Pilot-Killer 01
Data debt
The model works on curated sandbox data. Connect it to ERP, MES, CRM and 14 Excel sources — it starts hallucinating. Not because the model is weak, but because the data reality was never designed for AI consumption.
Seven out of ten enterprise AI pilots die at the production handoff. Not at the model — at the missing decision architecture. We don't paste AI on top. We re-think the value-bearing processes AI-natively — classified per decision, audit-grade per step, EU-AI-Act and FDA-21-CFR-Part-11 compatible.
Three real demo workflows. The methodology stays in the studio. You get the result.
Try it on your workflow →Any company still asking whether to "engage with AI" is asking a question nobody will understand in five years — same as "should we do something with the internet" in 2005.
The honest question isn't whether, but how. And that's where the mistakes begin.
When a pilot collapses, it's rarely the model. It's almost always one of these three structural omissions.
The model works on curated sandbox data. Connect it to ERP, MES, CRM and 14 Excel sources — it starts hallucinating. Not because the model is weak, but because the data reality was never designed for AI consumption.
The model makes a call. Nobody can later reconstruct with which inputs, what confidence, against which rule. Compliance asks. Nobody answers. The pilot quietly goes offline.
Who can override the model? Above what threshold must a human step in? What happens when model and planner disagree? Leave this open, and no one dares follow the system.
This choice rarely gets made consciously. It usually happens in passing — and then decides whether the investment becomes a production system or an expensive demo.
The old process stays. AI sits beside it: a copilot for email, a chatbot for FAQs, a forecast tool in the same workflow. Output gets faster — but the old decision logic, the gut-feel overrides and the unlogged manager calls remain.
The value stream is decomposed into decision points. Each is assigned to one of four logic classes. The target process is built around where AI is strong — and where rules, optimizers or humans are demonstrably better.
For each decision point, three questions get answered: how big is the risk of a wrong call? how complex is the decision logic? how reversible is the outcome? From there, one of four classes naturally follows.
"Do AI" usually means the second class — and forgets the other three. That's the most expensive mistake in enterprise AI: spending tokens where a rule suffices. Putting humans where the system decides more consistently. Letting machines decide where a human signature is ultimately required.
Many companies treat AI governance like a policy document. But governance doesn't emerge from a PDF. It emerges where the decision is made — or it doesn't.
Workflow Architecture forces every decision point in the target process to carry a complete, machine-readable record. None of it is optional. None of it gets filled in "later".
That makes the target process EU-AI-Act compatible (Art. 12 logging, Art. 13 transparency, Art. 14 human oversight) and FDA 21 CFR Part 11 compliant for regulated industries. Compliance review turns from a risk into a routine slot.
Each workflow in the studio produces three coordinated documents — one for your sponsor, one for your architect, one for your pilot team. From the same source. Versioned. Diffable.
Reality, AI-native target process, damage chain (which backstop would have prevented which historical loss), value card per decision. A4 print-optimized. Steering / board / investor-ready.
Every methodological step with rationale. Classification reasoning per decision. Layer path and crosswalk to your internal process library. The document the architecture board needs to see before greenlight.
Shared infrastructure, sequential steps per decision point, end-to-end run, implementation hints, pilot KPIs. Straight into the wiki, the Jira epic, the pilot brief.
The studio proposes at each step. You review, edit, approve. If you want to keep it short, click Auto-Workflow once and land at the final result.
6–20 lines of reality. What happened when, by whom. Auto-normalizer cleans the timestamps.
Each line becomes a structured entry: signal, interpretation, decision, action.
Micro-decisions get clustered into value-bearing macro decision points. IDs assigned.
Deterministic · AI · Verified · AI + HITL · Human only — with rationale and confidence.
Which layers does which decision need — and which can you skip. Crosswalk to your tech stack.
Shared infrastructure, sequential steps, end-to-end run, implementation hints, pilot KPIs.
Damage chain, value cards, customer PDF, internal audit, markdown. Steering-ready.
Workflow Architecture isn't "a prompt". It's a pipeline that classifies every decision point before the model and anchors each statement back to the source through three layers of quality control.
Invented timestamps? Phantom numbers without source anchor? Audit layer skipped on a "deterministic" class? Hard-coded checks catch it before the LLM speaks.
While one language model generates the target process, a model from a different family checks the result for story fidelity, DP coverage, drift. Family-specific blind spots get caught.
Findings aren't just found — they get healed. The studio fixes all blocking + strong findings and re-checks until the artifact stabilizes.
These five workflows together cover almost the entire decision logic in planning & operations. They're the typical entry point because they pay directly into forecast accuracy, service level, working capital and agility.
Which signal is real, which is noise — and when does the forecast get consciously overridden?
Which promo is material, how big are uplift and cannibalization, is supply feasible?
Material or capacity bottleneck + multi-customer conflict. Who gets partial shipped, who gets express?
When is inventory built up, shifted, reduced — or consciously put at risk?
Which escalation really belongs on the management pack — and which should be decided operationally?
Not limited to planning & operations. Underwriting, claims, invoice approval, QA — anywhere decisions carry value.
You don't pay for seats, you pay for workflow transformations. A single clean target process replaces a consulting brief that typically lands in the five-figure range.
A deliberately small group of process owners and architects whose workflow reality shapes the next iteration of the methodology. Seats are allocated by fit, not by first-come-first-served.
Program conditions: Architect tier at €590 / month, locked for 24 months. 4 hours of direct methodology coaching with Marcus Wolf per month. Prioritized treatment of your workflow patterns on the roadmap.
We believe in the method, so we carry the risk on the first three workflows. If your pilot team or your steering committee doesn't sign off on one of the first three — email us. Full refund. No questions. No fine print.
We rebuild your first workflow as proof, AI-natively. You get all three artifacts (Customer-PDF, Internal Audit, Markdown), the damage chain, the full decision classification — no obligation, no credit card.
What you concretely receive:
What isn't here, we answer by email — personally, not through a sales funnel.
If you want to see on your own workflow which step falls into which logic class, which backstop would have prevented which historical loss, and how the target process looks as an audit-grade artifact — the first transformation is free.
It's not a demo. It's your reality, cleanly structured for once.