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Artificial intelligence (AI) has been creating a lot of volatility across industries around the world. And in 2024, it is still creating so many waves that are rocking the boat for investors looking to ride the AI wave.
It’s easy to see the potential for AI to create massive disruption, but it can also create massive disasters if you don’t know how to sail the stormy seas.
My four rules here will help you cut through the noise and hype — in 2024 and beyond.
1. Mastering the regulatory context
These new developments will undoubtedly cause some turmoil in the industry, regardless of whether they coalesce into a full-fledged upheaval. In response, governments are rushing to establish new regulations to curb unchecked profiteering by weighing benefits against risks.
For instance, the AI Act, a proposed legislation for the EU, aims to taxonomize systems based on their riskiness, measured with social scoring, biometric ID and mass surveillance. These rules on transparency and accountability will therefore have a direct impact on the adoption of the systems, which in turn will affect the profitability of companies using them.
This would mean that countries with different policies would create challenges for firms operating across the world, however committed they might be to journeying with machine learning (ML) models. Given the variations in philosophical outlook and political frameworks, I suspect it could take a long time for the Global Partnership on AI(GPAI) and similar international initiatives to harmonize regulatory standards.
As an investor, it’s crucial to follow these international regulatory changes closely, as the rapid pace could lead to significant risks.
Related: AI Is Changing How Businesses Recruit for Open Roles — and How Candidates Are Gaming the System
2. Optimizing return on investment
Industries exist to be disrupted — efficiently and at scale. All the fashionable buzzwords promise substantial rewards that attract major investments.
However, you can’t discount the fact that it’s all about future rewards. Implementing AI technology requires very large initial investments—you need infrastructure, software, and all sorts of skilled talent. Take Nvidia, for example; its massive investment in semiconductors and AI technologies has led to a remarkable 239% increase in its capitalization by 2023. This highlights how initial investments can generate big returns, but only if adoption is widespread and competitive advantage is clearly demonstrated.
Indeed, that may still be wise so as not to fall into the trap of buying into hype and paying over the odds. That popped the bubble in the dot-com era in the 2000s.
Examining financials and market metrics — such as the P/E ratio, for example, to determine whether stock price reflects earnings — provides necessary navigational guidance. Additionally, analyzing technical indicators, such as candlestick charts to track trends, daily volatility and market sentiment toward the company can further enhance your understanding of market dynamics.
3. Technological infrastructure
Given AI’s substantial data demands, investments must establish robust technological foundations to fully capitalize on the technology’s potential.
AI relies on feedback from large-scale data sets, requiring companies to have the infrastructure to efficiently manage, store, and process this data. Is the hardware scalable and flexible? Is it secure?
Providing your own computing infrastructure isn’t always an option. AWS, the Amazon Web Services cloud computing platform, and Microsoft’s Azure can run basic AI-powered services. This allows organizations that don’t want to invest in in-house IT to exploit economies of scale, improving efficiency and innovation. Choose the right partners and review providers.
Data-hungry AI requires strong security, especially if it uses public networks. Ensure your AI systems incorporate advanced encryption, access controls and multifactor authentication to protect against potentially catastrophic cyberattacks.
In other words, your tech stack needs to respond to and be able to sustain AI’s data demands and nuanced needs, and this is where the toughest battles for future success will be fought.
4. Talent acquisition and development
Simply put, AI will only be as effective as the humans who help create it. In an era where specialist, hard-to-fill skills are in high demand, how are recruitment and retention being evaluated? Are efforts successful in attracting and retaining the best minds in specialized fields such as machine learning and data science?
By 2024, talent shortages in AI could make hiring highly competitive, with the AI recruitment market potentially reaching over $1 billion by 2030. To build your team, offering top rewards, captivating projects and a culture of innovation is essential.
Beyond recruitment, providing continuous education to develop skills is just as important, given the rapid changes in the world of AI. Investments must focus on ongoing training to keep talent at the cutting edge. Look for evidence of leadership in learning programs, mentoring, and other upskilling opportunities.
However, even the best human capital strategies can face challenges. Pay close attention to signs of personnel churn or skill set stagnation; these indicators can be as informative as the bottom line.
Today, wise investments in AI require assessing:
- Evolving regulations: monitor for compliance needs across geographies.
- Timelines to profitability: temper hype with realistic runway projections.
- Technical foundations: data, cloud and security elements should be robust.
- Human talent pipeline: recruitment, retention and skill-building are paramount.
When it comes to AI, the key is to be prudently evaluative, avoiding the extremes of pessimistic fatalism and blind optimism currently competing for attention. Through these lenses, you can act strategically and with a long-term focus. The future moves fast, but the insights above will help you time your investments in markets wisely.
