OI AI Adoption
Advice on bringing AI into work processes, with sourced effects, risks and rules for SI, HR, DE, AT
| Endpoint | https://operative-intelligence.com/ai-adoption (streamable-http) |
|---|---|
| Registry version | 0.1.0 |
| Now | 8 tools · grade A (97) · pin ts_b0e1d757c012d24a782ad0638ba5cb13 · protocol 2025-06-18 |
| First / last listed | 2026-11-11 00:32 UTC / 2026-11-11 00:32 UTC |
| Grade over time | 2026-11-11: A (97), 8 tools |
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Tools: get_readiness_questions assess_ai_readiness estimate_ai_costs plan_ai_rollout adoption_list_process_types adoption_find_patterns adoption_get_pattern adoption_get_regulations
get_readiness_questions | Returns the self-assessment questions on AI readiness in four areas (data, processes, people, compliance), each with its id, the reason it is asked and the allowed answer options. Answers are option ids, not free text. params: lang |
assess_ai_readiness | Scores the answers from get_readiness_questions with fixed rules: a level from 0 to 4 per area, the rule behind each limit, the gaps found and a next step for each gap. Unanswered areas are reported as not assessed. The result is a self-assessment. params: answers*, lang |
estimate_ai_costs | Estimates the costs of introducing AI, shown separately as external costs (contractor, DPIA, legal review, AI literacy training), internal staff time, licences per month and operations per month (hosting, model usage, maintenance), and the payback period in three savings scenarios. Public data with source and retrieval date: Eurostat labour costs (for internal staff time only), published licence, params: country, process_hours_per_week*, task_type, own_savings_share, external, compliance, internal_hours, internal_hourly_cost_eur, internal_it_hourly_cost_eur, licence, hosting, model_usage, maintenance_eur_per_month, lang |
plan_ai_rollout | Returns a phased rollout plan (discovery, pilot, rollout, operations) with tasks, roles in RACI form, risks and indicators for the chosen implementation pattern. When readiness answers are given, preparation tasks for areas at level 0 or 1 are added to the discovery phase. params: pattern*, answers, lang |
adoption_list_process_types | Returns the six process types of the knowledge base (data capture, communication, documents, planning, control, knowledge) with a definition and the ids of the adoption patterns of each type. params: lang |
adoption_find_patterns | Advises on using AI in a process the user describes, from a curated knowledge base with sources. The caller passes the description, the task that best matches it and the process properties it states; the server selects deterministically and does not use a language model. The result puts questions to ask first (ask_first) at the top, then one recommended way as a workflow (recommendation: how it wo params: description*, task, properties, process_type, user_unsure, lang |
adoption_get_pattern | Returns the user-facing recommendation for one pattern (workflow, sourced effects, needs, risks, a first step) and, in an internal part, the full pattern with every claim resolved to its sources (title, publisher, URL, date read, location) and evidence level, or marked as having no source. The internal part answers questions such as what the advice is based on. params: id*, sections, lang |
adoption_get_regulations | Returns the EU and national provisions in the regulation register for the chosen country, optionally only those linked to one pattern, each with what it requires in plain words, the article, the date checked and a link to the official text. params: country*, pattern_id, lang |
Entries describe changes in tool definitions. They are not statements about a server, its quality or its author. The grade measures how the tool definitions fit model providers (token cost, schema compatibility, confusable names) as scored by the ToolFit engine at the time of the listing. JSON: append .json. Opt-out: how or hello@youraitools.co.