Prada: Efficiency and Elegance

Prada, AI, and the difference between efficiency and elegance

I have been thinking about the Prada conversation from AI Week Europe, not because it was the most futuristic conversation in the room, but because it was one of the most grounded.

The AI conversation has become very noisy. Every founder is being told to adopt, automate, accelerate, optimise, and produce more. The implication is that the presence of AI inside a business is, in itself, a signal of sophistication.

But Prada gives us a more disciplined way to think about this.

In luxury, speed is not automatically impressive. More output is not automatically more valuable. Efficiency is not automatically elegance.

A brand like Prada is not built only on what it can produce. It is built on what it protects: craft, restraint, standards, cultural judgment, and trust. The commercial value of the brand depends on a form of discipline that cannot be reduced to productivity alone.

That is why Prada is such a useful reference point for founders considering the role of AI in their own businesses.

The question is not simply whether the technology can be used.

Of course it can.

The more important question is whether the technology strengthens the underlying business, or whether it quietly dilutes the very things that make the business valuable.

AI needs context before it can create advantage

One of the strongest ideas from the Prada discussion was the importance of context.

A generative AI system can sound intelligent. It can understand language, patterns, and categories. It can summarise, draft, analyse, generate and respond with increasing fluency.

But it does not automatically understand the internal logic of a business.

It does not automatically know how a company defines a customer order, calculates revenue, manages workflow, makes decisions, or protects the standards that matter most to the brand. It does not know the operating assumptions unless those assumptions have been made visible. It does not understand the nuance unless the business has enough structure to teach it.

Without context, AI can produce output. But output is not the same as advantage.

There is a temptation to treat AI adoption as a visible marker of progress, particularly when the market is rewarding anything that looks innovative. But funders are unlikely to be impressed by a vague statement that a business “uses AI.”

Therefore, ask yourself: what does AI actually improve?

Does it reduce friction in the operating model? Does it improve margin, consistency, evidence, customer experience, delivery capacity, or decision quality? Does it help the business scale without losing trust? Does it strengthen the commercial case, or does it simply make the business look more modern on the surface?

Each is a different question. And each requires a different level of founder thinking.

Prada understands that knowledge has to be trained

One of the reasons Prada is a useful example is that the brand has a long relationship with training, knowledge transfer, and craft. The Prada Group Academy has operated for more than 25 years as a place where skills, techniques, and innovative ideas are shaped, with learning pathways across leather goods, footwear, and ready-to-wear. Prada describes the Academy as part of its commitment to protecting craftsmanship and nurturing talent through strategic investment in training. 

This shows that Prada does not treat excellence as something that appears because the brand name is strong. The organisation invests in the knowledge behind the product.

In 2025, Prada marked the Academy’s twenty-fifth anniversary. From 2021 to 2024, the Academy trained 571 students from 18 nationalities, with women representing 69.7% of participants; in 2025, it launched seven training programs with 152 enrolled students. 

Skills development and investing into the artisinary of the brand is not a side note. It is central to the narrative.

One could argue that Prada’s value is visible in the product, but that value is sustained by what happens behind the product: the training, the standards, the handling of materials, the transfer of expertise, the judgment formed over time.

This is where many founder conversations about AI become wobbly.

The tool itself is not the strategy. The trained context around the tool is where advantage lives.

Efficiency can weaken credibility if it is not properly gounded

AI can make a business faster, but speed is only valuable when it is moving the business in the right direction.

In some contexts, speed can even weaken the perception of quality. If the use of technology makes a business feel less considered, less trusted, less precise, or less distinct, it may not be creating value. It may be introducing risk.

This is especially relevant in businesses where trust is part of the commercial model.

For women founders preparing for funding, the lesson is not to avoid AI. That would be the wrong conclusion. The lesson is to become more discerning about where AI belongs.

Not every process should be automated. Not every customer interaction should be delegated. Not every piece of content should be generated because it can be. Not every increase in output improves the strength of the business.

The founder has to be able to explain the logic.

What is AI being used for?
Why there?
What does it improve?
What risk does it introduce?
What remains human?
What must remain protected?

That clarity is more useful than simply saying the business is using AI.

The founder lesson

We are hearing this a lot lately, and I admit to being one of those voices.

Women founders are often told to keep up.

Keep up with AI.
Keep up with trends.
Keep up with productivity expectations.
Keep up with the pace of change.

But funding readiness asks for something calmer and more precise.

It asks whether the founder understands the business deeply enough to know where technology creates advantage and where it does not.

That means understanding the operating model, the evidence, the customer journey, the cost base, the constraints, the risks, and the commercial logic. AI can support that work, but it cannot replace the founder’s responsibility to understand it.

Similarly, a funder does not need to see that a founder is performing innovation. They need to see that the business has become stronger because of it.

Ah. Light bulb moment!

The invisible advantage

Prada reminds us that technology is not automatically the opposite of craft. It can sit alongside craft. It can support scale, consistency, learning, and operational intelligence.

But only when it is held inside a system that understands what must not be diluted.

The question is not whether AI is present. It is whether AI is connected to the business case.

Does it protect trust?
Does it improve delivery?
Does it strengthen evidence?
Does it support scale?
Does it sharpen judgment?
Does it make the business more credible under assessment?

The strongest AI use may not be the most visible. It may sit quietly behind the scenes, improving the parts of the business that make it more resilient, more legible, and more fundable.

It might not be loud. 

But it sits where it matters the most.