Artificial Intelligence
Intro
Artificial Intelligence (AI) is the capability of machines to perform tasks that typically require human intelligence, such as perception, prediction, and decision-making. In enterprise architecture, AI augments processes and systems to improve accuracy, speed, and outcomes.
Key points:
- Enhances automation with predictive and adaptive behavior.
- Unlocks insights from data at scale for better decisions.
- Common use cases across EA/BPM/Data/App/Tech include demand forecasting, intelligent routing, anomaly detection, and conversational interfaces.
- Pitfall: deploying AI without clear objectives, data quality, or governance.
Examples:
- Using machine learning to predict order cancellations and trigger proactive actions.
- Classifying service tickets to auto-route and recommend resolutions.
- Detecting fraud by flagging anomalous transactions in near real time.
In practice:
Start with well-defined business outcomes, ensure data readiness and ethics controls, then iterate models and integrate with workflows.
Related terms: Data; Key Performance Indicator
FAQs:
Q: Do we need big data to use AI?
A: Not always; start with focused use cases and sufficient quality data.
Q: How do we measure AI value?
A: Tie models to KPIs like cost, cycle time, accuracy, and risk reduction.
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