OUR AI RETURN

Validated Cohort & Organizational Benchmark Data

The Math Behind the Methodology

Every number on this page comes from a real engagement with a real person or organization. We track outcomes the way we teach them: structured, measured, and documented.

Aggregate Stat StripDirect participant & institutional records

Verified

$62,224

Documented annual value (single executive engagement)

Validated executive return

Verified

75%

Organizational timeline compression

3–4 months reduced to 30 days

Verified

~98%

Average workflow speed optimization

5+ hours reduced to <2 minutes

Verified

New software required

Leveraging existing infrastructure

These figures are validated from our AI Fluency Cohorts, Solomon Engine engagements, and embedded organizational partnerships. Updated as new engagements close.

Section 1: Individual Practitioners — AI Fluency Cohort Results

What Happens When Individuals Commit to the Process

These are real people from our cohorts who walked in with a problem and walked out with a working solution. The skills belong to them. We built the system that made it repeatable.

Filter Case:

Rapid AI Assistant BuildVerified Cohort Alum

AURILIS SANCHEZ

Instructional Designer / L&D

Built a working AI tool in under 2 hours.

Aurilis had to debug PowerBI formulas she didn't fully understand. Five-plus hours of work. Multiple failed attempts. She built a custom AI assistant in less than two hours using what she learned in the cohort. The assistant diagnosed the error in under a minute.

The Skill Gained:

A repeatable framework for AI assistant creation she now applies to other bottlenecks.

Documented MetricsResult

Time reduction98% (5+ hours → <2 minutes)

Build time for solution<2 hours

Resolution speed45–55 seconds per fix (confirmed live)

New tools purchased$0

Verified during live cohort audit & post-session workflow verification.

Multi-LLM BenchmarkingVerified Cohort Alum

TERESA WAGGONER

Learning Experience Designer

Tested 3 tools, picked the right one, saved weeks.

Teresa needed quality content for scenario-based learning modules. Accessing SMEs was expensive and slow, costing 10–80 hours per project just for the research. She ran the same custom system prompt across Gemini, ChatGPT, and Claude. Then she compared outputs critically, judged which was best, and made her decision in under 7 minutes. Claude won: developer-ready output, deeper scenarios, better structure.

The Skill Gained:

Critical evaluation of LLM outputs, the ability to run structured comparisons and make defensible tool decisions.

Documented MetricsResult

Tools tested head-to-head3

Best output generated in<7 minutes

Research time reduction64%

Hours saved per project10–80

Verified during live cohort audit & post-session workflow verification.

Institutional GovernanceVerified Cohort Alum

LEAH OTSIG

eLearning Developer / Corporate Trainer (University System)

Cut her approval bottleneck from 14 days to 5.

Leah spent 14 days and 40+ hour cycles waiting for storyboards to move from designed to approved. Four revision rounds. Inside a university system that doesn't move fast for anyone. She used university-approved tools to streamline the process: AI-assisted drafting, intelligent iteration, and structured revision. One revision round instead of four.

The Skill Gained:

Workflow optimization and scaling AI inside constrained, institutional environments.

Documented MetricsResult

Approval time14 days → 5 days (64% reduction)

Revision rounds4 → 1 (75% fewer)

Labor in revision loop80% less

AI readiness score before human review9.2 / 10

New tools purchased$0

Verified during live cohort audit & post-session workflow verification.

Aggregate Cohort Benchmarks

Cohort-Wide Outcomes

Across all participating practitioners

Confidence gains in AI use

66–80% increase

Measured through pre- and post-cohort diagnostic self-assessments

Weekly time freed for strategic work

2+ hours

Eliminating repetitive manual bottlenecks and triage friction

Average task completion improvement

75%+ faster in targeted workflows

Verified across prompt authoring, debugging, and synthesis pipelines

Results measured across AI Fluency Cohort participants using pre/post assessments and workflow documentation.

Section 2: Executive Leaders — The Solomon Engine Results

What Happens When a Leader Chooses to Master AI Personally

Executive leadership sets the operational ceiling for AI adoption. When leaders lead from the front, the ROI ripples across the entire organization.

The Solomon Engine Case Study6-Week Leadership Intensive

ANDY IVEY

Fundraising Lead, Family Legacy (8,000 sponsored children in Zambia)

From fear to $62K in annual value.

Andy told me on our first call: "I'm terrified of AI. I don't know where to start. And I'm not sure it's for me." He was willing to try. Six weeks later, he had a 5-year strategic plan, three custom AI assistants, and a clear path to scale his impact without scaling his team.

The first 6 weeks:

Execution Log

Line Item	Hours Recovered
Presentation scripts	10 hours
Stewardship reporting	20 hours
Strategic planning (2030 roadmap)	40 hours
Team members freed for mission-critical work	7
Immediate value captured	$20,000

Hours calculated based on executive time audit before and after deployment of 3 custom AI assistants for grant writing, presentation synthesis, and 2030 roadmap modeling.

Annual ROI:

Validated Return

Training investment$4,500

Projected annual return$62,224

Return per dollar investedDirect leverage ratio

$13.83

Benchmark comparison:

Outperforms Microsoft-IDC 2024 Global AI Research ($3.70 average, $10.30 "AI Leader" tier)

Validated through verified labor rate savings and mission acceleration metrics.

"I walked in convinced this wasn't for me. I walked out with a 5-year strategic plan, three custom AI assistants, and a clear path to scale my impact without scaling my team."

— ANDY IVEY, Fundraising Lead, Family Legacy

Section 3: Organizational Transformation — Embedded Partnership Results

What Happens When an Organization Redesigns How Work Moves

Enterprise scale requires more than individual prompting skills. It demands architectural overhaul of information flows, human triage gates, and multi-model operational pipelines.

Embedded Partnership (6 Months)Federal Program Evaluation & Maternal Health Research

NATIONAL CENTER FOR EDUCATION IN MATERNAL AND CHILD HEALTH (NCEMCH)

Six months embedded. Measurable transformation across every dimension.

NCEMCH operates at the intersection of public health research and federal program evaluation. Their executive director had a choice: continue pushing his small staff through bottleneck after bottleneck, or rethink how work moves through the organization entirely. He chose the second, and he led the way personally.

We embedded for six months and built four operational pillars: a unified knowledge architecture for 700+ evidence-based strategies, an automated intake and triage system, an AI-powered analyst engine, and a dynamic report generator. The human team shifted from data processing to judgment and strategy.

The Four Operational Pillars Built

1. Unified Knowledge Architecture

Centralized and vector-indexed 700+ evidence-based public health strategies for instantaneous reference.

2. Automated Intake & Triage System

Pre-screens, formats, and routes incoming evaluation submissions directly into structured workspaces.

3. AI-Powered Analyst Engine

Calculates preliminary evidence matching scores, pre-populating analytical drafts before review.

4. Dynamic Report Generator

Assembles review-ready executive briefs and federal reporting documentation in minutes.

Operational Benchmarks: Before vs. After

Empirically tracked across 6 months of organizational embedding

75% Max Cycle Compression

Benchmark	Before	After	Impact
Annual review cycle	3–4 months	30 days	75% compression
Executive coordination time	High-friction tactical management	Delegated execution	50% reduction
Research synthesis	3 weeks	8 hours	95% faster
Prompt iteration	45 minutes per setup	4 minutes	91% speed gain
Evidence matching (700+ strategies)	Manual cross-referencing	Instant semantic search	From hours to seconds

What the team said:

Direct feedback from participating researchers, project leads, and directors

Display Attribution:

⚠️ Build note: Confirm with Paige whether we use full stakeholder names (John Richards, Dr. Lan Le, Becky Burns, Sarah) or keep generic role titles. Use the toggle above to preview both layout variants live.

"I know I often become the bottleneck because I hold the big picture. Having structured AI assistants moves me from being the 'doer' to the 'approver,' protecting executive bandwidth for high-level strategy."

John Richards(Executive Director)

Focus: Operational Governance & Bandwidth

"Automating our data collection and synthesis through structured prompts removes the manual friction of literature reviews, allowing the team to focus on scientific rigor and strategy alignment."

Dr. Lan Le(Research Lead)

Focus: Literature Review & Scientific Rigor

"Taking ownership of the ESM testing with 'Scout' showed how quickly we can process measures when the initial extraction and evidence matching are pre-calculated for us."

Becky Burns(Project Lead)

Focus: ESM Testing & Measure Processing

"Learning to use delimiters and meta-prompts dropped our iteration times from 45 minutes down to under 5 minutes. It transformed AI from an unpredictable chat box into a reliable daily worktool."

Sarah(Research & Operations Staff)

Focus: Delimiters & Prompt Engineering

Section 4: Cross-Sector Breadth

The Industries Change. The Framework Adapts. The Results Are Consistent.

We've partnered with federal agencies, municipalities, nonprofits, and private-sector teams. From a university system that doesn't move fast for anyone, to a nonprofit managing 8,000 sponsored children in Zambia, to a federal research center processing hundreds of evidence-based strategies annually.

The common thread: every organization had capable people stuck in manual workflows. The frameworks we build are designed to be owned by your team, not dependent on ours.

Federal Agencies & Research Centers

Federal Program Evaluation & Maternal Health Center (NCEMCH)

The Baseline Challenge:

Heavy regulatory oversight, 700+ complex evidence-based standards, 3-week literature review bottlenecks.

Documented Transformation

Owned System. Measurable Outcome.

75% timeline compression; instant semantic cross-referencing; shifted scientists from data scrubbing to critical evaluation.

Zero ongoing agency dependency$0 New Software Licenses