How is AI redesigning the human experience of business?

When AI removes the first rung, how do people learn to climb?

Imagine you're a graduate fresh out of college. You studied hard, built your CV, landed the internship. You expected the first years to be repetitive, even tedious at times. But that was the deal: start with the basics, learn by doing, observe how others think, make mistakes in low-risk spaces, and slowly build judgment through practice.

Now imagine that first rung just disappeared.

That is what is beginning to happen. PwC cut around 5,600 roles globally last year, its first workforce decline since 2010, while graduate hiring across the Big Four is reportedly down by up to 40%.

AI is disrupting the career ladder. It is taking over many of the tasks that once formed the entry layer of work: research, analysis, first drafts, slide building. The very things that may have looked repetitive from the outside were, in reality, the exercises through which people learned how to think.

For companies, this can look like an obvious efficiency gain. Why ask a junior person to spend hours on a first draft or a first analysis when AI can do it in seconds?

But this is where the risk becomes bigger than the saving. If organizations use AI simply to remove people from the process, they may become faster today, but less capable tomorrow. They may reduce repetitive work, but also remove the spaces where people learn the basics, build confidence, and understand how good work is actually made.

The way we see it, is that AI can support human growth, it can accelerate it and it can open new possibilities. But it cannot replace the development that happens when people wrestle with a problem, form an opinion, test it, hear that it is not quite right, and try again.

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What if AI made user experiences more human?

AI is often framed as a way to remove human effort. But its most interesting applications are not just about automation. They are about changing how an experience feels.

Take XPeng, the Chinese EV company investing in autonomous driving, intelligent cockpits, and future mobility. What stands out is not just the technology, but how it reimagines being in transit.

In XPeng’s vision, AI does not simply replace the driver. It reshapes the relationship between person and vehicle. The human role becomes more fluid: sometimes active, sometimes assisted, sometimes fully hands off.

The car becomes less of a machine to control, and more of an intelligent environment that responds to people’s needs, context, and attention. This is where AI moves beyond efficiency. It helps products adapt to when people want control, support, reassurance, or release.

But there is a paradox. If AI is going to make experiences more human, we cannot remove humans from the process of designing them.

People still bring emotions, habits, doubts, contradictions, cultural codes, and unspoken needs into every interaction. AI cannot infer these from data alone. They need to be observed, interpreted, and understood. So yes, AI may help redesign UX at a deeper level. But that does not make human research less important. It makes it more essential.

The real question for experience design is not only what AI can do, but what it helps people feel: confidence, ease, safety, delight, control, care. The future of UX will be defined by how well we keep human insight at the center of intelligent systems.

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Rufus, which hiking bag should I buy?

AI sales agents are moving from novelty to infrastructure. Amazon’s Rufus shows what this looks like in practice: an AI shopping assistant built directly into the customer journey, helping people ask product questions, compare options, read across reviews and find recommendations without ever leaving the shopping experience. Amazon says Rufus is trained on its product catalogue, customer reviews, community Q&As and information from across the web, making it a new layer between the shopper and the brand.

For marketers, this changes the game. Because when an AI agent speaks to a customer, it is not just driving conversion. It represents the brand.

A growing part of the user experience is now mediated by AI. But AI does not naturally understand nuance, emotion, or meaning. It optimizes for what it is trained to optimize. If trained only on product data and conversion goals, it may drive performance while stripping away the things that build trust, desire, and brand equity.

This is why brand and performance marketing can no longer be treated as opposites. Performance without brand becomes pure extraction. Brand without performance becomes invisible.

In a world of AI sales agents, every interaction needs to be anchored in what the brand stands for: how it speaks, what it values, and what kind of relationship it wants to build with people. This gives brand owners a new responsibility: to train AI with a brand strategy, not just a sales script.

An AI sales agent should know the difference between persuasion and pressure. It should understand when to guide, when to reassure, when to hold back, and when a sale is not worth the cost to trust. In the age of AI commerce, your AI is not just a sales tool; it is your brand in action.