Baselines, quality-adjusted cost and outcome measures for agent investments.
Smaller models are not merely budget substitutes. Used behind explicit routing and escalation rules, they can improve latency, locality and architectural control.
A successful demonstration does not create a live service. Pilots stall when they test capability without resolving ownership, integration, economics and operating evidence.
Model concrete failure scenarios across detection, containment, correction, disruption and consequence, then use ranges to choose controls.
Model usage is one line in a wider cost stack that includes integration, evaluation, control, human work, incidents, change and exit.
Agents earn their complexity only when a workflow needs bounded adaptation. A disciplined no-agent test can reveal when rules, search or ordinary software is safer and cheaper.
A credible AI success story separates what was delivered from what changed, defines the baseline, shows the contribution chain and preserves uncertainty and permission.
Give decision owners a balanced view of outcomes, quality, human work, cost, risk and change—with every measure tied to an action.
Attendance, speaking and sponsorship are evidence of activity; useful conference claims require separate measures of learning, relationships, follow-through and resulting decisions.
Connect spending to accepted workflow outcomes by counting retries, review, correction and failures alongside model and platform charges.
Time saved is not an outcome until the original work, quality, case mix and destination of released capacity are measured on comparable terms.
Rank agent opportunities by decision value, workflow evidence, feasibility, consequence and reuse—not by enthusiasm, executive volume or demo polish.
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Read the method, assumptions and questions to ask.