Explore evidence-led notes on where AI should run, what agents need after the demonstration, and how technical mechanisms change workflow economics. Claims are bounded to their workload and evidence.
Sequence enterprise AI from one bounded workload through matched evidence, narrow operation, selective industrialization, and annual renewal. The 30-day, 90-day, 365-day, and 12-to-60-month roadmap gives every horizon an owner, executable budget, exit gate, evidence trigger, and renew, reroute, resize, or retire decision.
Read MoreStress-test enterprise AI capital across five scoped causal worlds without assigning probabilities or a hidden base case. The scenario wind tunnel separates commodity intelligence, differentiated systems, sovereign stacks, agentic operations, and permission ceilings, then connects signposts, precedence, falsifiers, and no-regret moves to named decisions.
Read MoreChoose an AI operating pattern by matching one workload's required control demand to demonstrated delivery capacity, not vanity headcount. The matrix shows when to self-deliver, use an eligible partner, narrow or isolate, retain manual service, or stop across compact, coordinated, federated, and global organizations.
Read MoreMove one AI opportunity from observed workflow friction to an owned, bounded operating decision. The eight-stage IMPAKT loop connects discovery, workload definition, complete alternatives, hard gates, matched tests, release evidence, operation, and renewal so prototype momentum cannot substitute for decision-changing proof.
Read MoreA synthetic probability model exposes why one-step accuracy cannot establish reliable long-workflow completion. The practical method replaces that toy arithmetic with repeated end-to-end tests covering state, permissions, tools, checkpoints, severe failures, intervention, latency, cost, recovery, and the exact authority an AI workflow may receive.
Read MoreUse a 20-question screen to evaluate claims about scaling, benchmarks, context, retrieval, tools, quantization, serving, work effects, adoption, security, governance, and frontier opacity. Every question carries an evidence class, countercondition, local test, owner, and expiry so a dated result stays inside its decision boundary.
Read MoreChoose among one call, a fixed chain, routing, parallel work, orchestration, and evaluator loops by the runtime discretion the workload actually needs. The pattern-and-authority contract connects goals, state, tools, permissions, verification, budgets, stops, recovery, and evidence before an AI system receives greater autonomy.
Read MoreMatch an observed AI failure to the least irreversible intervention that can resolve it. This decision ladder separates prompting, retrieval, fine-tuning, distillation, and routing by mechanism, new operating burden, evaluation need, countercondition, and rollback instead of treating them as a technology maturity sequence.
Read MoreDiagnose LLM serving by separating prefill, decode, queueing, batching, speculative decoding, and external work. The inference measurement record keeps phase metrics tied to representative arrival patterns, tail latency, failures, and accepted workflow goodput so a local optimization cannot masquerade as an operating improvement.
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