Don’t Just Talk About AI. Measure Business Outputs. Here’s How.

2026-03-19 · Source: Artificial intelligence - Crunchbase News · Field: Business & Management — Corporate Strategy & Leadership, Operations & Process Management, Project & Product Management · Depth: Intermediate, short

Summary

Many companies are experiencing widespread disappointment with AI pilot programs, shifting the focus from mere AI adoption to demonstrating tangible value. This article proposes a framework, drawing on Peter Drucker and Andy Grove's principles, to measure the success of agentic AI by focusing on business outputs rather than inputs like activities or anecdotes. Mathematician Dario Fanucchi's approach, developed with Strattam Capital, treats AI projects as mathematical optimization problems, defining a target business metric (e.g., throughput, working capital) and measuring AI's impact solely by its improvement. This method emphasizes starting with the desired output, continuously monitoring its change, and optimizing AI tools until a sustainable positive movement is observed, a metric termed "Time To Production." An example from Trax Technologies illustrates this, where an AI Audit Optimizer's success was measured by the fraction of exceptions resolved without human intervention, leading to a tripling of resolved exceptions to 2.5 million in Q4 through iterative tuning.

Key takeaway

For AI Product Managers evaluating pilot programs, you should shift your focus from activity-based metrics to concrete business outputs. Define the specific business outcome you want to improve, then rigorously measure your AI's impact on that metric. If the "output gauge" isn't moving positively and sustainably, iterate on your AI tools or approach until it does, ensuring your AI investments deliver quantifiable value.

Key insights

Measure AI success by business outputs, not activities, to ensure tangible value and avoid pilot failures.

Principles

• AI is an innovation only when it has a business model.
• Focus on measurable business outcomes, not inputs.
• Align AI evaluations directly to core business metrics.

Method

Define a target business metric, model variables influencing it, and continuously optimize AI tools until the metric shows sustainable positive change. This process aims to minimize "Time To Production."

In practice

• Define the business outcome before selecting AI tools.
• Monitor a single output gauge to track AI impact.
• Iterate on AI tools until the output metric improves.

Topics

• AI ROI
• Output-Oriented AI
• Agentic AI
• Business Metric Optimization
• AI Pilot Strategy

Best for: Executive, AI Product Manager, Director of AI/ML, CTO, Consultant

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Editorial summary, takeaway, and curation by AIssential. Original article published by Artificial intelligence - Crunchbase News.