fields may enter the business system · Separate extraction from validation · Use field and cross-field checks deliberately · Route exceptions with useful evidence · Work through a document exception record · Scope automation around the review workload
Table of Contents: Define the action that requires human approval · Bind approval to an exact proposal · Specify expiry, rejection and resubmission · Handle execution and uncertain outcomes · Review the approval-state worksheet · Choose owners and escalation before deployment
Table of Contents: Define the support workload the assistant may handle · Choose representative questions and approved evidence · Describe the expected answer or escalation · Include unanswerable and conflicting-source cases · Review answers by failure category · Manage reviewer disagreement and repeatable changes · Use the evidence to choose a bounded next step
Table of Contents: Define whose documents the assistant may use · Carry access decisions into retrieval · Relevance is not permission · Metadata must reflect the real policy · Plan for permission changes and stale copies · Write an access-change acceptance matrix · Review answers, citations and operational access separately · Agree the boundary before adding internal AI search
Table of Contents: How does AI automation work? · Which technologies make AI automation possible? · How is AI automation different from other automation? · How do generative AI, agents and chatbots fit together? · What can businesses automate with AI? · How does customer-facing automation work? · What happens in document and back-office workflows? · What are the business benefits and risks? · Which tasks should remain under human judgement? · How should businesses prioritise opportunities? · How is AI automation implemented? · What security and governance controls are needed? · Should businesses buy, build or use managed automation? · How do needs vary by organisation size? · What comes next for AI automation?
Table of Contents: How do POCs, prototypes, MVPs and pilots differ? · What is the central difference between a POC and an MVP? · What should you build first? · What should an AI proof of concept validate? · What must an AI MVP prove? · When should an MVP enter a pilot? · What makes an AI system production-ready? · How do costs, timelines and infrastructure change? · What monitoring is needed after launch? · What evidence should unlock the next investment?
Table of Contents: Which AI capability does the application actually need? · Should you choose a small or large language model? · Which AI model selection criteria matter most? · Should you use an open model, proprietary API or self-hosting? · Do you need prompting, RAG or fine-tuning? · How should models be evaluated before production? · How do cost and production operation affect selection? · What is the practical selection process?
Table of Contents: How does integration differ from development and automation? · What should an AI integration strategy decide? · Which business problem and data should you start with? · Should you buy a platform, use an API or build models? · What should the integration architecture look like? · How should AI API and LLM integration work? · When are retrieval and agents useful? · How do CRM, ERP, SaaS and legacy integrations differ? · How do you protect data and retain oversight? · How do you validate and release the integration? · How should costs, scale and maintenance be managed? · What is the safest practical starting strategy?
Table of Contents: What does the roadmap look like? · Stage 1: Which business problem should AI solve? · Stage 2: Is the data ready? · Stage 3: Is the use case technically feasible? · Stage 4: Which AI approach fits? · Stage 5: What architecture is needed? · Stages 6–7: How do POC, prototype and MVP differ? · Stage 8: How is the system developed and integrated? · Stage 9: How should AI testing work? · Stage 10: How should governance and security work? · Stage 11: What makes AI production-ready? · Stage 12: What should be monitored after launch? · Why can a POC fail to reach production? · What determines timeline, cost and delivery approach? · How should maturity and success shape the roadmap?
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