AI’s impacts on Thematic Investment strategy (Part 1)

Since the public release of OpenAI’s ChatGPT large language model at the end of 2022, the integration of Artificial intelligence (AI) in core business processes has been gaining cross-industry traction. With its growing significance and proliferation, AI is showing its disruptive potential to drive structural shifts in the way we live, work, produce, and collaborate. An unconstrained thematic investing approach to AI can help identify opportunities arising from these transformational processes, offering investors the chance to participate in cross-sectional growth prospects.AllianzGI have identified several use cases to show AI’s present and future impacts on the investable themes within  AllianzGI’s Thematica (3)Digital Life: Where there is cyber there should be AI-backed securityWith an increasing level of connection and with more services moving to a cloud environment, cyber security is growing in significance as complex and deeply integrated IT environments are exposed to new cyber-risks from multiple layers and access points.To protect companies from sophisticated cyber-threats, a multi-layered, holistic, scalable, and seamless cyber security approach is needed. With the integration of AI, features like automated threat detection, malicious patterns prediction, accelerated data protection, and risk-based conditional access can contribute to establishing a multi-layered defence of business processes, data, and IT infrastructure.For instance, two leading US-based companies that provide sophisticated cyber and cloud security services have developed solutions based on the idea of “zero trust”. Dependent on user identity, device security credentials, and access policies, access rights are granted or withhold. These real-time risk-based conditional access applications facilitate and speed up the steering of a multitude of individual access requirements from users. This, in turn, protects complex and thus more vulnerable IT environments from breaches.In parallel, cyber security solutions that help companies identify data leakages in AI applications or assist in designing and building safer AI environments are also gaining importance.The disruptive power of AI-powered cyber security solutions is also reflected in the projected double-digit market growth of this segment:04 (1)Source: AI in cybersecurity Market by Offering, Deployment Type, Security Type, Technology, Application, End User and Geography – Global Forecast to 2028. As of August 2023Health TechTrAIning for surgeons: “An experimental study of the National Center for Tumor Diseases Dresden (NCT/UCC) on the practical value of machine learning (ML) methods in abdominal surgery found that all of the four applied ML models outperformed at least 26 out of 28 human participants in pancreas segmentation demonstrating that ML methods have the potential to provide relevant assistance in anatomy recognition in minimally invasive surgery in near-real-time.”1In modern health tech, AI is deployed in the development of advanced surgical education. For example, a US based company that develops and manufactures robot-assisted minimally invasive surgery products, is working on building AI training applications.By collecting and evaluating information from millions of surgical interventions, and by comparing different surgical techniques, the company’s AI training applications will be able to make personalised recommendations during all steps of surgical learning, helping practitioners improve their skills both continuously and selectively.Not least, the AI-backed training of surgeons can also help to lower the likelihood of complications and improve clinical results, through training surgeons to use the most appropriate combinations of instruments and approaches in an intervention.05 (1)Source: Multidisciplinary Digital Publishing Institute: Artificial Intelligence in Surgical Learning. As of February 2023How AI-enhanced conversational agents can help control costs and deliver faster servicesAI-enhanced conversational agents can amplify personalised care through useful and time-saving features. For instance, a US-domiciled healthcare and insurance company, deploys Natural Processing Language (NPL) models – an area of AI – to understand callers’ requests, reply automatically, or redirect incoming calls to internally available resources and responsible departments. This can contribute to shortening the call duration and speeding up responses to patients’ questions.Automatically conducted real-time authorisations of patients’ insurance plans can also lead to significant cost savings, especially in terms of expensive labour time, compared to manual processes.Collecting and classifying data for an improved patient service experienceAI-enhanced conversational agents can collect and classify patient data gained from conversations and propose services that are tailored to individual needs and patient history. This, in turn, can significantly improve patient experiences.Based on the data and information gathered, AI can also predict certain conditions, leading to better clinical outcomes and further substantial cost-savings.Clean Water and LandAI in agricultureThe application of machine learning, a subset of AI, can help farmers create a cost-effective, fine-tuned seeding and spraying schedule that will optimise crop yield and quality, reduce weeds, while significantly diminishing the use of pesticides. The real-time differentiation and localisation of weeds permits a targeted deployment of herbicides and the shortest path to weed control. This is an essential contribution to food security.A global manufacturer of agricultural machinery and farm management software has developed a computer-vision and machine-learning backed precision agriculture system. This can contribute to a substantial reduction of pesticides, while supporting farmers in saving valuable resources and promoting better root health in crops.Higher adoption rates could drive further growth of precision agriculture marketAlthough the global precision farming market size is projected to amount to USD 20.84 billion by 2030, showing a staggering double-digit compound annual growth rate (CAGR) of 12.8%2 , there remains plenty of room for improvement.A still mediocre adoption rate of sophisticated information technology among global farmers is both hampering the advancement of precision farming market’s size and value, as well as offering investment opportunities to participate in the segment’s growth potential.This is especially true for Asia, which has the world’s largest amount of arable land3 and, in parallel, the lowest adoption rate for precision farming.06 (1)Source: McKinsey survey among 5,500+ farmers across the globe. 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