During my ~2 decades of experience of implementing advanced analytics & AI solutions at enterprises, I have seen the importance of use case selection. I analyzed 100+ AI use cases, their real-life examples and categorized them by business function and industry. Follow the links below based on your area of focus:
- AI Use Cases for Business Functions: Analytics, customer service, cybersecurity, data, finance, HR, marketing, operations sales, strategy and legal, and tech.
- AI Use Cases for Industries: Automotive & autonomous things, education, fashion, fintech, healthTech, manufacturing, non-profits, retail, and telecom.
- Other AI Use Cases
For all business AI applications and their real-life examples/ case studies, you can filter:
Artificial Intelligence (AI) Companies use cases with real-life examples
Natural language processing
Enabling machines to understand, interpret, and generate human language for communication, analysis, and automation.
Real Life Examples
Improving customer relations through the use of NLP and AI-based solutions for classifying customer claims and analyzing the content of customer calls.
BGIS, an energy company based in Canada, used KNIME Analytics Platform to quantify the return on investment for a lighting retrofit project. By applying natural language processing techniques, they were able to analyze 30,000 work order descriptions and identify the impact of the retrofit. The analysis revealed significant cost savings and improvements in lighting work order count and cost. The insights gained from the analysis helped inform future retrofit decisions and justify the project costs.
Fraud detection
Identifying suspicious transactions and behaviors to prevent financial loss and cybercrime.
Real Life Examples
Zip, a financial services company based in Australia, implemented DigitalGenius Autopilot to automate customer inquiries and offload ticket volume from their support team. With Autopilot handling over 2000 tickets a month, Zip achieved a Full Resolution Rate of 93.6% and saw significant reductions in Full Resolution Times and First Reply Times. The company experienced a return on investment of over 473% and freed up their customer service team to focus on complex tickets.
Finexkap, a leading fintech company in France, used Dataiku to build data projects and automate processes, resulting in 7x faster production. They leveraged Dataiku's user-friendly interface, easy data exploration, and analysis capabilities, as well as visual recipes and integrated notebooks. The company launched Finexpay, a machine learning-based service that helps B2B operators offer extended payment terms. With Dataiku, Finexkap improved their data ingestion, data wrangling, and release to production processes, significantly increasing their efficiency.
Dräger implemented Starmind to provide faster access to product expertise, resulting in improved customer service and reduced time spent searching for information. Within five months, the number of questions needing to be answered multiple times was reduced by 64%. Salespeople estimate a 12 percentage point increase in working time, and 94% of users are very satisfied with the Starmind solution.
Predictive maintenance
Forecasting equipment failures to schedule maintenance and reduce downtime.
Real Life Examples
Miele, a German manufacturer of high-end domestic appliances, used RapidMiner to improve the connection between production planning and product development. By transforming unstructured time series data, Miele was able to predict assembly times and optimize the production line. This data mining application led to essential added value for planning and decision-making, reducing product emergence time.
Insightera, a B2B targeting and personalization platform, used Qubole's Premium Service to accelerate their time to value for Hadoop. By leveraging Qubole's intuitive tools, automation, and cloud integration, Insightera was able to simplify their Big Data operations and focus more on data processing and turning insights into actionable results. With Qubole, Insightera reduced provisioning time, saved on operational costs, improved resource utilization, and achieved faster time-to-market.
National Grid leveraged Anaconda Enterprise to implement a risk-based maintenance methodology for their electricity transmission assets. By using open source Python tools and libraries, they were able to streamline their data science workflows, build reproducible maintenance models, and improve accuracy in modeling and predicting maintenance needs. This resulted in a more informed and cost-effective maintenance framework, reducing costs while maintaining the required level of safety.
Sales forecasting
Predicting future sales trends to guide business planning and resource allocation.
Real Life Examples
Heetch uses Dataiku and Kubernetes to treat large quantities of data while maintaining performance and controlling costs, ensuring a positive return on investment (ROI) and smooth execution on hundreds of data projects conducted throughout the organization.
A global retail chain increased coupon usage rate by up to 15% using AI. The company, dotData, provided an end-to-end AI automation platform that handled large amounts of POS data, automated model development, and delivered deeper insights. The marketing team was able to shorten campaign cycles from quarterly to monthly, resulting in improved coupon usage rate and increased sales. The success of this initiative has led the retailer to explore other use cases and consider projects to prevent supermarket defections.
InsideBoard, a technology company based in France, implemented their solution for sales forecasting, supply chain optimization, and data integration. The company experienced improved performance, increased visibility, and time-saving benefits.
Lead Generation
Identifying and engaging potential customers through data-driven targeting.
Real Life Examples
Chiesi Farmaceutici, an Italian pharmaceutical company, implemented KNIME Analytics Platform to automate the evaluation of drug compounds. This allowed medicinal chemists to prioritize the most promising candidates for further evaluation based on physico-chemical properties. The solution successfully integrated physico-chemical properties calculated by ACD/Labs Percepta into the corporate database, provided a user-friendly data visualization format, and automated the entire process without human intervention.
The A.H. Belo Corporation's The Dallas Morning News implemented Alteryx to achieve rapid development and deployment of analytics projects, resulting in significant time and cost savings. The implementation allowed the analytics team to spend more time on actual analytics work, leading to a 10x increase in analytics capability. The in-house development of advanced and predictive models using Alteryx also improved the quality and speed of deployment. The solution provided tangible return on investment and intangible benefits such as improved models and a strategic focus on innovation.
La Mutuelle Générale developed a decision support tool for sales using Dataiku. The tool helps prioritize prospects based on their likelihood of conversion and recuperation of acquisition costs. The company utilized data analysis on existing clients to establish 'look alikes' for each prospect. The tool also includes an interactive map to optimize travel for visiting prospects. Multiple teams and departments were involved in the project, including data and analytics, marketing and sales, and customer accounts division.
Personalized Marketing
Customizing advertising and content recommendations based on user behavior.
Real Life Examples
Howdoo, a decentralized social media platform, partnered with Shufti Pro to integrate KYC services into its portal. Shufti Pro's ID verification services seamlessly integrated with Howdoo on various platforms and languages, allowing for a user-friendly and trustworthy social networking experience. The collaboration aims to establish an authentic community based on ownership and trust.
Sephora partnered with Atos and Dell EMC to accelerate its digital transformation by moving its private cloud onto a new-generation platform. The collaboration resulted in improved performance, scalability, and resilience, enabling Sephora to deliver a seamless omnichannel experience to its customers and support ongoing innovation and global business expansion.
A global retail chain increased coupon usage rate by up to 15% using AI. The company, dotData, provided an end-to-end AI automation platform that handled large amounts of POS data, automated model development, and delivered deeper insights. The marketing team was able to shorten campaign cycles from quarterly to monthly, resulting in improved coupon usage rate and increased sales. The success of this initiative has led the retailer to explore other use cases and consider projects to prevent supermarket defections.
Data entry automation
Replaces manual data input with intelligent automation, reducing human errors and increasing the speed of data processing across systems.
Real Life Examples
DigitalGenius helped Odlo automate over 35% of chats in just one month, improving customer experience and reducing support costs.
Zip, a financial services company based in Australia, implemented DigitalGenius Autopilot to automate customer inquiries and offload ticket volume from their support team. With Autopilot handling over 2000 tickets a month, Zip achieved a Full Resolution Rate of 93.6% and saw significant reductions in Full Resolution Times and First Reply Times. The company experienced a return on investment of over 473% and freed up their customer service team to focus on complex tickets.
Heetch uses Dataiku and Kubernetes to treat large quantities of data while maintaining performance and controlling costs, ensuring a positive return on investment (ROI) and smooth execution on hundreds of data projects conducted throughout the organization.
Invoice processing
Automating the extraction, validation, and processing of invoices to streamline financial operations and reduce errors.
Real Life Examples
Acea Energia, an Italian multiutility company, upgraded its SAP Utilities system from ECC to S/4HANA to modernize its billing and credit management processes. The upgrade resulted in a 40% faster time to invoice, increased invoice throughput capacity, and zero disruption to users or processes. The project was completed in just eight months, delivering rapid value and setting high standards for future technology projects.
Atos was engaged by a large university Medical Center to redesign all aspects of cash management for both the acute and ambulatory settings. The solution involved shifting manual tasks to automated tools, reducing errors, and improving accuracy in financial transactions. By implementing streamlined processes and creating real-time dashboards, Atos achieved significant cost savings and enhanced controls for the healthcare team.
Atos helped Illumia, an Italian gas and electricity provider, transform its operating model with the implementation of DORA, a complete framework for selling gas and electricity in a deregulated market. The transformation resulted in accelerated processes, improved cash flow, and reduced operating costs. Illumia was able to expand its customer base and become a leading energy retailer in Italy.
Supply chain management
Managing and optimizing logistics, procurement, and distribution processes for efficiency and cost reduction.
Real Life Examples
SustainHub, a German technology company, uses RapidMiner's data mining solution for risk analysis in supply chains. They provide a platform for OEMs and suppliers to collaborate on regulatory compliance, including restricted and declarable substances. RapidMiner's functionality allows them to perform risk analysis, check for errors or omissions, flag certain substances or products, and search for alternatives. The platform also facilitates the exchange of bill of materials (BOM) data and automates data mining processes.
Domino's Pizza optimized their supply chain forecasting using RapidMiner's solution. By combining native operators and open source libraries, they were able to create accurate 8-week forecasts for over 4,000 locations. The solution addressed challenges such as food waste, labor pool usage, and strained supplier relations. RapidMiner's extensibility and scalability allowed for individual models for each location, resulting in a 10x performance boost. The company benefited from improved supply chain optimization and expanded their ecosystem of projects.
A specialty retailer partnered with Atos Healthcare to modernize its operations and beat digital competitors. The retailer shifted to the cloud, digitized its supply chain, and mobilized sales, resulting in a 5% jump in top-line revenue, 30% faster time to market for new digital features, 50% reduction in maintenance costs, and $22.8 million cost savings per year.
Supply chain optimization
Enhancing supply chain visibility and efficiency through predictive analytics and automation.
Real Life Examples
Switzerland's biggest retailer Migros partnered with Atos to implement a scalable and cost-efficient operating model for its data center platform services. The collaboration aimed to reduce IT costs, increase agility, and support digital transformation. Atos delivered robust and transparent services, optimized the Datacenter Platform IT service, and provided access to a world-class partner ecosystem. The partnership resulted in improved customer relationships, optimized supply chains, and increased profitability for Migros.
Sephora partnered with Atos and Dell EMC to accelerate its digital transformation by moving its private cloud onto a new-generation platform. The collaboration resulted in improved performance, scalability, and resilience, enabling Sephora to deliver a seamless omnichannel experience to its customers and support ongoing innovation and global business expansion.
InsideBoard, a technology company based in France, implemented their solution for sales forecasting, supply chain optimization, and data integration. The company experienced improved performance, increased visibility, and time-saving benefits.
Inventory optimization
Managing stock levels using automated demand forecasting and replenishment strategies.
Real Life Examples
Heetch uses Dataiku and Kubernetes to treat large quantities of data while maintaining performance and controlling costs, ensuring a positive return on investment (ROI) and smooth execution on hundreds of data projects conducted throughout the organization.
Acea Energia, an Italian multiutility company, upgraded its SAP Utilities system from ECC to S/4HANA to modernize its billing and credit management processes. The upgrade resulted in a 40% faster time to invoice, increased invoice throughput capacity, and zero disruption to users or processes. The project was completed in just eight months, delivering rapid value and setting high standards for future technology projects.
Switzerland's biggest retailer Migros partnered with Atos to implement a scalable and cost-efficient operating model for its data center platform services. The collaboration aimed to reduce IT costs, increase agility, and support digital transformation. Atos delivered robust and transparent services, optimized the Datacenter Platform IT service, and provided access to a world-class partner ecosystem. The partnership resulted in improved customer relationships, optimized supply chains, and increased profitability for Migros.
Compliance
Ensuring regulatory compliance through automated monitoring, reporting, and risk assessment.
Real Life Examples
Howdoo, a decentralized social media platform, partnered with Shufti Pro to integrate KYC services into its portal. Shufti Pro's ID verification services seamlessly integrated with Howdoo on various platforms and languages, allowing for a user-friendly and trustworthy social networking experience. The collaboration aims to establish an authentic community based on ownership and trust.
Enexis, a major utility company in the Netherlands, partnered with Atos to implement a secure data encryption solution for their smart metering project. The solution, called Cryptoserver, ensured the privacy and security of customer data, leading to increased customer acceptance of smart meters. The project was successful and resulted in the widespread adoption of smart meters in the Netherlands.
SustainHub, a German technology company, uses RapidMiner's data mining solution for risk analysis in supply chains. They provide a platform for OEMs and suppliers to collaborate on regulatory compliance, including restricted and declarable substances. RapidMiner's functionality allows them to perform risk analysis, check for errors or omissions, flag certain substances or products, and search for alternatives. The platform also facilitates the exchange of bill of materials (BOM) data and automates data mining processes.
Conversational AI & chatbots
Enhances customer service by automating responses, routing queries, and integrating with backend systems for real-time data access.
Real Life Examples
DigitalGenius helped Odlo automate over 35% of chats in just one month, improving customer experience and reducing support costs.
Skullcandy Inc., a technology company based in the United States of America, used DigitalGenius' solution to automate ticket resolution and handle customer queries. With over 60% of tickets fully resolved by DigitalGenius, the company achieved a 10% increase in customer satisfaction and automated 48% of tickets. The solution was particularly effective in handling WISMO queries and technical issues. The case study does not provide information about the creation year.
Siemens AG implemented Atos' Circuit solution, a cloud-based communications and collaboration application, to replace its outdated unified communications infrastructure. The solution provided an end-to-end communications infrastructure for over 350,000 employees worldwide. It offered messaging, audio and video conferencing, screen sharing, and integration with existing tools and processes. The implementation resulted in improved performance, increased visibility, and time savings for the company.
KPI monitoring
Automates the collection, visualization, and alerting of key performance indicators, providing real-time insights for decision-making.
Real Life Examples
Acea Energia, an Italian multiutility company, upgraded its SAP Utilities system from ECC to S/4HANA to modernize its billing and credit management processes. The upgrade resulted in a 40% faster time to invoice, increased invoice throughput capacity, and zero disruption to users or processes. The project was completed in just eight months, delivering rapid value and setting high standards for future technology projects.
Atos was engaged by a large university Medical Center to redesign all aspects of cash management for both the acute and ambulatory settings. The solution involved shifting manual tasks to automated tools, reducing errors, and improving accuracy in financial transactions. By implementing streamlined processes and creating real-time dashboards, Atos achieved significant cost savings and enhanced controls for the healthcare team.
The Hungarian Government implemented KNIME Analytics Platform to automate data reporting processes, provide real-time web-based reports for government executives, and improve decision-making. The solution enabled access to vital information, drill-down capabilities, and increased independence with data analysis. KNIME's out-of-the-box solutions, flexibility, scalability, and integration capabilities were key factors in choosing the platform.
Customer analytics
Analyzing customer behavior and trends to optimize business strategies and engagement.
Real Life Examples
DigitalGenius helped Odlo automate over 35% of chats in just one month, improving customer experience and reducing support costs.
Zip, a financial services company based in Australia, implemented DigitalGenius Autopilot to automate customer inquiries and offload ticket volume from their support team. With Autopilot handling over 2000 tickets a month, Zip achieved a Full Resolution Rate of 93.6% and saw significant reductions in Full Resolution Times and First Reply Times. The company experienced a return on investment of over 473% and freed up their customer service team to focus on complex tickets.
Finexkap, a leading fintech company in France, used Dataiku to build data projects and automate processes, resulting in 7x faster production. They leveraged Dataiku's user-friendly interface, easy data exploration, and analysis capabilities, as well as visual recipes and integrated notebooks. The company launched Finexpay, a machine learning-based service that helps B2B operators offer extended payment terms. With Dataiku, Finexkap improved their data ingestion, data wrangling, and release to production processes, significantly increasing their efficiency.
Workforce management
Optimizing workforce allocation and scheduling to enhance efficiency and reduce costs.
Real Life Examples
An Italian utilities company automated its field workforce activities with Atos to deliver paperless maintenance processes and improve customer experiences. Atos developed a Salesforce-based mobile and web workforce management solution, optimizing scheduling and resource management. The solution enabled field service technicians to manage customer service requests and planned maintenance activities via a mobile application or tablet, resulting in increased productivity, improved response time, and enhanced customer experience.
Network optimization
Optimizing network performance for speed, reliability, and resource utilization.
Real Life Examples
Transport for London uses RapidMiner to aid the performance of the road network by adopting a data-driven approach and automating data preparation processes, resulting in improved traffic flows and increased capacity for traffic optimization.
Rent-A-Center optimized their retail network using Alteryx, reducing the manual map creation process from 12.5 weeks to under 3 hours for 3,000 stores. The Alteryx solution provided improved data flow visibility and allowed for immediate adjustments. The demographic output from Alteryx also helped the merchandising department customize the merchandise mix in stores.
Atos implemented a multi-layered cybersecurity solution for Hard Rock Stadium to protect it during the Super Bowl LIV. The solution included network segmentation, real-time monitoring, and advanced detection and response techniques. Around 700 security events were managed and neutralized, ensuring the security of 6,500 fans and 7,100 devices. The implementation resulted in zero impact on the Super Bowl LIV and provided a replicable approach for future events.
Real-time analytics
Processing and analyzing data instantly for timely decision-making.
Real Life Examples
iflix, a Malaysia-based OTT service, leverages Qubole's big data platform to activate their data and make data-driven decisions. By decoupling storage and compute, iflix is able to leverage AWS infrastructure capabilities and achieve real-time analytics in a shorter period of time. Qubole's platform provides flexibility, resource allocation, and cost savings, allowing iflix to quickly resize clusters and complete jobs more efficiently.
Waze, a social navigation app owned by Google, uses RStudio Shiny Server Pro to process and visualize complex geospatial data. The solution allows for fast and interactive analysis, as well as the incorporation of statistical analysis and machine learning models. The company has successfully deployed multiple Shiny apps for various groups within the organization, resulting in happy data scientists and decision makers.
CityPulse, developed by Atos, is a pilot project in Eindhoven that uses big data analytics to manage a busy area of the city known for its nightlife. By combining data from multiple sources, including social media, the project helps authorities forecast and react to situations in real-time. The data gathered is primarily used to adjust street lighting levels, but the concept can be extended to other areas such as pollution alerts and traffic management.
Call classification
Categorizing calls based on topics, sentiment, or urgency.
Real Life Examples
Improving customer relations through the use of NLP and AI-based solutions for classifying customer claims and analyzing the content of customer calls.
Call intent discovery
Identifying the purpose behind customer calls to optimize responses.
Real Life Examples
FIRST ENERGY used RapidMiner solution to forecast staffing levels with more speed and precision. They implemented two forecast models using RapidMiner, achieving an average accuracy of 93% for a 90-day forecast. The company projected annual savings of $665K per call center by accurately staffing their call centers. They integrated the forecasts with Qlik for reporting and formalized a repeatable process for moving machine learning models to production. FIRST ENERGY aims to improve their advanced analytics function overall to gain a competitive edge.
Automated machine learning (AutoML)
Simplifying ML model creation without extensive expertise.
Real Life Examples
Sumitomo Mitsui Banking Corporation partnered with dotData to maximize their AI and machine learning investments. By implementing dotData's AutoML 2.0 platform, they were able to accelerate development times by 48X, generate 2 million features per project, develop over 100 models per year, and improve model accuracy by 30%. This automation solution provided significant benefits, including increased efficiency, scalability, and improved business impact.
Geo-analytics
Analyzing location-based data to uncover spatial patterns and trends.
Real Life Examples
Delhaize America, a grocery retailer with 1300+ stores in the Eastern USA, used Alteryx solution to improve their customer and trade area reporting. By implementing Alteryx, they were able to reduce manual steps, analyst wait-time, and software costs. The company achieved time savings, data availability, and analytical accuracy, leading to better utilization of consumer data insights.
Waze, a social navigation app owned by Google, uses RStudio Shiny Server Pro to process and visualize complex geospatial data. The solution allows for fast and interactive analysis, as well as the incorporation of statistical analysis and machine learning models. The company has successfully deployed multiple Shiny apps for various groups within the organization, resulting in happy data scientists and decision makers.
Data integration
Combining data from multiple sources for unified access and analysis.
Real Life Examples
InsideBoard, a technology company based in France, implemented their solution for sales forecasting, supply chain optimization, and data integration. The company experienced improved performance, increased visibility, and time-saving benefits.
Chipotle uses Alteryx to integrate data from various sources into a single workflow, allowing real estate managers to quickly apply their complex site selection model and sales forecasts. This has resulted in deeper insights, reduced time for site information entry, and improved workflow efficiency. The solution has helped Chipotle analyze more prospective locations with accurate data, increasing the number of successful real estate transactions.
Coyote, a European leader in real-time road information, uses Dataiku's solution to implement predictive analytics for churn prevention and predictive safety operations. They leverage IoT-derived data to improve road safety by identifying dangerous turns and developing a dynamic recommended speed limit model. Dataiku's centralized platform enables Coyote to connect, clean, and integrate diverse data sources, leading to improved driver assistance and road safety.
Data labeling
Annotating data to enhance machine learning model accuracy.
Real Life Examples
Diaceutics, a data analytics and end-to-end services provider, used KNIME Analytics Platform to standardize and label clinical data, streamlining data analysis and enabling domain expert input. The solution resulted in better data, better testing, and better treatment, with improved data collection, time savings, and increased visibility as key benefits.
Data visualization
Representing complex data through intuitive charts and graphs.
Real Life Examples
The University of Toronto partnered with Coursera to deliver bioinformatic courses using RStudio Server Pro. By setting up a web-accessible instance of Bioconductor on RStudio on Amazon Web Services, students were able to learn and analyze RNA-seq data without the need for individual installations. This solution reduced administration headaches and improved the student experience.
The University of Tasmania faced data storage issues and water damage to its infrastructure. With the help of Atos's HPSS solution, the university was able to revive its Research Data Management System and overcome these setbacks.
Audi of America used Alteryx and Tableau to consolidate and analyze their data, leading to improved sales forecasting, data visualization, and performance. The company experienced increased sales and saved time through the use of these tools.
Data transformation
Converting data into suitable formats for analysis and processing.
Real Life Examples
The University of Toronto partnered with Coursera to deliver bioinformatic courses using RStudio Server Pro. By setting up a web-accessible instance of Bioconductor on RStudio on Amazon Web Services, students were able to learn and analyze RNA-seq data without the need for individual installations. This solution reduced administration headaches and improved the student experience.
The University of Tasmania faced data storage issues and water damage to its infrastructure. With the help of Atos's HPSS solution, the university was able to revive its Research Data Management System and overcome these setbacks.
Miele, a German manufacturer of high-end domestic appliances, used RapidMiner to improve the connection between production planning and product development. By transforming unstructured time series data, Miele was able to analyze and predict assembly times, leading to improved planning and decision-making. The project resulted in essential added value for reducing product emergence time.
Data preparation
Cleaning and structuring data for analysis and modeling.
Real Life Examples
Transport for London uses RapidMiner to aid the performance of the road network by adopting a data-driven approach and automating data preparation processes, resulting in improved traffic flows and increased capacity for traffic optimization.
Idea Financial, a US online commercial lender, partnered with Explorium to streamline their pre-screening credit decision process and reduce data acquisition costs. By utilizing Explorium's External Data Platform, Idea Financial was able to access up-to-date data and develop accurate credit models more efficiently. As a result, they achieved a 50% reduction in data expenses, processed twice the amount of loan applications without increasing headcount, and improved their overall business operations.
The University of Nottingham used Alteryx to build a dynamic student planning and income model. By replacing Microsoft Excel and IBM Cognos, the university was able to handle complex calculations and improve decision making. Alteryx allowed for data cleaning and preparation, as well as the recreation of the data warehouse. The solution reduced planning model production time from hours to minutes and provided deeper insights into student numbers and funding.
Data management / monitoring
Ensuring data consistency, accessibility, and security.
Real Life Examples
The University of Toronto partnered with Coursera to deliver bioinformatic courses using RStudio Server Pro. By setting up a web-accessible instance of Bioconductor on RStudio on Amazon Web Services, students were able to learn and analyze RNA-seq data without the need for individual installations. This solution reduced administration headaches and improved the student experience.
The University of Tasmania faced data storage issues and water damage to its infrastructure. With the help of Atos's HPSS solution, the university was able to revive its Research Data Management System and overcome these setbacks.
Objenious, a subsidiary of Bouygues Telecom, partnered with Atos to design and implement France's first highly-secure LoRa® network for their IoT business. Atos' Horus security platform provided end-to-end protection for connected objects, ensuring top-performance and a high level of security. The solution was scalable, allowing Objenious to generate trusted identities for IoT devices in real-time. The collaborative relationship between Atos and Bouygues Telecom facilitated a fast implementation process, taking only twelve months.
Debt collection automation
Enhancing debt recovery through automated workflows and communication.
Real Life Examples
Webbankir, a leading microfinance company in Russia, implemented KNIME Analytics Platform to create an automated online loan application decision-making tool. The solution allowed for fast and accurate loan approvals, integrating data from external providers and client history. With KNIME, Webbankir reduced model implementation time from 1-2 months to 1-7 days, increased the share of fully automated decisions from 70% to 85%, and improved decision-making time by 30%. The company experienced increased sales, improved customer experience, and cost savings.
Credit lending & scoring
Assessing creditworthiness and risk based on data analysis.
Real Life Examples
Idea Financial, a US online commercial lender, partnered with Explorium to streamline their pre-screening credit decision process and reduce data acquisition costs. By utilizing Explorium's External Data Platform, Idea Financial was able to access up-to-date data and develop accurate credit models more efficiently. As a result, they achieved a 50% reduction in data expenses, processed twice the amount of loan applications without increasing headcount, and improved their overall business operations.
Maven Wave (an Atos company) partnered with a major retailer and a global bank to modernize the retailer's POS credit application capabilities. The project resulted in the successful deployment of a new POS credit application channel, leading to increased credit card applications and booking volume. The collaboration exceeded expectations for both the bank and the retailer.
Webbankir, a leading microfinance company in Russia, implemented KNIME Analytics Platform to create an automated online loan application decision-making tool. The solution allowed for fast and accurate loan approvals, integrating data from external providers and client history. With KNIME, Webbankir reduced model implementation time from 1-2 months to 1-7 days, increased the share of fully automated decisions from 70% to 85%, and improved decision-making time by 30%. The company experienced increased sales, improved customer experience, and cost savings.
Loan recovery
Automating strategies for tracking and collecting unpaid loans.
Real Life Examples
Webbankir, a leading microfinance company in Russia, implemented KNIME Analytics Platform to create an automated online loan application decision-making tool. The solution allowed for fast and accurate loan approvals, integrating data from external providers and client history. With KNIME, Webbankir reduced model implementation time from 1-2 months to 1-7 days, increased the share of fully automated decisions from 70% to 85%, and improved decision-making time by 30%. The company experienced increased sales, improved customer experience, and cost savings.
Employee monitoring
Tracking employee activity to optimize productivity and compliance.
Real Life Examples
ASML partnered with Atos to develop and implement a global Place to Work, Meet, Learn and Share program. The program aimed to align stakeholders, increase transparency, and engage employees, ultimately enabling ASML to become a global technology leader. Atos supported ASML in defining the vision, designing the operating model, and embedding the program at a local level. The program resulted in improved global transparency, better alignment among stakeholders, and clear measurement of performance.
Performance management
Monitoring and improving workforce efficiency and outcomes.
Real Life Examples
ASML partnered with Atos to develop and implement a global Place to Work, Meet, Learn and Share program. The program aimed to align stakeholders, increase transparency, and engage employees, ultimately enabling ASML to become a global technology leader. Atos supported ASML in defining the vision, designing the operating model, and embedding the program at a local level. The program resulted in improved global transparency, better alignment among stakeholders, and clear measurement of performance.
BP Germany partnered with Atos to improve application management, performance, and transparency. Atos successfully reduced costs, met service targets, and provided quality service. The partnership allowed BP to extend its best practices to other regions and industries.
Retail sales bots
Assisting customers with product recommendations and transactions.
Real Life Examples
Maven Wave (an Atos company) partnered with a major retailer and a global bank to modernize the retailer's POS credit application capabilities. The project resulted in the successful deployment of a new POS credit application channel, leading to increased credit card applications and booking volume. The collaboration exceeded expectations for both the bank and the retailer.
Knowledge management
Organizing and optimizing information access for efficiency.
Real Life Examples
PepsiCo R&D partnered with Starmind to improve knowledge management and accelerate innovation. By implementing the Starmind platform, PepsiCo R&D was able to connect R&D team members, streamline knowledge sharing, and provide the right information at the right time. The platform facilitated collaboration, reduced duplicated work, and improved decision-making. The company experienced increased engagement, efficient issue resolution, and a competitive advantage in the market.
ASML partnered with Atos to develop and implement a global Place to Work, Meet, Learn and Share program. The program aimed to align stakeholders, increase transparency, and engage employees, ultimately enabling ASML to become a global technology leader. Atos supported ASML in defining the vision, designing the operating model, and embedding the program at a local level. The program resulted in improved global transparency, better alignment among stakeholders, and clear measurement of performance.
Swisscom AG implemented Starmind to create a collaborative, open-book workplace culture. The solution provided a company-wide tacit knowledge platform, enabling employees to access the information they need quickly and easily. The implementation resulted in improved productivity, cost savings, and efficient product launches. The Starmind solution played a significant role in helping Swisscom adapt to remote working during COVID-19, ensuring important up-to-date information was readily available to employees.
Tutoring
Providing AI-driven educational assistance and personalized learning.
Real Life Examples
Atos provided the Sigmund-Schuckert-Gymnasium in Nuremberg with the RingCentral solution, enabling students and teachers to participate in digital teaching and work from home. The cloud-based solution allowed for virtual collaboration, social contacts, organization, and project work. The partnership between Atos and RingCentral provided a modern and adaptable solution tailored to the specific needs of the school.
Credit lending & scoring
Assessing creditworthiness and risk based on data analysis.
Real Life Examples
Idea Financial, a US online commercial lender, partnered with Explorium to streamline their pre-screening credit decision process and reduce data acquisition costs. By utilizing Explorium's External Data Platform, Idea Financial was able to access up-to-date data and develop accurate credit models more efficiently. As a result, they achieved a 50% reduction in data expenses, processed twice the amount of loan applications without increasing headcount, and improved their overall business operations.
Maven Wave (an Atos company) partnered with a major retailer and a global bank to modernize the retailer's POS credit application capabilities. The project resulted in the successful deployment of a new POS credit application channel, leading to increased credit card applications and booking volume. The collaboration exceeded expectations for both the bank and the retailer.
Webbankir, a leading microfinance company in Russia, implemented KNIME Analytics Platform to create an automated online loan application decision-making tool. The solution allowed for fast and accurate loan approvals, integrating data from external providers and client history. With KNIME, Webbankir reduced model implementation time from 1-2 months to 1-7 days, increased the share of fully automated decisions from 70% to 85%, and improved decision-making time by 30%. The company experienced increased sales, improved customer experience, and cost savings.
Patient data analytics
Extracting insights from medical data to improve care.
Real Life Examples
Aridhia, a clinical and translational informatics company, integrated RStudio Shiny into their AnalytiXagility platform to improve comprehension, efficiency, and communication in healthcare. The platform allows multidisciplinary teams to easily interact with and understand complex data, accelerating the development and deployment of transformational clinical applications. The integration of RStudio Shiny enables the creation of mini-apps that bring insights to stakeholders, enhancing productivity and impacting patient lives.
AstraZeneca's Scientific Computing Solutions team used RStudio and RStudio Server to develop life-changing medicines more efficiently. They created an R package and deployed RStudio Server to provide standardized tools and methodology for statisticians. The team also used Shiny Server Pro to deploy interactive tools for sharing analyses with non-experts. The use of R, RStudio, Shiny, and Shiny Server Pro allowed the team to explore historical clinical trial data, design clinical trials, and make go/no-go decisions across the drug portfolio.
Personalized medicine
Tailoring treatments based on individual health data.
Real Life Examples
Aridhia, a clinical and translational informatics company, integrated RStudio Shiny into their AnalytiXagility platform to improve comprehension, efficiency, and communication in healthcare. The platform allows multidisciplinary teams to easily interact with and understand complex data, accelerating the development and deployment of transformational clinical applications. The integration of RStudio Shiny enables the creation of mini-apps that bring insights to stakeholders, enhancing productivity and impacting patient lives.
GENyO, a biomedical research center in Spain, implemented an Atos supercomputer powered by Intel Xeon processors to support bioinformatics analysis and large-scale projects. The new system increased the center's scientific productivity, enabling them to contribute to precision medicine, pharmaceutical breakthroughs, and more efficient public health services. With improved performance, scalability, and reliability, GENyO researchers can run more processes in parallel, work with bigger datasets, and obtain faster answers.
Drug discovery
Accelerating pharmaceutical research through AI-driven analysis.
Real Life Examples
AstraZeneca's Scientific Computing Solutions team used RStudio and RStudio Server to develop life-changing medicines more efficiently. They created an R package and deployed RStudio Server to provide standardized tools and methodology for statisticians. The team also used Shiny Server Pro to deploy interactive tools for sharing analyses with non-experts. The use of R, RStudio, Shiny, and Shiny Server Pro allowed the team to explore historical clinical trial data, design clinical trials, and make go/no-go decisions across the drug portfolio.
GENyO, a biomedical research center in Spain, implemented an Atos supercomputer powered by Intel Xeon processors to support bioinformatics analysis and large-scale projects. The new system increased the center's scientific productivity, enabling them to contribute to precision medicine, pharmaceutical breakthroughs, and more efficient public health services. With improved performance, scalability, and reliability, GENyO researchers can run more processes in parallel, work with bigger datasets, and obtain faster answers.
Assisted /Automated diagnosis
Supporting healthcare professionals with AI-powered diagnostics.
Real Life Examples
AstraZeneca's Scientific Computing Solutions team used RStudio and RStudio Server to develop life-changing medicines more efficiently. They created an R package and deployed RStudio Server to provide standardized tools and methodology for statisticians. The team also used Shiny Server Pro to deploy interactive tools for sharing analyses with non-experts. The use of R, RStudio, Shiny, and Shiny Server Pro allowed the team to explore historical clinical trial data, design clinical trials, and make go/no-go decisions across the drug portfolio.
GENyO, a biomedical research center in Spain, implemented an Atos supercomputer powered by Intel Xeon processors to support bioinformatics analysis and large-scale projects. The new system increased the center's scientific productivity, enabling them to contribute to precision medicine, pharmaceutical breakthroughs, and more efficient public health services. With improved performance, scalability, and reliability, GENyO researchers can run more processes in parallel, work with bigger datasets, and obtain faster answers.
Generative AI Use Cases
Generative AI involves AI models generating output for tasks where there isn’t a single correct answer (e.g., creative writing). Since the launch of ChatGPT, its popularity has exploded. Use cases include content creation for marketing, software code generation, user interface design, and many others.
For more: Generative AI use cases.
AI Use Cases for Business Functions
Here are the most common artificial intelligence applications covering marketing, sales, customer services, security, data, technology, and other processes.
> Analytics
General solutions
- Analytics Platform: Empower your employees with unified data and tools to perform advanced analyses, quickly identify problems, and provide data insights.
- Analytics Services: Satisfy your custom analytics needs with these end-to-end solution providers. Vendors assist with your business objectives by offering turnkey solutions.
- Automated Machine Learning (autoML): AI powered machines can assist data scientists in optimizing machine learning models. With the rise of data and analytics capabilities, automation is increasingly essential in data science. AutoML automates time-consuming machine learning tasks, such as data entry, allowing companies to deploy models and automate processes more quickly.
Specialized solutions
- Conversational Analytics: Leverage conversational interfaces to analyze your business data. Natural Language Processing helps you work with voice data and more, enabling automated analysis of reviews and suggestions.
- E-Commerce Analytics: Specialized analytics systems designed to handle the surge in e-commerce data. Optimize your sales funnel and customer traffic to maximize profits.
- Geo-Analytics Platform: Analyze detailed satellite imagery for predictive insights. Utilize spatial data to achieve your business goals and capture real-time changes in any landscape.
- Image Recognition and Visual Analytics: Analyze visual data using advanced image and video recognition systems. Extract meaningful insights from large volumes of images and videos.
- Real-Time Analytics: Gain real-time insights for time-sensitive decisions. Act promptly to maintain your KPIs. Use machine learning to explore unstructured data without disruptions.
> Customer Service
- Call Analytics: Use advanced analytics on call data to uncover insights that improve customer satisfaction and operational efficiency. Identify patterns and optimize your results by analyzing customer reviews through voice data, pinpointing areas for improvement.
- Real-life example: Sestek indicates that ING Bank observed a 15% increase in sales quality score and a 3% decrease in overall silence rates after they integrated AI into their contact systems.
- Call Classification: Leverage natural language processing (NLP) to understand customer intentions, allowing agents to focus on higher value-added activities. Identify the nature of customer needs before routing calls, ensuring that the appropriate department handles the issue. This approach enhances efficiency and boosts satisfaction rates.
- Call Intent Discovery: Leverage NLP and machine learning to estimate and manage customer intent (e.g., churn) to enhance satisfaction and business metrics. Analyzing customer sentiment through voice level and pitch can reveal micro-emotions driving decision-making. Explore how to detect customer intent with chatbot intent recognition.
- Chatbot for Customer Service (Self – Service Solution): As AI algorithms improve, chatbots can understand more complex queries. Create 24/7 intelligent, self-improving chatbots that handle most inquiries and transfer customers to live agents when needed. This reduces customer service costs and increases satisfaction, allowing human representatives to focus on more specific customer needs. Read for more insights on chatbots in customer service or discover chatbot platforms.
- Chatbot Analytics: Analyze customer interactions with your chatbot to assess its overall performance. Identify shortcomings and areas for improvement, and measure customer satisfaction with the chatbot.
- Chatbot testing: Use semi-automated and automated testing frameworks to evaluate chatbot performance before deployment. Prevent catastrophic failures by identifying weaknesses in the conversational flow.
- Customer Contact Analytics: Apply advanced analytics to all customer contact data to gain insights that boost satisfaction and efficiency. Utilize NLP to achieve higher satisfaction rates.
- Customer Service Response Suggestions: Bots listen to agents’ calls, suggesting best practice responses to enhance customer satisfaction and standardize the customer experience. This approach can also increase upsells and cross-sells by providing the right suggestions.
- Social Listening & Ticketing: Use NLP and machine vision to identify customers needing contact and respond automatically or assign them to relevant agents, improving satisfaction. Analyze social media data to determine whom to sell to and what products to offer.
- Intelligent Call Routing: Route calls to the most qualified agents available. Intelligent routing systems use data from all customer interactions to optimize satisfaction. By considering customer profiles and agent performance, you can match the right service with the right agent to achieve superior net promoter scores. Feel free to read case studies about matching customer to right agent in our emotional AI examples article.
- Survey & Review Analytics: Use NLP to analyze text fields in surveys and reviews, uncovering insights that improve satisfaction. Automate the process by mapping relevant keywords to appropriate scores, reducing the time required for report generation.
- Real-life example: Protobrand used to manually analyze reviews through hand-coding but now automates much of the work with Gavagai, allowing the company to handle larger volumes of data while completing analyses efficiently. Learn more about survey analytics in our related article.
- Voice Authentication: Authenticate customers without passwords using biometric voice recognition, enhancing satisfaction and minimizing issues with forgotten passwords. Customers can access confidential information with their unique voice ID, providing a secure alternative to traditional authentication methods like SSN digits.
> Cybersecurity
DLP
Data loss prevention (DLP) software leverage AI technologies to achieve
- Real time detection of sensitive data beyond those identified using rules-based approached
- Intelligent access control learning from allowed data access patterns to reduce false positives
For more, see best practices for using AI in DLP.
Network monitoring
Typical use cases include:
- Anomaly detection in network traffic to identify cyberattacks
- Automated network optimization to manage peak loads at optimal cost without harming user experience.
For real-life examples: AI in network monitoring
> Data
- Data Cleaning & Validation Platform: Avoid garbage in, garbage out by ensuring the quality of your data with appropriate data cleaning processes and tools. Automate the validation process by using external data sources. Regular maintenance cleaning can be scheduled, and the quality of the data can be increased.
- Data Integration: Combine your data from different sources into meaningful and valuable information. Data traffic depends on multiple platforms. Therefore, managing this huge traffic and structuring the data into a meaningful format will be important. Keep your data lake available for further analysis.
- Data Management & Monitoring: Keep your data high quality for advanced analytics. Adjust the quality by filtering the incoming data. Save time by automating manual and repetitive tasks.
- Data Preparation Platform: Prepare your data from raw formats with data quality problems to a clean, ready-to-analyze format. Use extract, transform, and load (ETL) platforms to fine-tune your data before placing it into a data warehouse.
- Data Transformation: Transform your data to prepare it for advanced analytics. If it is unstructured, adjust it for the required format.
- Data Visualization: Visualize your data for better analytics and decision-making. Let the dashboards speak. Convey your message more easily and more esthetically.
- Data Labeling: Unless you use unsupervised learning systems, you need high quality labeled data. Label your data to train your supervised learning systems. Human-in-the-loop systems auto label your data and crowdsource labeling data points that cannot be auto-labeled with confidence.
- Synthetic Data: Computers can artificially create synthetic data to perform certain operations. The synthetic data is usually used to test new products and tools, validate models, and satisfy AI needs. Companies can simulate not yet encountered conditions and take precautions accordingly with the help of synthetic data. They also overcome the privacy limitations as it doesn’t expose any real data. Thus, synthetic data is a smart AI solution for companies to simulate future events and consider future possibilities. You can have more information on synthetic data from our related article.
> Finance
Finance business function led by the CEO completes numerous repetitive tasks involving quantitative skills which makes them a good fit for AI transformation:
- Billing / invoicing reminders: Leverage accessible billing services that remind your customers to pay with generative AI powered messages.
- Invoice automation & AP automation: Invoice processing is a highly repetitive process that many companies perform manually. This causes human errors and high costs, especially when a high volume of documents needs to be processed. Invoice automation solutions can extract relevant data from invoices of different formats (e.g. PDF, e-invoices), perform automated invoice validation and select the right expense codes for the invoice minimizing human involvement to edge cases. Invoice automation is available in most ERP systems via plugins. For ERP specific examples, check out:
For more, see AI use cases in AP automation.
> HR
- Employee Monitoring: Monitor your employees for better productivity measurement. Provide objective metrics to see how well they function. Forecast their overall performance with the availability of massive amounts of data.
- Hiring: Hiring is a prediction game: Which candidate, starting at a specific position, will contribute more to the company? Machine and recruiting chatbots‘ better data processing capabilities augment HR employees in various parts of hiring such as finding qualified candidates, interviewing them with bots to understand their fit or evaluating their assessment results to decide if they should receive an offer.
- HR Analytics: HR analytics services are like the voice of employee analysis. Look at your workforce analytics and make better HR decisions. Gain actionable insights and impactful suggestions for higher employee satisfaction.
- HR Retention Management: Predict which employees are likely to churn and improve their job satisfaction to retain them. Detect the underlying reasons for their motive for seeking new opportunities. By keeping them at your organization, lower your human capital loss.
- Performance Management: Manage your employees’ performance effectively and fairly without hurting their motivation. Follow their KPI’s on your dashboard and provide real-time feedback. This would increase employee satisfaction and lower your organization’s employee turnover. Actualize your employee’s maximum professional potential with the right tools.
You can also read our article on HR technology trends.
> Marketing
A 2021 survey conducted among global marketers revealed that 41% of respondents saw an increase in revenue growth and improved performance due to the use of AI in their marketing campaigns.
Marketing can be summarized as reaching the customer with the right offer, the right message, at the right time, through the right channel, while continually learning. To achieve success, companies can leverage AI-powered tools to get familiar with their customers better, create more compelling content, and perform personalized marketing campaigns. AI can provide accurate insights and suggest smart marketing solutions that would directly reflect on profits with customer data. You can find the top three AI use cases in marketing:
- Marketing analytics: AI systems learn from, analyze, and measure marketing efforts. These solutions track media activity and provide insights into PR efforts to highlight what is driving engagement, traffic, and revenue. As a result, companies can provide better and more accurate marketing services to their customers. Besides PR efforts, AI-powered marketing analytics can lead companies to identify their customer groups more accurately. By discovering their loyal customers, companies can develop accurate marketing strategies and also retarget customers who have expressed interest in products or services before. Feel free to read more about marketing analytics with AI from this article.
- Personalized Marketing: The more companies understand their customers, the better they serve them. AI can assist companies in this task and support them in giving personalized experiences for customers. As an example, suppose you visited an online store and looked at a product but didn’t buy it. Afterward, you see that exact product in digital ads. More than that, companies can send personalized emails or special offers and recommend new products that go along with customers’ tastes.
- Context-Aware Marketing: You can leverage machine vision and natural language processing (NLP) to understand the context where your ads will be served. With context-aware advertising, you can protect your brand and increase marketing efficiency by ensuring your message fits its context, making static images on the web come alive with your messages.
For more, check out AI use cases in marketing or AI for email marketing. AI-powered email marketing software is among the first AI tools that marketers should work with.
> Operations
- Cognitive / Intelligent Automation: Combine robotic process automation (RPA) with AI to automate complex processes with unstructured information. Digitize your processes in weeks without replacing legacy systems, which can take years. Bots can operate on legacy systems learning from your personnel’s instructions and actions. Increase your efficiency and profitability ratios. Increase speed and precision, and many more. Feel free to check intelligent automation use cases for more.
- Robotic Process Automation (RPA) Implementation: Implementing RPA solutions requires effort. Suitable processes need to be identified. If a rules-based robot will be used, the robot needs to be programmed. Employees’ questions need to be answered. That is why most companies get some level of external help. Generally, outsourcing companies, consultants, and IT integrators are happy to provide temporary labor to undertake this effort.
- Process Mining: Leverage process mining algorithms to mine your processes and understand your actual processes in detail. Process mining tools can provide fastest time to insights about your as-is processes as demonstrated in process mining case studies. Check out process mining use cases & benefits for more.
- Predictive Maintenance: Predictively maintain your robots and other machinery to minimize disruptions to operations. Implement big data analytics to estimate the factors that are likely to impact your future cash flow. Optimize PP&E spending by gaining insight regarding the possible factors.
- Inventory & Supply Chain Optimization: Leverage machine learning to take your inventory& supply chain optimization to the next level. See the possible scenarios in different customer demands. Reduce your stock, keeping spending, and maximize your inventory turnover ratios. Increase your impact factor in the value chain.
Admin
- Building Management: Sensors and advanced analytics improve building management. Integrate IoT systems in your building for lower energy consumption and many more. Increase the available data by implementing the right data collection tools for effective building management.
- Digital Assistant: Digital assistants are mature enough to replace real assistants in email communication. Include them in your emails to schedule meetings. They have already scheduled hundreds of thousands of meetings.
> Sales
Pre-Sales
- Sales Forecasting: AI allows automatic and accurate sales forecasts based on all customer contacts and previous sales outcomes. Automatically forecast sales accurately based on all customer contacts and previous sales outcomes. Give your sales personnel more sales time while increasing forecast accuracy.
- Real-life example: Hewlett Packard Enterprise indicates that it has experienced a 5x increase in forecast simplicity, speed, and accuracy with Clari’s sales forecasting tools.
- Lead generation: Use a comprehensive data profile of your visitors to identify which companies your sales reps need to connect. Generate leads for your sales reps leveraging databases and social networks
Sales
- Sales Data Input Automation: Data from various sources will be effortlessly and intelligently copied into your CRM. Automatically sync calendar, address book, emails, phone calls, and messages of your salesforce to your CRM system. Enjoy better sales visibility and analytics while giving your sales personnel more sales time.
- Predictive sales/lead scoring: Use AI to enable predictive sales. Score leads to prioritize sales rep actions based on lead scores and contact factors. Sales forecasting is automated with increased accuracy thanks to systems’ granular access to lead scores and sales rep performance. For scoring leads, these systems leverage anonymized transaction data from their customers, sales data of this specific customer. For assessing contact factors, these systems leverage anonymized data and analyze all customer contacts such as email and calls.
- AI-based agent coaching: Both AI and emotion AI can be leveraged to coach sales reps and customer service employees by:
- Sales Rep Response Suggestions: AI will suggest responses during live conversations or written messages with leads. Bots will listen in on agents’ calls suggesting best practice answers to improve sales effectiveness
- Sales Rep Next Action Suggestions: Your sales reps’ actions and leads will be analyzed to suggest the next best action. This situation wise solution will help your representatives to find the right way to deal with the issue. Historical data and profile of the agent will help you to achieve higher results. All are leading to more customer satisfaction.
- Sales Content Personalization and Analytics: Preferences and browsing behavior of high priority leads are analyzed to match them with the right content, aimed to answer their most important questions. Personalize your sales content and analyze its effectiveness allowing continuous improvement.
- Retail Sales Bot: Use bots on your retail floor to answer customer’s questions and promote products. Engage with the right customer by analyzing the profile. Computer vision will help you to provide the right action depending on the characteristics and mimics of the customer.
- Meeting Setup Automation (Digital Assistant): Leave a digital assistant to set up meetings freeing your sales reps time. Decide on the targets to prioritize and keep your KPI’s high.
- Prescriptive Sales: Most sales processes exist in the mind of your sales reps. Sales reps interact with customers based on their different habits and observations. Prescriptive sales systems prescribe the content, interaction channel, frequency, price based on data on similar customers.
- Sales Chatbot: Chatbots are ideal to answer first customer questions. If the chatbot decides that it can not adequately serve the customer, it can pass those customers to human agents. Let 24/7 functioning, intelligent, self-improving bots handle making initial contacts to leads. High value, responsive leads will be called by live agents, increasing sales effectiveness.
Sales analytics
As Gartner discusses, sales analytic systems provide functionality that supports discovery, diagnostic, and predictive exercises that enable the manipulation of parameters, measures, dimensions, or figures as part of an analytic or planning exercise. Machine learning algorithms can automate the data collection process and present solutions to improve sales performance. To have more detailed information, you can read our article about sales analytics.
- Customer Sales Contact Analytics: Analyze all customer contacts, including phone calls or emails, to understand what behaviors and actions drive sales. Advanced analytics on all sales call data to uncover insights to increase sales effectiveness
- Sales Call Analytics: Advanced analytics on call data to uncover insights to increase sales effectiveness. See how well your conversation flow performs. Integrating data on calls will help you to identify the performance of each component in your sales funnels.
- Sales attribution: Leverage big data to attribute sales to marketing and sales efforts accurately. See which step of your sales funnel performs better. Pinpoint the low performing part by the insights provided by analysis.
- Sales Compensation: Determine the right compensation levels for your sales personnel. Decide on the right incentive mechanism for the sales representatives. By using the sales data, provide objective measures, and continuously increase your sales representatives’ performance.
For more on AI in sales.
> Strategy & Legal
- Presentation preparation: Top management presentations in most companies involve slides (e.g. PowerPoint). Generative AI presentation software can prepare slides from prompts.
Legal counsels can rely on AI in:
- Contract drafting
- Contract review
- Legal research
For more: Legal AI software
> Tech
- No code AI & app development: AI and App development platforms for your custom projects. Your in-house development team can create original solutions for your specific business needs.
- Analytics & Predictive Intelligence for Security: Analyze data feeds about the broad cyber activity as well as behavioral data inside an organization’s network to come up with actionable insights to help analysts predict and thwart impending attacks. Integrate external data sources the watch out for global cyber threats and act timely. Keep your tech infrastructure intact or minimize losses.
- Knowledge Management: Enterprise knowledge management enables effective and effortless storage and retrieval of enterprise data, ensuring organizational memory. Increased collaboration by ensuring the right people are working with the right data. Seamless organizational integration through knowledge management platforms.
- Natural Language Processing Library/ SDK/ API: Leverage Natural Language Processing libraries/SDKs/APIs to quickly and cost-effectively build your custom NLP powered systems or to add NLP capabilities to your systems. An in-house team will gain experience and knowledge regarding the tools. Increased development and deployment capabilities for your enterprise.
- Image Recognition Library/ SDK/ API: Leverage image recognition libraries/SDKs/APIs to quickly and cost-effectively build your custom image processing systems or to add image processing capabilities to your existing systems.
- Secure Communications: Protect employee communications like emails or phone conversations with advanced multilayered cryptography & ephemerality. Keep your industry secrets safe from corporate espionage.
- Deception Security: Deploy decoy-assets in a network as bait for attackers to identify, track, and disrupt security threats such as advanced automated malware attacks before they inflict damage. Keep your data and traffic safe by keeping them engaged in decoys. Enhance your cybersecurity capabilities against various forms of cyber attacks
- Autonomous Cybersecurity Systems: Utilize learning systems to efficiently and instantaneously respond to security threats, often augmenting the work of security analysts. Lower your risk of human errors by providing greater autonomy for your cybersecurity. AI-backed systems can check compliance with standards.
- Smart Security Systems: AI-powered autonomous security systems. Functioning 24/7 for achieving maximum protection. Computer vision for detecting even the tiniest anomalies in your environment. Automate emergency response procedures by instant notification capabilities.
- Machine Learning Library/ SDK/ API: Leverage machine learning libraries/SDKs/APIs to quickly and cost-effectively build your custom learning systems or to add learning capabilities to your existing systems.
- AI Developer: Develop your custom AI powered solutions with companies experienced in AI development. Create turnkey projects and deploy them to the specific business function. Best for companies with limited in-house capabilities for artificial intelligence.
- Deep Learning Library/ SDK/ API: Leverage deep learning libraries/SDKs/APIs to quickly and cost-effectively build your custom learning systems or to add learning capabilities to your systems.
- Developer Assistance: Assist your developers using AI to help them intelligently access the coding knowledge on the web and learn from suggested code samples. See the best practices for specific development tasks and formulate your custom solution. Real-time feedback provided by the huge history of developer mistakes and best practices.
- AI Consultancy: Provides consultancy services to support your in-house AI development, including machine learning and data science projects. See which units can benefit most from AI deployment. Optimize your artificial intelligence spending for the best results from the insight provided by a consultant.
AI Use Cases for Industries
> Automotive & Autonomous Things
Autonomous things including cars and drones are impacting every business function from operations to logistics.
- Driving Assistant: Required components and intelligent solutions to improve rider’s experience in the car. Implement AI-Powered vehicle perception solutions for the ultimate driving experience.
- Vehicle Cybersecurity: Secure connected and autonomous cars and other vehicles with intelligent cybersecurity solutions. Guarantee your safety by hack-proof mechanisms. Protect your intelligent systems from attacks.
- Vision Systems: Vision systems for self-driving cars. Integrate vision sensing and processing in your vehicle. Achieve your goals with the help of computer vision.
- Self-Driving Cars: From mining to manufacturing, self-driving cars/vehicles are increasing the efficiency and effectiveness of operations. Integrate them into your business for greater efficiency. Leverage the power of artificial intelligence for complex tasks.
> Education
- Course creation
- Tutoring
For more: Generative AI applications in education
> Fashion
- Creative Design
- Virtual try-on
- Trend analysis
For more: Generative AI applications in fashion
> FinTech
- Fraud Detection: Leverage machine learning to detect fraudulent and abnormal financial behavior, and/or use AI to improve general regulatory compliance matters and workflows. Lower your operational costs by limiting your exposure to fraudulent documents.
- Insurance & InsurTech: Leverage machine learning to process underwriting submissions efficiently and profitably, quote optimal prices, manage claims effectively, and improve customer satisfaction while reducing costs. Detect your customer’s risk profile and provide the right plan.
- Financial Analytics Platform: Leverage machine learning, Natural Language Processing, and other AI techniques for financial analysis, algorithmic trading, and other investment strategies or tools.
- Travel & expense management: Use deep learning to improve data extraction from receipts of all types including hotel, gas station, taxi, grocery receipts. Use anomaly detection and other approaches to identify fraud, non-compliant spending. Reduce approval workflows and processing costs per unit.
- Credit Lending & Scoring: Use AI for robust credit lending applications. Use predictive models to uncover potentially non-performing loans and act. See the potential credit scores of your customers before they apply for a loan and provide custom-tailored plans.
- Loan recovery: Increase loan recovery ratios with empathetic and automated messages.
- Robo-Advisory: Use AI finance chatbot and mobile app assistant applications to monitor personal finances. Set your target savings or spending rates for your own goals. Your finance assistant will handle the rest and provide you with insights to reach financial targets.
- Regulatory Compliance: Use Natural Language Processing to quickly scan legal and regulatory text for compliance issues, and do so at scale. Handle thousands of paperwork without any human interaction.
- Data Gathering: Use AI to efficiently gather external data such as sentiment and other market-related data. Wrangle data for your financial models and trading approaches.
- Debt Collection: Leverage AI to ensure a compliant and efficient debt collection process. Effectively handle any dispute and see your success right in debt collection.
- Conversational banking: Financial institutions engage with their customers on a variety of communication platforms (WhatsApp, mobile app, website etc.) via conversational AI tools to increase customer satisfaction and automate many tasks like customer onboarding.
> HealthTech
- Patient Data Analytics: Analyze patient and/or 3rd party data to discover insights and suggest actions. Greater accuracy by assisted diagnostics. Lower the mortality rates and increase patient satisfaction by using all the diagnostic data available to detect the underlying reasons for the symptoms.
- Personalized Medications and Care: Find the best treatment plans according to patient data. Provide custom-tailored solutions for your patients. By using their medical history, genetic profile, you can create a custom medication or care plan.
- Drug Discovery: Find new drugs based on previous data and medical intelligence. Lower your R&D cost and increase the output — all leading to greater efficiency. Integrate FDA data, and you can transform your drug discovery by locating market mismatches and FDA approval or rejection rates.
- Real-Time Prioritization and Triage: Prescriptive analytics on patient data enabling accurate real-time case prioritization and triage. Manage your patient flow by automatization. Integrate your call center and use language processing tools to extract the information, priorate patients that need urgent care, and lower your error rates. Eliminate error-prone decisions by optimizing patient care.
- Early Diagnosis: Analyze chronic conditions leveraging lab data and other medical data to enable early diagnosis. Provide a detailed report on the likelihood of the development of certain diseases with genetic data. Integrate the right care plan for eliminating or reducing the risk factors.
- Assisted or Automated Diagnosis & Prescription: Suggest the best treatment based on the patient complaint and other data. Put in place control mechanisms that detect and prevent possible diagnosis errors. Find out which active compound is most effective against that specific patient. Get the right statistics for superior care management.
- Pregnancy Management: Monitor mother and fetus health to reduce mothers’ worries and enable early diagnosis. Use machine learning to uncover potential risks and complications quickly. Lower the rates of miscarriage and pregnancy-related diseases.
- Medical Imaging Insights: Advanced medical imaging to analyze and transform images and model possible situations. Use diagnostic platforms equipped with high image processing capabilities to detect possible diseases.
- Healthcare Market Research: Prepare hospital competitive intelligence by tracking market prices. See the available insurance plans, drug prices, and many more public data to optimize your services. Leverage NLP tools to analyze the vast size of unstructured data.
- Healthcare Brand Management and Marketing: Create an optimal marketing strategy for the brand based on market perception and target segment. Tools that offer high granularity will allow you to reach the specific target and increase sales of healthcare providers.
- Gene Analytics and Editing: Understand genes and their components and predict the impact of gene edits.
- Device and Drug Comparative Effectiveness: Analyze drug and medical device effectiveness. Rather than just using simulations, test on other patient’s data to see the effectiveness of the new drug, compare your results with benchmark drugs to make an impact with the drug.
- Healthcare chatbot: Use a chatbot to schedule patient appointments, give information about certain diseases or regulations, fill in patient information, handle insurance inquiries, and provide mental health assistance. You can also use intelligent automation with chatbot capabilities.
- Healthcare AI agent: Use an AI agent to schedule appointments, provide information about diseases or health regulations, document patient data, handle insurance questions, assist with mental health support, and automate clinical and administrative tasks with intelligent chatbot capabilities.
For more, feel free to check our article on the use cases of AI in the healthcare industry.
> Manufacturing
- Manufacturing Analytics: Also called industrial analytics systems, these systems allow you to analyze your manufacturing process from production to logistics to save time, reduce cost, and increase efficiency. Keep your industry effectiveness at optimal levels.
- Collaborative Robots: Cobots provide a flexible method of automation. Cobots are flexible robots that learn by mimicking human workers’ behavior.
- Robotics: Factory floors are changing with programmable collaborative bots that can work next to employees to take over more repetitive tasks. Automate physical processes such as manufacturing or logistics with the help of advanced robotics. Increased your connected systems by centralizing the whole manufacturing process. Lower your exposures to human errors.
> Non-Profits
- Personalized donor outreach and engagement based on historical data to increase fundraising levels while avoiding email fatigue.
- Donor identification via techniques like look-alike audiences.
See more use cases of AI in fundraising.
> Retail
- Cashierless Checkout: Self-checkout systems have many names. They are called cashierless, cashier-free, or automated checkout systems. They allow retail companies to serve customers in their physical stores without the need for cashiers. Technologies that allowed users to scan and pay for their products have been used for almost a decade now, and those systems did not require great advances in AI. However, these days we are witnessing systems powered by advanced sensors and AI to identify purchased merchandise and charge customers automatically.
> Telecom
- Network investment optimization: Both wired and wireless operators need to invest in infrastructure like active equipment or higher bandwidth connections to improve Quality of Service (QoS). Machine learning can be used to identify highest ROI investments that will result in less churn and higher cross and up-sell.
Other AI Use Cases
This was a list of areas by business function where out-of-the-box solutions are available. However, AI, like software, has too many applications to list here. You can also take a look at our AI in business article to read about AI applications by industry. Also, feel free to check our article on AI services.
It is important to get started fast with high impact applications and generate business value without spending months of effort. For that, we recommend companies to use no code AI solutions to quickly build AI models.
Once companies deploy a few models to production, they need to take a deeper look at their AI/ML development model.
- rely on autoML software to build complex AI models. Though most autoML software is not as easy to use as no code AI solutions, they can be used to build complex models.
- build custom AI solutions in-house
- work with the support of partners to build custom models
- run data science competitions to build custom AI models
- Use pre-trained models built by AI vendors
You can also check out our list of AI tools and services:
- AI Consultant
- AI/ML Development Services
- Data Science / ML / AI Platform
- AI governance tools, responsible AI software and AI compliance solutions to manage an AI inventory, mitigate AI bias and other generative AI risks.
These articles about AI may also interest you:
- Ultimate Guide to the State of AI technology
- Future of AI according to top AI experts
- Advantages of AI according to top practitioners.
Conclusion
AI is being applied across nearly every industry, with real-world examples showcasing its potential in marketing, manufacturing, finance, and beyond. This growing variety of use cases listed above highlights AI’s practical impact across business functions.
Yet, value creation requires more than just adopting AI. Organizations must align AI tools with specific goals, ensure ethical data use, and provide the right infrastructure and talent. The most successful use cases combine innovation with strategic execution.
FAQ
What is artificial intelligence (AI)?
Artificial Intelligence (AI) is the branch of computer science that focuses on creating machines capable of performing tasks that typically require human intelligence. This includes activities such as learning, problem-solving, understanding natural language, speech recognition, and visual perception. AI systems can analyze large amounts of data, identify patterns, and make decisions, often with speed and accuracy surpassing human capabilities.
AI is transforming industries and business functions, leading to growing interest in AI and its subdomains like machine learning and data science. With the launch of ChatGPT, interest in generative AI, a subfield of AI, has surged (see Figure 1). According to a recent McKinsey survey, 55% of organizations are using AI in at least one business function.1
What are the examples of AI in real life?
Artificial Intelligence (AI) is integrated into many aspects of daily life. Some common real-life examples include:
Virtual Assistants: Like Siri, Alexa, and Google Assistant, these AI-powered tools understand and respond to voice commands, performing tasks like setting reminders, answering questions, and controlling smart home devices.
Navigation and Maps: AI is used in services like Google Maps and Waze for route optimization, traffic prediction, and providing real-time directions.
Recommendation Systems: Streaming services like Netflix and Spotify use AI to analyze your viewing or listening history to recommend movies, shows, or music.
Autonomous Vehicles: Self-driving cars use AI to perceive the environment and make decisions for safe navigation.
Social Media: Platforms like Facebook and Instagram use AI for content curation, targeted advertising, and facial recognition in photos.
Security and Surveillance: AI aids in anomaly detection, facial recognition, and monitoring systems for enhanced security.
How does AI impact employment and job creation?
AI impacts employment by automating routine tasks, which can lead to job displacement in some sectors. However, it also creates new job opportunities in AI development, data analysis, and other tech-related fields, emphasizing the need for skill adaptation.
What are some misconceptions about AI?
Common misconceptions include the idea that AI can fully replicate human intelligence, that it’s always unbiased, or that AI-led automation will universally eliminate jobs. In reality, AI has limitations, can inherit biases from data, and often changes rather than replaces job roles.
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External links
Though most use cases have been categorized based on our experience, we also took a look at Tractica’s AI use cases list before finalizing the list. Other sources:
- 1. The state of AI in 2023: Generative AI’s breakout year | McKinsey. McKinsey & Company
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