Strategy
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Filtered field notes for Strategy work, newest first.
Prioritizing Legacy Modernization by Risk, Value, and Dependency
A practical framework for sequencing legacy modernization around operational risk, business value, and the dependencies that determine what can safely move next.
Reviewing Data and Model Terms in Enterprise AI Contracts
A practical engineering checklist for reviewing data use, model changes, security controls, and exit terms before buying enterprise AI.
Engineering Questions to Ask During AI Project Discovery
A practical discovery framework for clarifying AI workflows, data readiness, integrations, risks, acceptance criteria, and operational ownership.
After the PoC: Designing Production Ownership and an Operations Budget
A practical framework for assigning ownership, forecasting operating costs, and setting production gates for enterprise AI systems.
What Processes Should You Inventory Before Adopting an Enterprise AI Assistant?
A practical framework for assessing workflows, data, permissions, integrations, and exceptions before building an enterprise AI assistant.
Build or Buy an Internal AI Platform? A Practical Decision Framework
Evaluate differentiation, integration depth, governance, operating capability, and total cost before choosing a build, buy, or hybrid AI platform strategy.
Building an AI Adoption Roadmap That Avoids Demo-Only Projects
A production-minded AI roadmap must address workflow value, data, integration, risk controls, and ongoing operations—not just model capability.
How to Estimate the ROI of an AI Project
AI ROI depends on workflow impact, integration cost, data readiness, risk, and long-term operations, not just model fees.
Running Staged Acceptance for an AI Project
AI acceptance should verify requirements, data, models, integrations, and operations in stages instead of waiting until launch.
Picking Your First AI Project as an SME
Your first AI project should start with a frequent, measurable, low-risk workflow, not with the flashiest model.
Three common reasons AI adoption fails, and how to avoid them
Most AI projects fail not because the technology is weak, but because the starting point is wrong. Three recurring causes we see across delivery projects, and how to avoid each.
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