Executive Brief: June 1

Innovation Insights 

1. Four types of Gen AI risk and how to mitigate them (Harvard Business Review)
Organizations are cautious about adopting generative AI due to concerns such as privacy, security threats, copyright infringement, and potential biases. The authors identify four key risks: misuse (deliberate harmful use), misapplication (unintentional errors), misrepresentation (intentional misuse of AI-generated content), and misadventure (accidental use of misleading AI content). Mitigation strategies include aligning AI use with organizational values, watermarking AI-generated content, creating controlled AI environments, providing training, validating AI outputs, and having damage control plans in place.

2. The state of AI in early 2024 (McKinsey)
In early 2024, generative AI adoption surged, with 65% of surveyed organizations regularly using it, nearly double the previous year’s rate. Organizations are experiencing measurable benefits such as cost reductions and revenue increases, particularly in marketing, sales, and product development. High-performing companies in AI are leveraging these tools across multiple business functions and are proactive in mitigating associated risks like inaccuracy and intellectual property infringement.

3. Why Google’s AI Overviews gets things wrong (MIT Technology Review)
Google’s AI search feature, AI Overviews, has been providing unreliable and sometimes bizarre responses due to inherent limitations of AI systems and errors in information retrieval and generation processes. The feature struggles with accuracy, especially with less common or satirical queries. Experts suggest that while enhancements are possible, AI’s reliance on probability and language generation means that some level of error and “hallucination” will persist, necessitating caution and clear communication about the feature’s experimental status.

4. Four singularities for research (One Useful Thing)
Ethan Mollick identifies four transformative “singularities” in academic research due to AI. These include changes in how research is written and published, the research process itself, the meaning and interpretation of research, and the subjects of research. AI accelerates the writing and publication process, transforms research methodologies with advanced text analysis and simulations, bridges gaps between academia and the public, and opens new research avenues. However, it also introduces challenges like maintaining research integrity and addressing biases.

5. AI for financial analysis and executive work 
A new paper, not peer-reviewed yet, investigates GPT-4’s ability to perform financial statement analysis and predict future earnings, finding it outperforms human analysts even without narrative or industry-specific information. GPT-4’s predictions match those of specialized ML models, generate valuable insights, and lead to superior trading strategies (SSRN).

AI is increasingly making high-level executive positions potentially obsolete due to its efficiency in analyzing markets, communicating, and making unbiased decisions. A notable percentage of executives believe that AI could replace much of the CEO role. Companies are already experimenting with AI-driven leadership, suggesting a significant transformation in corporate management where AI could enhance efficiency and democratize decision-making while raising concerns about accountability and human oversight (New York Times).