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Market & Buyer Intelligence
Field notes
Each page earns its place through a distinct decision, credible evidence, and a practical next move.
Gushwork and dotSuper: Two Operating Models for AI-Search Inbound
A neutral comparison of Gushwork's broad AI growth system and dotSuper's evidence-controlled Inbound Engine, including fit, limitations, and buying questions.
Read the field noteManufacturing AI: How to Choose the First Use Case Without Buying a Demo
A practical method for selecting a first manufacturing AI workflow using operational value, data fitness, human oversight, integration, and a real stop rule.
Read the field noteBuild, Buy, Configure, or Partner? A 12-Factor AI Decision Scorecard
A vendor-neutral scorecard for choosing among packaged AI software, configurable platforms, custom builds, and delivery partners using evidence and whole-life trade-offs.
Read the field noteAI Consultancy, Product Studio, or Systems Integrator? Choose by the Work That Must Change
A practical comparison of three common AI partner models, the work each is built to do, and the evidence a buyer should request before selecting one.
Read the field noteThe Industrial AI Vendor Evaluation Checklist: 18 Questions Before a Pilot
A buyer-side checklist for evaluating industrial AI vendors across workflow fit, data, reliability, integration, human oversight, security, and transfer.
Read the field noteAI Proof of Concept vs Production Pilot: Know Which Evidence You Are Buying
A clear distinction between technical feasibility and operational proof, with gates for deciding what to fund and what each stage must produce.
Read the field noteAI Readiness Assessment vs Strategy Workshop: What Should Leave the Room?
A buyer’s guide to distinguishing a useful readiness assessment from an inspiration session, transformation roadmap, or generic AI workshop.
Read the field noteDigital Twin, AI Assistant, or Workflow Automation? Start With the Decision Loop
A fit guide for three different industrial system patterns and the operational problems each is equipped to solve.
Read the field noteHow to Evaluate a RAG Vendor: The SME Buyer Scorecard
A practical scorecard for retrieval quality, source control, permissions, evaluation, integration, and ongoing ownership in an AI knowledge system.
Read the field noteNIST AI RMF vs ISO/IEC 42001: A Practical Guide for Operational Teams
A plain-language comparison of two influential AI governance frameworks and how a smaller organisation can use them without turning governance into paperwork.
Read the field noteEU AI Act Checklist for Manufacturers Buying or Deploying AI
A non-legal operational checklist for inventorying AI uses, clarifying roles, risk classification, transparency, documentation, and supplier evidence.
Read the field noteThe Hidden Costs of Industrial AI: A Total-Cost Model Beyond the Licence
A practical model for estimating data preparation, integration, evaluation, change, monitoring, exception handling, security, and ownership costs.
Read the field noteHuman-Machine Collaboration in Operations: Define Authority Before Automation
A practical operating model for deciding what AI prepares, what people judge, how exceptions move, and who remains accountable.
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