AI at work: Where automation ends and judgment begins

AI has become the poster child of digital transformation. Every company wants it, every executive talks about it, and every roadmap seems to have “AI” somewhere on it.

But here’s the uncomfortable truth. The excitement often runs faster than the execution. Most companies want AI to deliver a big impact, and this typically involves cost reduction, efficiency, and speed.

Yet, it’s worth asking: what’s actually ready to be automated, and what still needs a human in the loop?

From what I’ve seen, and what recent Stanford research confirms, the answer is rarely black and white. The near-term opportunity of AI isn’t in full automation, it’s in collaboration.

What employees actually want from AI…

Stanford’s study on AI adoption offers a fascinating insight into how people view automation in their daily work. It mapped business tasks based on two things:

1. How much employees want them automated.
2. Whether AI is technically capable of handling them.

This has shown a clear divide between where AI can truly add value and where it would only add complexity.
In the “green light zone,” you’ll find tasks that are both automatable and desired to be automated. These are typically repetitive, manual, or low-risk activities, like scheduling, expense tracking, report generation, or routine communication. Employees welcome automation here because it removes friction and gives them time to focus on higher-value work.

In the “red light zone,” however, things look different. These are complex, creative, judgment-heavy, or interpersonal tasks, such as negotiating with a client, managing a team, or interpreting ambiguous data. Employees don’t want AI to replace them in these areas.

Then comes the “R&D zone,” and that’s where most of the real opportunity lies. Here, AI acts as a copilot rather than a driver, helping people make better decisions, structure information, and manage complexity, while keeping humans responsible for context and final judgment.

… and where they don’t want it

That is actually where the paradox of AI adoption today lies. People want automation but only when it feels human-controlled and value-driven.

Statistics back me up on this one.

Namely, 75% of workers welcome AI as a teammate, yet 70% reject it as a boss. Many already rely on AI quietly, hiding it from managers in what, research studies call, “AI shame.” They want automation that frees them, but they fear looking less capable, replaceable, or as if they’re cutting corners.

Their concerns are grounded:

• 45% cite doubts about accuracy and reliability;
• 23% fear job replacement;
• 16% point to the lack of human qualities AI cannot replicate.

The employer’s perspective: Between vision and reality

On the other side of the equation are companies under pressure to innovate, cut costs, and show results. Unsurprisingly, the temptation to “go big” with AI is a real one. I’ve seen countless organisations start with the ambition to entirely re-engineer their workflows, automate entire departments, or eliminate human dependencies altogether.

That’s the ambitious approach, but for most companies today, it’s simply not realistic. Why? Because a complete AI-driven transformation requires tearing down and rebuilding the organisation from the ground up. It demands data maturity, robust infrastructure, and deep process alignment, all of which take years, not months.

The pragmatic approach, on the other hand, starts with what already works. They don’t try to transform their operations overnight. Instead, they focus on areas where AI can truly make a difference and scale from there. The most successful implementations are those where AI is introduced as a copilot within existing processes.

That’s the essence of the human-in-the-loop model. Humans remain responsible for decision-making, supervision, and exception handling, while AI performs the time-consuming, repetitive, or data-heavy tasks.
AI scales when people trust it, and people trust it when they understand it.

That is when a few beautiful things happen:

• Adoption rates rise because employees see the benefit;
• Risk drops because people remain accountable for critical decisions;
• Value compounds over time because feedback loops make both humans and systems smarter.

AI scales when people trust it, and people trust it when they understand it.

The real value of AI does not lie in sensational promises but in how it helps people make better decisions and work more efficiently.

Companies that understand this start with practical, measurable use cases and gradually expand, rather than attempting an overnight revolution. This is the path to sustainable ROI, not a flashy trick that lasts for a single season.

And, this is exactly where Vega IT steps in. We have recently developed a comprehensive, six-step AI strategy framework that helps organisations turn that insight into action by:

• identifying high-impact tasks;
• defining clear processes and accountability;
• implementing strategically;
• continuously refining performance.

Want to learn more about how we can give your AI strategy a boost? Let’s talk.