Proprietary AI Model: A $40 Million Warning
Thomson Reuters spent $40 million to build its own AI model. The lesson for operators is not to copy the investment. It is to understand the conditions that made ownership rational.
Read more →Thomson Reuters spent $40 million to build its own AI model. The lesson for operators is not to copy the investment. It is to understand the conditions that made ownership rational.
Read more →Verizon’s AI now handles most inbound consumer calls and chats. The real lesson is the multi-year data consolidation that came first.
Read more →A $16,000 local AI cluster can keep data private. That still does not make it a sound business decision. Start with the workload, not the hardware.
Read more →The second largest GDPR fine in history was not about a data breach. It punished automated decisions nobody explained. Operators everywhere should take notes.
Read more →Customers’ AI assistants will soon draft their support messages. Fluent tickets are about to stop being a signal of anything.
Read more →New data puts a number on the resolution gap: fast AI answers, unresolved issues, and trust lost at the handoff. Here is how to actually close it.
Read more →Enterprise software is repricing from seats to meters. AI usage pricing will expose the true cost of every failed support interaction. Smart support leaders will start tracking cost per resolution now.
Read more →AI now closes 7 in 10 support sessions. The tickets that remain are the hard, physical, visual ones, and most support teams were never built to run a queue made entirely of them.
Read more →Your AI vendor reorganized its safety team on the way to an IPO. Your customers never got the memo. Accountability stays with you, and it starts with seeing the real problem.
Read more →OpenAI now earns most of its revenue from enterprises. Yet almost none of that enterprise AI spending touches the physical support problems that cost companies the most. Here is where the budget should go next.
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