Reframing How We See AI
When I was in 5th grade, I fell in love with computers. I used to spend hours in my bedroom writing BASIC computer code on notebook paper. I would spend my school recess time entering that same code into the Commodore Vic 20 computer in my classroom with high hopes. However, the results were often disappointing, frustrating and full of errors.
At those moments, I wished I could just talk to my computer and it would follow my instructions. I wanted that computer to act more like a human! When I was a little older, I watched the “WarGames” movie, where a teenager accidentally connects to a supercomputer (named Joshua) and ends up playing games with it that could have destroyed the world. Joshua could understand, respond, reason and hold a conversation. I was inspired! That was the future: a world where you could simply speak to a computer and it would understand.
Fast-forward to the present, and that once-fictional idea is part of our everyday lives. What we experience now is nothing like the cold and unforgiving computers of my elementary school years. The AI we interact with on our computers isn’t cold, rigid or waiting for us to give it complete instructions with flawless syntax. It doesn’t demand that we think like a machine.
Honestly, it feels a lot closer to Joshua, the supercomputer who just wanted to play a game. There’s something familiar about it: AI responds to conversations, it follows the thread of an idea, it adapts to context and it learns from the patterns we naturally use. It behaves a lot like a human in many ways. Spoiler alert: it does this because everything it knows, it learned from us.
For years, we’ve talked about artificial intelligence as if it were cold, mechanical and calculating, an advanced machine capable of replacing human thinking (or destroying the world). But that framing misses the truth entirely. AI isn’t an isolated, machine-like technology. It’s a system trained on human-created knowledge, shaped by human behavior and expressed through human language. Everything it knows, it learned from us.
The Human Mirror: Knowledge and Shortcomings
We often overlook a fundamental truth: AI only knows what we have taught it. Every model out there, no matter how advanced, has been trained on human stories, human writing, human decisions and human mistakes. It’s more like a giant remix of everything humans have ever published or shared than some sort of super-intelligence.
Because AI is built and trained on what humanity has produced, it carries our traits, both the good and the messy. When AI “hallucinates” or shows bias, it isn’t a software bug in a supercomputer. It’s a reflection. It carries our cultural blind spots, our historical prejudices and our very human tendency to guess confidently when we aren’t sure.
When the AI gets it wrong, it’s often because we, as a collective, haven’t always gotten it right. But this isn’t necessarily a bad thing. If we stop expecting AI to be a perfect, objective machine and start seeing it as a reflection of our own collective voice, we begin to use it differently. We stop wrestling with it like a computer full of errors and start collaborating with it like a brilliant but sometimes fallible colleague.
AI Frees Humans to Stop Acting Like Machines
Think about how we’ve worked for the last thirty years. We’ve spent our days filling out spreadsheets, memorizing keyboard shortcuts and following rigid processes just so the computer would understand us. We basically turned ourselves into human processors to bridge the gap between our ideas and the machine’s limitations.
AI flips that script.
It takes over the rote, the repetitive and the mechanical. It handles the “grunt work” of data and syntax so we don’t have to. And here is the good part: because you aren’t busy acting like a computer, you can finally begin acting like a person again. You can focus on strategy, empathy and the kind of creative leaps that don’t follow a formula. AI doesn’t turn us into robots; it finally lets us stop acting like them.
From Programming to Partnership
If AI is human-shaped, we need to stop “operating” it and start collaborating with it. Using these tools intentionally allows us to spend less time on repetitive work and more time on meaningful collaboration. Here are some approaches to that partnership that have worked for me and can hopefully work for you:
Mastering the Dialogue
Before you can collaborate, you have to know how to talk to AI. These strategies help you move past the “search engine” mindset and into a real conversation.
- Treat it as a Conversation — Stop trying to write the “perfect” prompt as if it were a line of code. The first prompt is just the beginning of the meeting. If the AI delivers a generic or unsatisfying response, clarify your intent.
- How to use it: If you are drafting a project proposal, start with a rough outline and ask the AI, “What am I missing here?” or “Challenge these three assumptions.”
- The Result: You move past surface-level ideas and get to deeper, more nuanced results in half the time. It’s no longer about getting an answer; it’s about refining your own thinking.
- The “Ask Me Anything” Strategy — Often, we don’t get good results because we haven’t given the AI enough context. Flip the script and ask the machine what it needs from you. If you’ve ever used Deep Research in ChatGPT, it does this automatically.
- How to use it: Tell the AI your goal (like writing a brand guide) and then say: “Before you begin, ask me 10 questions that will help you provide the most accurate and high-quality result possible.”
- The Result: This forces the AI to identify its own knowledge gaps and ensures the final output is grounded in your specific data and needs rather than generic patterns.
- Demand Reasoning Transparency — Don’t just ask for the answer; ask to see the “why” behind it. This helps you verify the logic and learn from the AI’s process.
- How to use it: When asking for a complex recommendation, include a phrase like this: “Think through this step-by-step and explain your reasoning before giving me the final conclusion.”
- The Result: This reduces the chance of hallucinations and allows you to spot where the logic might have veered off course. It keeps you in the driver’s seat of the decision-making process.
Expanding Perspective
AI is a remix of collective human knowledge. Use that to your advantage by challenging your assumptions.
- Build a Working Team of Models — Good decision-making has always relied on seeking out diverse perspectives before committing to a direction. This same principle can apply when using AI. Different models have different “personalities” and strengths based on their training and intended purpose, so putting more than one to work can help bring out the best in your thinking.
- How to use it: If you have access to multiple tools, take work you’ve created and feed it into one model with the instruction: “Identify any gaps or blind spots in this work.” Then feed those gaps into a second model to suggest where to look next. This can give you a concrete list of missing data points, unexplored angles or follow-up research to pursue.
- The Result: This creates a “multi-perspective” check that reduces bias and catches errors that a single model might miss. It turns the tech into a team of collaborators.
- Role-Play for Perspective — Because AI can adopt almost any professional persona, it provides a great way to “stress test” an idea before it goes live.
- How to use it: Ask the AI to act as a specific persona, such as a skeptical CFO, a Gen Z consumer or a highly technical engineer. Then, present your idea and ask for a critique from that specific lens.
- The Result: You gain instant access to different viewpoints. It helps you anticipate objections and refine your communication strategy.
- The “Don’t Blow Smoke” Strategy — AI models are often overly helpful and agreeable, which can lead to “sycophancy,” meaning they tell you exactly what they think you want to hear.
- How to use it: Explicitly tell the AI to prioritize accuracy over agreeableness. Use instructions like: “Do not try to please me. Base your answer solely on widely accepted peer-reviewed research, foundational business principles or established historical data. If the evidence contradicts my premise, tell me.”
- The Result: You cut through the “AI fluff” and get responses grounded in reality and external authority. It shifts the tool from being a “yes-man” to being a rigorous analyst.
Optimizing Workflow
This is where you can really start leveraging AI to handle the mechanical parts of your work, freeing you up for more important activities.
- Build Integrated ‘AI Chains’ — AI is most powerful when it isn’t just a chatbot, but a step in an automated chain. By putting AI in the middle of your workflows, you can connect different apps and have the AI handle the data processing in between.
- How to use it: Use platforms like Make.com, Zapier or Microsoft Power Automate to build “chains.” For example: a new email arrives (Trigger) -> AI extracts action items (Process) -> The summary is posted to Slack (Action).
- The Result: This removes the “middle-man” work of copy-pasting and manual sorting. The AI acts as the connective tissue between the tools you already use daily.
- Offload the “Less Human” Tasks — Identify the parts of your day that make you feel like a machine. These are the high-volume, low-meaning activities that drain your time and energy.
- How to use it: Use AI to summarize meeting transcripts, categorize customer feedback into themes, turn a scattered list of notes into a structured agenda or draft the first draft of a routine project status update.
- The Result: You reclaim “deep work” time. When the machine handles the formatting and the sorting, you have the extra time and mental energy to focus on the things that matter most: relationships, strategy and creative innovation.
The Most Human Tool
I didn’t know it at the time, but when I fell in love with computers in elementary school, it wasn’t about processing power or impressive technical specs. Instead, I fell in love with the possibility that computers could make life better. Yes, I wanted a machine that could do some of my grunt work but also meet me where I was, in my own language and help me bring my ideas to life.
AI isn’t some cold supercomputer looming over us. It is a profound synthesis of our stories, our shared wisdom and even our collective mistakes. It’s the most human tool we’ve ever created because it is effectively a mirror of us. By letting it handle the mechanical, rote parts of our work, we aren’t just gaining efficiency; we are reclaiming the time and mental space to be creative, compassionate and strategic. We are finally getting back the parts of ourselves that make us human in the first place.