When IBM’s Watson System Defeated the Two Biggest Winners Peril! champions in 2011, he helped propel artificial intelligence further into mainstream consciousness. Until then, AI was largely a science and research activity, especially after an effort to commercialize AI systems died down in the 1980s.
Watson himself went somewhat undercover after his quiz win, but the cognitive computing platform received a billion-dollar boost from IBM in 2014, leading to the development of Watson-based analytics offerings for healthcare, financial services, customer engagement, and other enterprise uses.
IBM is not alone: Microsoft, Google, Amazon Web Services, Salesforce and a growing horde of other vendors now offer business-focused AI technologies, from chatbot tools to machine learning platforms and software. deep learning.
As AI in enterprise applications moves from possibility to reality, organizations are learning to use these technologies to better serve internal users and external customers. For example, chatbots handle routine customer service requests, freeing up staff members to deal with higher-level issues. Advanced analytics programs integrate AI to segment customers for targeted marketing, to score leads, to identify potential issues 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 analytical uses. Learn how advanced analytics and cognitive computing systems powered by AI are driving business initiatives forward and get insights from industry experts and IT professionals who have been responsible for defining and implementing implement strategies for using AI in their company’s business operations.
1Overviews of Advanced Analytics and Enterprise AI
AI in business applications is changing the use of data in business and creating new technology needs and management challenges for organizations. In this section, learn about AI trends and issues you need to be aware of before embarking on deployments, including what it involves to build and maintain machine learning platforms, how AI could affect jobs and what the inherent analytical ambiguity of AI means for analytics teams.
2Methods and techniques for making sense of data
Enterprise applications, big data systems, and other data sources can produce large amounts of data about customers, products, machines, etc. But how can organizations take advantage of all this information? In this section, you will find guidance on how to implement predictive modeling, machine learning, and deep learning techniques, as well as other aspects of managing advanced analytics and analytics applications. AI.
3Putting Artificial Intelligence Technologies to Work in Enterprise Applications
Deploying AI in business environments is easier said than done, but business use cases are emerging for AI tools and associated advanced analytics technologies. In this section, learn more about these uses and the challenges some organizations have faced, as well as the lessons they’ve learned, such as the need to tie AI efforts tightly to business goals and be aware of the limitations of technology.
4Real-world advice on advanced analytics and AI in business
As technology vendors attempt to turn all the AI hype into reality, IT professionals are learning to take advantage of AI, machine learning, and cognitive computing platforms. In this section, get tips and insights from technology and analytics leaders who have led the charge in their organizations to modernize analytics approaches and deploy AI in business applications.