operationalization
What It Means
Operationalization is the process of taking an AI concept or system from development into actual business use where its performance can be tracked and measured. It's essentially the bridge between having an AI model that works in testing and having one that delivers measurable business value in production.
Why Chief AI Officers Care
CAIOs need operationalization to prove AI ROI and meet compliance requirements that demand measurable outcomes. Without proper operationalization, AI investments remain experimental costs rather than strategic business assets, and organizations can't demonstrate regulatory compliance or business impact to stakeholders.
Real-World Example
A retail company develops an AI recommendation engine that shows 85% accuracy in lab testing, but operationalizing it means deploying it on their e-commerce site, measuring actual conversion rate improvements, tracking customer satisfaction scores, and monitoring system uptime - turning the technical capability into measurable business results like '12% increase in average order value.'
Common Confusion
People often confuse operationalization with deployment, but deployment is just putting the system live while operationalization includes the measurement frameworks and business processes needed to track meaningful outcomes.
Industry-Specific Applications
See how this term applies to healthcare, finance, manufacturing, government, tech, and insurance.
Healthcare: In healthcare, operationalization involves deploying AI systems into clinical workflows while ensuring compliance with H...
Finance: In finance, operationalization involves deploying AI models for real-world applications like credit scoring, fraud detec...
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Includes:
- 6 industry-specific applications
- Relevant regulations by sector
- Real compliance scenarios
- Implementation guidance
Technical Definitions
NISTNational Institute of Standards and Technology
"Putting AI systems or related concepts into use so they can be measured."Source:
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