AI Decision Engine
AI AND ML
of digital transformation initiatives will be supported by AI in 2019
of executives say AI will drive innovation in their organization
Artificial intelligence is quickly becoming a fundamental building block of business operations, providing real gains such as improved processes, increased efficiency, and accelerated innovation. Advances in machine learning technology have combined with high-performance compute options and an abundance of data to create a perfect storm for AI to transform organizations of all sizes.
Impacting business goals
Early adopters are weaving AI across the organization to address business priorities. Some machine learning projects tackle incremental gains to automate processes to create efficiencies. Others are transformational initiatives aimed at innovation and competitive differentiation. While there are numerous applications of AI, enterprises already realize great value from use cases that provide new experiences for their customers and drive business growth.
product and service innovation
While finding new ways to innovate is top of mind, more mature AI enterprises already use machine learning pervasively. They inform their product roadmap through intelligence extracted.
Drive research and discovery
AI-driven research unlocks new ideas in diverse industries, where machine learning capabilities help uncover new formulas, new materials, and new ways of thinking about age-old problems.
Enhance customer experience
Across industries including financial services, healthcare, retail, and media and entertainment, targeted recommendations have become important for personalization that delights and engages customers.
Improve customer service
AI technology such as conversational agents bridge the gap, modernizing the contact center to provide quick responses to customer calls and chat requests.
Goals for AI innovation
1 Enable product and service innovation
2 Drive research and discovery
3 Enhance the customer experience
4 Improve customer service
5 Improve healthcare outcomes
6 Increase efficiency and productivity
7 Improve security and compliance
8 Optimize supply chain operations
9 Improve decision making
Increase efficiency and productivity
Across business operations, pervasive use of machine learning to automate and streamline processes has resulted in increased efficiency and reduced costs.
Improve security and compliance
Use of machine learning across security, risk, and compliance use cases is a fast-growing trend especially in the financial services sector.
Optimize supply chain operations
From point-of-sale to freight-delivery management to forecasting demand, AI plays a pivotal role across the supply chain.
Machine learning-based predictions augment decision making across all functions of the organization and across use cases.
Getting started with AI
AI presents new opportunities to realize foundational gains such as efficiency and cost savings, as well as higher-value gains such as product innovation and spurring discovery and research. But how do organizations get started? For many, AI adoption begins by identifying workflows and business processes that suffer from low efficiency or where human mistakes abound. They consider all their data sources and existing data strategy. They determine the best cloud-based infrastructure and tools to scale AI. And last, they ensure that the right skills are on board for machine learning projects to be successful.
Understand business objectives
Advance your data strategy
Leverage the cloud
Enable your organization
Getting started with BI:=AI
BI:=AI has the broadest and deepest set of AI and Machine Learning services for your business. AWS and Intel have been working together for over a decade to deliver the most comprehensive set of resources, tools, training, and services. On behalf of our customers, we are focused on solving some of the toughest challenges that hold back AI adoption.
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