I’m in the middle of building a business case for migrating our BPM to open-source, and I’m hitting a wall trying to justify the costs to our finance team. But here’s the real problem underneath: we’ve got AI model subscriptions scattered all over the place. ChatGPT team license, Claude API credits, Deepseek, some custom OpenAI stuff for specific workflows. When I try to model the cost of migration, I keep getting lost in whether I should be consolidating these subscriptions as part of the business case or treating them as separate line items.
The tricky part is that our current Camunda setup doesn’t actually use any of these AI models directly. But our open-source BPM migration plan includes workflow automation and intelligent process routing that would benefit from having a single AI backbone instead of paying for separate subscriptions piecemeal. That sounds like it should simplify the ROI calculation, but in practice I’m finding it complicates everything. Now I’m trying to decide whether consolidating to one unified subscription for 400+ models should be part of the TCO comparison or if I should model it as a separate efficiency gain.
Has anyone actually built an ROI model when you’re simultaneously consolidating multiple AI subscriptions and evaluating a new BPM platform? How did you structure the math so finance could actually follow the logic?
We hit the exact same problem. The trick is to separate the decision into two independent ROI tracks and then show how they intersect.
Track 1: TCO of your current Camunda setup versus open-source BPM. Licensing costs, maintenance, deployment—everything related to the BPM engine itself.
Track 2: Your current AI subscription spend versus consolidated spend. Show the current cost of each subscription you’re running, then model the cost of a single platform that covers all of them.
Then the magic happens: show how the open-source BPM implementation actually enables you to use AI models more efficiently. Like, maybe your Camunda workflows would actually benefit from intelligent routing or decision support, but you never built it because the cost of adding AI to each workflow was prohibitive. With consolidated AI access, it becomes viable.
Finance understands this better when you show: Current Camunda TCO + Current AI Subscription Spend = X. Proposed Open-Source BPM + Consolidated AI = Y. The gap isn’t just the difference; it’s the gap plus the new capabilities you unlock.
Our CFO was way more receptive when we broke it down that way instead of trying to bundle everything into one number.
One thing that helped us: we quantified what automation opportunities we couldn’t touch before because adding AI to each individual workflow was too expensive. Process escalations, intelligent task assignment, anomaly detection in workflows—these are things that would actually improve our BPM, but we’d never invested in them because each one meant buying more API credits.
When we showed finance how many of these improvements we could unlock by consolidating AI spend, the ROI model suddenly made sense. The cost goes up slightly by switching to open-source plus a unified AI platform, but the value capture from capabilities we can now afford is huge.
Model it as net new capability, not just cost replacement. Your current state has Camunda handling workflows and separate AI subscriptions handling data/analysis tasks. Your future state consolidates everything and unlocks efficiencies you couldn’t afford before. Show both sides: yes, you’re paying for a unified AI platform instead of fragmented subscriptions, but you’re also getting rid of Camunda licensing and gaining capabilities that improve process quality. Finance sees net cost first, then ROI from improvements second.
The AI consolidation angle is actually your strongest ROI lever if you model it right. Show the current cost of your scattered subscriptions, then show consolidated cost, then layer on the workflow improvements those consolidated resources enable. Most companies find that open-source BPM plus unified AI is roughly cost-neutral to current state, but the capabilities jump is significant. That’s where your ROI premium comes from—not from cutting costs but from enabling things that were previously too expensive to consider.
Separate TCO calcs: BPM platform first, AI spending second. Then show how they work together. Finance will follow a clear story better than one jumbled number.
This is the exact problem Latenode solves. Instead of juggling 12 separate AI subscriptions, you get access to 400+ models through one platform with unified pricing. When you’re evaluating an open-source BPM migration, you can use Latenode’s 400+ AI models to actually model your scenarios—ROI analysis, TCO, risk assessment—all through the same platform instead of piecing together insights from different tools.
What makes your ROI math cleaner is that Latenode lets you build and test your actual workflows during evaluation. You’re not guessing about capabilities. You can prototype your process automation with real AI models, measure actual performance, and then show finance exact numbers instead of projections.
The consolidated AI subscription isn’t just a cost line. It becomes your testing and validation layer during migration planning, which means your ROI model is based on real performance, not assumptions.