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Much of the artificial intelligence and automation discussion going on in the enterprise recently involves something that, in most cases, actually isn’t artificial intelligence at all: robotic process automation, or RPA.

Artificial intelligence is transforming a variety of banking functions and allowing tech startups to compete with some of the largest banks for market share of key services, including lending and wealth management. Business news and media sites have been heralding the downfall of the banking industry as we know it because fintech companies are going to feel comfortable leveraging AI long before banks.

Our sector-wide research suggests that natural language processing (NLP) is one of the more common AI approaches in banking AI use-cases today. Sentiment analysis is a capability of NLP which involves the determining whether a segment of open-ended natural language text (which can be transcribed from audio) is positive, negative, or neutral towards the topic being discussed.

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.

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:

Investment in AI by banks and financial institutions for risk-related functions such as fraud and cybersecurity, compliance, and financing and loans has grown dramatically in the last half-decade compared to customer-facing functions.

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.

Event Title: The launch of our Emerj AI in Banking Vendor Scorecard and Capability Map 2019 report

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.

AI and machine learning have had successful applications in the financial sector even before the entry of the mobile banking ecosystem. AI is being used to leverage insights from data for financial investing and trading, wealth management, asset management, and risk management.