// ASSESSMENT | 10 DAYS | FIXED PRICE
AI Status Quo Sprint:
The Truth About Your Data and Processes.
This AI audit is not a theoretical readiness check; it is an operational assessment. Many companies believe they are not ready for AI, while their employees already use tools like ChatGPT or Claude, often going around IT (shadow AI). We show you in black and white where you stand: data maturity, tech stack, shadow AI usage, team skills. Outcome in 10 days: a traffic-light scorecard that feeds directly into the roadmap.
// DEVELOPING PRODUCTION-READY AI SOLUTIONS FOR
Fixed price | Clarity in 10 days | No consulting loop
// DEFINITION
What is the AI Status Quo Sprint?
// THE BOTTLENECK
Assumption or reality
Assumption (what boards believe):
Our data is well structured. We barely use AI in the company. IT has everything under control. Our people are well-trained, we have standards. When we introduce AI, we start on a clean slate. Compliance is covered by existing policies. We know what our competitors are doing in AI.
Reality (what we typically find):
In 80 percent of audits, 10 to 50 different AI tools are running in the company, most without approval. Customer data ends up in free ChatGPT accounts. Four out of five critical data fields sit in Excel silos. Central systems are connected, but via CSV export. Employees are motivated but wonder what they are allowed to do. There is no AI policy in operational use. In the market, smaller and more agile companies are overtaking the industry leaders.
// COMPARISON
Assumption vs. reality, what audits typically show
Area
Assumption
Reality (typical finding)
Shadow AI
We barely use it
10 to 50 tools in use, often without approval
Data quality
Is in order
4 of 5 critical fields in Excel silos
System integration
Everything via APIs
CSV exports by email, data copied by hand
Compliance
Covered by standard policies
No AI-specific policy operationally in effect
Team skills
We start from zero
Employees already use tools, need guardrails
The 10-day sprint: Discover, Diagnose, Deliver
/ APPROACH
01
Days 01–04
Discover
We start with a kickoff workshop (90 minutes remote) plus 6 to 10 structured stakeholder interviews across sales, marketing, IT, operations, finance. In parallel, an anonymized employee survey on tool usage runs. We pull samples from data systems, we don't need full access, schemas and selected tables are sufficient. Day-to-day work isn't blocked.
02
Days 05–07
Diagnose
Data analysis offline. Data maturity gets placed on a 4-stage scale (stage 1: heads and paper, stage 2: Excel and silos, stage 3: data warehouse, stage 4: AI-ready APIs). Shadow AI inventory gets classified by risk. Tech stack gets checked against typical integration patterns (SAP, AS/400, Dynamics, custom in-house). Gap analysis per area.
03
Days 08–09
Synthesis
From the findings comes the traffic-light scorecard: red (action needed), yellow (okay but old), green (ready). Concrete next-step recommendation per finding. Quick wins get identified: what can be lifted with today's state immediately? What needs 6 months of groundwork?
04
Day 10
Deliver
Presentation of results in the stakeholder group (2 hours remote). 15-page report plus traffic-light scorecard plus prioritized recommendations list. Optional: direct transition into the roadmap workshop for follow-up weeks.
// SCORECARD
Your report after 10 days: the traffic-light scorecard
No 100-page strategy deck, no legal jargon, and no glossy consulting language. You get a clear red-amber-green scorecard that every executive stakeholder can understand in two minutes.
A typical finding looks like this: red on data quality (action needed, Excel silos in master data), amber on the tech stack (old but functional, AS/400 stable but CSV-driven), and green on team skills (employees are ready, but need clear guardrails).
The roadmap follows directly: quick wins in the green areas, investments in the red areas, monitoring in the amber areas.
// FAQ
Frequently Asked Questions
Little. For each stakeholder, a 45- to 60-minute interview, plus a 90-minute kickoff and a 2-hour closing presentation. Employee survey runs asynchronously, we pull data samples ourselves from schemas. We don't block day-to-day work, we connect briefly.
No. Schema structures and samples are enough. We don't need to see the full customer dataset to assess data maturity. Pseudonymized samples plus documented table structures suffice. NDA is standard, GDPR-compliant procedure is a given.
No. The sprint is a technological and operational audit, not a legal or accounting audit. We assess data maturity, shadow AI risks, and tech stack capability, not legal compliance against GDPR paragraph X or EU AI Act article Y. For legal assessments we refer you to your in-house legal team or law firm, ideally in combination with our governance cluster.
// PROVEN RESULTS
How an industrial mid-market company discovered 27 shadow AI tools and sorted them in 6 weeks
27 shadow AI tools identified
8 released as quick wins
4 stopped as security risk
Data maturity: stage 2 (Excel-dominated)
Anonymized case study from manufacturing mid-market, 380 employees, traditional mechanical engineering. Starting point: the board believed AI was "not yet a topic." Sprint finding: 27 tools in use, of which 4 with customer data in public models. Within 6 weeks after the sprint: tool cleanup, employee handbook, quick-win sprint on master data enrichment. Today: AI use enabled and controlled.
// READY?
Book a Sprint, Clarity in 10 Days.
Fixed-price sprint, 10 working days, defined output. We deliver the traffic-light scorecard, prioritized recommendations list, and the foundation for your roadmap. No consulting loop, no open end. Directly usable next Monday.