In classic Mittelstand teams, AI rarely fails because of the model — it fails because of chaos: tools, data, owners, approvals. Build a small internal AI‑intake mini‑app that collects use‑cases, triages risk, assigns owners, and stores evidence. You get speed + control instead of “AI ban vs. wild growth”.
Primary source (quick but real)
The EU’s AI Act (Regulation (EU) 2024/1689) follows a risk‑based approach. Practically, that means you need a reliable way to capture AI use‑cases and handle them by risk/scope before scaling.
Primary source:
- EU AI Act (Reg. (EU) 2024/1689) on EUR‑Lex: https://eur-lex.europa.eu/eli/reg/2024/1689/oj
Not legal advice — this is an implementation/process play.
Pain/Case hook: “AI in the company” turns into shadow IT
Common 2026 pattern:
- Marketing uses 3 tools, sales uses 2, HR adds another.
- Nobody can answer: what data goes in, who is accountable, how it’s evaluated.
- Legal/Security responds with stop signs (“please clarify first…”) — and everyone hates it.
Better: don’t “allow/ban AI”. Build a single intake flow.
The minimal system: one front door for every AI idea
Goal: every AI idea has one place, one owner, a status, and an evidence set.
The 80/20 building blocks:
- Use‑case form (purpose, users, data types, vendor/model, output)
- Lightweight risk triage (Low / Medium / High / Unknown)
- Owners + approvals (business, IT, privacy/security)
- Pilot gate (tests can start, but with clear rules)
- Evidence vault (links, contracts, DPIA artifacts, approval log)
What triage looks like (without turning it into a legal project)
You don’t need a perfect compliance engine. You need early signals.
Useful triage questions:
- Does it process personal data or confidential information?
- Does AI influence decisions about people (hiring, credit, access, scoring)?
- Is the output external (customers) or internal (assistive)?
- Any automated decisions without meaningful human control?
- What controls exist (logging, human‑in‑the‑loop, prompt/policy guardrails)?
Result: you quickly see whether it’s a fast pilot or a governance project.
Checklist 1: AI use‑case intake (copy/paste)
- Business goal (1 sentence) + expected impact (time/€, quality)
- User group + accountable owner
- Input data (public / internal / confidential / personal)
- Output (internal / external) + consequence if wrong
- Vendor/model (tool, hosting region, sub‑processors)
- Integrations (Slack, CRM, files, email, ticketing)
- Human‑in‑the‑loop: where must a person approve?
- Logging/monitoring (what is stored, for how long?)
- Pilot go/no‑go criteria
Checklist 2: AI‑intake mini‑app spec (shippable in 14 days)
- MUST: form + single intake channel (no spreadsheets)
- MUST: status board (Submitted / In Review / Pilot / Approved / Rejected)
- MUST: owner + reviewer roles (business/IT/privacy/security)
- MUST: evidence uploads + audit trail (who approved what when)
- SHOULD: triage wizard (risk flags + default recommendations)
- SHOULD: templates for common use‑cases (writing assist, support bot, summarization)
- NICE: automated checks (PII detection, DLP, prompt policy)
- NICE: export an “AI Use‑Case Dossier” (PDF/ZIP)
A realistic 14‑day plan
- Days 1–2: define the form + roles/approvals
- Days 3–5: status board + evidence vault + audit trail
- Days 6–8: triage wizard + standard pilot rules (do/don’t)
- Days 9–11: integrations (SSO, Slack/Teams, ticketing)
- Days 12–14: dossier export + a 1‑page pilot playbook
CTA: we’ll build your AI‑intake mini‑app
Send us:
- your top 5 AI use‑cases (short)
- what data is involved (internal/PII/confidential)
- who blocks/approves today (IT/privacy/security)
We’ll turn it into a small internal AI‑intake app that unblocks pilots, makes risk visible, and gives you an auditable trail.