AI enterprise accelerator
Building artificial intelligence (AI) systems involves more than learning how to perform a specific task from data; it requires a strong data foundation and infrastructure architecture.
The AI enterprise accelerator offers foundational IBM showcases for you to replicate. These showcases serve as examples of where and how AI-transformed business processes generate value:
Data strategy
Start with understanding your business strategy. Then, create a data strategy that aligns AI and data resources accordingly. This is your compass for all future data-driven AI initiatives.
AI enterprise data architecture
The bedrock of an AI enterprise is formed by two entities: A multicloud architecture plus associated workflows for seamless integration and movement of data across AI and analytic workloads.
Automated metadata generation
Generate metadata automatically for your AI enterprise data lake by using deep learning and natural language understanding.
Data privacy
Deploy AI resources and automation that address data privacy regulations such as GDPR. A framework for governance, security, communication and automation can provide strong support for current and future data privacy regulation compliance.
AI Applications:
AI sales enablement
Get a 360-degree view of clients with AI-powered seller recommendations. The view can be used throughout your enterprise as the “single truth” for client insights, and is coupled with product insights that are delivered through an AI-powered chatbot.
Contractual insights
Use annotators and machine learning for proactive client contractual relationship management and expansion management.
Risk insights
AI lets you identify how natural disasters and other disasters will impact your data centers and supply centers in real time.
Start your journey today
Work with the IBM Global Chief Data Office to implement these solutions and jumpstart your journey to becoming an AI enterprise.
IBM leaders on extracting value from data
Beth Smith, GM Watson Data and AI, and Inderpal Bhandari, IBM Global Chief Data Officer, talk about how companies can identify and scale projects where there is value to be gained from data.
Infrastructure for the end-user
Ed Walsh, GM of Storage, IBM, and Steven Eliuk, VP of Deep Learning, IBM GCDO, cover simplifying architecture to make data-driven insights easier to achieve.
Harnessing data starts with breaking down silos
Asim Tewary, Head of Data Science and Advanced Analytics, Verizon, and Seth Dobrin, VP and Chief Data Officer, IBM Analytics, stress the importance of businesses working together internally to truly transform into an AI enterprise.
Leading industry analyst weighs in
Brandon Purcell, Principal Analyst at Forrester Research, joins Caitlin Halferty, IBM Client Engagement Executive, to discuss the future of AI, data privacy, and more.
What is an AI enterprise?
IBM AI enterprise data platform architecture
Discover how your Chief Data Officer can help harness the power of data
It’s a slam dunk: the NBA and IBM
IBM Global Chief Data Officer Inderpal Bhandari created a program that analyzed data from NBA games and found patterns to help teams optimize their lineups and rotations during playoff games. Orlando Magic coach Brian Hill used insights gleaned from data patterns to slow down "Mr. Crossover," also known as Tim Hardaway Sr.
Contact our Global Chief Data Office
Embark today on your journey to become an AI enterprise.