Gains:
- Can list the types of tasks where AI is strong
- Recognizes areas where AI is structurally weak
- Can decide whether it's appropriate to outsource a job to AI
The secret to using generative AI effectively is to see it not as an “all-knowing magic box” but as an assistant that is very strong on certain tasks and structurally weak on certain tasks. Giving the wrong job to the wrong tool wastes time and has risky consequences. In this unit, we will outline the strengths and weaknesses of AI with concrete examples so that you can make the right decision before outsourcing a job to it. The key sentence is this: AI's weaknesses are not random; It stems directly from how it works. Remember the second unit — the model makes a probability prediction, not a reality check.
Jobs Where AI is Strong
Language models shine on language-related tasks that have "no one clear cut answer":
- Convert text: Summarize a long report, simplify a formal text, change an email to a gentler tone.
- Producing drafts: First draft email, blog post, job posting, presentation plan. It solves the "blank page" problem.
- Idea generation: 20 slogans for a campaign, possible solution titles for a problem, agenda suggestions for a meeting.
- Reorganizing and formatting: Turning scattered notes into bullet points, turning a text into a table.
- Language work: Translation, grammar correction, tone adjustment.
- Explanation and teaching: Explaining a complex concept by simplifying it and producing examples.
Tip: The safest and most powerful use of AI is for tasks you source: “Summarize that text,” “edit those notes.” Here the model does not fit information, it just transforms what you give it. This is the region with the lowest risk and highest benefit.
Jobs Where AI is Weak
- Exact calculation and mathematics: May make mistakes in complex arithmetic; Because it doesn't calculate, it guesses. (Some tools compensate for this with calculator add-ons, but that's the nature of the basic model.)
- Current and real-time information: The model's information is up to the date it was trained. Today's exchange rate, yesterday's news or your company's internal data alone are not reliable.
- Facts that require certainty: Can fabricate (hallucinate) verifiable information such as date, name, number, source, quote.
- True judgment and judgment: Cannot have the final say in ethical, legal or strategic decisions; People carry the context and responsibility.
- Physical world: Doing manual work and observing in the field are already out of its scope.
Attention: The most dangerous thing about AI is that it answers confidently, as if it were strong, even in a subject it is weak on. Can produce a fluent fabrication instead of saying "I don't know." Therefore, it is necessary to verify the output in weak areas.
Decision Guide: Should I Give This Job to AI?
Question
If "yes"
If "No"
Is the work mostly language/text work?
AI may be suitable
be careful
Is there more than one acceptable answer?
AI is powerful
Risky if precision is required
Am I providing the source/data?
safe
The risk of fabrication increases
Will a human control the output?
safe
Unsupervised use
Is the mistake costly (money, health, legal)?
Extra verification required
Can be used comfortably
Three Mini Cases
Case 1 — Contract summary (strong use). One manager had AI summarize a 42-page service agreement; He received a clear list of 8 items, and his reading time decreased from 40 minutes to 6 minutes. But "should we sign this contract?" He left the decision to his legal team. Correct boundary: AI prepares summary, human makes the decision.
Case 2 — Made-up account (poor usage). An employee asked, "Each of 12 products is 37.50 TL, how much is the total with 18% VAT?" he asked and put the resulting number directly into the offer. The number was wrong; The model didn't calculate, it predicted. Result: wrong price went to the customer. Lesson: use spreadsheets for numerical precision, not AI.
Case 3 — Outdated information. An assistant asked the AI about the deadline to apply for a government grant; The model was old and gave an incorrect date. Since the employee did not confirm this on the official institution's website, he shared incorrect information at the meeting. Lesson: current/official information is always verified from the primary source.
Weak Prompt / Strong Prompt
Weak prompt: Give 2024 inflation figures and their source.
Conclusion: The model can fit up-to-date and verifiable numerical data — high risk.
Powerful prompt: Based on the official report I pasted below, create a summary using only the data in this text. Do not add any numbers that are not in the text.<report>[paste official text here]</report>
Result: The risk of fabrication is greatly reduced because you provide the source.
Copiable Templates
Consider whether I should give the following task to the AI:Task: [write task]Answer these questions: (1) language task? (2) Is there only one truth? (3) Am I providing the source? (4) Is the cost of error high? Finally decide "suitable/careful/not suitable for AI".
Summarize this text. Rules:- Only use the information in the text, do not add anything from outside.- If you are not sure about something, write "not specified in the text".Text: [paste here]
Generate (brainstorm) 10 ideas on this topic. Diversity, not accuracy, is important. Subject: [subject]. List ideas under short headings.
In the draft below, mark all the points that need to be verified (number, date, name, source) and a "to-verify" list will appear.Draft: [paste here]
Common Mistakes
Common mistakes
- Using AI for calculation and exact number. Arithmetic and financial precision are AI's weak area; Use a spreadsheet or official source.
- Receiving current information without verification. The model's information is old; Confirm current data such as date, price, legislation from the primary source.
- Delegating the decision to AI. AI generates options and drafts; The responsibility for decisions such as recruitment, signing and investment lies with people.
- Trusting when you see the "sure" tone in the weak area. The confident language of the model is not evidence of accuracy.
Correct Mindset: Partner, Not Authority
The healthiest approach is to view AI as a smart but inexperienced intern. He works fast, knows a lot, does not get tired; But important work should not be undertaken without being checked, because even if it seems sure, it can be mistaken. When you position him/her not as an "authority" but as a "partner" that accelerates your business, you will both get efficiency and be protected from risks.
In summary
- AI; Strong in language tasks such as summarizing, drafting, coming up with ideas, translating and reformatting.
- AI; It is inherently weak in tasks that require precise calculation, up-to-date information, verifiable facts, and final judgment.
- The biggest risk is that he appears confident even on subjects he is weak in.
- Before assigning a job, ask: is it a language job, is there only one right way, am I providing the source, will people check it, is the cost of error high?
- Position AI as a regulated partner, not an authority.
Application Task
Choose 3 of your jobs this week. For each of the above “Should I give it to AI?” Apply the template and classify as "appropriate / careful / not suitable". Try at least one "suitable" task with the AI and verify the output yourself.
Checklist
- [ ] I can think of at least 4 types of missions where AI is strong.
- [ ] I know areas where AI is structurally weak.
- [ ] I can follow the decision guide before delegating a task.
- [ ] I know not to rely on AI for accurate calculations and up-to-date information.
- [ ] I position AI as a “controlled partner.”