Ayn de Jesus

Ayn serves as AI Analyst at Emerj - covering artificial intelligence use-cases and trends across industries. She previously held various roles at Accenture.

Articles by Ayn

30 articles
Augmented Reality in Healthcare - Current Applications

Augmented Reality in Healthcare – 2 Current Applications

Most people may associate augmented reality (AR) with smartphone apps and video games, perhaps even Pokemon Go. But augmented reality is gaining traction in a variety of industries, including manufacturing and healthcare.

Big Data Analytics in EcOmmerce - Data Platforms and Artificial Intelligence

Big Data Analytics in eCommerce – Data Platforms and Artificial Intelligence

Digitally-native eCommerce businesses are used to working with their customer data in order to write copy for marketing campaigns, run PPC ads, calculate customer lifetime value, and make decisions based on core metrics within CRM dashboards.

Artificial Intelligence in the Food and Beverage Industry

AI in the Food and Beverage Industry – 3 Current Use-Cases

Although it's apparent that AI development is slow-moving in the industry, food and beverage companies may be able to use AI for food processing, in particular:

Big Data in Banking - AI and Data Management Use-Cases

Big Data in Banking – AI and Data Management Use-Cases

Banks are in one of the best positions for leveraging AI in the coming years because the largest banks have massive volumes of historical data on customers and transactions that can be fed into machine learning algorithms. We recently completed our Emerj AI in Banking Vendor Scorecard and Capability Map in which we explored which AI capabilities banks were taking advantage of the most and which they might be able to leverage in the future.

Artificial Intelligence in Life Sciences - Vendor Landscape and Use-Cases

Artificial Intelligence in Life Sciences – Vendor Landscape and Use-Cases

Life sciences companies are likely to begin experimenting further with AI in their workflows in the coming years, but they face challenges in AI adoption due to strict regulations. Machine learning has a "black box" problem, meaning that it's in many cases impossible to know how a machine learning algorithm comes to its conclusions.

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Artificial Intelligence in Epidemiology – Current Use-Cases

Artificial intelligence is changing the way healthcare networks do business and physicians perform their routine activities from medical transcription to robot-assisted surgery. Although the more mature use-cases for AI in healthcare are those built on algorithms that have applications in various other industries (namely white-collar automation), we believe that in the coming three to five years, AI solutions for healthcare will become increasingly specialized to individual use-cases.

Predictive Maintenance in Oil and Gas - Current Applications

Predictive Maintenance in Oil and Gas – Vendors and Use-Cases

The International Energy Agency’s latest annual gas market report, Gas 2018, estimated that global gas demand could reach more than 4,100 billion cubic meters (bcm) in 2023. This is an increase from 3,740 bcm in 2017. Greater gas demands mean more oil rigs, and the machines on these rigs break down.

The Internet of Things in the Oil and Gas Industry - Current Applications

Internet of Things in the Oil and Gas Industry – Current Applications

According to McKinsey, global gas demand is expected to reach 4,503 billion cubic meters (bcm) in 2035, showing a more than 1% annual growth from 3,736 bcm in 2017. Asia is expected to be the biggest consumer of the resource at 47%, followed by the rest of the world (24%), the Middle East (16%), and the United States (14%).

Big Data in Healthcare - 4 Current Applications

Big Data in Healthcare – 4 Data Management Software with AI Capabilities

The healthcare industry is perhaps second only to finance when it comes to the sheer amount of historical data available for use with artificial intelligence. Data from EMRs, insurance claims, clinical trials, and drug research and development can all be pulled into a machine learning algorithm to generate insights on patient behavior, patient risk, and effective treatments for a variety of conditions, among a variety of others.

Machine Learning in Asian Pharmaceuticals - Current Applications

Machine Learning in the Asian Pharmaceutical Sector – Current Applications

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.

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