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Prompt or drown
We have become heavily addicted to one specific tool: the hammer. What do I mean? LLMs. And with a hammer, everything looks like a nail. ChatGPT, Claude, Gemini, all wonderful tools for language. Summarizing, rewriting, exploring ideas, developing rough concepts: fast, cheap, often good enough. But language ≠ knowledge, and language ≠ decision. An LLM predicts the next word; it has no understanding of cause and effect. That is a fundamental limitation.
What does that mean for executives and IT leaders? I'll put it simply, but it's not: the realization that not every problem is a language problem.
Let's look at a few examples where it is better NOT to use AI/LLM:
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Do you want to predict whether a customer will churn? Use classic machine learning with clear features and a validatable metric.
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Does a route, schedule, or inventory need to be optimized? Then you need optimization algorithms (operations research), not prompts.
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Looking for anomalies in log streams? Anomaly detection (statistics + ML) is more robust and reproducible.
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Image, sound, sensor data? Use computer vision or signal models trained on that data.
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And when it comes to ethics, compliance, or context that falls outside the scope? Then you need experienced people. Period.
LLMs can support but not guide. Decisions require reasoning, verification, and accountability. LLMs hallucinate, can reinforce bias, and are difficult to audit. {Say something like: Unfortunately, LLMs are also declining in quality, so people are doing more and more of the work themselves}. You can build safeguards—retrieval, tool use, rules, evaluations—but that doesn't make a language model a reliable decision-maker. At best, you increase its usability in a narrow, controlled workflow.
The pitfall is organizational: as soon as “AI” is deployed everywhere, ownership and measurability disappear. Teams start prompt-tuning instead of clearly defining the problem. Costs rise, quality becomes unpredictable, and no one can explain why an answer is “that way.” Best practices? Choose the right model for the right job:
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Define the business problem and the decision criteria.
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Determine whether it is primarily linguistic, predictive, optimizing, or detecting.
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Select the appropriate AI type (or human process) with measurable metrics.
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Use LLMs where they excel: interface, orchestration, and language tasks—not as the brain of your business.
In short: LLMs are a powerful screwdriver. But you can't build a bridge with them. For some problems, you choose a different type of AI. And for the rest, you choose {teach} people who know when not to choose AI.
Stop prompting—get real gains with real AI
LLMs are language. Many business problems are not. These are the alternatives that do deliver benefits such as speed, and where you can use them.
1) Classic Machine Learning (supervised) - predict & explain
Applications: churn prediction, lead scoring, bankruptcy risk, fraud detection with labels.
Why: fast training, clear features, explainable (“these 5 variables drive 80% of the result”).
KPIs: AUC, precision/recall, uplift relative to baseline.
2) Time-series forecasting - look ahead, not behind
Applications: demand forecasts, budget & capacity planning, stock levels.
Why: seasons, trends, and events are modeled; fewer stockouts, less dead stock.
KPIs: MAPE, sMAPE, service levels.
3) Anomaly detection - find deviations early
Applications: log streams, payments, IoT/sensors, performance spikes.
Why: unlabeled data, real-time alerts, few false positives with good tuning.
KPIs: false-positive rate, mean time to detect.
4) Optimization / Operations Research - plan what is possible
Applications: routes, schedules, allocation of people/machines, cloud resource planning.
Why: hard constraints, guaranteed best (or proven near-best) solution.
KPIs: cost per order, utilization rate, SLA hit rate.
5) Computer Vision & Signal Models - see what is really happening
Applications: quality control, counting/recognition, OCR + invoice processing, predictive maintenance (vibration/noise).
Why: trained directly on pixels/sensors; fewer human hands, fewer errors.
KPIs: accuracy/F1, turnaround time, first-pass yield.
6) Recommenders - increase revenue without discounts
Applications: up-/cross-sell, content suggestions, dynamic bundles.
Why: personalized without spam; proven revenue increase.
KPIs: CTR, conversion uplift, extra revenue per session.
7) Rules + Knowledge Graph + Bayesian - when explainability is crucial
Applications: compliance, claims triage, decision trees with context.
Why: transparent, auditable, combinable with ML.
KPIs: turnaround time, error rate, audit pass rate.
8) Reinforcement Learning - for control & policy
Applications: dynamic pricing, real-time energy management, intraday scheduling.
Why: learns from feedback; optimizes policy instead of individual decisions.
KPIs: reward per episode, cost/benefit vs. rule-based.
Practical: start with the problem, choose the class, define a measurable KPI, and validate against a simple baseline. LLMs? Fine as an interface or “glue.” The profit comes from the right model and from the person who decides when you don't need a model.
AI doesn't think. You do—so act like it ;)
We call it “intelligence,” but AI is primarily statistics that recognize patterns. Powerful? Certainly. Intelligent like a human? No. The difference lies in five things that you, as a CIO/CEO, cannot ignore.
1) Understanding vs. correlation
AI sees connections; humans understand causes. A model can perfectly predict that two events will occur together and still recommend the wrong course of action. Strategy requires causality, not just correlation.
2) Goal setting and values
AI optimizes the function we choose. People choose the goal, weigh interests, make moral considerations, and accept responsibility. If you formulate the goal incorrectly, you will get perfect optimization of the wrong thing.
3) Reasoning and transfer
Models generalize within the data on which they are trained. People can reason aloud, test assumptions, and apply knowledge to completely new situations. Out-of-distribution is an abyss for AI and a challenge for humans.
4) Context & common sense
AI has no ‘world model’ with common sense and no theory of mind. It has no implicit rules of office politics, culture, or power. Humans do. That is why a model often breaks down in borderline cases, precisely where decisions are made that affect profits or reputational damage.
5) Verifiability & responsibility
AI outcomes are often difficult to audit. People can explain their assumptions and steps, and above all, they can be held accountable. Governance without accountability is false security.
What does this mean for decision-making?
Use AI to see (patterns, deviations, scenarios) and people to judge (goals, trade-offs, action). Use models for detection, prediction, and optimization; use people for prioritization, ethics, context, and accepting consequences.
Practical rule:
• AI for analysis and options.
• Humans for direction and decision-making.
• Hybrid for speed with control: AI prepares, humans sign off.
AI can get you to an answer faster. Only humans can determine whether it is the right answer.
Faster, more stable, cheaper? Choose Sciante.
This is how we work at Sciante: targeted AI for the heavy computing work, human intelligence for the choices that matter. We measure where performance is lagging, costs are rising, or stability is leaking. We use the right techniques (ML, OR, anomaly detection) to uncover the facts.
We then translate that into clear decisions that you can implement immediately. So that your IT performs again: faster, more stable, cheaper. Cloud or on-prem? It doesn't matter to us. We optimize where the value lies.
Want to stop prompting and start performing?
Schedule a no-obligation appointment. We'll show you where you're losing money, which interventions yield the most, and how to regain control—without hassle, without vendor bullshit (sorry vendors, but someone has to say it).
Ready for optimal IT?
Let AI do the work. Let people determine the direction. With Sciante, you get both—neatly substantiated.