Artificial Intelligence is revolutionizing the way organizations can transform and accelerate their core business operations. It empowers stakeholders to streamline business processes, optimize resources, and pave the way for innovation across core Finance, Procurement, Manufacturing, Supply Chain, Customer Services, Sales & Marketing and Human Resources.
In this article, I have tried to give perspective on various AI use cases which an organization can adopt across this line of businesses. There could be multiple use cases beyond below listed, however I have tried list important ones.
Manufacturing
Applicable industries: Heavy industries like manufacturing, where machinery downtime can be costly.
| Use cases | Benefits |
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Predictive Maintenance – Data Collection and Monitoring: Collecting real-time data from sensors and IoT devices embedded in machinery to monitor performance metrics such as temperature, vibration, and energy consumption. – AI Analytics and Pattern Recognition: Using AI algorithms to analyze collected data, identify patterns, and predict potential equipment failures or maintenance needs. – Condition-Based Alerts: Generating alerts and notifications based on predictive models to notify maintenance teams about the need for servicing or repairs. |
– Reduced Downtime: Proactive maintenance prevents unexpected breakdowns, minimizing downtime and optimizing machinery uptime. – Cost Savings: Avoiding catastrophic failures saves on repair costs and prevents disruptions to production schedules. – Improved Equipment Lifespan: Timely maintenance based on predictive insights can extend the lifespan of machinery and equipment.
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Quality Control: – AI-powered image recognition and machine learning can inspect products for defects or anomalies, ensuring quality control in the production process. |
– Improved production quality: Automated AI based inspection enhances accuracy of production quality. – Improves the productivity and enhances accuracy of shop floor operators by automating visual inspection tasks.
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Process Optimization: – Optimize production schedule based on machine capacity, labor availability, and demand fluctuations. – Optimize energy consumption by identifying inefficiencies in machinery operations. – Minimize waste reduction by optimizing material usage. – Workforce optimization based on existing capacity and demand |
– Optimize cost of manufacturing operations – Improve profitability |
Case Studies:
- Foxconn Utilizes AI-powered visual inspection systems for quality control in electronics manufacturing, ensuring precision and accuracy in identifying defects, leading to better product quality.
- Siemens Energy along with Nvidia created AI powered predictive maintenance solution which could save utility providers more than $1+ billion / year.
Supply Chain
Applicable industries: All industries: Manufacturing across core industries, life science, healthcare, Retail, CPG and distribution, where efficient supply chain management significantly impacts operational costs and customer satisfaction.
| Use cases | Benefits |
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– Demand Forecasting: AI algorithms analyze historical sales data, market trends, weather patterns, social media and various other factors to predict future demand accurately. – Inventory Optimization: AI can help optimize inventory levels by considering various factors like demand variability, lead times, supplier performance and others and decide replenishment schedules. |
– Optimized Inventory Management: Aligning inventory levels with actual demand, optimizing inventory levels, reducing overstock or stockouts, minimizes waste and improves customer satisfaction. – Improved Efficiency: Enhancing supply chain operations through accurate forecasts and optimized inventory. Increase supply chain efficiency with accurate delivery date prediction. – Cost Savings: Reduced inventory carrying costs including that of slow-moving materials and better resource utilization. |
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– Supply Chain Optimization: Route optimization for logistics, scheduling and distribution thereby reducing transportation cost, delivery times and carbon footprints. Last-Mile Delivery Optimization considering factors like traffic, weather, and package size. – Integrating IoT feeds from moving goods across the globe to optimize inventory management or use prediction models and correlation analysis to understand causes and effects in supply chain interactions. – Traceability: integrating AI with blockchain technology can enhance transparency and traceability in supply chains. AI helps analyze and interpret the vast amounts of data stored in a blockchain, ensuring better tracking and transparency across the entire supply chain. |
– Enhanced traceability & transparency – Optimized cost of supply chain operations |
| – Warehouse Automation: AI-driven robotics and automation streamline warehouse operations by optimizing picking, packing, and sorting processes. | – Faster order fulfillment, reduced errors, and improved overall efficiency. |
| – Compliance: Assist in navigating complex international regulations and customs processes, while streamlining cross-border operations integrating with Global Trade services applications | – Ensuring business compliance |
Case Studies:
- Amazon uses AI for demand forecasting to anticipate shopping patterns, optimizing inventory levels and reducing stockouts.
- UPS is innovatively performing warehouse automation to create optimal space and automated goods movement.
- Walmart Utilizes AI to optimize inventory, reducing overstock situations and ensuring products are available when needed especially during peak holiday seasons, improving the overall customer experience.
- DHL uses AI in automating warehouse operations.
Sales & Marketing
Applicable industries: Across various industries including retail, e-commerce, and B2B sectors, where accurate sales forecasts, lead prioritization, and optimized marketing strategies significantly impact revenue and customer acquisition.
| Use cases | Benefits |
| – Sales Forecasting: AI algorithms analyze historical sales data and market trends to predict future sales trends | – Improved Sales Performance: Accurate sales forecasting leads to better planning and performance |
| – Lead Scoring: Using predictive models to identify and prioritize potential leads for sales teams |
– Enhanced Lead Prioritization: Identifying high-quality leads improves sales efficiency. – |
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– Marketing Optimization: Automated customer segmentation for focused marketing campaign. – AI insights used to refine and optimize marketing strategies for better targeting – market demand, competitor pricing and other influencing factors |
– Optimized Marketing Campaigns: AI-driven insights enhance the effectiveness of marketing strategies. – Maximized Profitability: Optimized pricing based on demand and market conditions increases revenue |
| – Dynamic Pricing Models: Using AI-driven models to adjust prices in real-time |
– Competitive Advantage: Dynamic pricing strategies provide a competitive edge. – |
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– Customer Insights and Personalization: AI can analyze customer behavior and preferences, enabling more accurate forecasting, personalized pricing strategies and better customization of products and services, thereby improving customer satisfaction. – Sentiment Analysis from social media in order to steer sales strategy |
– Personalized Pricing: Tailoring prices to customer behavior enhances customer satisfaction |
Case Studies:
- Delta is experimenting with AI to establish pricing.
- Uber and other online cab service providers business is completely on dynamic pricing.
Customer Service
Applicable industries: E-commerce, retail, and services, where personalized and efficient customer service can significantly impact customer satisfaction and retention.
| Use cases | Benefits |
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– Service Automation: Automate customer inquiries resolution – AI-Powered Chatbots: Integration of AI chatbots with required applications to handle customer inquiries. Natural Language Processing (NLP): AI algorithms understand and respond to customer queries naturally. – Leveraging Generative AI to draft customer service requests over email, provide effective solution based on past similar issues and so on. – Automated case management – Categorizing and prioritizing service cases leveraging AI |
– 24/7 Support: Chatbots provide round-the-clock customer assistance, improving accessibility |
| – Cross sell / up sell – Customer Data Analysis: Leveraging customer data for personalized interactions and recommendations | – Personalization: Tailored interactions based on customer history and preferences enhance satisfaction |
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– Automate Field Service: Auto agent assignment based on customer service request / required skill match. – Predict driving times and distances |
– Field service automation – Enhanced customer satisfaction |
| – Customer sentiment analysis – Analyze customer feedback – Understand customer sentiment, concerns, and satisfaction levels and plan appropriate actions. | – Enhanced customer experience |
| – AI for knowledge mgmt. – Enabling service agents to get instant information from knowledge base. | – Efficient Query Handling: Quick and accurate responses streamline customer service. |
Case Studies:
- Bank of America provides AI powered personalized banking services to its customers
- Amazon, Netflix and several other organizations provides its customer’s personalized recommendations based on previous behaviour.
Procurement
Applicable industries: Applicable to all industries where efficient procurement and inventory management directly impact operational costs and customer satisfaction.
| Use cases | Benefits |
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– Demand Forecasting: AI algorithms analyze historical sales data, market trends, weather patterns, Social media and various other factors to predict future demand accurately. – Inventory Optimization: AI can help optimize inventory levels by considering various factors like demand variability, lead times, supplier performance and others and decide replenishment schedules |
– Cost Reduction: Optimized procurement and inventory lead to cost savings by reducing excess stock and avoiding shortages. – Operational Efficiency: Timely contract renegotiations with predictive analytics for contract consumption. Generating key questions for RFP / bid documents. – Streamlining procurement processes. – Accurate Demand Planning: Aligning inventory with actual demand improves customer satisfaction. Predict delivery delays (from vendors) |
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– Supplier Management: Analyzing supplier behavior, performance, and reliability to make informed decisions. AI can assess various data sources to evaluate and predict supplier performance, potential risks, and disruptions. It helps in identifying potential issues before they impact the supply chain and allows for proactive risk mitigation strategies. – Cost Optimization: Identify cost saving opportunities in procurement based on historical trend on consumption, pricing trends, raw material costing. |
– Improved Supplier Relationships: Better understanding and management of suppliers enhances partnerships. Discover quality suppliers. |
| – Dynamic Procurement Strategies: AI insights help in negotiating contracts, optimizing purchasing volumes, and exploring alternative sourcing | – Optimized cost |
| – Internal / indirect procurement: Employees can get personalized recommendations that streamline purchasing from the company’s catalogs | – Enhanced user experience |
| – Contingent workforce – Resume matching to nearest Job Description, ranking based on skills/JD | – Optimized efforts in contingent workforce selection |
| – Process Automation – leveraging AI powered bots for getting real time visibility on process status, automate interaction with suppliers and internal business stakeholders. | – Process Automation, enhanced user experience |
| – Fraud detection – Detect anomalies in process or compliance. | – Improved compliance |
Case Studies
- Walmart began exploring the possibility of automating procurement negotiations leveraging AI
Finance
Applicable industries: All industries
| Use cases | Benefits |
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– Automated invoice processing – Utilizing AI, particularly Optical Character Recognition (OCR) technology, to extract and process invoice data automatically – 3 ways matching (Purchase Order, Invoice, Goods Receipt), streamlining, and enhancing the accuracy of financial operations. – Accounts Payable Automation: Automated routing of invoices for approval and payment processing based on predefined rules and validation. – Accounts Receivables reconciliation with incoming payment advice – Complex Intercompany reconciliations – Reconciliation of Goods Receipt to Invoice Receipt in complex intercompany reconciliations |
– Efficiency Gains: Automation reduces manual effort, speeds up invoice processing, and minimizes errors. – Cost Savings: Reduction in manual processing saves time and reduces operational costs. – Accuracy Improvement: AI verification enhances accuracy and reduces the risk of human errors. |
| – Anomaly Detection: Algorithms to detect anomalies in financial transactions, enabling the identification of potential fraud or errors for enhanced security and data integrity. Potential scenarios – employee theft, corruption, incorrect transactions, warranty fraud and so on. |
– Anomaly Detection – Enhanced Security: Early anomaly detection helps in preventing fraud and security breaches. – Improved Data Integrity: Ensuring financial data accuracy and reliability. – Cost Savings: Preventing financial losses due to fraudulent activities or errors. |
| – Cashflow prediction – Accounts payable departments can predict when to expect payments from at-risk customers | – Helps the financial team optimize payment strategies and cash flow management |
| – Audit Compliance – AI and machine learning analyze expense reports, speed up auditing, and resolve noncompliance issues | – Compliance & audit automation |
Case Studies
- JPMorgan Chase is leveraging AI in various financial use cases
Human Resource
Applicable industries: All industries
Utmost care is required in anonymizing data and ensuring privacy and confidentiality of an individual while training AI models. Regulatory compliance and transparency a must in deploying HR use cases.
| Use cases | Benefits |
| – Learning and development – Tailored learning recommendations, personalized development plans based on current role of an individual and further talent development. | – Enhanced learning experience |
| – Encourage growth & retention – by matching employee career goals and skills sets to open opportunities | – Talent retention |
| – Employee engagement – Solving employee queries, FAQs, company policies through AI powered chatbot |
– HR Automation – Enhanced employee experience |
| – Recruitment and Talent Acquisition – Profile screening – profile to JD matching. Also helps in screening profiles by eliminating biases and promoting fair evaluation. |
– Eliminate biases. – Automated recruitment – shorter lead time to onboard resource |
| – Employee feedback analysis – Understand employee sentiment, concerns, and satisfaction levels | – Enhanced employee experience |
| – Workforce planning & optimization – Forecasting future workforce needs based on trends – business requirements, forecasted sales of an organization, existing sales pipeline, existing workforce utilization | – Optimized workforce utilization |
| – Employee Well-being: AI powered employee well-being surveys to detect signs of stress or burnout and suggest health and wellness programs based on individual needs and preferences | – Enhanced employee experience ensuring their well-being |
| – Learning and development – Tailored learning recommendations, personalized development plans based on current role of an individual and further talent development. | – Enhanced learning experience |
Case Studies:
- Unilever uses AI powered talent marketplace for new opportunities for their employees
Knowledge Management
Leveraging cognitive search capabilities to retrieve relevant information from various documents, manuals, and databases, aiding in quick issue resolution and decision-making.
| Use cases | Benefits |
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AI for knowledge mgmt. – Train AI to fetch results from Internal data sources – IT Service Mgmt. tool, Known Error Data Base, Knowledge Portals, Run Books and business documents. – Generative AI to generate feed from External public data – Application / solution specific external knowledge articles, help documents and other specific materials. |
– Efficient Issue Resolution: Quick access to relevant information aids in faster problem-solving. – Improved Decision-Making: Access to comprehensive data helps in informed decision-making. – Enhanced Productivity: Saves time by quickly retrieving necessary information.
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