Great post, Karthik. Your posts are a staple for me to stay current on the AI landscape; I always appreciate the perspective.
I’ve been reflecting on ServiceNow’s move to these "AI-native" tiers (Foundation/Advanced/Prime). While collapsing the add-on fees simplifies the bill, I’m curious if you’re seeing a significant jump in the base license costs for customers moving to these new tiers?
The part that really has me thinking, though, is the shift in the "automation lifecycle." In the "good old" workflow model, we followed a build-once/run-forever approach. With this new AI-native architecture, do you think we’re moving toward a model where the logic is effectively being "designed" every single time the process runs via an AI agent? Or is there a layer of optimization happening where a flow is "designed" once by AI and then cached/re-used?
If it's the former, aren't customers essentially paying a "design cost" (via usage-based Assists/tokens) for every execution, rather than amortizing a one-time build cost? It feels like high-volume, simple automation that we already "solved" with standard, deterministic flows might be getting pulled into a recurring consumption bucket.
I'd love to get your take; Is this a genuine step forward in flexibility, or are we seeing a rebranding of standard automation into "Generative Orchestration" to justify the new metering?
Hey Banu, I appreciate your insights as well. Thank you for engaging in this.
I'm closely following the SaaS pricing model and its evolution with AI. It's clear that seat-based licensing is dying, and outcome-based licensing is probably going to replace it. We are in this odd moment where the providers are unable to price the outcomes yet. It makes sense for ServiceNow to take an interim approach along with SAP, but we'll see where the industry goes.
You are touching on a very important point of automation versus agentic AI. I think there is still applicability of a risk-based approach to deterministic outcomes where traditional workflows are still the best fit. Where there is an element of acceptance for probabilistic outcomes, agentic AI is going to continue to take more share.
It'll be interesting to see if deterministic vs probabilistic/stochastic outcomes are priced differently in the upcoming renewals.
Great post, Karthik. Your posts are a staple for me to stay current on the AI landscape; I always appreciate the perspective.
I’ve been reflecting on ServiceNow’s move to these "AI-native" tiers (Foundation/Advanced/Prime). While collapsing the add-on fees simplifies the bill, I’m curious if you’re seeing a significant jump in the base license costs for customers moving to these new tiers?
The part that really has me thinking, though, is the shift in the "automation lifecycle." In the "good old" workflow model, we followed a build-once/run-forever approach. With this new AI-native architecture, do you think we’re moving toward a model where the logic is effectively being "designed" every single time the process runs via an AI agent? Or is there a layer of optimization happening where a flow is "designed" once by AI and then cached/re-used?
If it's the former, aren't customers essentially paying a "design cost" (via usage-based Assists/tokens) for every execution, rather than amortizing a one-time build cost? It feels like high-volume, simple automation that we already "solved" with standard, deterministic flows might be getting pulled into a recurring consumption bucket.
I'd love to get your take; Is this a genuine step forward in flexibility, or are we seeing a rebranding of standard automation into "Generative Orchestration" to justify the new metering?
Hey Banu, I appreciate your insights as well. Thank you for engaging in this.
I'm closely following the SaaS pricing model and its evolution with AI. It's clear that seat-based licensing is dying, and outcome-based licensing is probably going to replace it. We are in this odd moment where the providers are unable to price the outcomes yet. It makes sense for ServiceNow to take an interim approach along with SAP, but we'll see where the industry goes.
You are touching on a very important point of automation versus agentic AI. I think there is still applicability of a risk-based approach to deterministic outcomes where traditional workflows are still the best fit. Where there is an element of acceptance for probabilistic outcomes, agentic AI is going to continue to take more share.
It'll be interesting to see if deterministic vs probabilistic/stochastic outcomes are priced differently in the upcoming renewals.
Good times, eh?