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Guide to using advanced analytics and AI in business applications

Last updated:February 2018

Editor's note

When IBM's Watson system defeated the two highest-earning Jeopardy! champions in 2011, it helped propel artificial intelligence further into the mainstream consciousness. Until then, AI was largely a scientific and research pursuit -- particularly after an effort to commercialize AI systems flamed out in the 1980s.

Watson itself went somewhat undercover after its quiz show victory, but the cognitive computing platform got a $1 billion push from IBM in 2014, leading to the development of Watson-based analytics offerings for healthcare, financial services, customer engagement and other enterprise uses.

IBM isn't alone: Microsoft, Google, Amazon Web Services, Salesforce and a growing horde of other vendors now offer enterprise-focused AI technologies, from chatbot tools to machine learning and deep learning platforms.

As AI in business applications moves from possibility to reality, organizations are learning how to use such technologies to better serve both internal users and external customers. For example, chatbots handle routine customer service inquiries, freeing up staff members to attend to higher-level issues. Advanced analytics programs incorporate AI to segment customers for targeted marketing, to score sales leads, to identify potential problems in internet of things (IoT) devices and more.

In this guide, we cover the current state of AI technology in the enterprise and the role of platforms like IBM Watson in enabling new types of analytics uses. Learn how AI-powered advanced analytics and cognitive computing systems are furthering corporate initiatives, and get advice from industry experts and IT professionals who've been tasked with setting and implementing strategies for using AI in business operations at their companies.

1Big-picture views of advanced analytics and enterprise AI

AI in business applications is changing the use of data in the enterprise -- and creating new technology needs and management challenges for organizations. In this section, learn about AI-related trends and issues to be aware of before jumping into deployments, including what's involved in building and maintaining machine learning platforms, how AI tools could affect jobs, and what AI's inherent analytical ambiguity means for analytics teams.

2Methods and techniques for making sense of data

Business applications, big data systems and other data sources can produce a great deal of data on customers, products, machinery and more -- but how can organizations take advantage of all that information? In this section, you'll find advice on how to implement predictive modeling, machine learning and deep learning techniques, plus other aspects of managing advanced analytics and AI applications.

3Putting AI technologies to work in business applications

Rolling out AI in business environments is easier said than done, but business use cases are emerging for AI tools and related advanced analytics technologies. In this section, learn more about those uses and the challenges that some organizations have faced -- plus lessons they've learned, such as the need to tightly tie AI efforts to business objectives and to be aware of technology limitations.

4Real-world advice on advanced analytics and AI in business

As technology vendors try to turn all the AI hype into reality, IT professionals are learning how to take advantage of AI, machine learning and cognitive computing platforms. In this section, get advice and points of view from technology and analytics managers who've led the charge in their companies to modernize analytics approaches and deploy AI in business applications.