15. AI in Practice

AI in Practice

Predictive Analytics

AI models analyze historical data to predict future outcomes. Common applications include sales forecasting, demand prediction, customer churn prediction, and risk assessment.

# Simplified time-series prediction
import numpy as np
from sklearn.linear_model import LinearRegression

# Historical monthly sales
months = np.array([1, 2, 3, 4, 5]).reshape(-1, 1)
sales = np.array([100, 120, 135, 150, 170])

model = LinearRegression()
model.fit(months, sales)

# Predict month 6
prediction = model.predict([[6]])
# Returns approximately 187

Customer Personalization

AI drives recommendation engines that personalize user experiences. Collaborative filtering (users with similar tastes), content-based filtering (similar items), and hybrid approaches are common.

Manufacturing

• Predictive Maintenance — Sensors + ML predict equipment failures before they occur
• Quality Inspection — Computer vision detects defects on assembly lines
• Supply Chain Optimization — AI optimizes inventory levels and logistics
• Robotic Process Automation (RPA) — Automates repetitive tasks

Healthcare AI

• Medical Imaging — Detecting tumors, fractures, and abnormalities in X-rays, MRIs, CT scans
• Clinical Decision Support — AI assists doctors with diagnosis and treatment recommendations
• Drug Discovery — AI accelerates identification of drug candidates
• Remote Monitoring — Wearable devices + AI detect health anomalies

Autonomous Vehicles

Self-driving cars use a pipeline of perception (object detection, lane detection), localization (GPS + SLAM), prediction (predicting other vehicles' trajectories), planning (path planning with A*/RRT), and control (steering, acceleration, braking). Challenges include edge cases, weather conditions, and regulatory approval.

Decision Making

AI supports business decisions through data-driven insights, scenario simulation, and optimization. Decision intelligence combines AI with decision theory to improve organizational decision-making.