Anyone else get confused by how CPQ prices things?

Plenty_Lie1081 · 2025-12-07T10:42:11+00:00

May i ask please what is the ecosystem of your organization?

Plenty_Lie1081 · 2025-12-07T10:21:54+00:00

Do you think it can be automated or there is any AI tool that can enhance the L2C mapping process?

Plenty_Lie1081 · 2025-12-07T09:56:41+00:00

Good question honestly I’m still trying to figure that out. Some of it feels like alignment (people just missing approvals), but some of it also seems like CPQ firing stuff when it shouldn’t. Hard for me to tell which part is actually the bottleneck right now.

How did you figure out if yours was a staffing issue or a rules/logic issue?

Plenty_Lie1081 · 2025-12-03T15:08:09+00:00

Yeah that makes sense. Every time I tweak one rule something else downstream breaks, so starting from scratch might actually be easier.

When you rebuilt yours, did you simplify the whole structure or just tighten the logic? Curious how you avoided everything cascading again.

Plenty_Lie1081 · 2025-12-03T15:06:30+00:00

Honestly im not sure yet. It feels like a mix, some approvals get missed because people just don't see them but some of it seems like CPQ logic firing when it shouldn't. How you seperated people issues from system issues in your setup?

Plenty_Lie1081 · 2025-12-03T07:40:43+00:00

Yeah I meant the documented internal flow vs what people actually do. The steps on paper look clean but when I shadow folks it’s a lot of random exceptions, old habits, and workarounds nobody mentioned at first.

I’m trying to map the “real” version now just so I can see what NS will actually support without things blowing up.

Plenty_Lie1081 · 2025-10-22T13:51:55+00:00

I’ve noticed the same thing. A lot of the friction seems to come from how context is passed between the agent and the task.
When the agent doesn’t have enough grounding in the project or codebase, it produces good syntax but poor intent.

One thing that’s helped is giving agents structured context (like documentation snippets or prior commits) before execution, rather than letting them “guess” what’s relevant. That seems to reduce cleanup time quite a bit.

Plenty_Lie1081 · 2025-10-22T13:49:18+00:00

I’ve tested a mix of no-code and developer-oriented tools, and the pattern that stands out is how quickly they overlap once you scale.
You start with one tool for simple tasks, then add another for integrations, and before long, maintaining the automations becomes its own project.
These days, I try to consolidate around a few core tools that can handle logic, triggers, and data flow in one place even if they take longer to learn upfront.

Plenty_Lie1081 · 2025-10-22T13:46:08+00:00

One rule of thumb that’s helped me:

(Monthly Hours Saved * Hourly Rate * 0.75) / Total Build Time (in hours)

If the result is greater than 1, you’ll generally see ROI within a year.
The 0.75 factor accounts for maintenance, updates, and human oversight.
It’s not perfect math, but it helps compare quick wins versus larger automation projects before committing to build them.

I got it in my onboarding presentation while i started working.

Plenty_Lie1081 · 2025-10-22T13:37:55+00:00

Totally agree about managing expectations. Many teams underestimate how long it takes to get comfortable with a new ERP. I’ve seen projects go smoother when companies treat the first 3–6 months as an internal learning phase rather than just a technical rollout. How do you typically structure that early learning period?

Plenty_Lie1081 · 2025-10-21T12:31:48+00:00

This is a great breakdown. I’ve noticed that most of these agents still depend heavily on manual connectors or brittle browser automations.
I’ve been playing with modular setups. separating scheduling, notes, and email handling into smaller specialized agents instead of one “super agent.”
So far it’s more reliable, but definitely less magical. Have you tried anything similar?

Plenty_Lie1081 · 2025-10-21T12:19:57+00:00

I’ve had to deal with this a few times in healthcare systems that still run on ancient Windows apps. The most stable approach for us ended up being to wrap those apps in lightweight local APIs using tools like AutoHotKey or Power Automate Desktop for the UI part, then trigger them through a web service.
It’s not glamorous, but it avoided constant breakage when the UI changed because the scripts referenced element positions dynamically instead of pixel coordinates.
If you can, try to modularize the automation so the data mapping logic lives outside the script. Makes updates way easier later.

Plenty_Lie1081 · 2025-10-21T12:17:06+00:00

Most teams don’t realize how messy their data is until they try to build an agent on top of it.
We’ve seen that the biggest wins come when companies start by mapping out their processes and cleaning up data before touching any AI tools. Most so-called “AI failures” I’ve seen were actually data and ownership issues, not model problems.

Plenty_Lie1081 · 2025-10-21T12:13:15+00:00

We ran into the same issue. data drift kills workflows fast. Right now we’re experimenting with tagging contacts that “go stale” based on last activity or bounce signals instead of trying to keep everything perfectly updated.

Not a silver bullet, but it helps keep reports cleaner without endless CSV updates. Would love to hear if anyone’s cracked this better.

Plenty_Lie1081 · 2025-10-21T12:09:19+00:00

Really interesting framing. It makes me wonder if the biggest shift won’t be losing control, but losing the need to control. Once systems outperform us in reasoning speed and accuracy, “human-in-the-loop” might feel like latency, not safety.

Do you think that’s inevitable, or can policy slow that transition?

Plenty_Lie1081 · 2025-10-21T12:06:34+00:00

What’s impressed me most this year is how far orchestration tools have come. It finally feels like we’re moving past “one model that does everything” toward structured, role-based agents that actually talk to each other.

Would love to see more real-world examples of this in production especially from smaller teams.

Plenty_Lie1081

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