In a previous article, we introduced the basics of AI. Here we delve into AI’s impact on various investment industries from health care to automotive, and the opportunities and risks AI poses for investors. 

The good news is that investors with a diversified portfolio probably already hold AI-related companies. This is because successful companies do not sit idle when they can improve their business by exploring new technologies to remain competitive. The trick, however, is identifying companies which will have the most success with AI integration over the long term. There are also inherent investment risks, not limited to: user-adoption, consumer privacy protection, large upfront-capital expenditures and legal/regulatory considerations. This is why diversification remains paramount to disperse investment risk and isolation in unsuccessful companies. Learn more about what a good diversified portfolio looks like in this article. 

Here are a few examples of sectors found in a typical diversified portfolio that have benefitted from AI advancements, ultimately benefiting shareholders as well.

Technology is the most recognized investment sector for AI products, as it extends from suppliers of AI infrastructure (semiconductor companies like Nvidia and AMD) to US tech conglomerates such as Apple, Meta (formerly Facebook), Microsoft and Amazon which are heavily investing in or producing AI products. For example, Amazon announced an investment of up to $4 billion to rival Microsoft’s investment in ChatGPT developer OpenAI. As we discussed in our first article, ChatGPT is a generative AI product. Generative AI products generate (hence the name) text, images, sounds or animations and are used by companies to produce computer code, analyze documents, or even write essays, in response to user-prompted questions.  

Investment risks:- User-adoption of programs, future demand for AI infrastructure, client-data privacy concerns, and large-scale enterprise investment are typically required.

Although it is unlikely that AI will replace a human health care specialist anytime soon, it has shown promise in performing diagnostic and procedural support. AI can assess a patient’s history, symptoms, and even flag deviations from historical baseline health. AI built around machine or deep learning can improve the accuracy of its health care services over time. Robotic versions of AI are already being used to perform surgeries (robot-assisted surgery-RAS), with the guidance of health care professionals, since robots possess a steady hand and visual sensors far surpassing that of humans. As it stands now, health care AI is generally viewed as a complement to health care diagnosis and procedures versus a formal human replacement.  

Investment risks: Humans are inherently unique and face a myriad of unusual ailments. AI therefore will require substantial amounts of data to make accurate predictions and conclusions. In addition, there are regulatory restrictions around sharing confidential patient information, as data privacy protection remains paramount. There is also the innate aspect that patient care often requires a human touch, and AI may not be able to comprehend difficult emotional circumstances or know how to properly respond.

Robotic forms of AI used in automotive manufacturer assembly lines are well known. Robotics helps to perform repetitive and manual tasks more efficiently with less error than humans. Future automotive AI advancements can also extend to autonomous vehicles (self-driving) or even vehicle finance support. Autonomous vehicles combine AI subsets discussed in our first article: visual analysis (sensing the environment around it when driving), natural language processing (responding to written or spoken in car-prompts and questions), and deep learning to constantly learn and modify a user’s driving preferences to varying road conditions.

Investment risks: In terms of autonomous vehicles, consumer trust is a top priority. Despite humans making mistakes every day on the road, AI may be unable to properly perform under extreme weather conditions and adverse environments—putting the safety of others at risk. This makes the long-term hurdle of consumer trust and regulatory approvals a steep hill to climb.

The concept of financial robo-advice can extend to mortgage financing, insurance products, investment advice and much more. AI takes the information you provide and makes suggestions on potential financial solutions and products. Similar to the health care industry, AI is most often thought of as a complement to human advice from a financial advisor because AI can’t provide the human touch required for specialized financial situations and client-specific goals. 

Investment risks: The financial service industry inherently gathers and stores confidential client information so data privacy and consumer acceptance are large hurdles. In addition, robo-advice may not be sophisticated enough to cater to clients who can’t subscribe to commoditized financial products and require personalized advice and tailored solutions. 

AI can assist in cost optimization for logistical solutions. For example, it can identify the shortest and fastest route to deliver a package, or perhaps the most cost-effective one for less urgent packages. This path could include railway or truck logistics. Some view the benefits of AI for autonomous trucking as a massive use-case given limitations around 24-7 runtimes when operated by a human. 

Investment risks: AI is already being employed for various forms of logistical support. Autonomous transportation faces similar consumer trust and regulatory hurdles.

AI-based product customization and visualization tools can show customers what a house kitchen renovation could look like, or how they might look in a brand new pair of shoes. AI in a retail setting can even assist in forecasting product and service demand, automate inventory management, or evaluate customer feedback. The retail industry is vast in terms of product and service offerings and how AI might apply.

Investment risks: User-data privacy, relevance of products and services shown, large upfront company costs.

Consumer use-cases of AI in the energy space include smart meters that track energy consumption in your home, analyze data and optimize user preferences (such as lowering costs). From an energy company perspective, AI is used to predict future commodity prices, optimize production schedules, supply-chain logistics and inventory management. 

Investment risks: User-data privacy for retail products collecting sensitive client information. Large-scale industrial investment costs for energy companies employing new systems for optimization of production, supply-chain and inventory management.

Many are already familiar with tailored ads and marketing communications, perhaps to our disdain. Nonetheless, AI is able to gather detailed information about our search patterns, and predict products and services that we might be interested in seeing more of. 

Investment risks: Client confidentiality is paramount in the sense that some may not feel comfortable with providing personal data that is used to generate tailored ads and marketing communications, or having their personal data sold.