6 Examples of AI in Business Intelligence Applications

Enterprise does seem to be of course entering into a new era that is ruled by data. What at one time was accepted as science fiction, now AI in business intelligence is rather evolving into everyday business as one knows it. Companies are able to make use of machines algorithms in order to identify trends and insights in vast realms of data and thus are able to make faster decisions.

It is a complex process for companies to be able to incorporate machine learning into their operating business intelligence systems. Organizing data collection as well as testing an algorithm with this data for accuracy is difficult indeed and business at times gets held up.

AI in Business Intelligence Applications

AI has no doubt gained much momentum and prominent application providers have rather gone beyond creating traditional software in order to develop more holistic platforms as well as solutions that better automate business intelligence as well as analytics processes such as:

• A brief overview of the product or service
• Successful case studies
• Potential use cases in the industry

Business intelligence applications will indeed be one of the fastest growing areas for no doubt leveraging AI technology over the coming next five to 10 years.

Business Intelligence Apps Built on Machine Learning and also artificial intelligence in medicine

SAP – AI for Turning Databases into Useful Intel

HANA is SAP’s cloud platform that often companies make use of to manage databases of information that they have collected.

This platform can be installed to run on-premise via a company’s servers, or via the cloud. HANA does take in information that is gathered from access points well across the business that does include mobile as well as desktop computers, financial transactions, sensors, and equipment at production plants.

DOMO – AI for Business Dashboards

Companies using Domo can pull data from Salesforce, Square, Facebook, Shopify, and many other applications that are made use of to gain insight into their respective customers, sales, or product inventory.

Apptus – AI in Sales Enablement

One does come across numerous ways for machine learning to enhance applications.

The Apptus eSales solution is indeed designed to, among many other features, to automate merchandising based on a predictive understanding of consumers. The software combines big data, as well as machine learning, is geared towards determining which products might appeal to a potential customer as they do a search online or get required recommendations.

Avanade – AI for Business Insights

Avanade is a joint venture between Microsoft and Accenture. Avanade makes use of smart technologies whereby machines do take on more of the work that people traditionally do.

Machine learning is largely made use of in service sectors, such as insurance and retail. It is also used to address tasks related to customers, sales, and operations. No doubt, AI has indeed also merged with BI applications in manufacturing and industrial domains.

BI and AI – Applications in Heavy Industry

General Electric – Predicting Repairs and Upkeep for Machinery

There is an increased prevalence of sensors in machinery, vehicles, production plants, and other hard equipment spaces mean physical equipment that can be digitized and also be monitored by artificial intelligence. The Internet of Things is not merely about consumer gadgets; commercial trucks, trains, oil rigs, and cargo ships can also be digitized, monitored as well as assessed via networks.

Siemens – AI for Monitoring Machine Fleets and Factories

This application can be made of by industrial companies in order to keep a track of machine tools at plants around the world and also see performance stats of their assets. This does help schedule preventive maintenance and does allow managing how their equipment is made use of in order to improve their operational lifespan.


Business and industry are indeed focusing on machine learning might weave their way into how operations are handled, the way decisions are made as well as how resources are managed.

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