Most AI projects fail not because of technology, but because of unrealistic ROI expectations. When the promised 10x improvement turns into a modest 20% gain, stakeholder support evaporates.
The Problem with Traditional ROI Models
Traditional ROI frameworks assume:
- Linear scaling of benefits
- Perfect data quality
- 100% user adoption
- No implementation friction
Reality is messier.
A Better Framework
Here's what we use at Datablooz:
1. Conservative Baseline Assumptions
Start with industry benchmarks, then discount by 30%. Yes, really. Better to under-promise and over-deliver.
Example:
- Industry benchmark for automation: 60%
- Our starting assumption: 40%
- Actual results often land at 50-55%
2. Phase-Based Adoption Curves
Don't assume day-one adoption. Model realistic ramp-up:
- Month 1-3: 20% adoption
- Month 4-6: 50% adoption
- Month 7-12: 80% adoption
- Year 2+: 90% steady state
3. Include Hidden Costs
Most models miss:
- Change management time
- Training and support
- Data quality remediation
- Integration complexity
- Ongoing maintenance
Rule of thumb: Add 25-40% to development costs for these hidden factors.
4. Sensitivity Analysis
Build three scenarios:
- Conservative: What if adoption is slow and benefits are 50% of projected?
- Expected: Realistic middle ground
- Optimistic: Best case with favorable conditions
Share all three with stakeholders. Make decisions based on the conservative case.
Real Example
A client wanted to automate invoice processing:
Their Initial Projection:
- 90% automation rate
- $500K annual savings
- 6-month payback
Our Adjusted Model:
- 60% automation rate (after accounting for edge cases)
- $280K annual savings
- 14-month payback
- Plus $80K in data cleanup costs they hadn't considered
Actual Results After 12 Months:
- 65% automation rate
- $310K annual savings
- On track for 13-month payback
They were thrilled because we'd set realistic expectations.
The Takeaway
Under-promise, over-deliver. Build ROI models that account for reality, not ideal conditions. Your stakeholders will thank you when the project actually delivers.
Want help building a realistic ROI model for your AI initiative? Get in touch.