Articles
Field notes on AI transformation, modernization, and trust.
Practical executive writing from Sam M. Sweilem on building AI-native systems that regulated enterprises can actually operate, govern, modernize, and scale.
Enterprise-Managed Authorization Should Arrive Before Shared MCP RolloutWhy shared MCP rollout becomes an authorization design problem with centralized policy, revocation, approvals, and audit ownership.
The Autonomy Dividend Belongs To Teams That Survive Production ReviewWhy enterprise AI value will accrue to teams that can prove controls, evidence, and recovery under real production review instead of just running more pilots.
Failure-Recovery Drills Should Arrive Before Another Enterprise AI Prompt WorkshopWhy enterprise AI training should include pause, approval, replay, rollback, and evidence drills before broader rollout.
Agent Release Lanes Should Arrive Before More AI ChannelsWhy enterprise AI channel growth becomes a release-lane problem with validation, approvals, evidence, and rollback ownership.
Incident Response Playbooks Should Arrive Before More Agent AutonomyWhy enterprise AI autonomy fails without tested stop-the-line procedures, replayable checkpoints, and named incident owners.
Data Residency Maps Should Arrive Before Global AI RolloutWhy enterprise AI expansion across the US, EU, Japan, and Australia fails when teams collapse storage, inference, endpoint, and tool-path boundaries into one residency claim.
Contact Center AI Breaks at the Fact BoundaryWhy serious evaluations turn on deterministic facts, supervision, escalation, and audit evidence instead of voice quality alone.
Connector Entitlement Maps Should Arrive Before AI SearchWhy enterprise AI search becomes a connector governance problem once assistants can reach more systems.
Task Contracts Should Arrive Before More Coding AgentsWhy AI-assisted delivery scales through task contracts, review ladders, and tool boundaries before another enterprise coding-agent rollout.
Enterprise AI Needs Prompt Release DisciplineWhy prompt, routing, and reasoning-setting changes need version history, eval baselines, trace review, and rollback discipline before another model upgrade ships.
The Harness LayerWhere agentic AI becomes enterprise work: context, tools, memory, evals, tracing, governance, and human review.
The Briefing LayerWhy enterprise AI needs more than better prompts: intent, context, constraints, tools, evidence, and success criteria.
Context Is the Control PlaneWhy AI quality depends on what the enterprise system can know, retrieve, prove, and safely act on.
The Agentic Operating ModelWhy agents need roles, permissions, review gates, evidence, and ownership before they can perform accountable work.
Interview: Sam Sweilem on Enterprise AIEditorial Q&A on enterprise AI, modernization, healthcare technology, and the operator-builder perspective.
The Enterprise AI Builder StackWhy credible enterprise AI delivery needs modernization, evidence, workflow, and implementation working together.
The Enterprise AI Operating ModelHow organizations move from AI pilots to production proof with business workflow, data, agents, evidence, and value loops working together.
How Evidence-Governed AI Lets Regulated Enterprises Move FasterGovernance cannot be a policy binder. It has to be built into the operating system of delivery.
AI-Native vs. Agentic AIWhy the architecture layer and operational workforce layer are different but inseparable.
2026 Is the Inflection PointWhy modernization risk is reversing in insurance and financial services.
The Jevons Paradox of TokensWhy cheaper AI expands demand instead of merely reducing cost.