Regarding willingness to pay, especially in the enterprise and highly regulated world, where any variability quickly raises yellow flags, based on my experience, this transition would require a clear top-down disruption.
It is not only about better pricing conversations, but about breaking through organizational inertia and the internal burden that often prevents companies from translating rich, “deep” insights into consistent, data-backed pricing decisions.
Thanks for pitching in here - if asking WTP questions is too delicate, I like to pivot to behavioral questions - how are you currently solving X + how much does that cost you/what are the consequences of not solving it. The other thing that works like a charm for me is "vision casting + upside" - if you implement this, all you need for positive ROI is "5 new deals" - do you think we can make that happen? Happy to exchange more thoughts and best practices at the webinar https://us06web.zoom.us/webinar/register/6917685513299/WN_nyod9T1IRsmZnqmisbEryA if you can make it and I've asked Fynn to check out your questions here too. Thank you for your valuable contribution to this discussion - loved it.
It made me think about whether hybrid models are a natural response to change in the face of AI cost uncertainty, or whether they can truly establish themselves as a value-anchored pricing choice.
When inference costs become more predictable, will pricing metrics naturally simplify, or has complexity already become part of the package?
I was thinking the same - is hybrid the combo that connects "best of both worlds" or an intermediate form before we find something more solid? For now, most of the companies I work with and analyze swear by it.
Regarding willingness to pay, especially in the enterprise and highly regulated world, where any variability quickly raises yellow flags, based on my experience, this transition would require a clear top-down disruption.
It is not only about better pricing conversations, but about breaking through organizational inertia and the internal burden that often prevents companies from translating rich, “deep” insights into consistent, data-backed pricing decisions.
Thanks for pitching in here - if asking WTP questions is too delicate, I like to pivot to behavioral questions - how are you currently solving X + how much does that cost you/what are the consequences of not solving it. The other thing that works like a charm for me is "vision casting + upside" - if you implement this, all you need for positive ROI is "5 new deals" - do you think we can make that happen? Happy to exchange more thoughts and best practices at the webinar https://us06web.zoom.us/webinar/register/6917685513299/WN_nyod9T1IRsmZnqmisbEryA if you can make it and I've asked Fynn to check out your questions here too. Thank you for your valuable contribution to this discussion - loved it.
A thought-provoking reflection. Happy to find!
It made me think about whether hybrid models are a natural response to change in the face of AI cost uncertainty, or whether they can truly establish themselves as a value-anchored pricing choice.
When inference costs become more predictable, will pricing metrics naturally simplify, or has complexity already become part of the package?
I was thinking the same - is hybrid the combo that connects "best of both worlds" or an intermediate form before we find something more solid? For now, most of the companies I work with and analyze swear by it.
So glad this one resonated. Hopefully I'll finally have the chance to meet you here https://us06web.zoom.us/webinar/register/6917685513299/WN_nyod9T1IRsmZnqmisbEryA#/registration :))) Happy weekend!