Over the past year or two, many companies have started using AI tools in the same way they previously tried out a new piece of software or an online subscription. Someone on the team signed up for ChatGPT, someone else started using Claude for documents, the marketing team tested Perplexity for research, and in the meantime image generators, video generators, meeting note-takers, browser-based assistants and automation add-ons all started appearing. In the beginning, this often happened spontaneously, which is completely understandable: the technology was evolving quickly, and everyone was trying to figure out how it could support their own work.
Now, however, we have reached a different stage. AI tools are no longer just experimental novelties. In more and more companies, they are becoming part of daily operations. This also means that their cost, accessibility and reliability are becoming management-level questions. It increasingly matters who subscribes to what, which package they use, what kind of data they upload, and how much a given workflow depends on a single provider or feature.
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What does AI overhead mean?
By AI overhead, I don’t just mean monthly subscription fees. It includes everything that becomes part of the operating cost of using AI: software subscriptions, credits, API usage, automation platform fees, business-tier plans, internal training, redesigning workflows, creating the right data security conditions, and even the situation where a previously used feature suddenly moves to another package and forces the company to rethink how it works.
This is becoming increasingly common. With many tools, a feature first appears under very favourable conditions, everyone tries it, builds it into their work, and later it becomes tied to extra credits, a higher-tier plan or an enterprise subscription. From a business perspective, this is understandable, because these systems have serious computing and development costs. From a leadership perspective, however, it is worth recognising that what we consider a natural part of a monthly subscription today may become a separate cost item tomorrow.
So the cost of AI is no longer only what we see on the company card statement. It also includes the time the team spends learning the tools, the risk of building a process on an uncertain provider, and the dependency that develops when an important workflow becomes tied to a single platform.
Access to AI tools is becoming increasingly important
Alongside costs, the question of access is also becoming more interesting. A good example is the case of Anthropic’s Fable 5 and Mythos 5 models. These models were briefly made available, then Anthropic had to suspend access because of an export control directive from the US government. The official explanation referred to national security considerations, and the practical result was that customers suddenly could no longer use those models. The other Claude models remained available, but the case clearly shows that the strongest AI capabilities are now surrounded not only by technological and business questions, but also by regulatory ones.
I see this as an important turning point. In recent years, we have become used to the idea that the most advanced models, in some form, are accessible to everyday users, freelancers, small companies, marketers, consultants and developers. This has been, and still is, a huge opportunity. A small team can access analytical, content creation, automation and visual capabilities that would previously have been imaginable only with large corporate resources.
At the same time, the question arises: how long will this era continue in its current form?
As models become stronger and more capable, restrictions around them are likely to become more common. These will not necessarily appear as obvious bans. It is much more likely that the most powerful capabilities will gradually move into more expensive plans, behind enterprise access, into credit-based systems or regulated environments. For a large corporation, this may often be a manageable cost. For an SME or a smaller expert team, however, it makes a huge difference whether AI costs amount to tens of thousands, hundreds of thousands or even millions per month.
Why is AI tool usage a leadership question?
AI adoption cannot simply mean giving the team a few subscriptions and letting everyone use them however they can. This may seem practical in the short term, but over time it can easily turn into a system that is almost impossible to oversee.
One colleague works from a private account, another copies company data into a free tool, and a third builds an automation that depends on a single feature from a single provider. Monthly costs become scattered, knowledge remains inside individual accounts, and leadership often only becomes aware of the problem when a feature disappears, pricing changes, or a data security issue arises.
Choosing AI tools is therefore now also an operational, financial and risk management decision. It is not enough to look at which model gives the nicest answers or generates the most impressive demo. We also need to understand what task it is being used for, what data it processes, who has access to it, under what conditions the information is stored, how stable the given feature is, and whether there is a backup plan if the service or pricing changes.
How can we assess AI overhead within a company?
One of the first practical steps in corporate AI usage can be a simple AI subscription audit. It does not need to be overcomplicated, and it does not require a large consulting project. As a first step, a spreadsheet is enough. It is worth listing which AI tools the company currently pays for, who uses them, what they use them for, how much they cost per month, what kind of data is entered into these systems, and whether any workflow already depends on them.
- For the assessment, these are the types of questions worth going through:
- What AI tools does the company currently use?
- Who pays for them: the company, the employee, or a mix of both?
- Which tool is used for which task?
- Is there overlap between several subscriptions?
- Is company, customer or personal data entered into these systems?
- Is there any process that already depends on a specific AI feature on a daily basis?
- Do you know what happens if that feature disappears or moves into a more expensive package?
Very often, this first step already reveals that several tools are performing the same role, while none of them were selected consciously. It is also common that the biggest risk is not the cost itself, but data handling or organisational disorder: important processes run in private accounts, there is no shared knowledge base, and no one has a clear overview of how many AI subscriptions the company actually has.
Not everyone needs the same AI tool
The next question is what kind of AI environment each role actually needs. A leader, a marketer, a salesperson, a finance professional and a customer support colleague will not necessarily benefit from the same tool.
If everyone receives the same tool in the same package, the result is rarely optimal. Some colleagues end up using an expensive tool for simple tasks, while others try to do work in a weaker or unsuitable system where quality and data security would matter much more.
This is why, when introducing AI tools, it is worth defining not only the tool list, but also which role can use them for which tasks. A good AI strategy does not start with the question “Which is the best model?” It starts with the question: which workflow do we want to improve?
Data security
When someone works with public information, their own notes or general marketing ideas, the risk is lower. But customer data, contracts, internal financial documents, HR materials or strategic information require a completely different level of awareness.
What happens to the data? Does the system learn from it? What contractual terms apply to the use of the tool? Who takes responsibility if sensitive information leaves the controlled environment?
A business leader does not need to become a technical expert here, but the basic questions must be asked. Which plan are we using? Are we working in a business account or private accounts? Is personal data being entered? Do employees know what they should never paste into an AI system? Is there an internal policy, or is everyone making decisions based on their own judgement?
Experimentation and implementation
Another common mistake is that companies build on a newly released feature too quickly. The enthusiasm is understandable, because AI tool demos can be genuinely impressive. A new agentic feature, a browser-based assistant or an automated research tool immediately makes us think: we should build this into our process, this will save time, this will take several hours of work off the team’s shoulders every week.
However, a business process should only be deeply built on a solution where access, cost, data handling and long-term sustainability have all been considered.
This does not mean companies should avoid experimentation. Quite the opposite: I believe the companies that gain an advantage now will be the ones that can test quickly and intelligently. The difference lies between controlled experimentation and rushed implementation. A new feature can be tested on a smaller task. Time savings can be measured, quality can be reviewed, and costs can be assessed. But once daily operations, customer processes or decision preparation depend on it, it becomes a leadership-level decision.
Annual subscriptions
In the past, annual software plans often made sense. The price was more favourable, the features were relatively stable, and companies more or less knew what they were paying for.
The AI market is changing faster than that. A tool can take a completely new direction within a few months, the available models can change, extra credits may appear, limits may be restructured, or a better alternative may enter the market. If there is a significant discount, an annual plan can of course still be a rational decision. But as a business leader, it is worth carefully considering how long you want to commit to a market that is moving this quickly.
What should be the next step?
I think the next phase of AI will be much less romantic than the first wave. There will be fewer free miracles, and more package logic, credits, access levels, regulatory questions and internal company decisions.
AI creates real value for a company when there are not only enthusiastic users, but also a conscious operating framework.
The good news is that this does not need to be handled as one huge project. You can start with a simple assessment: what tools are being used, what they are used for, how much they cost, what kind of data goes into them, and which workflows they genuinely improve. From there, you can decide where it makes sense to standardise, where a business plan is needed, where training is required, where internal rules should be created, and where a completely new workflow is worth building.
If you would like to better understand how AI could support your company’s operations, feel free to get in touch. I run practical AI workshops based on real company examples, and I can also support you through personalised mentoring if you want to think through specific tools, workflows or directions for AI adoption.
Corporate AI Training and Consulting
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I conduct corporate training sessions on generative AI topics, covering areas such as the use of AI in daily work, AI assistants and agents, AI marketing, GEO and SEO, and image and video generation.
