Building the Business Case for Enterprise AI: Costs, Value, and Honest Math
How to build a defensible AI business case: total cost of ownership, value levers, measurement plans, and the assumptions to stress-test before you commit.
Guides
Deep knowledge,
written to be used.
Long-form, practical guides on enterprise AI adoption, AI governance, retrieval systems, platform engineering, and cloud — written for technology leaders and the teams doing the work.
How to build a defensible AI business case: total cost of ownership, value levers, measurement plans, and the assumptions to stress-test before you commit.
The human side of AI adoption: workflow redesign, training, trust, resistance, and the operating changes that turn pilots into habits.
A practical guide to AI governance: risk-tiered controls, model inventories, audit trails, and the operating cadence that keeps AI systems accountable.
How enterprises buy AI well: structuring RFPs, scoring technical and commercial proposals, running demos and pilots, and negotiating terms that protect you.
Architecture decision records capture the why behind technical choices. Format, workflow, and how ADRs strengthen architecture governance.
How Canadian public-sector technology procurement works: standing offers, competitive processes, and what vendors need to know before bidding.
Reduce cloud spend without slowing delivery: FinOps fundamentals, the architecture levers that matter, and how to sustain savings over time.
A practical playbook for enterprise cloud migration: portfolio assessment, the 6 Rs, landing zones, migration waves, cutover strategies, and operating after the move.
Designing an enterprise data platform: warehouse vs. lakehouse vs. mesh, data products, governance, and the sequencing that makes AI possible.
A phased playbook for enterprise AI adoption: assess readiness, run disciplined pilots, scale what works, and govern the full lifecycle.
A plain-language glossary of 40+ enterprise AI terms — RAG, agents, evals, guardrails, fine-tuning, and more — for technical and business readers.
Designing enterprise integration: API strategy, event-driven architecture, iPaaS build-vs-buy, and the governance that keeps integrations from becoming spaghetti.
How to evaluate LLM applications systematically: eval harnesses, quality metrics, human review, and red-teaming basics for production readiness.
Modernizing legacy estates without big-bang rewrites: strangler fig patterns, replatform vs. refactor vs. replace, risk management, and realistic sequencing.
What observability means for AI systems: LLM tracing, evaluation in production, quality signals, and cost telemetry for every request.
Platform engineering done right: internal developer platforms, golden paths, and self-service that speed teams up without removing their autonomy.
How Canadian public-sector organizations modernize legacy IT: digital service standards, delivery models, funding realities, and sequencing that survives elections.
Take retrieval-augmented generation from demo to production: chunking strategy, retrieval evaluation, grounding, access control, and ongoing operations.
How enterprise buyers assess vendor security: questionnaires, SOC 2 and certifications, penetration tests, data handling, and right-sizing reviews by risk.
Weighted scoring frameworks for technology vendor selection — criteria design, scoring discipline, demos, reference checks, and decisions that survive audit.
Shorter reads
The insights collection carries shorter, opinionated pieces on enterprise AI, responsible architecture, and modernization.
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