lab
Lab: working notes on decision systems
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Decision simulation for mid-sized companies: less room for error
Decision simulation means testing a commercial decision against historical data and scenarios before implementing it in the real world: “What would have happened if we had made this decision?” For mid-sized companies, this is even more critical than it is for large enterprises, because the relative cost of a bad decision is much heavier for a smaller business. While large corporations focus on the most advanced tools, mid-sized FMCG and retail companies are often left in a blind spot.
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Consultancy as code: not a consulting report, but a decision prescription
Consultancy as code means designing the consulting deliverable as an executable decision prescription rather than a static report. A traditional consulting report offers an impartial analysis and leaves the reader alone with the question, “So what should we do?” This Lab does something different: each article is not merely an analysis, but an actionable decision framework. Not an idea, but a prescription.
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Multi-agent decision lab: a simulation in which agents review one another
A multi-agent decision lab does not leave a decision to a single artificial intelligence model. Instead, agents with different roles, a proposer, a challenger, and a judge, test one another’s reasoning. The decision is not the output of one model, but the result of a debate. The reason is simple: a single AI agent cannot see its own blind spot.
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AI fatigue: rebuilding trust after failed pilots
AI fatigue is the cautious frustration that develops within an organization after a series of failed or unfinished artificial intelligence pilots. The solution is counterintuitive: not a larger project, but a smaller and faster proof. Before spending months building a system, manually simulate the decision it would produce and demonstrate its value, the “Wizard of Oz” approach.
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Why can’t you measure the ROI of your AI investment? Because you are not simulating it
Companies are investing in artificial intelligence but cannot demonstrate the return. This appears to be a measurement-tool problem, “we are not tracking the right metrics”, but the deeper issue is that there is no basis for comparison against which ROI can be measured. The AI system is put into production without first simulating what it would do in a real context. As a result, its value can neither be estimated beforehand nor compared with a reference point afterwards.
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Decision stress testing: not whether AI gives the right answer, but whether it avoids the wrong move in a crisis
Decision stress testing evaluates an AI decision system not under average conditions, but under the worst plausible scenarios, such as a price war, inventory shock, or demand collapse. It does not measure whether the system gives the “right answer.” It measures the quality of its behavior during a crisis: Does it know when to stop, when to escalate to a person, and how to avoid a catastrophic move? The most expensive mistakes in a decision system usually happen not on an ordinary day, but under pressure.
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The anatomy of an AI decision failure: where expensive mistakes begin
Expensive enterprise AI decision failures often originate not in the model’s “stupidity,” but in the design of the system surrounding it. The same model may work reliably in one structure and produce a serious error in another. This article is an attempt at diagnosis: it examines where a failure begins across five structural layers. The first condition for preventing an error is looking for it in the right place.
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The profitability paradox: is your largest customer your most valuable customer?
The profitability paradox is the possibility that a company’s highest-revenue customer becomes its least profitable, or even loss-making, customer after all service costs are deducted. For FMCG manufacturers, distributors, and companies with recurring B2B sales, this is not a theoretical nuance. It is an error that directly distorts resource allocation: the largest customer receives the best terms even though its net contribution may already have turned negative.
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Promotion growth or margin erosion?
A revenue increase during a promotion is not the same as a profit increase. Much of the uplift visible on the screen is often not genuine new demand. It is volume that would have been sold anyway, now sold at a lower price, plus future purchases pulled forward into the promotion period. For FMCG manufacturers and distributors, this distinction is critical because the feeling that “sales increased” can hide margin that is quietly disappearing underneath.
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Sell-out is not a historical report; it is a commercial alarm system
Sell-out monitoring for mid-sized FMCG companies. A practical framework for seeing a store-SKU deviation before it shows up in the monthly result.
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Silent churn: the loss Excel sees too late
Silent churn is the gradual loss of a customer who never formally “leaves,” but slowly reduces order frequency and basket size until the relationship has effectively disappeared. For B2B companies and FMCG distributors built on recurring orders, it is one of the most dangerous forms of customer loss because there is no cancellation and no email saying, “We are no longer working with you.” The customer does not suddenly go; the relationship slowly fades. A company looking only at monthly total revenue often notices the decline only after the opportunity to intervene has passed.
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Not a visit list, but next best action
A visit list tells the field team whom to visit and when. Next best action tells them what to do during each visit: the one concrete action most likely to create value for that customer. This distinction matters in FMCG field sales, distributor coverage, and retail operations. The list fills the route, but leaves the content of the visit empty. What fills that gap is the right customer-specific action.
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The “beautiful dashboard” trap in management reporting
The “beautiful dashboard” trap occurs when a management report is optimized for visual appeal and comprehensiveness rather than for changing a decision. The screen looks impressive and may be admired once, yet no decision changes afterwards. It is a common blind spot for mid-sized companies trying to become data-driven and for BI and reporting teams. The problem is not that the report looks good; it is that visual quality is mistaken for value.
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Managing AI hallucination
AI hallucination occurs when a language model produces an output that sounds fluent and confident but is actually incorrect or entirely fabricated. The critical point for enterprises is that this is not a temporary defect that the next model release will solve. Theoretical research and model providers’ own analyses indicate that hallucination is a structural risk that must be managed. For companies using and building AI, the real question is not “How do we eliminate it?” but “How do we manage it?”
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Turning market monitoring into a system
A practical approach to market monitoring for FMCG teams, covering pricing, promotions, pack changes and trade activity before they show up in commercial results.
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From Excel to a decision system
Excel is a calculation tool. A decision system is a structure that records how a decision was made, which data supported it, who approved it, and what the result was. When the two are confused, a company entrusts its most critical decisions, pricing, discounts, inventory commitments, and budgets, to a file without an audit trail, dependent on one person’s knowledge, and containing errors that may remain invisible. For FMCG, retail, and mid-sized companies, this is a quiet but structural risk.
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