Artificial intelligence is becoming mainstream. CEOs and CFOs are investing in AI in order to automate processes and productivity growth and innovation.
However, there is another aspect of the equation. As companies become increasingly dependent on AI providers, the economics behind these solutions can change quickly. Pricing models evolve, usage grows and premium functionality gradually becomes necessary rather than optional. Without active management, the expected return on AI investments may decrease over time.
A recent Dutch report on the legal sector illustrates this trend. Multiple AI providers are moving away from fixed subscriptions towards usage-based pricing, meaning that organisations pay more as adoption grows. Succesfully scaling AI therefore also means scaling costs.
Driving Forces Behind AI Cost Inflation
The challenge is rarely a single price increase. Several developments often reinforce each other as AI adoption matures. Organisations increasingly face:
- Usage-based pricing, where costs increase alongside business adoption and transaction volumes.
- Premium functionality becoming operationally essential, including larger context windows, advanced reasoning models and enhanced monitoring.
- Inefficient solution design, with long prompts, excessive context and unnecessary model calls driving avoidable consumption.
- Vendor dependency, reducing negotiating power and making switching providers increasingly difficult.
- Hidden operational costs, including model upgrades, policy changes and revalidation efforts that consume valuable internal expertise.
Individually, these developments may appear manageable. Together, they can significantly reduce the financial benefits that originally justified the investments. Organisations are no longer running isolated pilots, they are scaling AI across functions and embedding it into daily operations.
Building Sustainable AI Economics
Managing AI costs is not about slowing adoption. It is about putting the right commercial, technical and governance disciplines in place so organisations can continue to scale AI while maintaining control over costs and value creation.
In practice, this means focusing on several key capabilities:
- Measure value, not usage: Focus on cost per business outcome rather than cost per token, while monitoring quality indicators such as accuracy, rework and exception rates.
- Maintain flexibility: Avoid unnecessary vendor lock-in by using modular architectures, portable data and routing different workloads to the models best suited for the task.
- Engineer for efficiency: Optimise prompts, apply sensible guardrails, use caching where possible and reserve human intervention for high-value exceptions.
- Strengthen governance and sourcing: Treat AI as a strategic procurement category through dual sourcing where appropriate, outcome-based contracts and executive reporting on cost, quality and model performance.
Together, these disciplines allow organisations to scale AI while maintaining control over both costs and performance.
From Experimentation to Operational Excellence
For many organisations, the challenge is no longer proving that AI works—it is ensuring that it continues to deliver value at scale.
The journey typically starts with establishing transparency: understanding where AI is used, which contracts are in place and what each use case actually costs. From there, organisations can optimise solution design, introduce technical guardrails, improve prompt engineering and reduce unnecessary model consumption. Reviewing commercial agreements and reducing unnecessary vendor dependency further strengthens long-term resilience.
The final step is embedding AI into the operating model through clear governance, engineering standards and executive reporting. This ensures AI costs, quality and performance remain transparent as adoption continues to grow.
What Good Looks Like
Organisations that successfully scale AI do more than control costs, they create a sustainable operating model where value creation and financial discipline reinforce one another.
Typical indicators of a mature AI capability include:
- A 20–40% reduction in cost per business outcome, while maintaining or improving quality.
- The majority of AI workloads running on right-sized models, with premium models reserved for use cases where they deliver measurable additional value and fewer than 10% of requests exceeding context limits.
- Dual-sourced coverage for critical AI capabilities, reducing dependency on a single providers.
- Predictable AI expenditure, with month-on-month spend remaining stable and fewer unexpected cost increases.
These metrics are not goals in themselves. They demonstrate that organisations have successfully combined technical optimisation, commercial governance and operational discipline to scale AI in a sustainable way.
Protecting the Return on AI Investments
The objective is not to slow AI adoption, but to ensure it remains economically sustainable as organisations scale. Organisations that treat AI as an operational capability will be far better positioned to capture its long-term value.
At Argon & Co, we help organisations combine AI strategy with procurement, operational excellence and technology governance. By improving cost transparency, reducing vendor dependency and designing scalable operating models, we help clients maximise the return on their AI investments while maintaining the flexibility to adapt as the market evolves.
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