Aevon.ai · Insights
Every post contains a specific number from a named primary source, a named mechanism, or an uncomfortable truth about enterprise AI that consulting firms won't say out loud.
S&P Global surveyed 1,006 enterprises. MIT NANDA reviewed 300 AI deployments. RAND analysed 65 data scientists. They all found the same thing. Here is what the data actually says — and why most people are reading it too charitably.
2,100+ GCCs now operating in India. 62% of organisations plan to use theirs as an AI hub (Accenture's own data). Most have no production AI system. The gap between GCC setup and AI delivery that nobody is naming.
The non-technical explanation of the serving framework that most mid-market ML teams have never deployed — and why its absence is costing you 10–20× on inference throughput. Stanford: inference cost dropped 280-fold. Most companies captured none of it.
The due diligence question most PE portcos cannot answer. RAND: 80%+ of AI projects fail — twice the rate of non-AI IT. The ones that survive become valuation assets. Here is how the conversation goes in the data room.
The reference post. Token costs, GPU utilisation, batching, quantisation, model right-sizing — all explained for the person who signs the infrastructure budget. Every claim sourced. Bookmark this one.
Most consulting firms are structurally incentivised to make you dependent. We built the opposite. The founding logic behind an implementation partner that is contractually required to leave — and why that changes everything about how we work.
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