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"Should we automate this?"
It’s a question many leaders have been asking over the last decade, and that’s certainly accelerated recently. But it’s also the wrong question.
It no longer fits the reality of work in 2025. Artificial intelligence (AI) is already embedded in how we get things done in workplaces across the globe. AI agents are quietly handling multi-step workflows, gaining access to tools and completing tasks that used to take teams of people. Employees are using AI independently, deploying their digital sidekicks in both the routine and the novel.
So why are we still acting like automation is a yes-or-no switch? That old binary lens doesn’t help anymore. If anything, it’s become a way to waste money on the wrong AI tools, alienate employees or worse, build systems no one trusts.
The real question isn’t "Should we automate?" It’s "What kind of partnership between people and AI will help this work shine?"
AI Isn’t All or Nothing
It’s tempting to think of AI as a yes-or-no question. Either the machine does it, or the human does.
Simple, right? Reality is messier, though.
Some tasks are perfect candidates for full automation. Others? Not so much. And then there’s everything in between.
The automate-or-not mindset forces us into oversimplified choices. It encourages us to push automation where it doesn’t belong, just because it’s technically possible. It tempts us to overlook opportunities where AI could make life easier without erasing human value.
We’ve already seen where this leads. Venture capital dollars foolishly chase shiny objects and me-too projects. In fact, 41% of recent AI startup investment focuses on the wrong zones: low-desire or high-risk areas where workers don’t want AI meddling with their tasks. Meanwhile, those sweet spots where people actually want AI help and the technology’s ready for it are getting neglected.
The result? Lots of money spent, not a lot of progress made.
The Human Agency Scale: A Better Way to Think About AI at Work
So what’s the smarter path? Enter the Human Agency Scale, or HAS.
HAS comes from a landmark study from Stanford University, "Future of Work with AI Agents: Auditing Automation and Augmentation Potential across the U.S. Workforce." The researchers who wrote the study focus on labor economics, AI capability and workforce transformation. The study was part of a collaboration between academics and applied technologists aiming to help businesses move beyond the simplistic automation mindset.
The researchers analyzed huge datasets such as national workforce surveys, detailed job task data, technical evaluations of AI systems and real-world usage patterns. They looked at what workers wanted AI to take on, where they wanted to retain control and what AI was realistically ready to do. If you like nerding out on AI potential in the workplace, it’s a grounded piece of research.
What they found was simple but applicable to the realities of using AI in the workplace: People don’t want an all-or-nothing answer. They want the right balance.
Here are the levels covered in the research:
- H1: AI fully drives the task. No human involvement. Imagine something like automated data transcription, a task that is boring, repetitive and better off handled by the machine that, outside of edge cases, can probably do a better, faster job.
- H2: AI drives, humans provide minimal oversight. AI can do most of the job, but needs some supervision. Think about automated payment processing. The AI handles it all, but there’s a person ready to step in if something looks weird.
- H3: Equal partnership. Here, the AI system and the human work side-by-side. Picture AI-powered strategic planning or co-created content. Both bring something to the table that the other can’t, improving results.
- H4: Human leads, AI supports. The human’s in charge, but AI lends a hand on some niche tasks. An example is providing insights or ideas for a designer or helping solve a complex problem by looking for patterns that might be difficult to see.
- H5: Human essential, AI minimal. Tasks where people want to keep full agency. Negotiations, interpersonal communication — anything where nuance matters and AI should stay in the background.
Here’s the real key to these levels: There’s not one that’s automatically "better." The key is in picking the right one for each task. That’s what HAS helps leaders do.
Getting AI Automation Right
When you look at what workers actually want automated and assisted by AI, the research is clear: They’re asking for relief from the grind.
Almost everyone has repetitive, stressful, tedious tasks that drag down their day. In fact, workers are positive about automating nearly half (46%) of occupational tasks, mainly because they want to free time for more meaningful work.
But where is AI actually getting applied? Often in the wrong places. The Stanford research shows that top tasks where workers are most eager for automation represent just 1% of AI chatbot use today. “Existing usage patterns may be skewed toward early adopters or specific job types, rather than reflecting broader demand,” the Stanford study speculated. That’s a huge missed opportunity for leaders.
Meanwhile, leaders and AI solution makers are pouring time and money into automating tasks where people would rather keep control: creative work, interpersonal communication and judgment-heavy decisions. It’s a recipe for internal resistance and, in some cases, public backlash.
So what should smart leaders be doing?
For starters, stop chasing hype. Use HAS to map out where AI supports your people and your business in a less binary way. Do you have a lot of H2-H4 level tasks? Focus on finding the right fit, not just automating it to the greatest effect.
Another key is to focus on those areas where both worker desire and AI capability are high. Design your AI strategy to improve and expand human agency. And make sure your training, role design and governance plans reflect this balance.
Before You Fund Another AI Initiative, Do This
Before you chase the next shiny AI tool, pause for a second.
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Map the work against the Human Agency Scale. Instead of asking, “Can we automate this?” we should be asking:
- What’s the smartest way for AI and people to partner on this task?
- Where will AI elevate performance without eroding the human difference?
- Where will it build trust and momentum rather than sow fear and slow progress?
- Where can it drive performance instead of being a distraction?
That’s the shift, and it could be the difference between AI success and an expensive, embarrassing failure.
Editor's Note: Read more tips on successful AI implementation:
- What AI Upskilling Looks Like at Every Level of the Organization — A 3-tiered approach to AI upskilling for leaders, managers and individual contributors.
- Are Business Leaders Prioritizing AI Innovations Over Existing Jobs? — Finding the balance between technological progress and workforce well-being.
- Workforce Planners Say AI Isn't Ready for Prime Time — The volatility of the current business environment is making workforce planning tough. AI can help, but not in the way you might think.
