What We Can Share
Case Studies
Real-world stories analyzed through the same framework: data, processes, people, and decision-making. Because AI doesn't fail because of the technology—it fails because of the strategy used to integrate it into your business.
The right automation for the wrong company
Founder · E-commerce · Over 10% of EBIT lost
Data
Unmapped company data. The agency built on top of chaos, not clarity. The raw material was missing.
Processes
Zero documentation prior to implementation. Automation amplified the disorder rather than solving it.
People
The Founder lacked the tools to evaluate the agency's proposal or understand the technical risks before signing.
Decision-Making
A purchase driven by vendor hype, without an independent assessment. No internal governance on AI spending.
The Intervention
Immediate project halt. Paused redundant automations to restore the e-commerce channel. Redesigned core processes before writing a single new line of code.
The Result
Online revenue restored. Avoided additional spending to "patch" a system built on a broken foundation.
You can't cut costs if you don't know what they are
Founder · Manufacturing & Distribution · About to sign off on €15,000 of useless tech
Data
Revenue forecasts were an illusion—manually copied and unverifiable. Calculating ROI was impossible without knowing the actual starting costs.
Processes
Existing in-house systems already supported the functions they wanted to automate. No one knew. Untracked processes kept real operating costs invisible.
People
Management made decisions based on gut feeling rather than data. They lacked the ability to read real margins or create verifiable forecasts.
Decision-Making
They were about to buy automation to solve a non-existent problem. No internal assessment preceded the vendor's quote.
The Intervention
Blocked technical implementation. Strategic shift toward mapping real processes and reconstructing actual cost data.
The Result
€15,000 saved. For the first time, management gained real visibility into margins and the ability to make data-driven financial forecasts.
AI hallucinated, and three departments were already blaming an innocent supplier
Head of Operations · Italian Enterprise · Supply chain at risk due to a "perfect" but entirely false report
Data
A 12,000-row database that exceeded the model's context window. The team was unaware of this technical limit. The AI hallucinated critical, non-existent anomalies.
Processes
No protocol for validating AI output. The report was immediately treated as "operational truth," mobilizing three departments within hours.
People
The Head of Operations knew how to use Claude, but didn't understand LLM limits on large datasets. Two different skills; two very different outcomes.
Decision-Making
No one stopped to verify the raw data. In the company's governance structure, a professional-looking report was treated as verified data.
The Intervention
Raw data audit and hallucination identified. The task force was halted immediately before formal accusations were made against the supplier.
The Result
Diplomatic fallout avoided. Weeks of wasted work saved. Internal protocols for AI data analysis implemented.