Insights
Frameworks for buyers, sponsors, and operators of agentic AI.
Twelve articles for the decisions every AI program runs into — pilot selection, ROI, build vs buy, vendor choice, production readiness, governance design, and organizational fit.
How to Pick Your First AI Pilot: A Selection Rubric That Actually Predicts Success
Score candidate workflows on impact, data readiness, governance fit, and reversibility — and write success criteria before the build. The four-factor rubric most pilots skip.
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How to Calculate ROI on an AI Agent Pilot (Without Fooling Yourself)
Count direct labor reclaimed, error costs avoided, cycle-time value, and opportunity cost — and subtract the eval and ops costs every vendor spreadsheet hides. The defensible four-line model.
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Build vs Buy for Agentic AI: A Decision Framework for 2026
Buy horizontal copilots. Extend platforms when 80% of data lives there. Build custom when the workflow is differentiation, spans systems, or requires governance vendors do not cover.
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From AI Pilot to Production: The Checklist Most Pilots Skip
Working output is the easy half. Eval suite, kill-switch, on-call, observability, drift monitoring, versioning, and change management — the operational layer that gets pilots actually shipped.
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Agentic AI Vendor Evaluation: The Criteria That Actually Predict Outcomes
Production references with KPIs, evaluation methodology, human-in-the-loop stance, governance posture, build philosophy. The criteria that predict 18-month outcomes vs the ones that win RFPs.
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Measuring AI Agent Quality in Production: Beyond Accuracy
Task completion, factual accuracy, policy compliance, escalation precision, latency, cost, override rate, downstream business KPI. The panel beats any single accuracy score.
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Human-in-the-Loop Design Patterns for Agentic AI
Review-before-send, approval-on-exception, sampled review, dual-control, shadow mode. The five patterns, when each fits, and the failure mode of using one pattern for everything.
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AI Agent Security & Data Controls: What Procurement Will Ask
Model data handling, identity and ACL propagation, prompt-injection defenses, audit logging, residency, secrets, incident response. The eight controls procurement will require before sign-off.
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When RAG Is Not the Answer: A 2026 Retrieval Decision Guide
RAG is the default — and often the wrong default. The five scenarios where fine-tuning, long context, structured queries, graph traversal, or hybrid retrieval beats vector search alone.
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MCP vs Custom Integration: When to Use Each in 2026
MCP for reusable, well-maintained tool surfaces. Custom for fine-grained auth, latency-critical paths, or one-offs. Most production stacks run both — here is the dividing line.
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AI Pilot Budget Planning: What Each Dollar Actually Buys
15% discovery, 35% build, 20% evaluation and governance, 10% change management, 20% post-launch ops. The line-item allocation that actually ships pilots vs the one that stalls them.
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Organizational Readiness for Agentic AI: The Non-Technical Checklist
Executive sponsorship, operating-team ownership, data policy, change management, decision rule. The five non-technical dimensions that predict pilot outcome more than any model choice.
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