Categories of Use Cases #

Artificial intelligence systems driven by machine learning enable organizations to analyze large amounts of historical data to discover hidden insights and relationships that would be impossible for any one person to identify. This enables new possibilities and efficiencies that can be categorized into four buckets:
Increase revenue: for example, identify opportunities to upsell or cross-sell, design sales and marketing programs that drive the most revenue, and increase customer acquisition, retention and satisfaction
Minimize risk: for example, avoid risk of non-compliance, reduce employee churn, reduce customer risk (e.g., credit risk, fraud, churn, or multiple low value pilferage), anti-money laundering, face recognition to enhance physical security, and detect intellectual property infringement
Decrease costs: for example, identify customers that are likely to respond to a marketing campaign, eliminate the product features with the least impact, find the optimal production schedule in manufacturing, and automate capabilities previously delivered by humans
Gain competitive edge: for example, anticipate market demands, personalize customer experience, develop new products (even new markets), improve existing products, optimize after-sales support, and reverse engineer competitor’s services or processes

Examples Across Business Functions #

Marketing: predicting Customer Lifetime Value, estimating wallet share, churn prediction, customer segmentation, market basket analysis, cross-selling/upselling, product recommendation, channel optimization, discount targeting
Risk management: credit risk, fraud detection, accounts payable recovery, anti-money laundering, regulatory compliance, detecting multiple low value pilferage, physical security, intellectual property infringement
Human resources: resume screening, employee churn prediction, training recommendation, talent management, predictive planning of leave, employee onboarding using chatbots Customer service: chatbots, call routing, call center message optimization, call center volume forecasting, warranty management, customer complaint resolution
Sales: lead scoring, demand forecasting, sales script personalization, CRM data input automation, sales attribution
IT operations: construction of configuration management database, information search and retrieval, cyber security management, data center environment management, server load balancing, automating or optimization helpdesk tasks
Finance and accounting: revenue, expenses and deviation forecasting, automating writing commentary on financial analysis graphs and tables, retrieving data for reporting, identifying spending anomalies and expense abuses, detecting deviation from procedure Supply chain management: demand forecasting, securing inventory, predicting individual purchases for purposes of pre-shipping, route optimization
Procurement: identifying procurement errors and fraud, identifying attempts at evading existing controls and procedures

Examples Across Industries #

Healthcare: claims review prioritization, medical aid fraud detection, medical resources allocation, alerting and diagnostics from real-time patient data, prescription compliance, staff churn, medication, effectiveness, readmission risk prediction
Retail: price optimization, location of new stores, product layout in stores, merchandising, inventory management, market basket analysis, cannibalization analysis, next best offer analysis, in store traffic pattern analysis
Travel: aircraft/vehicle scheduling, seat/gate management, crew scheduling, dynamic pricing, predicting arrival time of aircraft/vehicle
Insurance: claims prediction, agent and branch performance, claims handling, investment decisions, claims fraud detection, price sensitivity analysis
Manufacturing: predictive maintenance, production optimization, failure analysis, quality management, inventory management, warranty management, pricing, selling analytics-enabled service rather than selling the product directly
Electric power utilities: predictive maintenance, real-time load balancing, grid optimization and aggregation, distributed power generation, advanced demand response, campus/building energy management
Telecommunications: reducing cost of network operation, demand forecasting, predicting equipment requirements, detecting fraud, predicting churn, determining spares inventory, drawing maintenance schedule, cybersecurity, customer support chatbots, call center optimization
Government and public sector: detecting water leakage and theft, optimizing traffic flows, detecting and foiling cyber-attacks, detecting epidemics in their early stages, fleet management, facial recognition and voice recognition by law enforcement agencies

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