The conversation around AI is moving beyond adoption. Organisations are now asking a more difficult question: What does AI actually cost, and are we getting enough value from it?
The assumption is that AI gets cheaper over time, the way most technology eventually does. In many ways, that is true. Model costs are falling, infrastructure is becoming more efficient, and access to increasingly powerful tools is becoming easier. However, the reality for many organisations is more complicated. As AI usage grows, so does the complexity around managing it. The cost is no longer confined to the licence fee: it also includes the time, processes, and investment required to make AI work effectively within teams.
Beyond the Tools
When businesses talk about AI costs, the conversation often starts with the obvious numbers: licences, API usage, model pricing, and infrastructure. But from speaking with developers who use these tools every day, another cost is becoming increasingly visible: teams spending time experimenting with different tools, evaluating outputs, adapting workflows, learning where AI adds value, and figuring out where human judgement is still essential.
Someone needs to decide which tools teams should use. Someone needs to consider security, access, governance, and how AI fits into existing engineering processes. From my recent conversations with clients and AI implementors, it is clear that the organisations getting the most value from AI are those that understand where it works well, where human judgement remains essential, and how their teams need to adapt around it.
AI Usage and Impact
After sitting down with a Director of Engineering recently to discuss how organisations can scale AI adoption without losing sight of the value it is supposed to create, one thing stood out: increased usage does not always mean increased impact. One of the biggest differences between AI and traditional software is how quickly costs can scale. A SaaS licence typically gives a business a predictable monthly cost per user, whereas AI introduces a consumption model where costs can increase based on usage: prompts, context size, model selection, agent usage, and the complexity of workflows being automated.
It often starts small: a developer creates an AI agent to automate one task, another team sees the potential and starts exploring their own ideas. Over time, AI usage can spread across different teams, sometimes faster than organisations can track where it is being used, who owns it, and what value it is creating.
Organisations want teams to embrace AI, but the challenge is ensuring increased usage creates meaningful outcomes. The companies that benefit most will be those that balance experimentation with discipline: encouraging innovation while still understanding where time, money, and resources are being invested.
The challenge many organisations are discovering is that AI costs do not behave like traditional software costs. A SaaS licence typically gives a business a predictable monthly cost per user. AI introduces a consumption model where costs rise with activity: prompts, context size, model selection, agent usage, and the complexity of workflows being automated.
As adoption spreads, small experiments can quickly become significant spending lines. A developer creates an AI agent to automate one workflow. Another team sees the benefit and creates their own. Soon multiple teams are running AI workloads, often without a clear view of total consumption, ownership, or the value being generated. This challenge is already becoming visible across the industry.
The Talent Cost of AI
Another cost discussion is happening alongside the technology conversation: what AI means for engineering teams themselves.
In Ireland, and indeed further afield, the market concern is that fewer graduate positions are being advertised than before, though universities describe a mixed picture rather than a clear trend, with graduate intake reported to be falling in some organisations and fluctuating without a steady decline in others.
Experienced engineers with the right tools can often deliver more, reducing some routine development work that previously helped create entry-level opportunities. This creates a longer-term question for the industry: If businesses reduce investment in early-career engineers today, where do tomorrow’s experienced engineers come from?
AI may reduce some short-term labour costs while creating a longer-term challenge around building technical capability, and the organisations that benefit most will be those that automate more tasks while continuing to invest in engineering talent. The same trade-off applies to cost more broadly: right now, while the technology is still relatively new, that investment is real, as organisations work out which tools and subscriptions are most cost-effective, but the time savings developers are already making on more routine tasks suggest the balance is shifting from an added cost towards greater efficiency, and potentially lower cost overall.
Takeaway
AI will continue to become more powerful and more accessible, but cheaper technology does not automatically mean cheaper engineering.
The organisations that create the most value will treat AI as an engineering investment: measured, managed, and connected to real outcomes. More importantly, these organisations will pair automation with continued investment in strong engineering capability.
Connect with Rachel at rachel.mcguckian@barden.ie or on LinkedIn>>>
Additional Reading on this Topic:
TechCrunch: the rise of AI cost management
- Recent reporting from TechCrunch highlighted how organisations are beginning to reassess AI spending as usage scales. While the cost of individual AI interactions continues to fall, increased adoption of AI tools and more complex agent-based workflows are driving overall consumption higher.
- The conversation is shifting from simply adopting AI tools to understanding usage, setting controls and measuring whether the value created justifies the spend. The Token Bill Comes Due: Inside the Industry Scramble to Manage AI’s Runaway Costs – TechCrunch
McKinsey: moving from AI pilots to production
- Research from McKinsey has highlighted that many organisations underestimate the cost of moving AI from experimentation into production.
- The investment is not just in model access or licences. Scaling AI requires infrastructure, governance, integration work and operational processes to ensure solutions deliver measurable value. McKinsey QuantumBlack AI Insights
FinOps Foundation: the rise of AI cost management
- The FinOps community, which originally focused on managing cloud expenditure, is now expanding its thinking into AI consumption.
- The emergence of concepts such as “AI FinOps” and “token economics” reflects a broader shift in how organisations think about AI spend: not just controlling costs but understanding whether usage is creating enough value to justify the investment. FinOps Foundation – Token Economics: The Atomic Unit of AI Value
