In this article, Alison McKinnon, managing director of CM.OSX, challenges one of AI’s biggest assumptions that more automation automatically means lower costs. While AI is delivering productivity gains across marketing, McKinnon argues the next phase of enterprise adoption will introduce a hidden layer of operational, governance and infrastructure costs that many organisations have yet to account for.
There’s no shortage of discussion about what AI can automate. Yet one issue receives surprisingly little attention: the economics of running AI at scale.
Because while automation can reduce effort, large-scale AI orchestration introduces a new category of costs that many marketers have yet to factor into their business case.
Mythbuster: AI and automation won’t automatically reduce costs
One of the most common beliefs about AI is that automation will reduce costs. Logically, if AI can perform work that was previously done by humans, organisations should become more productive and costs should fall.
In many cases, that is true. We find efficiencies through faster production, reduced time per task and improved workflows.
But as the conversation shifts towards agentic AI and interconnected ecosystems of specialised AI agents, a different reality is beginning to emerge. Large-scale AI orchestration is going to be expensive.
And we’ve all seen companies get caught in the cross-fire of AI enterprise billing, with Axios recently reporting a company racked up a whopping $US500million in a single month on Clause, because of no usage limits on employee licenses.
So, will the cost of operating complex AI systems begin to erode the efficiency gains they were designed to create?
The first generation of AI savings in marketing
The initial business case for AI empowered marketing is relatively straightforward. Adopt AI to accelerate content creation, analysis, reporting and workflow execution. Increase productivity. Produce more output. Reduce manual effort. If this is where your organisation is on its transformation journey, you’re probably already seeing value from your investment.
But what happens when you attempt to scale? Using a single AI tool is relatively simple. Operating an ecosystem of AI agents is not.
As organisations automate more complex processes, they introduce specialised agents performing specific tasks. One gathers information. Another analyses it. Another generates recommendations. Another executes actions. Another performs quality assurance. Yet another monitors performance.
Individually, each interaction appears efficient. Collectively, they create a network of continuous AI interactions, and every interaction consumes resources. Every handoff consumes resources. Every decision consumes resources.
Suddenly, while work is being completed faster and more efficiently, operating costs begin to compound, fast.
The hidden economics of AI-powered marketing orchestration
Nothing valuable comes without cost, and AI is no exception. Large-scale deployment introduces a range of operational expenses that often sit outside the initial business case. Every interaction carries a computational cost. Every workflow consumes tokens. Every layer of orchestration creates additional overhead. As workflows become more sophisticated, the cost of operating them can increase significantly.
And computational costs are only one part of the equation.
As AI ecosystems expand, so does the need for governance, oversight and risk management. Will there come a time when enterprise AI systems require independent auditing? Anyone who has engaged a Big Four consulting firm knows that compliance and assurance activities are rarely inexpensive.
Like all systems, there will be needs for monitoring. Performance, errors, QA, maintenance, prompt optimisation, model updates, system updates, integration management. All of these require human intervention. And while we are on those costly humans, someone still needs final accountability. Review outputs, managing updates, resolving conflicts, correcting errors. The more autonomous systems become, the more important these controls are. AI does not eliminate management, it changes where management effort is applied.
The efficiency trap marketers must avoid
This creates what could become one of the biggest misconceptions in enterprise AI, the assumption that more automation automatically creates more value.
If you are currently asking “How much can we automate?”, try reframing the question as “Which workflows create measurable commercial value when automated?”.
If automation expands, governance expands and operating costs expand, but business outcomes remain largely unchanged, all you’ve done is introduce complexity without proportional return.
How do you create value if it all costs so much?
Trade the idea of large ecosystems for disciplined ones. Focus on clear business outcomes, well defined workflows, strong governance, efficient architecture and measurable value created. When you understand that every automated process carries a cost, you move from thinking the goal is maximum automation to maximum value per automated process.
The real question marketers need to be talking about
Much of the conversation around AI focuses on capability and what can AI do to make us faster, smarter and more productive. These are important questions.
But as AI adoption matures, another question becomes equally important. Are we prepared for the cost of operating AI effectively at scale?
Because automation that delivers more value than it consumes requires discipline, not just technology.
And this is the challenge many organisations will face as they move from AI experimentation to enterprise-wide transformation.
Why marketers need to treat AI as a commercial investment, not a tech experiment
The marketers that succeed will be ones that treat AI as a commercial investment, not a technology experiment. That means designing for value, not volume. Measuring outcomes, not activity. Importantly, those who will thrive will recognise that every automated marketing workflow should earn its place through wider tangible business impact.
The next chapter of AI won’t be defined by how much we automate. It will be defined by how intelligently we choose what to automate. Because at enterprise scale, competitive advantage won’t come from having more AI, it will come from operating AI more effectively than everyone else.
