The hidden cost of scaling AI
A guest blog post from Shannon Harris, Managing director of HP New Zealand
Artificial intelligence is moving rapidly from experimentation into everyday use. What began as pilots and proofs of concept is now embedded in how people write, analyse information, collaborate and make decisions at work. For many organisations, the conversation has shifted from whether to adopt AI, to how it can be scaled in a way that is effective and sustainable over time.
The more useful AI becomes, the harder it is to control the cost of running it. As AI becomes woven into daily operations, the challenge for leaders is no longer access to capability, but whether it can be run at scale without compromising performance, security or control. This is where AI moves from being a technology discussion to an economic one.
When scale changes the equation
Much of today’s AI relies on the cloud, with costs tied directly to usage. Every interaction, whether it is a prompt, a document summary or an automated workflow, consumes processing power. Individually, these interactions feel small and inexpensive. But when multiplied across hundreds or thousands of employees, those costs can grow quickly and unpredictably.
This is not a flaw in the technology, but a reflection of how valuable and widely used it has become.
There is a well-established principle in economics known as the Jevons Paradox, which says that when something becomes more efficient and easier to use, overall consumption tends to increase rather than decrease. In other words, the challenge shifts from what AI can do, to what it costs to run at scale.
As models improve and access becomes more seamless, usage accelerates. Even as the cost per interaction falls, total spend continues to rise. More efficient AI, in practice, does not automatically translate into cheaper outcomes.
At the same time, expectations of AI are changing. It is no longer viewed as an occasional tool, but as something that should be immediate, responsive and always available. When AI becomes part of everyday work, delays feel more noticeable, trust matters more, and tolerance for friction drops quickly. Systems that rely entirely on sending data back and forth to the cloud start to feel the strain, particularly as AI moves from experimentation to everyday reliance.
The future of AI at the Edge
This is why we are beginning to see a shift towards intelligence running closer to where work actually happens. Advances in hardware, software optimisation and model design mean that AI capabilities once reserved for data centres can now operate directly on modern devices. This is often referred to as AI at the Edge, but the idea itself is simple: put intelligence where it is used most.
The implications for businesses are significant. Running AI on-device reduces reliance on recurring cloud compute for high-frequency tasks, while improving responsiveness through real-time processing. It also strengthens privacy and security by keeping sensitive data local, which is increasingly important in regulated environments. Perhaps most importantly, it makes AI more economically sustainable as usage grows, allowing organisations to scale capability without a corresponding surge in ongoing costs.
This shift is not about replacing the cloud, but about designing a hybrid future where intelligence runs in the right place for the right task. Some workloads will continue to benefit from centralised cloud processing, while others are better suited to being handled locally. Designing for that balance requires a different mindset from leaders.
AI strategy needs to factor in long-term cost, scalability and trust from the outset. The organisations that succeed will be those that ask not only what AI can do, but how often it will be used, by whom, and at what cost over time. They will consider not just the power of a model, but how it fits into everyday work without introducing friction, risk or unsustainable expense.
At HP, we are seeing this shift clearly in our conversations with customers and partners. AI is becoming part of the fabric of work, and that means it needs to be efficient, secure and built to last. Bringing more intelligence onto the device is a critical part of making that possible, particularly as organisations look to embed AI more deeply across their workforce.
The next phase of AI will be shaped by smarter deployment decisions, and leadership that understands how the economics of AI matter as much as innovation itself.
