Counting the full cost
The sticker price of an AI model is usually the smallest line. A real business case includes build, integration, ongoing usage, and maintenance, plus the human review that most serious use cases require.
- Build and integration effort, not just the model
- Ongoing per-use or subscription costs at your real volume
- Maintenance, monitoring, and tuning over time
- Human review time where outputs need checking
- Change management: training and adoption
- A contingency for the parts that always cost more than planned
Estimating the benefit honestly
Benefits are where business cases get inflated. We push for benefits you can actually measure and defend, such as time saved, error reduction, faster turnaround, or capacity freed, and we're explicit about which are hard savings versus softer gains.
- Tie benefits to a measurable baseline you have today
- Separate hard savings from softer "productivity" claims
- Use conservative ranges, not single optimistic numbers
- Account for the ramp-up before benefits are fully realized
- Note benefits that are real but genuinely hard to quantify
Payback and the decision
With honest cost and benefit ranges, payback becomes a straightforward comparison, and sometimes the honest conclusion is "not worth it," which is a good decision to reach before spending. We also favor structuring projects so you can validate assumptions cheaply before the full commitment.
- A payback range, not a false-precision single figure
- Best-case and conservative scenarios side by side
- A small pilot to test the biggest assumptions first
- A clear go/no-go threshold agreed up front
- Willingness to recommend against a project that doesn't clear it
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Frequently asked questions
Can you promise a specific ROI or savings number?
No, and be cautious of anyone who does before understanding your data and process. We give honest ranges tied to your real numbers and assumptions, and we structure a small pilot to test them before you commit the full budget.
What costs do people usually forget?
The ongoing ones: per-use fees at real volume, maintenance and monitoring, and the human review time serious use cases need. Leaving these out is the most common reason an AI project looks better on paper than in reality.
What if the business case doesn't justify the project?
Then you've spent a little analysis to avoid a large mistake, which is a win. We'd rather tell you a project doesn't pencil out than build something that won't pay back. Often we can suggest a smaller version that does.