By Renato Lima, Chief Commercial Officer
Thirty-two percent of financial institutions have recently walked away from buying at least one piece of software because agentic coding tools made building it themselves look viable, according to research from McKinsey. The obvious reason is that coding agents have made writing a first version of almost anything fast. Really fast.
What happens after version one ships is the part that matters.
A coding agent can write a matching engine over a weekend. It cannot write 150+ financial counterparty connections, tried and tested across decades of real market conditions. The trust that underpins our connectivity took Pirum over 25 years to build. Every one of those connections involved a legal entity, a workflow quirk, a message format probably nobody else uses, and a permissioning model somebody had to get right the first time and keep right ever since.
You can copy the matching logic. But, as in our social lives, what is virtually impossible to copy is relationships, and the relationships are what matter most.
Building v1 is the cheap part today. The expensive part was always what comes after the MVP. The requirements keep evolving with every counterparty that changes its message format, every regulator that redraws the reporting rules, and every corporate action that behaves slightly differently to the one your test data anticipated. That work does not stop once an in-house tool ships. It repeats and evolves indefinitely, whether or not the team that built the tool is still around to maintain it.
On Pirum’s platform, that cost is spread across the network. Build it yourself, and you unfortunately carry all the cost.
According to the same McKinsey report, 44% of enterprises say they are scaling AI this year. Only 37% can attribute that scaling to an actual EBIT number, and that share remains unchanged from last year.
That gap is, from what I have seen, a workflow problem. Firms are bolting AI onto processes built for a slower, more manual world, then wondering why the productivity gain never reaches the P&L. The firms closing the gap are the ones redesigning the workflow itself.
What we are selling is a data and connectivity layer that has been accumulating counterparty behaviour for over a quarter of a century, and that gets stronger every time another institution joins it.
This is AI-powered securities finance, across the full trade lifecycle. A coding agent can offer you a weekend’s head start on a model, but it lacks the other twenty-five years: the network, the accumulated expertise and the trusted relationships. That is what will eventually separate the institutions pulling ahead from the ones still catching up.
If you want to talk through what AI-readiness looks like for your own operation, get in touch.
Also included in the Quarterly Panorama:
Ben Challice, CEO, on Vision 2030, and why scale and connectivity are now table stakes, with the real advantage coming from turning network-wide data into intelligence and automation.
Zoë Balkwell on pre-trade intelligence, and why the real value of AI is surfacing opportunities a desk would never have known to look for.
Ray Wong, Director of Product, AI & Data, on why there has never been a better time for Pirum to launch AI, and why infrastructure, people, platform, deep securities finance knowledge and client-validated use cases make Pirum unusually ready for this moment.
The Panorama also includes a regulatory and events calendar.