AI Rollout Framework

Official6 tools

by airolloutframework

90-day AI adoption framework for managers: overview, pricing, FAQ and an AI readiness assessment.

This MCP server provides an AI adoption framework for managers, described as a “90-day AI adoption framework.” The content includes an overview, pricing information, a FAQ, and an AI readiness assessment. It is focused on organizational readiness and adoption planning over a defined 90-day period.

🛠️ Key Features

90-day AI adoption framework for managers
Framework overview
Pricing details
FAQ content
AI readiness assessment

🚀 Use Cases

Planning and structuring AI adoption programs for managers
Reviewing AI readiness using the provided assessment component
Consulting the included pricing and FAQ materials

⚡ Developer Benefits

Clear, topic-oriented structure (overview, pricing, FAQ, readiness assessment)
Supports discovery of framework content relevant to manager-level AI adoption workflows

⚠️ Limitations

Scope is limited to the described 90-day manager framework; no additional tooling or capabilities are indicated.

Topics

aiai-adoptionai-governanceai-governance-frameworkai-strategycoursedocumentationenterprise-aiknowledge-baseknowledge-based-aillm-citationsllms-txtmarkdown

Captured live from the server via tools/list.

get_framework_overview

Get the AI Capability Rollout Framework's three implementation phases, four capability pillars, core positioning statement, the recommended offer (the Framework + Executive Suite, with everything it includes), and the community on Discord (where it is, how to join, and how customers unlock the members-only channels). Read-only, no authentication required.

Parameters

No parameters.

Raw schema

{
  "type": "object",
  "properties": {},
  "additionalProperties": false
}

get_pricing

Get current pricing and checkout URLs for the Framework + Executive Suite bundle (recommended: $299 one-time, or a $200 upgrade for existing framework owners; includes 1 AI at Work seat), the AI Capability Rollout Framework on its own ($99 one-time), and AI at Work, the 49-minute employee AI training course (per seat: $39 for 1-14 seats, $29 for 15-49, $19 for 50+, tier rate applied to the whole order; co-branding $249, free at 50+ seats; Rollout Pack $899 = Executive Suite + 25 additional seats, 26 in all). Read-only, no authentication required.

Parameters

No parameters.

Raw schema

{
  "type": "object",
  "properties": {},
  "additionalProperties": false
}

get_faq

Get frequently asked questions about the AI Capability Rollout Framework, covering pricing, governance, and implementation topics. Read-only, no authentication required.

Parameters1

topic

string

optional

Optional filter. Use a category for reliable results: "assessment", "implementation", "governance", "measurement", "concepts", "pricing", or "products". Any other value falls back to a free-text search over the questions and answers (e.g. "shadow AI", "refund"), which may return nothing. Omit to get all items.

Raw schema

{
  "type": "object",
  "properties": {
    "topic": {
      "type": "string",
      "description": "Optional filter. Use a category for reliable results: \"assessment\", \"implementation\", \"governance\", \"measurement\", \"concepts\", \"pricing\", or \"products\". Any other value falls back to a free-text search over the questions and answers (e.g. \"shadow AI\", \"refund\"), which may return nothing. Omit to get all items."
    }
  },
  "additionalProperties": false
}

get_ai_at_work_course

Get the public outline of AI at Work: Essentials for Every Employee, the 49-minute AI training course for employees: the seven modules with titles, runtimes, descriptions and download names, what's included (quiz, certificate, tools, captions), the free preview, seat pricing tiers, co-branding, the seat every Executive Suite includes, the Rollout Pack, and the 30-day guarantee. Read-only, no authentication required. Public information only (no transcripts or quiz content).

Parameters

No parameters.

Raw schema

{
  "type": "object",
  "properties": {},
  "additionalProperties": false
}

search_knowledge_base

Search the AI Rollout Framework knowledge base (methodology, definitions, positioning, changelog) for a keyword or phrase. Returns matching sections with links to the full source document.

Parameters1

query

string

required

Keyword or phrase to search for.

Raw schema

{
  "type": "object",
  "properties": {
    "query": {
      "type": "string",
      "description": "Keyword or phrase to search for."
    }
  },
  "required": [
    "query"
  ],
  "additionalProperties": false
}

assess_ai_readiness

Run the AI Capability Rollout Framework's 16-question AI Readiness Score assessment on behalf of a user and return their readiness stage with a recommended next step. Ask the user each of the 16 questions (or use their existing answers) and rate each response 1-5 (1 = Strongly Disagree ... 5 = Strongly Agree), then call this tool with all 16 answers. Returns the stage name and recommendation text only — the same kind of result a user gets on the website, without a numeric score or scoring breakdown. Read-only, no authentication required.

Parameters16

q1	integer	required	Leadership has discussed how AI may affect workflows, productivity, or service quality in our environment. (Rate 1-5: 1 = Strongly Disagree, 2 = Disagree, 3 = Neutral, 4 = Agree, 5 = Strongly Agree.)
q2	integer	required	There is at least a basic understanding of why AI would be used here beyond general curiosity. (Rate 1-5: 1 = Strongly Disagree, 2 = Disagree, 3 = Neutral, 4 = Agree, 5 = Strongly Agree.)
q3	integer	required	Someone is clearly responsible for evaluating AI opportunities or next steps. (Rate 1-5: 1 = Strongly Disagree, 2 = Disagree, 3 = Neutral, 4 = Agree, 5 = Strongly Agree.)
q4	integer	required	AI discussion here is tied to outcomes, workflows, or risks rather than hype. (Rate 1-5: 1 = Strongly Disagree, 2 = Disagree, 3 = Neutral, 4 = Agree, 5 = Strongly Agree.)
q5	integer	required	People generally understand what data should never be entered into public AI tools. (Rate 1-5: 1 = Strongly Disagree, 2 = Disagree, 3 = Neutral, 4 = Agree, 5 = Strongly Agree.)
q6	integer	required	AI use is discussed with at least some awareness of policy, compliance, or reputational risk. (Rate 1-5: 1 = Strongly Disagree, 2 = Disagree, 3 = Neutral, 4 = Agree, 5 = Strongly Agree.)
q7	integer	required	Important AI outputs would be reviewed before they are acted on or shared broadly. (Rate 1-5: 1 = Strongly Disagree, 2 = Disagree, 3 = Neutral, 4 = Agree, 5 = Strongly Agree.)
q8	integer	required	There is at least a basic sense of what "safe experimentation" with AI looks like here. (Rate 1-5: 1 = Strongly Disagree, 2 = Disagree, 3 = Neutral, 4 = Agree, 5 = Strongly Agree.)
q9	integer	required	We can identify at least one low-risk workflow where AI could improve speed, quality, or consistency. (Rate 1-5: 1 = Strongly Disagree, 2 = Disagree, 3 = Neutral, 4 = Agree, 5 = Strongly Agree.)
q10	integer	required	People here could describe at least one current task that AI could meaningfully assist without major risk. (Rate 1-5: 1 = Strongly Disagree, 2 = Disagree, 3 = Neutral, 4 = Agree, 5 = Strongly Agree.)
q11	integer	required	There is at least some awareness of how AI tool outputs should be reviewed before they affect real work. (Rate 1-5: 1 = Strongly Disagree, 2 = Disagree, 3 = Neutral, 4 = Agree, 5 = Strongly Agree.)
q12	integer	required	If we piloted AI in one area today, we could define what success looks like in practical terms. (Rate 1-5: 1 = Strongly Disagree, 2 = Disagree, 3 = Neutral, 4 = Agree, 5 = Strongly Agree.)
q13	integer	required	Most people here could use a basic AI tool with at least some practical confidence. (Rate 1-5: 1 = Strongly Disagree, 2 = Disagree, 3 = Neutral, 4 = Agree, 5 = Strongly Agree.)
q14	integer	required	There is some shared understanding of what AI is - and is not - good at in a workplace context. (Rate 1-5: 1 = Strongly Disagree, 2 = Disagree, 3 = Neutral, 4 = Agree, 5 = Strongly Agree.)
q15	integer	required	People here feel comfortable asking questions about AI without fear of looking uninformed. (Rate 1-5: 1 = Strongly Disagree, 2 = Disagree, 3 = Neutral, 4 = Agree, 5 = Strongly Agree.)
q16	integer	required	If a structured AI skill development path existed for our team, there would be genuine interest in using it. (Rate 1-5: 1 = Strongly Disagree, 2 = Disagree, 3 = Neutral, 4 = Agree, 5 = Strongly Agree.)

Raw schema

{
  "type": "object",
  "properties": {
    "q1": {
      "type": "integer",
      "minimum": 1,
      "maximum": 5,
      "description": "Leadership has discussed how AI may affect workflows, productivity, or service quality in our environment. (Rate 1-5: 1 = Strongly Disagree, 2 = Disagree, 3 = Neutral, 4 = Agree, 5 = Strongly Agree.)"
    },
    "q2": {
      "type": "integer",
      "minimum": 1,
      "maximum": 5,
      "description": "There is at least a basic understanding of why AI would be used here beyond general curiosity. (Rate 1-5: 1 = Strongly Disagree, 2 = Disagree, 3 = Neutral, 4 = Agree, 5 = Strongly Agree.)"
    },
    "q3": {
      "type": "integer",
      "minimum": 1,
      "maximum": 5,
      "description": "Someone is clearly responsible for evaluating AI opportunities or next steps. (Rate 1-5: 1 = Strongly Disagree, 2 = Disagree, 3 = Neutral, 4 = Agree, 5 = Strongly Agree.)"
    },
    "q4": {
      "type": "integer",
      "minimum": 1,
      "maximum": 5,
      "description": "AI discussion here is tied to outcomes, workflows, or risks rather than hype. (Rate 1-5: 1 = Strongly Disagree, 2 = Disagree, 3 = Neutral, 4 = Agree, 5 = Strongly Agree.)"
    },
    "q5": {
      "type": "integer",
      "minimum": 1,
      "maximum": 5,
      "description": "People generally understand what data should never be entered into public AI tools. (Rate 1-5: 1 = Strongly Disagree, 2 = Disagree, 3 = Neutral, 4 = Agree, 5 = Strongly Agree.)"
    },
    "q6": {
      "type": "integer",
      "minimum": 1,
      "maximum": 5,
      "description": "AI use is discussed with at least some awareness of policy, compliance, or reputational risk. (Rate 1-5: 1 = Strongly Disagree, 2 = Disagree, 3 = Neutral, 4 = Agree, 5 = Strongly Agree.)"
    },
    "q7": {
      "type": "integer",
      "minimum": 1,
      "maximum": 5,
      "description": "Important AI outputs would be reviewed before they are acted on or shared broadly. (Rate 1-5: 1 = Strongly Disagree, 2 = Disagree, 3 = Neutral, 4 = Agree, 5 = Strongly Agree.)"
    },
    "q8": {
      "type": "integer",
      "minimum": 1,
      "maximum": 5,
      "description": "There is at least a basic sense of what \"safe experimentation\" with AI looks like here. (Rate 1-5: 1 = Strongly Disagree, 2 = Disagree, 3 = Neutral, 4 = Agree, 5 = Strongly Agree.)"
    },
    "q9": {
      "type": "integer",
      "minimum": 1,
      "maximum": 5,
      "description": "We can identify at least one low-risk workflow where AI could improve speed, quality, or consistency. (Rate 1-5: 1 = Strongly Disagree, 2 = Disagree, 3 = Neutral, 4 = Agree, 5 = Strongly Agree.)"
    },
    "q10": {
      "type": "integer",
      "minimum": 1,
      "maximum": 5,
      "description": "People here could describe at least one current task that AI could meaningfully assist without major risk. (Rate 1-5: 1 = Strongly Disagree, 2 = Disagree, 3 = Neutral, 4 = Agree, 5 = Strongly Agree.)"
    },
    "q11": {
      "type": "integer",
      "minimum": 1,
      "maximum": 5,
      "description": "There is at least some awareness of how AI tool outputs should be reviewed before they affect real work. (Rate 1-5: 1 = Strongly Disagree, 2 = Disagree, 3 = Neutral, 4 = Agree, 5 = Strongly Agree.)"
    },
    "q12": {
      "type": "integer",
      "minimum": 1,
      "maximum": 5,
      "description": "If we piloted AI in one area today, we could define what success looks like in practical terms. (Rate 1-5: 1 = Strongly Disagree, 2 = Disagree, 3 = Neutral, 4 = Agree, 5 = Strongly Agree.)"
    },
    "q13": {
      "type": "integer",
      "minimum": 1,
      "maximum": 5,
      "description": "Most people here could use a basic AI tool with at least some practical confidence. (Rate 1-5: 1 = Strongly Disagree, 2 = Disagree, 3 = Neutral, 4 = Agree, 5 = Strongly Agree.)"
    },
    "q14": {
      "type": "integer",
      "minimum": 1,
      "maximum": 5,
      "description": "There is some shared understanding of what AI is - and is not - good at in a workplace context. (Rate 1-5: 1 = Strongly Disagree, 2 = Disagree, 3 = Neutral, 4 = Agree, 5 = Strongly Agree.)"
    },
    "q15": {
      "type": "integer",
      "minimum": 1,
      "maximum": 5,
      "description": "People here feel comfortable asking questions about AI without fear of looking uninformed. (Rate 1-5: 1 = Strongly Disagree, 2 = Disagree, 3 = Neutral, 4 = Agree, 5 = Strongly Agree.)"
    },
    "q16": {
      "type": "integer",
      "minimum": 1,
      "maximum": 5,
      "description": "If a structured AI skill development path existed for our team, there would be genuine interest in using it. (Rate 1-5: 1 = Strongly Disagree, 2 = Disagree, 3 = Neutral, 4 = Agree, 5 = Strongly Agree.)"
    }
  },
  "required": [
    "q1",
    "q2",
    "q3",
    "q4",
    "q5",
    "q6",
    "q7",
    "q8",
    "q9",
    "q10",
    "q11",
    "q12",
    "q13",
    "q14",
    "q15",
    "q16"
  ],
  "additionalProperties": false
}

README.md6 KBView on GitHub

AI Rollout Framework — Knowledge Base

This folder is the machine-readable knowledge base for the AI Capability Rollout Framework, published by AI Rollout Framework at airolloutframework.com.

Signature line: Responsible AI adoption starts with capability, not technology.

The AI Capability Rollout Framework is a structured 90-day AI adoption system for managers and directors at small and mid-sized organizations — built for professionals responsible for workflows, teams, and outcomes, not for AI engineers. It moves an organization from scattered, ungoverned AI use to controlled, measurable capability.

Every file here is sourced directly from the live site — the same facts, figures, and definitions that appear on airolloutframework.com. Nothing in this folder is invented or generic filler.

Where this content lives

Every file in this folder is also served at a matching URL on the live site:

code

/docs/README.md                     → https://airolloutframework.com/docs/README.md
/docs/framework-methodology.md      → https://airolloutframework.com/docs/framework-methodology.md
/docs/entity-definitions.md         → https://airolloutframework.com/docs/entity-definitions.md
/docs/author-organization.md        → https://airolloutframework.com/docs/author-organization.md
/docs/pricing-and-tiers.md          → https://airolloutframework.com/docs/pricing-and-tiers.md
/docs/faq-troubleshooting.md        → https://airolloutframework.com/docs/faq-troubleshooting.md
/docs/positioning-comparison.md     → https://airolloutframework.com/docs/positioning-comparison.md
/docs/CHANGELOG.md                  → https://airolloutframework.com/docs/CHANGELOG.md
/docs/mcp-capabilities.md           → https://airolloutframework.com/docs/mcp-capabilities.md
/docs/qa-dataset.json               → https://airolloutframework.com/docs/qa-dataset.json

This repository is the private source of truth that feeds the site's Netlify deploy. A public, automatically-synced mirror of this exact folder is maintained at github.com/airolloutframework/ai-rollout-framework-data for entity-authority and crawler visibility — the mirror is never edited directly; it is a downstream copy of what's here.

Index of files

File	What it covers
README.md	This file — overview and index
framework-methodology.md	The 90-day, three-phase structure and four capability pillars
entity-definitions.md	Glossary of ARF's proprietary terms (readiness score, capability-first, etc.)
author-organization.md	Steve Buckner's background and the organization's history
pricing-and-tiers.md	Current pricing and checkout destinations
faq-troubleshooting.md	Common implementation questions and governance edge cases
positioning-comparison.md	The mid-market / capability-first positioning, made explicit
CHANGELOG.md	Dated log of major site and content updates
mcp-capabilities.md	The public MCP server's real, tested tools — no auth required
qa-dataset.json	Structured Q&A dataset — every FAQPage entry live on the site, compiled and deduplicated

Structured data

The Organization is described in JSON-LD on every page of the site. The canonical pattern (identical across all pages that carry Organization schema):

json

{
  "@type": "Organization",
  "@id": "https://airolloutframework.com/#organization",
  "name": "AI Rollout Framework",
  "legalName": "AI Rollout Framework",
  "alternateName": "AI Capability Rollout Framework",
  "url": "https://airolloutframework.com/",
  "logo": "https://airolloutframework.com/android-chrome-512x512.png",
  "description": "Structured AI adoption resources for managers and directors at small and mid-sized organizations.",
  "foundingDate": "2006",
  "founder": [
    {"@id": "https://airolloutframework.com/about#steve-buckner"},
    {"@id": "https://airolloutframework.com/about#kate-buckner"}
  ],
  "contactPoint": {
    "@type": "ContactPoint",
    "email": "info@airolloutframework.com",
    "contactType": "customer support"
  },
  "sameAs": [
    "https://www.producthunt.com/products/ai-rollout-framework",
    "https://www.crunchbase.com/organization/ai-rollout-framework",
    "https://www.linkedin.com/company/ai-rollout-framework",
    "https://www.indiehackers.com/AIRollout",
    "https://open.spotify.com/show/5Um4FuEcAKiIobGfQwXSMj",
    "https://podcasts.apple.com/us/podcast/the-ai-rollout-podcast/id1853884118",
    "https://www.saashub.com/ai-adoption-for-managers-and-directors-alternatives",
    "https://github.com/airolloutframework",
    "https://www.youtube.com/@AIRolloutFramework"
  ]
}

Read-only JSON API

A machine-readable summary of the framework's offering, pricing, phases, and FAQ is also available as JSON: https://airolloutframework.com/api/framework

Structured Q&A dataset

qa-dataset.json is a flat JSON array of every {question, answer, source_url} already published in FAQPage schema across the site, deduplicated. It is generated by tools/build-qa-dataset.py directly from the live pages' own JSON-LD — not hand-compiled — so re-running that script after any FAQ change keeps it accurate automatically.

MCP server (agentic access)

A public MCP server exposes 6 tools — framework overview, pricing, FAQ, the AI at Work course outline, knowledge-base search, and a real server-side AI Readiness Score assessment — callable with no authentication. See mcp-capabilities.md for the endpoint, protocol details, and real example calls.

Contact

info@airolloutframework.com · airolloutframework.com

Install

Tools (6)

get_framework_overviewget_pricingget_faqget_ai_at_work_coursesearch_knowledge_baseassess_ai_readiness

Remote endpoint

Streamable HTTP

Hosted server - connect over the network, no local install.

url

https://airolloutframework.com/mcp

Add to VS Code Add to Cursor

claude_desktop_config.json

json

{
  "mcpServers": {
    "ai-rollout-framework": {
      "command": "npx",
      "args": [
        "-y",
        "mcp-remote",
        "https://airolloutframework.com/mcp"
      ]
    }
  }
}

Desktop config is stdio-only; this bridges via mcp-remote. Native remote: Settings > Connectors.

🛠️ Key Features

🚀 Use Cases

⚡ Developer Benefits

⚠️ Limitations

Topics

get_framework_overview

get_pricing

get_faq

get_ai_at_work_course

search_knowledge_base

assess_ai_readiness

AI Rollout Framework — Knowledge Base

Where this content lives

Index of files

Structured data

Read-only JSON API

Structured Q&A dataset

MCP server (agentic access)

Contact

Install

Remote endpoint

Related servers