Evaluating the ‘Generational’ Impact of AI in Information Services

Video Transcript

Information services companies primarily disseminate data, analysis, and research to decision makers across a variety of end markets and they rely heavily on AI technology in order to do so.

With recent groundbreaking advancements in AI technology, we look to answer the question, how will AI impact the information services industry?

We believe the technology will have a long-lasting, positive impact on the fundamental performance of the sector. But the extent to which a company can leverage AI to improve its business and the relative risk of disruption requires a more detailed evaluation.

As artificial intelligence and gen AI are leveraged throughout the economy, we expect information services companies to benefit across three primary dimensions. First, we believe it will increase supply. AI already helps identify patterns in data, but we think enhancements to these technologies will support companies’ ability to find new correlations and allow for a “fail faster” approach to product development.

Second, we believe it will increase demand. We believe one of gen AI’s most valuable contributions to the broader data ecosystem will be its ability to make data more accessible.

And then lastly, we expect AI to also improve the operational efficiency of information services companies. These benefits include more efficient software development processes, enhanced customer service, particularly for highly repetitive, simple customer inquiries, and the streamlining of lower value sales and marketing tasks which we would expect to enhance salesforce productivity.

All told, the combination of increased supply, particularly via data, content, and product that is hard to replicate and increase demand for that same information, and increase demand for that same information has the potential to meaningfully enhance the value of both individual data assets and the companies that own them.

With these tailwinds at the sector’s collective backs, we then set out to come up with an evaluation framework to help us answer the two-sided question, who will benefit the most and who is most at risk?

In the end, we settled on eight primary considerations in our evaluation framework, which we split into two buckets.

At the data asset level, we believe how proprietary the data is, its accuracy, its richness, and its applicability will determine how valuable the asset is within this new AI paradigm determine how valuable the asset is within this new AI paradigm and the likelihood of competitive disruption over time. and the likelihood of competitive disruption over time.

At the product and/or company level, we believe a company's scale, brand, the stickiness of its products, and its data and technology infrastructure are the factors that will help extend or enhance the data assets’ competitive positioning in the long run. The combination of increased new product velocity enhancements to existing products, better, more data driven, or at least data comfortable customers, and an increase in the value of great data will drive stronger revenue growth for the sector.

Higher incremental margins on this revenue resulting from information service companies’ “build it once, sell it many times” model, plus AI-driven operational efficiencies should result in even faster earnings growth.

Ultimately, we believe that if you own high-quality, accurate, rich, and widely applicable data—and manage it via a well-thought-out data and technology infrastructure—you’ll be able to build sticky products that customers trust.