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For a tech subsector on everyone's proverbial lips' at the moment, things can hardly be described as 'easygoing' for the AI vendor startup market in 2023.

PwC (PricewaterhouseCoopers) is a professional services firm that provides a range of consulting, audit, and advisory services to clients worldwide. Per its annual report, the company's revenue was USD 50.3 billion, employing 328,000 people in 152 countries.

DHL is a German logistics company that offers parcel delivery, express mail, freight forwarding, and third-party logistics. Its parent company is Deutsche Post (‘DPDHL’), the world’s largest logistics enterprise, operating in over 220 countries and employing more than 510,000 people worldwide. 

Bank of Montreal (BMO) is a North American bank that provides personal and commercial banking, global markets, and investment banking. The bank caters to over 12 million customers with over 1000 branches. Per the bank's 2022 Annual Report, BMO earned approximately CAD 33.71 billion in revenue.

Although not often regarded as a technological first-mover, the insurance industry has recently seen robust, even rapid, adoption and deployment of AI capabilities, particularly in those related to natural language processing (NLP). 

Traditional investors have several mechanisms for achieving their desired portfolio risk level. One of the most common methods involves the use of correlations. The more well-known 60/40 and 80/20 models distribute investments in a specific ratio of equities/bonds. These models are based on correlations between markets.  

Like many other economic and industrial sectors, AI is beginning to transform operations across life sciences. In a 2021 survey of life science organizations published by the International Data Corporation (IDC), respondents cited a 65% average increase in AI spending in their organizations over the next year.

While machine learning might be as close to a household name as any AI capability in 2023, any serious historian will tell you the technology itself is nothing new. The term 'machine learning' was coined in an IBM study on computer gaming and AI dating back to 1959.

A paradigm shift is happening in the manufacturing industry. Advancement in big data and machine learning is changing traditional manufacturing processes into the era of intelligent manufacturing. The concept of what gets called "industry 4.0" encourages the use of smart sensors, devices, and machines – going beyond the motives of collecting data about production. 

This article is sponsored by Arkestro, and was written, edited and published in alignment with our transparent Emerj sponsored content guidelines.