Campfire's V40 release embeds AI directly into Opportunity Management and CPQ, so sales leaders can ask questions of their own pipeline, quote, and program data in plain English and get answers in seconds instead of days. There is no report request queue, no analyst backlog, and no new headcount required. You use the system as you normally do, build up your data, and when you need insight, you simply ask.
McKinsey estimates knowledge workers spend about one-fifth of their time, roughly one day per week, searching for and gathering information (McKinsey, The economic potential of generative AI, 2023).
Automotive suppliers feel the reporting problem harder than most because cost data, BOMs, and RFQ history live in incompatible systems while OEMs modernize their procurement and data stacks faster than suppliers can keep up.
Campfire V40 lets sales leaders ask questions like "which quotes have the thinnest margins?" or "which opportunities are going stale?" and get direct answers, plus follow-ups, in natural language.
Resource-constrained teams get analyst-grade reporting without hiring data specialists, report writers, or standing up a BI infrastructure.
AI reporting is only as good as the data feeding it. McKinsey's State of AI research finds data quality and siloed systems are among the top blockers keeping most companies stuck in AI pilots, which is why Campfire's unified OM and CPQ data backbone matters (McKinsey, The State of AI, 2025).
If you run sales or commercial operations at a Tier 1 or Tier 2 supplier, your answers are scattered by design. Quote costing lives in spreadsheets, BOMs sit in PLM, RFQ history is buried in email and shared drives, and pipeline status lives in a CRM that was never built for program-based automotive business. Meanwhile, your OEM customers are modernizing their procurement and data stacks faster than most suppliers can respond.
Reporting is slow not because your people aren't working. It's slow because the data is scattered. And the numbers back this up:
McKinsey estimated that knowledge workers spend about one-fifth of their time, or one day each work week, just searching for and gathering information.
For a lean commercial team supporting multiple OEM programs, that's an entire team member's worth of capacity spent hunting for data instead of acting on it.
The short version: it turns your reporting process into a conversation. You use Campfire the way you always have. Opportunities, quotes, cost models, and program data build up in the system as part of daily work. When you want insight, you ask in natural language:
Is my pipeline healthy? Which opportunities are going stale, and where are things stuck
Which quotes have the thinnest margins, and what should we target for our first round of cost reductions?
And the roadmap goes further. As Campfire's AI capabilities expand beyond the initial V40 release, you'll be able to ask predictive questions too: which quote has the best chance of getting awarded, what the risk looks like on a soon-to-launch program, or why the last launch hit delays and where the bottleneck was.
Got a follow-up question? Ask again. The AI works across the unified V40 Opportunity Management and CPQ data model, so answers draw on the full commercial picture, not one module's slice of it. And when you're ready to present, take those answers to your AI assistant of choice (Claude, Copilot, or whichever your company uses) and have it build the board deck. Done before you finish your first cup of coffee.
Today, it looks like this:
An executive calls a review meeting. The team spends days preparing the data in the requested format. Five minutes in, the executive asks a question no one anticipated. Then a few more. The team exchanges glances, promises answers by tomorrow, and calls the report writer to pull additional data, pivot it five more ways, and build a new chart. This cycle repeats three or four times. By the end of the week, the executive has most of his answers, but for the rest he extrapolates and makes assumptions, because the team simply can't react that fast or think of everything in advance. Everyone worked hard. It still wasn't fast enough or complete. That's why he starts prep two weeks early: he knows the process.
And behind that meeting sits an entire infrastructure: a unified data repository someone had to build, data specialists, report writers, analysts to define requirements, IT to wire it together, and for high-profile projects, a project manager and an executive sponsor. That's just the standard reports. Then you still need to expose the data for ad hoc reporting, in case someone wants to slice it differently.
With V40:
The executive, or anyone on the team, asks the question directly in Campfire and gets the answer in seconds. The unanticipated follow-up question gets answered in the meeting, not next week. The two weeks of prep collapses into the conversation itself.
Both, and the second matters more. Speed is the obvious win, but the real shift is context.
When a CRO can ask a question, see the answer, and immediately ask the next three questions it raises, decisions get made with the full picture rather than with whatever slice of data fit into last week's report request.
As McKinsey put it, natural language access to enterprise data lets teams rapidly make better-informed decisions, not just faster ones.
For resource-constrained suppliers, this is the core payoff: analyst-grade reporting and analysis without dedicating headcount to it. Small commercial teams no longer have to choose between selling and reporting on selling.
Here's the honest caveat: an AI engine sitting on top of fragmented, inconsistent data will confidently give you fragmented, inconsistent answers. McKinsey's State of AI research finds that most organizations remain stuck in AI pilots that never scale, and data quality and siloed tech stacks are consistently among the top blockers.
This is exactly why Campfire's approach starts with the data layer, not the chatbot. V40 is built on a unified data model across Opportunity Management and CPQ, so quotes, cost models, opportunities, and program data already live in one consistent backbone. And when relevant data lives outside the system, Campfire's APIs bring it in, into the right places, so the AI engine has a reliable foundation to reason over. The AI is the visible feature. The unified data is what makes it trustworthy.
Automotive suppliers need to modernize to stay competitive in an evolving OEM world, or risk getting left behind. The traditional reporting path, with its infrastructure, headcount, and two-week prep cycles, is exactly the kind of hard work AI should eliminate. With Campfire V40, you get better answers, in less time, with less headache.
Q: Do I need data analysts or report writers to use Campfire's AI reporting?
A: No. The AI answers natural language questions directly against your Campfire data, so resource-constrained teams get analyst-grade insight without dedicated reporting staff or BI infrastructure.
Q: What kinds of questions can I ask across OM and CPQ?
A: Anything your commercial data can answer: pipeline health, stale opportunities, quote margin analysis, and program bottlenecks, plus any follow-up questions the first answer raises. Predictive capabilities like award likelihood and launch risk scoring are on the near-term roadmap.
Q: What happens if my data lives in multiple outside systems?
A: Campfire provides APIs to bring relevant external data into the platform, into the right places in the unified data model, so the AI engine works from a complete and consistent picture.
Q: Is AI reporting reliable enough for board-level decisions?
A: It's as reliable as the data underneath it. Because V40 unifies OM and CPQ data in one prebuilt model, the AI reasons over a consistent source of truth rather than stitching together conflicting spreadsheets, which is the failure mode McKinsey identifies as the top blocker to AI value.
Q: Does this replace our existing standard reports?
A: You can keep standard reports where they add value, but most ad hoc and exploratory questions no longer require building a report at all. You just ask.