In short: DSM.promo measures AI automation ROI using a 4-dimension framework: direct labor savings, error reduction value, compliance cost avoidance, and revenue acceleration. Every projection is calculated using standardized formulas with transparent assumptions, benchmarked against published industry data from McKinsey, Gartner, and Forrester. Last validated: February 2026.
The DSM.promo ROI Framework
Most automation vendors quote a single savings number and hope you don't ask how they got there. We take the opposite approach. Our ROI framework measures value across four distinct dimensions, each with its own formula, data source, and margin of error.
Hours Freed, Dollars Returned
The most straightforward dimension. We measure the hours a task takes before automation, subtract the hours it takes after, and multiply by the fully-loaded hourly cost of the employees performing that work.
Formula: (hours_before − hours_after) × hourly_rate × 52 weeks
Fully-loaded cost includes salary, benefits, taxes, and overhead — typically 1.25–1.4× base salary. We use Bureau of Labor Statistics data for industry-specific rates when client data is unavailable.
Confidence interval: ±8% for task-level measurements (tightest of all dimensions). Variance comes primarily from differences in employee experience level and seasonal workload fluctuations.
Every Mistake Has a Price Tag
Manual processes introduce errors. Every error carries a remediation cost. We calculate the reduction in error rate multiplied by the average cost per error and the annual volume of transactions.
Formula: (error_rate_before − error_rate_after) × cost_per_error × annual_volume
Common benchmarks: data entry errors cost $50–$200 each to correct, missed appointments cost $150–$300 in lost revenue and rebooking labor, and invoice errors average $53 per correction according to the Institute of Finance and Management.
Confidence interval: ±15% due to variance in error categorization across organizations. Some errors go undetected, making baseline error rates inherently approximate.
The Cost You Don't Pay
Regulated industries spend significant labor on audit preparation, evidence gathering, and control monitoring. AI automation typically reduces compliance labor by 15–40%, and more importantly, reduces the risk of penalties from non-compliance.
Formula: (compliance_hours_saved × hourly_rate) + (penalty_risk_reduction × average_penalty_cost)
We calculate penalty risk reduction conservatively, using the organization's historical audit findings and published enforcement data from the relevant regulatory body.
Confidence interval: ±20%. Regulatory enforcement varies by jurisdiction and administration. We use a 5-year rolling average of enforcement actions to smooth annual volatility.
Faster Processing, Faster Revenue
When you process orders, approvals, or onboarding faster, revenue arrives sooner. We measure the improvement in days-to-revenue and calculate the financial impact of that acceleration.
Formula: (days_before − days_after) × daily_revenue_impact × 12 months
This dimension carries the widest confidence interval, so we weight it conservatively in total ROI projections. For most clients, revenue acceleration accounts for 10–25% of total calculated ROI.
Confidence interval: ±30%. Revenue attribution is inherently complex — multiple factors influence revenue timing beyond process speed. We cap this dimension at 25% of total ROI to prevent overstatement.
Our 6-Step Calculation Process
Every ROI projection we publish follows the same six steps. No shortcuts, no black boxes.
Data Sources & Benchmarks
Our projections are only as good as the data behind them. Here are the published sources we use to calibrate our models.
| Source | What We Use | Published |
|---|---|---|
| McKinsey Global Institute | AI adoption rates by industry, labor displacement projections | 2024 |
| Gartner | Automation market sizing, RPA vs. AI efficiency comparison | 2025 |
| Forrester | Total Economic Impact methodology, composite organization modeling | 2024 |
| DSM.promo Internal | Implementation timelines, deployment patterns, client outcomes | 2025–2026 |
We update our benchmark library quarterly. When a new source contradicts an existing assumption, we re-run affected projections and notify clients whose ROI estimates may have changed. (Benchmarks current as of February 2026.)
Assumptions & Limitations
Transparency is the point of this page. Here is what our projections assume and where they may fall short.
- All case study figures are projected outcomes, not guaranteed results. Actual savings depend on implementation quality, organizational readiness, and adoption rates.
- Labor cost calculations assume a standard 8-hour workday and US-based compensation. International engagements use local Bureau of Labor Statistics equivalents.
- Payback periods exclude the opportunity cost of internal resources allocated to implementation.
- Industry benchmarks represent medians across a range of organization sizes. They may not apply uniformly to all organizations within a given vertical.
- We update our benchmarks quarterly as new research is published. Projections created before a benchmark update are not retroactively revised unless a client requests it.
If any assumption in a specific projection does not apply to your organization, we flag it explicitly in the deliverable and adjust the model accordingly.
How to Use Our Data
This methodology underpins every ROI figure you see on our site — from industry research pages to individual case studies. Here is how to go deeper:
- AI Automation ROI Research — Full industry-by-industry data with detailed projections and source citations.
- ROI Calculator — Input your own numbers to generate a personalized estimate using our framework.
- Request a Custom Analysis — Our team will run the full 6-step process against your actual workflows and deliver a detailed ROI report.
Every number we publish can be traced back to a formula, a data source, and a set of stated assumptions. If you have questions about any specific figure, contact us and we will walk you through the calculation.
Confidence Intervals by Industry
Not all industries have equal data quality. The table below shows the confidence interval range we apply to ROI projections in each vertical, based on the maturity of available benchmarks and the number of comparable deployments in our dataset.
| Industry | Labor Savings | Error Reduction | Compliance | Revenue Accel. | Sample Size |
|---|---|---|---|---|---|
| Healthcare | ±6% | ±12% | ±18% | ±25% | 12 orgs |
| Finance | ±5% | ±10% | ±15% | ±22% | 18 orgs |
| Legal | ±8% | ±14% | ±20% | ±28% | 9 orgs |
| E-Commerce | ±7% | ±12% | N/A | ±20% | 15 orgs |
| Manufacturing | ±10% | ±18% | ±22% | ±30% | 8 orgs |
| Insurance | ±6% | ±11% | ±16% | ±24% | 11 orgs |
| Logistics | ±9% | ±15% | N/A | ±26% | 10 orgs |
Tighter intervals indicate more available benchmark data and greater cross-organization consistency. Industries with fewer than 8 comparable deployments receive wider intervals by default. "N/A" means the compliance dimension does not apply to that vertical in most engagements.
Scoring Methodology for Website & AI Assessments
In addition to ROI projections, we publish website performance and AI readiness scores. These follow a separate but equally transparent methodology.
In short: Our scoring methodology achieves ±3% accuracy across repeated measurements, following evaluation principles established by the NIST AI Risk Management Framework. Scores are calibrated against industry benchmarks and validated through automated regression testing with 32 test cases.
| Source | Data Type | Frequency | Weight |
|---|---|---|---|
| Google PageSpeed Insights API | Performance, Core Web Vitals | Real-time | 25% |
| Custom SEO Scanner | Meta tags, schema, headings, links | On-demand | 25% |
| Lighthouse Audit | Accessibility, best practices | On-demand | 15% |
| AI Readiness Assessment | WebMCP, llms.txt, schema coverage | Weekly | 15% |
| Content Analysis | Word count, readability, E-E-A-T signals | On-demand | 10% |
| Security Headers Scan | CSP, HSTS, permissions policy | Daily | 10% |
Each score is the median of 3 consecutive measurements to reduce variance from network conditions and server load. Outliers beyond 2 standard deviations are excluded.
Scores are normalized against a reference dataset of 500+ enterprise websites. A score of 80 means the site outperforms 80% of the reference set on that dimension.
32 automated tests validate scoring logic on every update. Tests cover edge cases including missing meta tags, invalid schema, slow servers, and redirect chains.
Composite Score Formula
Our composite score combines six weighted dimensions:
Composite = (PSI × 0.25) + (SEO × 0.25) + (A11y × 0.15) + (AI_Ready × 0.15) + (Content × 0.10) + (Security × 0.10)
Individual dimension scores are weighted to reflect their relative impact on search visibility and user experience. The formula is reviewed quarterly and adjusted based on search engine algorithm updates.
Get Your Custom ROI Analysis
We will run our full 6-step methodology against your actual workflows and deliver a detailed projection with all assumptions documented.
Request a Free Assessment