AI pilots can look successful while the business case quietly deteriorates through review, correction, exceptions, leadership burden, knowledge maintenance, or unrealized capacity. AI Value Assurance provides a framework for determining whether AI-enabled work creates enough credible, durable value to justify continued investment.
AI can work technically and still fail operationally. Learn how decision authority, agent design, human judgment, resilience, and value fit together.
AI governance defines rules and risk boundaries. An AI operating model determines how decisions, agents, humans, knowledge, resilience, and value work in practice.
A Production Readiness Framework covering data, ownership, review, exceptions, integrations, support, and measurement, with a 60-day rollout plan.
Your best AI users may be building capabilities the company cannot reproduce. Learn how to turn expert AI use into institutional knowledge and resilient capability.
Review 12 AI use cases that often fail because of weak processes, data, controls, judgment, ownership, or operating economics.
The Model Counterpoint Method uses independent AI analysis, structured challenge, human reconciliation, and verification to strengthen consequential decisions.
Pair AI productivity measures like time saved and output volume with outcome measures like revenue, conversion, and customer impact.
AI disagreement can expose weak evidence, hidden assumptions, context gaps, and judgment needs. Learn how executives should use model differences.
Identify AI workflows that increase corrections, reviews, transfers, exceptions, and maintenance, and measure the complete operating cost.
Get Sterling’s Sparks
Weekly fire, straight to your inbox.
Clarity prompts. AI shortcuts. Productivity that doesn’t wreck your peace.
This is how bold moves begin—one Spark at a time.
