AI-first company: evidence on AI adoption

Start here for any question about adopting AI in a company: the map of the ai-first.company evidence. Returns the company cases by the kind of work AI was used for (customer service, legal work, office assistant and others), the six themes (with counts of practices, cases and studies), the claim checks of widely repeated AI statistics, the groups of other figures, the company cases by outcome, the assessments by kind, and what each filter value and rating means. Then open the part that fits and fetch records with get_evidence.

{
  "type": "object",
  "properties": {},
  "$schema": "http://json-schema.org/draft-07/schema#"
}

Open one part of the evidence map: a list (practices, cases, studies, figures, claim-checks, assessments) with filters such as theme, outcome, industry, company size, stage, priority, figure group (line) or assessment kind. Returns every matching row in one line with its id, or, when there are more than 25, a menu splitting them by the next filter, with counts. Use the arguments the menus show. Then fetch the rows you pick with get_evidence.

{
  "type": "object",
  "properties": {
    "list": {
      "type": "string",
      "enum": [
        "practices",
        "cases",
        "claim-checks",
        "figures",
        "studies",
        "assessments"
      ],
      "description": "Which list: practices, cases, studies, figures, claim-checks or assessments."
    },
    "theme": {
      "description": "A theme key from start, e.g. governance-and-risk or people-and-skills.",
      "type": "string",
      "maxLength": 500
    },
    "task": {
      "description": "Cases only: the kind of work AI was used for, a task key from start, e.g. customer-service-and-support or legal-research-drafting-and-filings.",
      "type": "string",
      "maxLength": 500
    },
    "line": {
      "description": "Figures only: the group key from start, e.g. time-freed or running-cost.",
      "type": "string",
      "maxLength": 500
    },
    "kind": {
      "description": "Figures: the kind of number, e.g. measured-in-a-study or self-reported. Assessments: the kind key from start, e.g. readiness-surveys.",
      "type": "string",
      "maxLength": 500
    },
    "outcome": {
      "description": "Cases with this outcome: measured, company-reported, went-wrong or walked-back.",
      "type": "string",
      "enum": [
        "measured",
        "company-reported",
        "went-wrong",
        "walked-back"
      ]
    },
    "industry": {
      "description": "Cases in this industry: professional-services, agencies, ecommerce-retail, manufacturing, or other (none of the four, e.g. banks, airlines, public bodies). Practices apply to all industries.",
      "type": "string",
      "enum": [
        "professional-services",
        "agencies",
        "ecommerce-retail",
        "manufacturing",
        "other"
      ]
    },
    "size": {
      "description": "Company size band: micro-small, medium, mid-market, enterprise (1–49, 50–249, 250–999, 1,000+ employees). Or pass employees instead.",
      "type": "string",
      "maxLength": 500
    },
    "employees": {
      "description": "Number of employees, e.g. 120.",
      "type": "integer",
      "exclusiveMinimum": 0,
      "maximum": 10000000
    },
    "stage": {
      "description": "How far the company is: explore, adopt, redesign or run.",
      "type": "string",
      "enum": [
        "explore",
        "adopt",
        "redesign",
        "run"
      ]
    },
    "priority": {
      "description": "Practices with this priority.",
      "type": "string",
      "enum": [
        "do first",
        "do next",
        "do later"
      ]
    },
    "free_only": {
      "description": "Assessments only: free to use.",
      "type": "boolean"
    }
  },
  "required": [
    "list"
  ],
  "$schema": "http://json-schema.org/draft-07/schema#"
}

Get the full records for ids from the menus and lists (such as "case:klarna-ai-customer-service" or "claim-check:mit-95-percent-of-ai-pilots-fail"): figures, ratings, verdicts, caveats, primary sources, the ai-first.company link, and the ids of related studies, cases and practices to follow. Up to 20 ids per call. Quote and cite only from what this returns.

{
  "type": "object",
  "properties": {
    "ids": {
      "minItems": 1,
      "maxItems": 20,
      "type": "array",
      "items": {
        "type": "string",
        "minLength": 1,
        "maxLength": 200
      },
      "description": "Ids as the menus and lists give them, e.g. [\"case:klarna-ai-customer-service\", \"practice:ask-ai-vendors-for-proof\"]."
    }
  },
  "required": [
    "ids"
  ],
  "$schema": "http://json-schema.org/draft-07/schema#"
}

Before quoting an AI statistic (e.g. '95% of AI pilots fail', 'only 13% of companies are ready for AI'), check where it comes from, who was asked and what it covers. Returns every claim check of a widely repeated statistic and the groups of other figures; fetch the matching claim check, or open the group the figure belongs to. Use it for figures from surveys, reports and the press. For a claim a vendor makes about its own product, use check_vendor_claim.

{
  "type": "object",
  "properties": {},
  "$schema": "http://json-schema.org/draft-07/schema#"
}

Check a claim made by an AI vendor or consultant, such as '30% productivity gain', 'cuts support costs by 40%' or 'our agents replace 700 staff'. Returns the questions to ask the vendor, the claim checks, the groups of figures and the company cases by the kind of work AI was used for, so you can find the same result and companies that tried the same kind of tool. Use it whenever someone selling or proposing AI quotes a result. For a statistic from the press or a report, use check_ai_figure.

{
  "type": "object",
  "properties": {},
  "$schema": "http://json-schema.org/draft-07/schema#"
}

Review an AI transformation proposal, business case or rollout memo, for example before it goes to a board. Returns the do-first practices for the company’s size as a checklist, each with its evidence rating and wording that may show it, and how to find similar cases. You read the plan and decide for each practice whether the plan commits to it, rules it out or leaves it out. Pass the size and industry; the plan itself stays with you. For one figure in the plan, use check_ai_figure.

{
  "type": "object",
  "properties": {
    "size": {
      "description": "Company size band: micro-small, medium, mid-market, enterprise (1–49, 50–249, 250–999, 1,000+ employees). Or pass employees instead.",
      "type": "string",
      "maxLength": 500
    },
    "employees": {
      "description": "Number of employees, e.g. 120.",
      "type": "integer",
      "exclusiveMinimum": 0,
      "maximum": 10000000
    },
    "industry": {
      "description": "The site’s industry category, if one fits: professional-services (consulting, accounting, audit, law), agencies (marketing, advertising, creative), ecommerce-retail (online shops, retail, wholesale), manufacturing. Map the user’s words, in any language, to one of these; if none fits (a bank, a hospital, a public body), leave it out.",
      "type": "string",
      "enum": [
        "professional-services",
        "agencies",
        "ecommerce-retail",
        "manufacturing"
      ]
    }
  },
  "$schema": "http://json-schema.org/draft-07/schema#"
}

List practices for adopting AI that fit a company's size, rated by strength of evidence. Use it for 'Where should we start with AI?' or 'What should a 120-person manufacturer do first?'. Pass the number of employees or a size band, and optionally the theme, stage or priority. Returns the practices in one line each, or a menu by theme when there are many; fetch the ones you use with get_evidence for their studies, cases and links.

{
  "type": "object",
  "properties": {
    "size": {
      "description": "Company size band: micro-small, medium, mid-market, enterprise (1–49, 50–249, 250–999, 1,000+ employees). Or pass employees instead.",
      "type": "string",
      "maxLength": 500
    },
    "employees": {
      "description": "Number of employees, e.g. 120.",
      "type": "integer",
      "exclusiveMinimum": 0,
      "maximum": 10000000
    },
    "theme": {
      "description": "A theme key from start, e.g. governance-and-risk or people-and-skills.",
      "type": "string",
      "maxLength": 500
    },
    "stage": {
      "description": "How far the company is: explore, adopt, redesign or run.",
      "type": "string",
      "enum": [
        "explore",
        "adopt",
        "redesign",
        "run"
      ]
    },
    "priority": {
      "description": "Practices with this priority.",
      "type": "string",
      "enum": [
        "do first",
        "do next",
        "do later"
      ]
    }
  },
  "$schema": "http://json-schema.org/draft-07/schema#"
}

List real companies that used AI and how it turned out: measured result, company-reported result, went wrong, or partly walked back. Use it for 'Who has done this?' or 'What went wrong for others?'. Filter by the kind of work AI was used for (task), outcome, theme, industry or company size; returns the cases in one line each, or a menu with counts when there are many. Fetch the ones you use with get_evidence for the story, who produced the figures and the public sources.

{
  "type": "object",
  "properties": {
    "task": {
      "description": "The kind of work AI was used for: a task key from start, e.g. customer-service-and-support.",
      "type": "string",
      "maxLength": 500
    },
    "outcome": {
      "description": "Cases with this outcome: measured, company-reported, went-wrong or walked-back.",
      "type": "string",
      "enum": [
        "measured",
        "company-reported",
        "went-wrong",
        "walked-back"
      ]
    },
    "theme": {
      "description": "A theme key from start, e.g. governance-and-risk or people-and-skills.",
      "type": "string",
      "maxLength": 500
    },
    "industry": {
      "description": "Cases in this industry: professional-services, agencies, ecommerce-retail, manufacturing, or other (none of the four, e.g. banks, airlines, public bodies). Practices apply to all industries.",
      "type": "string",
      "enum": [
        "professional-services",
        "agencies",
        "ecommerce-retail",
        "manufacturing",
        "other"
      ]
    },
    "size": {
      "description": "Company size band: micro-small, medium, mid-market, enterprise (1–49, 50–249, 250–999, 1,000+ employees). Or pass employees instead.",
      "type": "string",
      "maxLength": 500
    },
    "employees": {
      "description": "Number of employees, e.g. 120.",
      "type": "integer",
      "exclusiveMinimum": 0,
      "maximum": 10000000
    }
  },
  "$schema": "http://json-schema.org/draft-07/schema#"
}

List published questionnaires, tests and task maps to assess AI readiness, AI literacy, staff attitudes or which tasks suit AI, best tested first, optionally by kind or free to use. Fetch the ones that fit with get_evidence for how well each has been tested, who answers it, its length, licence and cost. For advice on what to do, use find_practices.

{
  "type": "object",
  "properties": {
    "kind": {
      "description": "The kind key from start, e.g. readiness-surveys or ai-knowledge-tests-and-self-ratings.",
      "type": "string",
      "maxLength": 500
    },
    "free_only": {
      "description": "Only assessments that are free to use.",
      "type": "boolean"
    }
  },
  "$schema": "http://json-schema.org/draft-07/schema#"
}

Prepare a one-page board brief on a question about AI adoption, from rated evidence only. Returns the company cases by the kind of work AI was used for, the themes (with practice counts for the company’s size), the claim checks and figure groups, and how to write the brief: open the cases for the work in question and one to three themes, fetch eight to twelve records and write from them. Use it when someone needs a summary for a board, an owner or an investment committee. To review a written plan, use review_ai_transformation_plan.

{
  "type": "object",
  "properties": {
    "size": {
      "description": "Company size band: micro-small, medium, mid-market, enterprise (1–49, 50–249, 250–999, 1,000+ employees). Or pass employees instead.",
      "type": "string",
      "maxLength": 500
    },
    "employees": {
      "description": "Number of employees, e.g. 120.",
      "type": "integer",
      "exclusiveMinimum": 0,
      "maximum": 10000000
    },
    "industry": {
      "description": "The site’s industry category, if one fits: professional-services (consulting, accounting, audit, law), agencies (marketing, advertising, creative), ecommerce-retail (online shops, retail, wholesale), manufacturing. Map the user’s words, in any language, to one of these; if none fits (a bank, a hospital, a public body), leave it out.",
      "type": "string",
      "enum": [
        "professional-services",
        "agencies",
        "ecommerce-retail",
        "manufacturing"
      ]
    }
  },
  "$schema": "http://json-schema.org/draft-07/schema#"
}