Synthèse
The instinct in many organizations is to treat AI spend as an IT line item, something to be minimised, benchmarked and squeezed for cost efficiency. SLMs deserve a different treatment. They are closer to R&D than infrastructure: An investment in converting accumulated institutional knowledge into a durable, defensible capability. That argument holds in the private sector too. A PE-backed portfolio company, a regulated financial services firm, a healthcare provider — each has years of proprietary
The instinct in many organizations is to treat AI spend as an IT line item, something to be minimised, benchmarked and squeezed for cost efficiency. SLMs deserve a different treatment. They are closer to R&D than infrastructure: An investment in converting accumulated institutional knowledge into a durable, defensible capability. That argument holds in the private sector too. A PE-backed portfolio company, a regulated financial services firm, a healthcare provider — each has years of proprietary operating data sitting idle in case files, transaction logs and service records.
An SLM built on that data is a way of turning a sunk cost, decades of operational history, into a forward-looking asset that compounds with every additional case it processes. Boards and executive committees that are still asking “what is our AI strategy? ” as a single, undifferentiated question are asking the wrong thing. The better question is: Which parts of our operation are rich enough in proprietary data and judgement to justify owning the model outright, rather than renting someone else’s?