Evaluating Robust AI: Techniques Proven Effective in Assessing Evolving Technologies
In the rapidly evolving world of artificial intelligence (AI), making long-term investments requires a strategic and nuanced approach. A holistic framework, as opposed to relying on traditional valuation metrics, is essential for a robust understanding of a company's AI-driven value potential.
This approach emphasizes four key evaluation elements: data moats, adjacency, regulation, and long-term value creation.
Data Moats
Assessing the strength and uniqueness of a company's proprietary data assets is crucial. Companies that build large, diverse, and high-quality data lakes or accumulate intelligence through network effects create "data moats" that are difficult for competitors to replicate, enhancing long-term defensibility.
Adjacency
Considering how AI capabilities extend into adjacent markets or business units is another important factor. Horizontal and vertical integration can create synergies, lead to scalable AI applications, and multiply value creation beyond isolated projects.
Regulation
Evaluating the regulatory landscape surrounding AI is essential. Companies with mature AI governance frameworks and proactive regulatory alignment are better positioned to avoid costly disruptions and gain trust from customers and regulators.
Long-term Value Creation
Focusing on a broader set of value metrics, not just immediate financial returns, is vital. Leading indicators include improvements in operational efficiencies, enhanced customer engagement, and innovation velocity.
Practical metrics and approaches to gauge these aspects include tracking interim operational KPIs, using AI ROI ratios, monitoring competitive advantage development, and synthesizing multiple data points to form a holistic investment thesis.
For instance, Bank of America's virtual financial assistant, Erica, started as a front-end customer support tool and has reduced IT service desk calls by over 50%. This demonstrates the value of AI in operational efficiency improvements and customer engagement.
In industries with evolving regulatory pressure, regulatory inflection points are as important as product milestones for AI investments. Compliance with certain regulations, like the EU's AI Act, can be the difference between market access and market exclusion.
Getting in early, before a data portability mandate or AI audit requirement kicks in, can unlock temporary advantages and reorder market positions.
Achal Singi, Vice President at WestBridge Capital, focuses on investment and strategic guidance for emerging technology companies, particularly those in the AI sector. He emphasises the importance of smart investments that deepen a company's proprietary data edge, such as backing products that can mine insights from operational exhaust or capabilities that allow firms to license internal data externally.
The AI market is driven by novelty, but it's important to ground decisions in what holds up over time for long-term compounding. Smart investments should be made in areas where AI capabilities are adjacent and compounding existing strengths, rather than focusing solely on cost savings or new technologies.
Access to unique data is crucial for product differentiation in AI, as public data access is tightening and regulatory scrutiny over training data is growing.
In summary, evaluating long-term AI investments requires a holistic framework combining proprietary data and network effects (data moats), strategic expansion across related markets (adjacency), proactive regulatory and governance alignment, and multi-pillar value measurement capturing both leading and lagging indicators of AI impact. This comprehensive approach helps investors identify AI investments likely to generate durable competitive advantage and sustained value creation over time.
- Achal Singi, an investor in emerging AI technology companies, often stresses the importance of investments that strengthen a company's proprietary data edge, such as backing products that can mine insights from operational exhaust or capabilities that allow firms to license internal data externally in finance and technology sectors.
- The adoption of technology like AI in finance requires a strategic and nuanced approach in the long run, focusing on access to unique data for product differentiation, strategic expansion across related markets, proactive regulatory and governance alignment, and multi-pillar value measurement capturing both leading and lagging indicators of AI impact, to ensure a robust understanding of a company's AI-driven value potential for long-term value creation.
