From boosting productivity to radically transforming industries, the vast field of AI applications presents a complex panorama that is difficult for investors to fully comprehend. This complexity is an excellent opportunity for Investor Relations Officers and senior management to explore how to enhance their equity story through the adoption of AI. In this article, we aim to provide you with clues for such reflection. We hope that industry examples and investor questions can help you initiate internal discussions on crafting and communicating a robust AI implementation strategy that aligns with your company's positioning and vision.
What is AI?
Artificial intelligence is an umbrella term that covers different methodologies that enable machines to reason, learn, and act. Generative AI, currently gathering much attention, specializes in generating new content based on patterns it has learned from existing data. To achieve this, generative AI uses different techniques of one of the AI subfields, machine learning. Other subfields of AI are natural language processing, robotics, computer vision, expert systems, etc. Unlike human learning which encompasses cognition, emotions, and social interactions, AI's learning typically relies on vast amounts of data. Paradoxically, what makes current AI extremely powerful (its vast knowledge built on historical data) also determined its limitations. It has a limited capacity to adapt to unfamiliar situations, a trait where humans still excel.
Why such a frenzy among investors?
AI is top of mind for many investors because it has the potential to drive multiple aspects of investment return. It can ignite productivity gain across industries, alter competitive landscapes, and support some of the investment megatrends.
For most investment managers, AI represents a means to enhance productivity. Schroders estimates that even if AI displaced a conservative 15% of the work done by knowledge workers, it could generate an addressable market of around USD 450bn annually. Industry leaders think the same, the latest Deloitte survey on corporate AI adoption found that close to 80% of the respondents view AI’s main goal as cost reduction.
Additionally, McKinsey predicts that AI will catalyze transformation across various corporate functions. They estimate that about 75% of the added value will fall across sales & marketing, operations, R&D, and software engineering.
Finally, managers, like Schroders and ARP Investments, see AI as a partial solution to the aging population. Developed countries like Japan, and emerging countries like China, could leverage AI to become more productive and offset the loss of workers. For ARP Investments, AI will also accentuate other megatrends such as the rising wealth gap, the rise of Asian nations, and a new wave of technological progress.
Where do investment managers see investment opportunities?
Most investment managers express caution about the rally in AI stocks. They admit that the long-term impacts of AI are difficult to predict, like most new technologies. That is why they prefer to invest in the upstream of the AI value chain, or what Wellington Management qualifies as “enablers”.
Franklin Templeton, Capital Group, Allianz GI, Wellington Management, Ninety One, and MFS all favor companies that are at the bottom of the technology stack (i.e. the one operating in semiconductor materials, semiconductor equipment, foundry) or larger tech companies as they have the resources and data capabilities to invest in AI technologies (cloud computing, AI models). Research by Man Group observes that this round of technology stock rally was more selective. Based on their NLP analysis themes like AI, digital transformation, and IOT were driving stock selection.
Gone past the low-hanging fruits, investment managers have been engaging with companies across industries to assess how they plan to leverage AI for profit growth. Industries like healthcare and life sciences are frequently mentioned as promising fields. MFS and Capital Group highlight how the application of AI can reduce the time needed for drug discovery, lower research costs, increase the speed to market, and help doctors make better diagnoses. Another industry is education, for example, a few investment managers cited Chegg, whose shares plunged 48% in early May after the company indicated that ChatGPT hurt its customer growth rate. Neuberger Bergman insists on the promising role of AI in the mobility sector, specifically in establishing a wide network of autonomous vehicles, traffic systems, and emergency services. AI could also be useful in high-risk industries, such as mining and construction, by using more robots for dangerous tasks. Another industry is advertising, where AI content creation facilitates creative productivity, freeing employees from mundane tasks, and ultimately increasing revenues. The IT service industry should also change. J O Hambro foresees IT outsourcing shifting from human-centric models to machine-based models. Other investment managers think that cybersecurity will grow further as AI will trigger more frequent and more complex cyberattacks. For some industries, adopting AI may be key to corporate survival, Franklin Templeton highlights the risks in sectors driven by manual processes and consumer content with low barriers to entry as highly vulnerable to software disintermediation.
The macro picture, too, holds exciting prospects. AI's influence on supply chains in Asia is a notable consideration. Neuberger Bergman sees Thailand becoming a hub for network equipment production, Malaysia for semiconductor assembly, Singapore for semiconductor testing, and Vietnam and India for IT services.
What are the unknowns/concerns?
AI's adoption brings concerns at both macro and microeconomic levels.
At the macro level, Franklin Templeton and MFS question the impact of automation on Jobs and services. MFS expresses concerns about AI's effect on the middle class and knowledge workers, and how it may lead to social upheaval. The benefits of AI may be monopolized by a select few, hence worsening the wealth gap. Capital Group highlights that building an effective regulatory framework will be complex. The industry is at such an early stage that there is not even agreement on which type of risks to prioritize.
At the micro level, AllianceBernstein insists on the need for real competitive advantage when corporates adopt AI. It warns against mere declarations and blanket assumptions without actual economic benefits and emphasizes on how AI fits into a company’s existing business models and industries. On the ESG front, Fidelity International highlights that checks and balances systems are yet to be established in AI-focused corporates. It mentions the lack of AI risk committees, internal audit teams, or incident reporting procedures.
How can corporates gain a competitive advantage using AI?
We found that investment managers focused on assessing how corporates are strategically planning to integrate AI into their operations.
Capital Group believes that thoughtful AI implementation will be key to competitive advantages in various functions, such as research, product design, and logistics. MFS lays out general questions for corporates such as: “How are they (corporates) thinking about AI? What are they doing? How will it benefit them? What is your AI roadmap? How will it be executed? Who are the vendors that you want to work with?”
Going a step further, GSAM writes that corporate leaders who are considering using generative AI in their business must see it as an integral part of their strategy, instead of a side concern that can be outsourced or delegated. They highlight key considerations for such a strategic plan as competitive surveillance, business model adaptation, risk mitigation, ethical considerations, and governance. The actual questions can be read here. To get a sense of what your industry peers may be doing, the AI adoption matrix produced by McKinsey shows the corporate functions in selective industries that have been the most prone to AI adoption (available on p. 202 of this report). As the field of possibility for AI adoption is wide, a way to start could be to map out your competitive advantages and weaknesses across your corporate functions, then assess where cost and benefit would be the most beneficial for your growth strategy. And if you look to include ESG considerations, Alphinity Investment provides a high-level, yet useful, materiality mapping of AI-related ESG considerations across sectors.
In general, we found that Chapter 4 of The State of AI in the Enterprise report, published by Stanford University, is a good source of information to initiate reflection. For example, it details the barriers to both starting and scaling AI projects, including proving business value, securing executive commitment, choosing technologies, managing risks, obtaining data, and designing proper implementation.
All in all, as AI stands to become a potent force driving change across industries, influencing investment decisions, for some investor relationship officers, gaining an understanding of AI will be more than an opportunity, but a necessity. Articulating a thoughtful AI integration strategy within your equity narrative, it's not just about showcasing to investors the progressive nature of your management; it's about revealing the quality of your internal communication, the depth of your organizational strategic thinking, and a commitment to innovation that will set your company apart in the long-term.
We hope this article gave you fruit for thoughts on how your organization can approach AI adoption in the context of investor communication. Irostors is the first SaaS platform that helps IROs and CFOs to simplify investor targeting through IR data. Feel free to contact us to discuss the solutions we offer.
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Capital Group GSAM Wellington Management Franklin Templeton Fidelity International AllianceBernstein Neuberger Berman Schroders MFS Man Group American Century Investments Ninety One J O Hambro Alphinity Deloitte ARP Investments
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