The biggest advantage of AI is speed on tasks that suit it: drafts, summaries, rewording, sorting notes. Its biggest disadvantages are errors that sound convincing, open questions about data protection and biases that come from the training data. Whether the advantages outweigh the drawbacks depends less on the AI than on the task and on whether a person checks the result.
I work with AI every day. My fourteen AI employees run on Claude Team, some also on open models or through the API. So I know both sides: how much work the AI takes on, and errors that would have gone out without a review. Here I weigh both, with evidence instead of gut feeling. All AI basics are on my AI Basics page.
The advantages of AI
- Speed on suitable tasks. In a field experiment by Harvard Business School with the Boston Consulting Group, 758 consultants worked on realistic tasks in 2023. Those allowed to use GPT-4, an AI model from OpenAI, completed on average 12.2 percent more tasks on work that suited the AI, were 25.1 percent faster and delivered significantly better quality.
- No expertise needed to start. You describe what you need in plain language. You don't have to program anything for a draft letter or a spreadsheet formula.
- Easier access to knowledge. The European Parliament lists as an advantage that AI can make access to information, education and training easier.
- More output in the business. According to the Parliament, AI can help companies boost sales, improve machine maintenance and improve customer service, among other things.
- Relief from routine. What happens the same way every week, such as replies to similar requests, the AI can prepare, and you only check.
An example from my business: from 27 August to 21 September 2026, Sebastian, my AI employee for SEO, and I grew kevinwelter.com in three stages from 10 to 224 posts per language. Before the start, from 1 to 26 August, the website had 5 Google clicks; by 17 September 2026, Search Console showed 1,060. That is a look back, not a promise for other websites.
The disadvantages and risks of AI
- Errors that sound convincing. The US standards body NIST calls this confabulation: confidently stated but false content, colloquially known as hallucinations. How to spot them is in AI Hallucinations: Why AI Makes Things Up.
- A jagged frontier. The same Harvard study also shows the flip side: on a task deliberately chosen to lie outside what the AI does well, consultants using AI reached the correct solution noticeably less often than those without AI. The researchers call this a "jagged" frontier because it runs even between tasks of similar difficulty.
- Bias. What an AI produces depends on how it was designed and what data it uses. The European Parliament warns that both can be biased, intentionally or unintentionally, and that numbers can make an AI seem more precise than it is.
- Data protection. NIST counts the leakage and unauthorised use of personal data among the risks of generative AI, the kind of AI that produces text, images or code.
- Too much trust. NIST also lists over-reliance on the AI and the tendency to attribute human qualities to it.
- The wrong use. According to the Parliament, investing in AI that proves not to be useful wastes resources, and AI is sometimes applied to tasks it isn't suited for.
- Environment. Training and running large models takes a lot of computing power. NIST lists the effects on the environment as a risk of its own.
On top of that come legal questions, such as who owns a text or image from the AI and when you have to label AI content. I cover copyright in a separate post on AI and copyright. What applies to labelling is in the post on labelling AI content.
Pros and cons at a glance
Is AI dangerous?
In your day-to-day work, the dangers rarely lie in science fiction but in three very practical things: false statements that go out unchecked, data that ends up in the wrong place, and deception. As deception, the European Parliament names, for example, extremely realistic fake videos, audio and images, known as deepfakes.
In its risk profile for generative AI, NIST describes twelve risks and explicitly limits itself to those for which evidence already exists today. The profile deliberately leaves out speculative risks of future systems. For you this means: it isn't AI as such that is dangerous, but using it without rules. According to the European Parliament, the EU adopted its own Artificial Intelligence Act in June 2024 to limit such risks.
An example: five errors only the review caught
Ralf, my AI employee for websites, rebuilt a client's website from their existing texts. All the core data, prices and quotes were correct. The separate review still found five errors, and all of them were in sentences Ralf had written himself as transitions: a response time, a hosting claim, an overly strong legal statement. None of it was in the sources, and none of it went live.
For me, this is the heart of the trade-off. The AI did the build, and the errors sat exactly where it was free to phrase things itself. The advantage held because a second role did the review.
What to watch out for
- The task first, then the tool. Check beforehand whether the task suits AI: texts, summaries and sorting yes; decisions with liability or current facts without a source no.
- Have someone review who didn't create it. My rule: whoever creates something does not correct it. That can be a person or a second AI with a clear review task.
- Ask for sources. For important statements, ask where they come from and look them up.
- Don't put customer data into personal plans. What belongs in the chat and what doesn't is in AI for Beginners: What to Know First.
- Measure the benefit. Write down how long a task took before. After four weeks you'll know whether the AI pays off.
Frequently asked questions
What is the biggest advantage of AI?
Time on recurring text and sorting tasks. On suitable tasks, the Harvard study measured 25.1 percent more speed and better quality. That only holds for tasks that suit the AI, though.
What is the biggest disadvantage of AI?
That its errors sound good. An AI doesn't tell you on its own when it is unsure, and it writes false statements just as fluently as correct ones. That's why every important result needs a review.
Is AI worth it for small businesses?
Often yes, if you start with a concrete, recurring task and measure the time. Then after a few weeks you'll see whether the time saved outweighs the review and the cost. Without a clear task it quickly turns into tinkering.
Do the disadvantages shrink with better models?
Not automatically. The security vendor Veracode has been testing code from AI models regularly since 2025. According to its July 2026 report, almost all of the code runs, but across all test rounds and more than 100 models only 56 percent pass the security tests on average, virtually unchanged from the year before. Better models don't fix every weakness by themselves. The review remains your job.
Do I need rules for AI in my business?
As soon as more than one person uses AI, yes. Even a few sentences help: which tools, which data, who checks. What such a policy looks like is in the post on an AI policy for your business. What the EU AI Act says about AI literacy in your team, I explain in AI Literacy Under the AI Act: Training Staff.
Where to go from here
One of the most effective ways to counter invented answers is explained in What Is RAG in AI? RAG Systems Explained: the AI then answers from your own documents. Next, it's worth looking at the most common kind of error, AI hallucinations. If you want to start right away, read AI for Beginners: What to Know First. How others weigh benefits and risks is what we discuss in my German-speaking community Claude Practitioners.
