InsightsSeptember 20, 2026

Does Your Company Actually Need AI? A 5-Question Test

Not every problem needs AI. Answer these five questions to find out whether you need AI, automation, or plain classical software.

ByQorAi.Space

Every company says “we should do something with AI.” But not every problem needs AI.

Ask these five questions. Your answers will show you which technology you actually need.

Question 1: Is your problem repetitive and rule-based?

Example: “We enter 200 invoices manually every day.” / “We answer customer emails from the same templates.”

Yes → Simple automation / RPA / scripting (not AI)
No → Go to Question 2

Why: If the rule is fixed, the data shape is stable, and there is no judgment call → AI is expensive and unnecessary. A simple script or RPA (robotic process automation) solves it.

Question 2: Is your problem about a “right answer” or a “best option”?

Example: “A customer asks ‘Where is my order?’” (Right answer: the tracking number)
Example: “Which route burns the least fuel?” (Best option: optimization)

“Right answer” (information access, Q&A, document search) → RAG / LLM / semantic search
“Best option” (planning, routing, allocation) → Optimization / operations research / quantum-inspired

Why: LLMs are excellent at retrieving information. But in mathematical optimization — constraints, objective functions — they make mistakes. There you need operations research (OR) or quantum-inspired algorithms.

Question 3: Do you have labeled historical data?

Example: “We have three years of churn data — which customers will leave?” (Yes → ML)
Example: “We are launching a new product and need demand forecasting.” (No → prediction will be hard)

Yes (enough quantity, quality, labels) → Machine learning (classification / regression / forecasting)
No (no data, too little, dirty, unlabeled) → Collect data first / use simple heuristics / try LLM few-shot

Why: An ML model is only as good as its data. Garbage in, garbage out. Without data, data engineering comes first — ML second.

Question 4: Is your problem about generating content or understanding it?

Generating: “Write a report”, “draft an email”, “write code”, “translate this” → Generative AI (LLM) Understanding: “Does this contract contain a risky clause?”, “What category is this complaint?”, “Extract common themes from these documents” → NLP / classification / extraction (LLM or classical NLP)

Both can be done with an LLM, but for understanding tasks, smaller fine-tuned models or classical NLP (rule-based, regex, established NLP libraries) can be faster, cheaper, and more transparent.

Question 5: Should the system decide, or assist?

Decider (autonomous): “Stock drops → place the order automatically” → High risk, requires auditing, human approval is a must Assistant (supportive): “Stock drops → suggest a reorder to the procurement specialist” → Low risk, fast value, human in the loop

Autonomous decisions → You need rigorous testing, an audit trail, human-in-the-loop, and a rollback mechanism. “The model works” is not enough.
Assistant → Fast pilot, low risk, immediate value. Most companies should start here.

Result: Which Technology Do You Need?

Your answers	Recommended approach	Example tools
1=Yes, 2=—, 3=No	Simple automation / scripting / RPA	Python scripts, n8n, Zapier, Power Automate
1=No, 2=Right answer, 3=No	RAG / LLM + your own data	LangChain, LlamaIndex, OpenAI/Anthropic API, local LLMs
1=No, 2=Best option, 3=Yes/No	Optimization / OR / quantum-inspired	OR-Tools, Gurobi, CP-SAT, QAOA, D-Wave
1=No, 2=Any, 3=Yes (data exists)	Machine learning	scikit-learn, XGBoost, PyTorch, AutoML
1=No, 2=Generating, 3=No	Generative AI	GPT-4, Claude, Mistral, local LLMs (Ollama)

Summary: The Decision Tree

Is your problem repetitive + rule-based?
  ├─ YES → Simple automation (NOT AI) → DONE
  └─ NO → Continue

Do you have labeled historical data?
  ├─ YES + a "best option" problem → Optimization / OR / quantum-inspired
  ├─ YES + a "right answer / prediction" problem → Machine learning
  └─ NO → Continue

Is your problem about understanding/extracting content?
  ├─ YES → RAG / NLP / LLM (a small model may be enough)
  └─ NO → Is it about generating content?
      ├─ YES → Generative AI (LLM)
      └─ NO → Unclear → Define the problem more precisely first

Final Word

AI is not the answer to everything. Ask the right questions, sharpen the problem, and start from the simplest solution that works.

“If all you have is a hammer, everything looks like a nail.” — Abraham Maslow

At QorAi.Space, we understand the problem first and pick the technology second. We validate with a pilot and scale only once the value is proven.

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