When frustration with artificial intelligence bubbles up, it’s common to hear, “AI just doesn’t work,” or “I tried to use AI, but it just gave me garbage.” But often, the real issue isn’t the technology; it’s how we’re trying to use it.

People frequently approach AI expecting instant results with minimal effort. But working effectively with AI isn’t a matter of clicking a button and walking away; it requires a collaborative effort, thoughtful prompting and iterative refinement.

Working with AI is a lot like onboarding the world’s smartest intern. At first, it sounds like a dream: an overachiever who never gets tired, is eager to help and has access to nearly limitless information. But then you realize they need detailed instructions for even the simplest job. Artificial intelligence is a lot like that intern — full of promise, capable of greatness but completely dependent on the quality of the guidance it receives.

AI expert Daniel Shorstein described a common scenario in a recent LinkedIn post: “People tell me they tried using ChatGPT for something and it didn’t work. So I asked them whether they tried a follow-up prompt or changing the prompt, and usually the answer is no.” Shorstein hit the nail on the head: “Working with AI is similar to working with a human; if you don’t give them enough context or instructions, you won’t like the results.”

People attempt a task once with AI, become frustrated by subpar results and give up immediately. Rarely do they invest time in refining their prompts or providing additional context. But, as Shorstein rightly points out, effective AI usage demands careful and clear instructions.

The misunderstanding goes deeper. Many people still approach AI as if it were just a smarter search engine: type in a query, get instant results and move on. But AI is fundamentally different. It demands clarity and specificity because, unlike a seasoned coworker, it possesses zero intuitive context. Without clear instructions, it simply guesses, often missing the mark.

This was sharply illustrated by leading AI strategist and former AWS head of machine learning startups Allie K. Miller, who shared an eye-opening Zoom call with a celebrity client struggling with AI-generated product descriptions. Miller quickly diagnosed the issue, noting that the employee responsible had barely provided context: “Turns out the employee gave it ONE example. Described the company in TWO sentences. Didn’t even describe the audience. Used her free ChatGPT account.”

And there’s the rub. Most of us treat AI as a quick fix rather than a collaborative partner. Just as we wouldn’t hand a new employee a vague task and walk away, we shouldn’t expect optimal results from a simple prompt entered into a free AI platform. Effective use demands repeated refinement, experimentation and an iterative, conversational approach.

To unlock AI’s full potential, users must stop seeing it as a shortcut and start treating it as the highly capable but utterly context-dependent intern that it is. Only then can we move beyond the false conclusion that “AI doesn’t work” and instead leverage it as a transformational partner.

Ultimately, it’s not AI that’s letting us down. It’s our own misunderstanding of how to work alongside it. The potential is boundless, but only if we invest in understanding the tools we have and learning to use them with the depth and diligence they demand.

Miller didn’t hold back: “I told her flat-out: that’s not the AI’s fault. That’s the employee’s, and maybe even hers for not knowing how to fix it.” To illustrate her point, Miller showcased an 18-page standard operating procedure tailored for AI writing in her own voice and demonstrated how multiple specialized AI tools could be employed simultaneously. The response? The client was floored by the capabilities she’d overlooked.

“A lot of people think the AI ‘doesn’t work.’ IT WORKS. You’re just using it like Google,” Miller emphasized. AI requires users to “benchmark your inputs and literally overwhelm the model with clarity and specificity and intent.”

Take a recent situation I encountered, which shows exactly why this collaborative approach matters. I faced a complex problem on one of my web servers — a situation that I knew from experience would usually take several hours to resolve. Instead, I spent an intense 30 minutes collaborating with ChatGPT. The session involved detailed troubleshooting, several failed attempts, adjustments and clarifications. But the collaborative effort dramatically shortened the process. What would have normally taken hours was solved swiftly through careful, iterative prompting and active collaboration.

Investing the time to properly use AI isn’t always about speed, either. Often, AI enables significantly better results within the same amount of time. Tasks that used to take four hours can still take four hours, but the quality or scope of the finished product far surpasses what could have been achieved alone.

To effectively leverage AI, consider the following tangible steps:

  • Start small and iterative: Begin with simple, clear prompts. Review the output, adjust your instructions and progressively add complexity.

  • Provide detailed context: Explain your goal, audience, style and specific needs. AI thrives on clarity and specificity.

  • Ask questions: Use the AI to get better results from itself! Tell the AI what you’re trying to achieve and ask it what it needs to know to help you. This can lead to more refined and useful outputs. If the output isn’t what you expected, ask it to clarify or expand on certain points. This iterative questioning can lead to much more refined results.

  • Brainstorm with AI: Use it as a sounding board for ideas. Ask it to generate multiple options or a variety of styles, which can help you refine your own thinking.

  • Collaborate actively: Treat the AI interaction as a conversation. Ask follow-up questions, refine your prompts and clarify when outputs aren’t matching expectations.

  • Be honest: It might sound odd, but being honest with the AI about your opinion of its output can help it learn and adjust. If you don’t like what it produced, tell it why and ask for a different approach. If you do like it, explain what you found useful. This feedback loop can significantly improve the quality of future interactions.

  • Experiment across platforms: Different AI tools excel at different tasks. Be open to trying multiple platforms to find the best fit for your needs.

  • Benchmark your inputs: Regularly assess the quality of your prompts and the AI’s responses. Adjust your approach based on what works best.

  • Learn from examples: Study successful AI interactions and prompts shared by others. This can provide valuable insights into effective strategies and techniques.

Ultimately, the effort invested in learning to collaborate effectively with AI is profoundly rewarding. Instead of treating it like the new kid you shove in the corner with busywork, we need to recognize the partnership and responsibility involved. AI, like any talented intern, will only reach its full potential if we take the time to train, guide and collaborate with it. When we do, the payoff can be enormous: productivity and quality on a level we simply can’t reach alone.