The industry's real problem was never value. It was distribution & monetisation. 

The challenges faced by Sell-Side Equity Research since the financial crisis have been well documented. I know - I had a front-row seat. Funding was squeezed by falling commissions and then by MiFID II. The rise of passive investing hollowed out the traditional client base. It became increasingly hard to justify a large Research department as a direct revenue generator. 

We can now add Artificial Intelligence to the list of perceived threats. Why employ armies of analysts to write Research reports when AI agents might do it faster and cheaper? 

Yet this diagnosis misses something important. Sell-Side research never really suffered a value problem. Investors always wanted good insights. The constraint was distribution. 

The traditional model rationed access. Analysts had only so many hours in the day to speak to clients or colleagues who had neither the time nor inclination to trawl through dense reports to find answers to specific questions, in real-time. Distribution depended on sales bandwidth, commission wallets and geography. The result was chronic under-utilisation of expensive intellectual capital. 

This is where AI can help. Of course, it can make analysts more productive. But its more immediate impact could be on distribution & monetisation - making research insights far more scalable. 

It is not simply a matter of loading PDFs into a generic chatbot that can then be questioned. A company initiation note from two years ago may answer an investor’s question better than a pre-results preview from last week - one of many nuances generic models struggle to capture. 

This is why I have worked with Engine AI to create Research Intelligence – an agentic solution that has the required domain knowledge to deliver the most relevant analyst insight for each question and questioner. Clients, bankers, wealth managers and traders can interrogate research almost as if speaking directly to the analyst. Ongoing user feedback refines Research Intelligence, improving accuracy and relevance. 

Written research is complemented by the underlying models and other relevant data such as news sentiment and consensus forecasts, providing users with the context needed to make better informed, timely decisions.   

That changes the economics. Research stops being a scarce service rationed by human bandwidth and becomes scalable core infrastructure. In that world, AI looks less like a threat to Sell-Side Research and more like the enabling technology that finally allows it to scale.