We’re in the early stages of modeling ROI for a potential consolidation project. Right now, we have Make handling the core automation work, and we’re managing OpenAI and Claude separately for AI-heavy workflows. Leadership wants a clear number: how much better does our financial picture look if we consolidate everything.
I built a straightforward model at first: current Make spend plus current AI licensing, versus a unified platform’s total cost. The savings looked reasonable—maybe 35-40% over two years, factoring in reduced engineering overhead.
But I realized I might be missing something important. If we consolidate to a single platform with unified AI access, does that actually change how we build workflows? Like, are there operational efficiency gains that go beyond just the licensing costs?
I’ve read some case studies suggesting that consolidation also reduces the complexity of managing workflows, which supposedly leads to faster deployment and fewer errors. But I’m not sure how to quantify that in a model. It feels like I’m either missing real value or overthinking this.
How are other teams factoring operational efficiency and deployment speed into their ROI models? Is it worth trying to build that into the forecast, or should I stick to the straightforward licensing math?
Your instinct is right—there’s real operational efficiency hiding in the consolidation story, but most finance models miss it because it’s hard to quantify.
When we did our consolidation, we focused on licensing costs at first too. But what actually mattered more was that our team stopped losing time to API key management, credential rotation, and dealing with rate limits across multiple services.
One of our engineers was spending probably 5-6 hours a week managing connection strings, API keys, and fixing workflows when rate limits were hit. When we consolidated, that work basically went away. That’s roughly 250 hours per engineer per year that went back into actual automation development.
Here’s how I quantified it: I tracked the time our team spent on non-productive overhead for three months, calculated an annual burn rate, and added that to the licensing savings. Suddenly the ROI picture looked way better.
It’s tricky to model prospectively, but if you talk to your team about how much time they actually spend managing credentials and integrations, you’ll find a number. That’s real cost recovery.
The operational efficiency gain is real, but it’s subtle and easy to underestimate. When you’re managing separate platforms and AI services, there’s cognitive overhead your team is carrying. Decisions take longer because you have to think about which service to use. Troubleshooting takes longer because problems could be in five different places.
We estimated this by tracking deployment cycle time and error rate before and after consolidation. Our deployment time went down about 18% and error rates dropped about 25%. In a more complex environment, those numbers could be bigger.
For your ROI model, I’d suggest building two scenarios: one with just the licensing math, one that includes a conservative estimate of time savings (maybe 5-10% of your team’s automation-focused work). The difference between the two shows you the operational upside versus the downside if those efficiency gains don’t materialize.
Finance teams usually respond better to that two-scenario approach because it derisks the forecast.
The licensing consolidation is the straightforward part. The operational efficiency gain is where real value sits, but it requires thoughtful measurement.
When we modeled the full ROI impact of consolidation, we tracked three things: direct licensing cost savings, engineering time allocation before and after, and reduction in unplanned remediation work (the firefighting that happens when integrations break).
The direct savings were about 35% as you calculated. But engineering time reallocation added another 25% because the platform was simpler to work with and required less maintenance. The reduction in firefighting firefight was harder to quantify but definitely material.
For your model, I’d separate these out clearly: licensing savings (definite), operational efficiency (probable), and firefighting reduction (possible but significant). That gives finance a clearer view of what’s certain versus what’s assuming things go well.
The enterprise ROI is usually much better than the licensing math alone suggests, but only if you’re honest about what operational improvements you’re actually expecting to achieve.
This is exactly where I went wrong the first time we were modeling consolidation. I focused on the licensing math and missed the operational transformation happening in the background.
When we switched to a unified platform, the direct licensing consolidation saved us about $400-500 per month. But what actually moved the needle was that our team stopped juggling different services, different pricing models, and different ways of thinking about API usage.
Our deployment cycle time went down because we weren’t constantly context-switching between services. Error rates dropped because we had visibility into the entire workflow in one place instead of having to debug across five different platforms. One of our people estimated they were gaining about 8-10 hours per week of productive time because they weren’t managing API key issues anymore.
When we quantified that in the ROI model, it roughly doubled the value proposition. Licensing savings were the floor. Operational efficiency was the upside.
For your model, talk to your team about where they’re losing time right now. That’s your operational efficiency baseline. Estimate how much of that overhead goes away with consolidation. Be conservative, but include it in the forecast.
Finance will respect a model that shows licensing savings plus operational recovery separate from overhead elimination. It’s more credible.