Overview and Purpose
Agentic AI Solutions is a consulting firm, not a research institution. Our benchmarks are a by-product of client work — we collect, anonymize, and aggregate data from our engagements to build reference points that help new clients calibrate their expectations. We publish this work because we believe the market is badly underserved by speculative analyst estimates that cannot be validated against actual outcomes.
This page is the canonical methodology reference. All benchmarks we publish link here so that readers can evaluate the provenance and limits of each statistic before relying on it.
Data Sources
Our benchmarks draw from three primary sources:
- 1. Direct Engagement Data: Metrics captured from client implementations — project timelines, costs, measured output changes, and time-to-value observations. This data is collected with client consent and reported in aggregate with all identifying information removed.
- 2. Structured Post-Engagement Reviews: We conduct structured reviews with clients 30, 90, and 180 days after go-live. These reviews capture sustained operational changes versus one-time gains, allowing us to distinguish durable ROI from initial novelty effects.
- 3. Comparative Pre/Post Measurement: Where clients agree to share baseline metrics, we establish a documented pre-engagement baseline before project start. This enables apples-to-apples attribution of outcome changes to specific interventions rather than broader business trends.
Sample Definitions
Metric Construction
Each benchmark report specifies which metrics are reported and how they are calculated. The following conventions apply across all reports unless a specific report states otherwise:
- ✓ROI figures are presented as net ROI: (measured benefit − total project cost) / total project cost, expressed as a percentage.
- ✓Cost ranges represent the 25th–75th percentile of observed engagement costs, excluding outliers more than 2 standard deviations from the mean.
- ✓Time-to-value figures use median rather than mean to limit the distortion caused by unusually long or short engagements.
- ✓Industry breakdowns require a minimum of five observations per category before we report a finding. Categories with fewer than five observations are excluded or noted as "insufficient sample."
- ✓Where clients provide only directional feedback ("significantly improved," "no change," etc.) rather than hard numbers, we exclude those data points from quantitative statistics but may reference them qualitatively.
Time Window and Currency
Each benchmark report carries a year designation (e.g., "2026") reflecting the primary time window of the engagements included. The conventions are:
- ✓A "2026" report draws primarily from engagements that concluded or reached their 90-day post-go-live review point between January 2025 and March 2026.
- ✓We include earlier engagements where the technology type and market conditions are sufficiently similar to current deployments to remain representative.
- ✓Reports are updated when the underlying engagement data changes materially (typically annually) or when a methodological improvement warrants revision.
- ✓Cost figures are denominated in USD and reflect U.S. market conditions unless otherwise noted.
Limitations You Should Know
We believe honest reporting of limitations is more valuable than inflated confidence intervals. The following constraints apply to all our benchmarks:
Selection Bias
Our sample consists of clients who chose to work with Agentic AI Solutions. These are companies that had sufficient organizational readiness and budget to engage a consulting firm. They are not representative of all mid-market companies and likely skew toward better AI readiness and more favorable implementation conditions than the broader population.
Small Sample Sizes
We are a focused consulting firm, not a large research institution. Most benchmark categories are built from tens of engagements, not hundreds. We flag this by reporting ranges rather than point estimates and by noting when a category has fewer than ten observations.
Survivor Bias in Client Reporting
Clients who achieve strong results are more likely to participate in follow-up reviews than clients whose outcomes were disappointing. We attempt to mitigate this by conducting structured reviews proactively, but some survivor bias likely remains in our reported outcomes.
Attribution Uncertainty
Even with pre/post measurement, it is difficult to attribute all outcome changes to the AI implementation. Business conditions change, markets shift, and client teams improve independently. We do not claim full causal attribution — we report associations between implementation and measured outcomes.
Generalizability
Our clients are concentrated in Colorado and adjacent Western U.S. markets. Labor costs, regulatory environments, and technology adoption rates may differ in other geographies. Adjust our benchmarks accordingly if your context is materially different.
How to Cite Our Research
If you cite our benchmarks in articles, presentations, or reports, please attribute as follows:
"[Statistic]. Source: Agentic AI Solutions, [Report Name], [Year]. agentic-ai-solutions.com/research/[slug]. Methodology: agentic-ai-solutions.com/research/methodology."
We ask that citations always include a link to this methodology page so that readers can evaluate the provenance and limitations of any statistic they encounter.
Questions About Our Methodology
If you have questions about how a specific finding was produced, want to discuss the applicability of our benchmarks to your situation, or have suggestions for improving our methodology, we welcome the conversation. Use the contact form below.
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