AI adoption data model (DataPool)

Canonical DataPool schemas for AI-adoption discovery, scoring, and roadmaps. Assessment playbooks read and write these tables. Implementation on Tealfabric is a later step (implementation_links only).

Kind

Resource / canonical model.

When to use

Install first. Discovery, assessment, roadmap, operating-model, HR/back-office, and bridge playbooks depend on these schemas. Do not store assessment records in platform Entities, products, or contracts.

Depends on

None.

Prerequisites

Import contract

Suggested DataPool columns

Use these when creating a missing schema. Confirm with request_human_input before create_datapool_schema. If the tenant already has equivalent tables, bind names — do not invent a second model.

ai_engagements

as_is_processes

stakeholder_inputs

ai_use_cases

roadmap_items

implementation_links

Filled only by ai-tealfabric-bridge. Assessment playbooks must not write this table.

Tenant SKILL.md (copy this body)

After Prerequisite binding, replace tokens.

# AI adoption data model (DataPool)

Canonical AI-adoption records for this tenant live in DataPool. Load `load_platform_skill` with `skill_id: datapool`. Call `describe_datapool_table` before any row write.

Do not use platform Entities as the system of record. Do not put ProcessFlow / WebApp / Autonomic Agent ids on roadmap rows — those belong in `{{implementation_links_schema}}` after `ai-tealfabric-bridge`.

Deliverable markdown lives under `{{engagement_folder}}`.

## Bound schemas

| Role | schema_name |
|---|---|
| Engagements | {{engagements_schema}} |
| As-is processes | {{as_is_processes_schema}} |
| Stakeholder inputs | {{stakeholder_inputs_schema}} |
| Use cases | {{use_cases_schema}} |
| Roadmap items | {{roadmap_items_schema}} |
| Implementation links | {{implementation_links_schema}} |

## Sequence

1. `describe_datapool_table` for the bound `schema_name`.
2. Read with `query_datapool`. Filter by `engagement_id`; do not scan unbounded.
3. Writes: `insert_datapool_rows` / `update_datapool_rows` using **only** columns from describe. Confirm before `delete_datapool_rows` or schema drops.
4. Status and type values must match the enumerations in this playbook.

## Stop

- Do not invent column names.
- Do not copy platform strategy fields into use-case rows; cite `get_platform`.
- Assessment playbooks must not insert `implementation_links`.
- If describe fails, stop and report the missing schema.

Optional skill.json

{
  "name": "ai-adoption-data-model",
  "version": "1.0.0",
  "description": "Canonical DataPool schemas for AI-adoption discovery, scoring, and roadmaps.",
  "capabilities": {
    "prompt": [
      "Describe DataPool tables before writes. Bound schema_name values are the system of record.",
      "Do not write implementation_links except from ai-tealfabric-bridge."
    ],
    "resources": [],
    "tools": [
      "describe_datapool_table",
      "query_datapool",
      "create_datapool_schema",
      "insert_datapool_rows",
      "update_datapool_rows",
      "request_human_input"
    ]
  }
}

See also

• AI adoption advisor (user guide)
• AI discovery
• Model skills index