AI Adoption: Why Now Is the Right Moment to Start

We believe that now is the right moment for Artificial Intelligence. Not because a trend demands it, but because starting with AI is not a switch you can flip later without cost. Delay it, and you accumulate effects that no single quarter can recover. No drama, just a clear look at the items we keep seeing in conversations.

When and how a company opens itself to Artificial Intelligence is a strategic decision, not a question of right or wrong. From our own projects, though, we know a set of recurring effects that build up when the start is put off for too long. We do not see them as a threat, but as a solid basis for an unsparing assessment of where you stand.

Why now is the right moment for AI: eight effects of waiting

In our view, delaying AI adoption builds up a whole bundle of disadvantages, imperceptibly and cumulatively. Eight of them we see especially often:

• A productivity gap: AI-fluent competitors handle routine work in a fraction of the time, and the distance widens at an accelerating pace.
• Structurally high staff costs: tasks a model solves in seconds are still done by hand.
• Knowledge becomes a commodity: what once earned a margin as specialist knowledge is now available at the push of a button. Value now comes from judgement, context, and trust.
• Talent drain: qualified specialists expect modern tools.
• Innovation blindness: a competitor's new business models stay invisible until they displace your own market.
• Shadow AI: a company that does not actively shape Artificial Intelligence gets it anyway, only without governance, data protection, or compliance.
• A customer expectation gap: fast, personalised, around-the-clock services become the standard.
• An infrastructure bottleneck: fast memory and bulk storage are scarce, lead times are long, and data centre power is rare.

Knowledge loses its asset character

For decades, accumulated expertise was a central competitive advantage. Specialist knowledge was scarce, expensive, and hard to access. Artificial Intelligence changes that equation at its root, because it makes expert knowledge retrievable in seconds, from a first legal review to a medical differential diagnosis to tax advice. A company that built its business model purely on providing knowledge, without securing that lead through advisory quality, contextual understanding, or data integration, loses its most important asset. The value shifts from the information to its application.

What it means in detail when knowledge becomes freely available is something we explore in our essay Knowledge Without Limits. For this piece, the finding is enough: the asset character of knowledge is melting away, and it does not come back.

Shadow AI is the more dangerous scenario

The most frequently underestimated risk does not arise because a company bans Artificial Intelligence, but because it acts as if it did not exist. The workforce uses it anyway, mostly through private accounts, often on private devices. Confidential data ends up in public systems, with no data protection review, no audit trail, no compliance. A company that does not actively shape Artificial Intelligence gets it in its riskiest form. A ban does not solve the problem, it only moves it to where nobody is looking.

Infrastructure: scarcer than many realise

The current AI wave has a peculiarity that risk lists usually miss: it consumes hardware on a scale the market cannot yet reliably supply. Contrary to what is often repeated, the GPUs are only part of the problem. The real bottleneck lies in fast memory, in high-grade SSDs, and in classic HDDs for the bulk data that AI pipelines produce and consume. How deep this bottleneck now runs is shown by an observation circulating in the industry: for fast memory in AI systems, production for 2026 is sold out, and a substantial share of 2027 capacity is already reserved. Order today and you do not join a supply chain, you join a waiting list.

On top of that comes power for AI data centres, which is becoming a headline of its own. Lead times for the most important hardware are often longer than quarterly planning. We see it in our own purchases: what was deliverable in weeks two years ago now takes months, sometimes more. The demand peak we are living through may only be the beginning. With every further industry that gets serious, every model that needs more compute, every additional data centre waiting on power, the bottleneck runs deeper. Invest early in your own capacity, or have a partner who already has, and you buy predictability that will not be available later.

A question of sequence, not obligation

Adopting AI is rarely a question of duty or extra credit, more often one of sequence: what to begin with, where first, at what depth. The good news: every one of these points is reversible once a starting point is found. Where that point sits for a specific company can usually be sorted out quite quickly in an open conversation. How we go about it is set out on our Business page, and to sort out your case directly, the contact page is the place to start.