Artificial intelligence is playing an increasingly important role in business operations and value chains. But one fundamental question remains: what does success actually look like for an AI project?
Organizations typically evaluate projects using established measures such as cost, revenue, and job creation. While these indicators remain essential, they do not tell the whole story. An AI system can perform well technically without being widely adopted, improving operations, or generating meaningful business value.
Measuring AI success therefore requires a broader view. Technical performance must be considered alongside operational improvements, adoption by the people expected to use the solution, the costs and risks associated with its deployment, and the business outcomes it ultimately supports.
Operational metrics play a critical role in connecting these dimensions. Shorter cycle times, fewer errors, improved quality, increased capacity, and changes in how work is performed can establish a measurable link between the performance of an AI system and the value created for the organization.
This connection is becoming even more important as generative AI and AI agents are integrated into business processes. Strong results in technical evaluations do not necessarily translate into reliable performance in real operating environments. Organizations need to assess the complete solution; including how it integrates into existing workflows, how consistently it performs, what it costs to operate, how people use it in practice, and what controls are in place.
At ALL IN 2026, Yassine Lazraq, Investment Director at Scale AI, will present a practical approach to defining and measuring AI success. Drawing on lessons from more than 200 applied AI projects supported by Scale AI, he will explain how organizations can establish success criteria from the outset, track impact throughout implementation, and evaluate whether a project has delivered the expected results.
Yassine has more than 15 years of experience in analytics, data strategy, and AI investment. Before joining Scale AI, he was a Vice President within J.P. Morgan Chase’s Quantitative Research team.
Participants will be introduced to a practical framework for assessing technical performance, operational improvements, workforce adoption, responsible deployment, and measurable business outcomes. The goal is not to replace traditional financial indicators, but to complement them with the evidence needed to understand how AI creates value, and ultimately determine whether an initiative is ready to scale.
Register for ALL IN 2026 to attend the session!