Reframing the Question: From "Can We?" to "Should We?"
In the rush to adopt AI tools, many organizations start with the question: "What can this automate?" That question can unlock new value—but it can also lead us to overlook what makes our work work. In human-centered disciplines, success often comes not from speed or scale, but from resonance, relevance, and trust.
Instead of asking only what AI can do, we should also ask what it should do—and where it still relies on us to close the gap between efficiency and empathy.
AI as an Amplifier, Not a Replacement
Artificial intelligence excels at what it was designed to do: recognize patterns, analyze data, and automate repetitive tasks.
For example:
- AI can sift through massive survey datasets in seconds
- It can identify clusters in user behavior, trends in reviews, or sentiment in qualitative feedback
- It can generate dozens of messaging variations, helping us test tone, clarity, or engagement more quickly
But these capabilities don’t equate to insight. Knowing what people did is not the same as understanding why they did it—or how they felt about it. That’s where humans still lead.
AI can guide us toward better questions. But human-centered practitioners are the ones who ask them.
The Role of Empathy and Lived Experience
Human-centered work begins with people. Not just as subjects, but as collaborators, co-creators, and informants of lived reality. AI can help summarize transcripts or highlight patterns in user feedback. But it can’t conduct a thoughtful interview. It can’t notice body language, sense emotional undercurrents, or understand cultural references that matter deeply.
Empathy doesn’t live in the data. It comes from presence, observation, and dialogue. If we skip those steps, we risk building solutions that are technically sound—but disconnected, tone-deaf, or even harmful.
When to Use AI in Human-Centered Work
AI can absolutely help us do human-centered work better—when it's applied intentionally.
Some high-impact use cases include:
- Automating research analysis (e.g., sorting and categorizing survey responses)
- Generating first-draft content to accelerate brainstorming or prototyping
- Synthesizing large data sets to identify behavioral trends or test hypotheses
- Exploring creative variations of copy, design, or visual content to iterate faster
In all of these cases, AI supports efficiency, not empathy. It frees up our time to do the listening, collaborating, and decision-making that machines cannot.
Where Human Insight Is Irreplaceable
While AI can accelerate tasks, it cannot make ethical calls, interpret social context, or take responsibility for decisions. In human-centered work, these moments matter.
They include:
- Facilitating interviews or co-design sessions
- Making value-based trade-offs in design
- Navigating stakeholder priorities, sensitivities, or power dynamics
- Understanding the historical, cultural, or emotional layers behind user behavior
In short: AI can augment our process, but it can’t replace our presence. The most meaningful insights still come from people in conversation with people.
A New Kind of Collaboration
As AI tools become more embedded in how we work, we have a choice. We can use them to speed up business as usual. Or we can use them to deepen our practice—to give ourselves more space to think, connect, and understand.
That means:
- Designing research processes that pair AI synthesis with human interviews
- Using AI to reduce busywork, not human input
- Training teams to critically evaluate AI output, not blindly accept it
The future of human-centered work isn’t AI vs. people. It’s people who know how to work alongside AI—responsibly, creatively, and with care.
The Hidden Costs of AI
While AI can make human-centered work more efficient, it’s important to acknowledge its hidden costs—both to the environment and to ourselves.
Environmental Burden:
Training and running large AI models requires massive computational power. A single AI model can emit as much carbon as five cars over their lifetimes. As we integrate AI more deeply into everyday workflows, we also need to consider its ecological footprint—and balance convenience with sustainability.
Artificial intelligence is not immaterial. Every prompt, every output, every iteration is powered by data centers that consume enormous energy and water resources.
Cognitive and Emotional Strain:
There’s also a human toll. Constant engagement with AI tools can lead to decision fatigue, reduced critical thinking, or even creative flattening. When we outsource too much ideation or analysis to machines, we risk losing the deeper intuition that makes our work truly human-centered.
Some early research even suggests that overreliance on AI may erode empathy, as people begin to defer ethical or interpersonal decisions to algorithmic suggestions rather than wrestling with them firsthand.
Efficiency is not neutrality. It has a shape, and often that shape is extractive.
Final Thoughts
AI is a powerful tool. But like any tool, it reflects the values of those who use it. In human-centered work, our value lies not just in what we produce, but how and why we produce it. We ask better questions. We build trust. We design for nuance.
That work still requires us.
So yes—use AI. But keep people at the center. That’s where the real intelligence lives.
