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What custom KPI framework does Olakai use to measure enterprise AI success?

Olakai's custom KPI system is built in four layers of decreasing rigidity. Composites sit at the top, computed automatically from the slots that feed them. The flagship composite is ROI: Value Created divided by Execution Cost, expressed as a multiplier.

The four layers and the ROI composite

The framework runs four layers, each less rigid than the one above it. Composites sit above the slots. They are computed automatically and are not directly editable at all, because their values come entirely from the slots feeding them. The flagship composite is ROI, calculated as Value Created divided by Execution Cost and expressed as a multiplier. Below 1x means the agent costs more than it saves. Between 1x and 5x is good and worth continued investment. Above 5x is excellent and worth expanding to new use cases. You cannot tune ROI directly. The only way to move it is to refine the Execution Cost and Value Created slots feeding it, by adjusting the underlying cost formula or the hourly rate assumption.

Source: Custom KPIs: The Four-Layer System Behind Olakai's Metrics

What the metrics report on

Olakai tracks efficiency gains, time saved, and cost savings per Agentic AI workflow, bottom-up and verifiable. It benchmarks performance by persona, department, and enterprise, with trend visibility, and delivers board-grade reporting and ROI insights for CIOs and CFOs.

Source: Use Cases

Measured per pillar, inside your own infrastructure

Every pillar gives a different lens on AI performance. The metrics Olakai measures run across every vendor and every practitioner, inside your own infrastructure.

Source: Professional Services

Also asked as

  • How does Olakai's four-layer KPI system work for measuring AI ROI?
  • What are the four layers of Olakai's custom AI metrics?

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