The Myth of the DIY AI Solution: Why Build vs. Buy Matters for Investment Research

Insights from our recent webinar "

Beyond Data Management: Accelerating Investment Opportunities in a Changing Market

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A common misconception in the investment world is that firms can easily build their own AI research capabilities using widely available large language models. Our recent webinar with industry leaders revealed why this approach often falls short.

The Data Engineering Challenge:

"Many clients underestimate these challenges," explained our AI expert. "Implementing AI on a larger scale, such as analyzing thousands of companies or screening the entire market for interesting investment opportunities, presents a much bigger data engineering problem."

While building a proof of concept might be relatively straightforward, scaling it becomes exponentially more difficult. The panelists emphasized that data engineering "is not an area where you can simply throw in GPUs and servers and expect everything to work magically."

Controlling Hallucinations:

Generic AI models can produce unreliable results when applied to specialized investment analysis. The solution isn't just about having a model but ensuring it receives the right information and follows structured logic.

"Most of the effort goes into curating documents and unstructured data stores," noted one speaker. This curation process – collecting, organizing, and preparing millions of documents in multiple languages – represents the hidden iceberg beneath the surface of successful AI implementation.

The ROI Consideration:

Investment firms must weigh the cost and time required to build internal solutions against partnering with specialized providers. One fund manager shared that their payback time was "maybe four months, maybe less" when working with an established AI research partner.

For firms evaluating the build-versus-buy decision, the webinar highlighted that successful AI implementation requires three key components: high-quality data sources, powerful language models, and sophisticated systems to connect them – a combination that's challenging to develop internally without significant specialized resources.

Interested in the full discussion?

Watch the complete webinar recording here to hear our expert panel's detailed analysis.