Executives worry about their businesses.
They often have to navigate, with limited resources, a stormy market made of customers, competitors, and regulators, and the interactions between all these actors make finding answers to business questions a complex process.
This article is based on a presentation given by Emerj CEO Daniel Faggella in Geneva, at the 2019 New Shape Forum: Weapons Governance for the Geneva Disarmament Platform. To learn more about Emerj's AI presentations and speaking, visit our presentations page.
Several key insurance carriers began to experiment with AI in the last decade, including Progressive, All-State, and State Farm. Although not as large as the banking and retail industries, the AI vendor landscape in insurance is growing.
Emerj Technical Advisor, German Sanchis Trilles, PhD, defines natural language processing as:
“...everything which is related to human language. If you have a system that needs to recognize what a human wrote, that’s NLP. If you have a system that tries to understand what a human said with his voice or with her voice, that’s NLP as well. If you want a system to speak and to do some speech synthesis, that’s NLP as well.
The insurance industry is dominated by large global firms that deal with thousands of customers filing insurance claims every day. Claims processing is a huge part of the insurance business process and improving turnaround time for each claim is critical to reducing operational costs at insurance firms.
This article is part 4 of a 7-part series called “AI Zeitgeist,” where we’ll be mapping out the details of AI adoption over the next 10 years and explore the critical changes in the AI ecosystem that business leaders need to understand.
McKinsey estimated that embarking on digital transformation to restructure value chains and drive R&D innovation across the pharmaceutical industry could be worth $50–150 billion of earnings before interest, taxes, depreciation, and amortization. In particular, machine learning is likely to continue finding a place in the pharmaceutical industry. Pharmaceutical companies have found applications for machine learning ranging from drug discovery to clinical trial retention.
The State of AI in the Asian Pharmaceutical Industry
AI seems to be making its way into the pharmaceutical space in Asia over the last two or three years, particularly in China and Japan. For the most part, the companies offering or using AI for drug discovery are just starting to acquire funding and talent. XtalPi seems to have the highest density of talent with a decent likelihood of being able to work with machine learning.
Deloitte estimated the size of the global travel and tourism industry at around $1.6 trillion in 2017. When adding in the indirect and induced economic contributions of related activities, the travel and tourism industry accounts for 10.4% of the world’s gross domestic product (GDP).
Adoption of chatbots—coded programs that can engage in some degree of conversation with human inputs, often through the help of artificial intelligence (AI) or machine learning—is undoubtedly a growing trend. There are thousands of chatbots in use today, on websites, messaging apps, and social platforms. It follows that bots would find a prominent place in the retail world.