AI is transforming the way we live, work, and play. It’s altering how we make decisions and interact with technology. But for all its power, it still needs humans (for now) — not just any humans but those who understand how AI works, the dependencies between good data and useful AI outputs, and where human judgement is irreplaceable.
Amidst a world rushing towards automation, data and AI literacy isn’t just a skill — it is how you become THE human in the loop.
What Does It Mean To Be “The Human In The Loop”?
The phrase “human in the loop” (HITL) comes from AI and machine learning, referring to the humans who step in to guide, correct, or make sense of AI-driven processes. Sometimes, it means reviewing AI-generated decisions to catch mistakes (think fraud detection or medical diagnoses). Other times, it’s about injecting human expertise where AI lacks context, nuance, or ethical reasoning. If you’ve attended a conference in the past year, the HITL is what vendors point to when assuring people with AI concerns that humans still will be a part of key governance structures and decision-making. What is often overlooked is how many humans will be in the loop, what the loops might look like, or how many AI/software loops one human can be responsible for.
Here is our reality: Not all humans in the loop will be equal.
Some will be passive overseers, clicking “approve” or “reject” on AI recommendations (the hospital scene from the 2006 film “Idiocracy” comes to mind here). Others will be active decision-makers driven within a culture of inquiry who shape how AI is used, train models with better data, and ask questions before being prompted by an algorithm. The key difference between passive human drones and those actively involved in guiding AI decisions is data and AI literacy within a culture of inquiry.
Why AI And Data Makes You Indispensable
Two short anecdotes illustrate the point:
Over the past year, I’ve been showing a friend who works at a bank how the simple use of AI tools outside of her company can help her improve engagement and impact at work. She was just highlighted at work for being “forward-thinking and proactive” for getting creative without sacrificing security.
KPMG recently gave me a demo of its “Curiosity Workbench,” an AI tool that helps its employees locate and leverage decades of knowledge, data, and expertise to help with clients and get them moving quickly.
Both of these examples depend on humans interpreting information and learning more by being curious and inquisitive. After all, AI is only as good as the data it learns from — and data is only as useful as the humans interpreting it. If you want to be the human in the loop, you need:
Data literacy: the foundation | AI depends on clean, consistent, relevant, and representative data. Without data literacy, you’re just a spectator to the AI revolution. With it, you’re the one shaping impact. Ask yourself:
Can you spot bad data before it leads to bad outcomes?
Do you understand how bias can slide into datasets like a creepy social media stalker can slide into your DMs?
Can you interpret AI-driven insights to make business decisions, rather than just accepting whatever a model spits out?
AI literacy: the next level | AI literacy isn’t about coding your own model from scratch. It’s about understanding how AI influences decisions, where it’s useful, and where it needs a human course interaction. In 2025, I ask our clients to imagine that AI is like the world’s best intern: It can do 80% of most common jobs very well, but that remaining 20% is still pretty suspect and needs the guidance of a wiser mentor who can work with it to get you 100% there. Ask yourself:
Do you know how AI models make predictions and where they can go wrong?
Can you question AI outputs instead of blindly trusting them?
Are you aware of ethical risks, compliance issues, and real-world AI failures?
Enterprise culture of (data) inquiry | AI is just software, but without a body of users who are enabled to find it, ask questions of it, grow using it, communicate with it, and trust it, it is as worthless as the grains of sand that its chips are built from. A culture of inquiry is one where all are empowered in a psychologically safe environment to ask questions and share commentary. A culture of data inquiry ensures that, within that safe environment, users can locate, leverage, trust, and communicate those insights found within data without fear. Ask yourself:
Do I work within an environment where all can locate data?
Do I work in an environment where all can leverage data?
Do I work in an environment where all can trust data?
Do I work in an environment where all can communicate data?
Be The One Behind The AI
Automation is here for many routine tasks. But organizations will need humans who:
Understand when AI is making good vs. bad recommendations.
Know how to validate AI insights before acting on them.
Can explain AI-driven decisions in clear, human terms — to coworkers, executives, regulators, and customers.
Can translate business challenges to more technical and data-focused AI engineers while also listening and learning from them in turn.
Being the human in the loop isn’t about resisting AI. It’s about being the person who knows how to use it responsibly, effectively, and strategically.
Now What?
Reach out for an inquiry ([email protected]) with me today to uncover your natural strengths and purpose, via your own roles, goals, and values VIP evaluation, to improve your own data communications and data storytelling skills, and then to discover how to build your enterprise culture of data inquiry via curiosity velocity and data and AI literacy programming. I look forward to working with you!
If you are a vendor looking to share insights on your AI literacy offerings or have a use case of how you’ve helped others with the above, I’d welcome the opportunity to hear from you. Learn more here: Analyst Briefings – Forrester.
