AI is taking over businesses at high speed, but without clear strategy and governance, hidden costs, technical complexity and team fatigue risk causing a false start
AI is moving into businesses at an unprecedented speed. In a few months, management activated “co-pilots” everywhere and piled up licenses. Behind the enthusiasm, another dynamic is emerging: explosion of hidden costs, stacks difficult to evolve, team fatigue. Without informed governance, many AI initiatives risk hitting a wall in the next six months. There is still time to correct the trajectory.
A major innovation… deployed like a gadget
AI is becoming a working infrastructure. It speeds up writing, facilitates analysis, allows you to simulate scenarios and explore options that you would never have had time to test. It also opens the way to agentic systems capable of chaining together several tasks: searching for information, structuring it, producing a deliverable, triggering a workflow.
The problem is not the technology. This is how we deploy it.
AI: between promises and strategic wall (illustration generated by IAG)
In many organizations, the response to AI looks like a headlong rush:
- Activate AI modules from existing editors,
- Buy additional licenses “just in case”,
- Let each business department choose its tools,
- Launch isolated pilots, without guidelines.
In other words: we treat a profound transformation of work as a succession of “functionalities” to be added. It’s comfortable in the short term. It is dangerous in the medium term.
Hidden costs that will explode
Over the next six months, three categories of costs will converge if nothing is regulated.
1. Direct costs of licenses and tokens
By multiplying the AI integrated into each tool, we multiply the invoicing lines. Each publisher charges for its “intelligence layer”: in the office suite, in the CRM, in the project tool, in the IT department. Individually, each amount seems reasonable. Collectively, the bill rises quickly, for uses that are often redundant and partially exploited.
2. Human supervision costs
The more AI produces, the more it needs to be verified. We massively underestimate the time spent rereading, cross-checking, correcting. This time does not appear in any invoice, but it saturates calendars. Without governance, we shift the burden rather than reduce it: less time to produce, more time to control, with no net gain.
3. Architecture and exchange costs
Each embedded AI comes with its own constraints: formats, models, security rules, integrations. In the short term, we accept compromises. In the medium term, we find ourselves with stacks that are difficult to evolve, strong dependencies on certain suppliers, and cross-functional projects slowed down by the complexity of integrations.
In a context of budgets under pressure, these costs can become the perfect argument for “going backwards”: freezes, deactivation of services, widespread disillusionment. This is a plausible scenario over the next six to twelve months if nothing is structured.
The strategic false start that threatens companies
The danger is not only financial. It is strategic.
By stacking AI initiatives without consistency, we take several risks:
- Dilute confidence: unequal, sometimes disappointing experiences create weariness (“another tool”, “another driver”).
- Discuss the subject internally: teams are getting tired, political sponsors are getting worn out, management is becoming more cautious.
- Closing doors: hasty choices of stacks and contracts limit the use of more advanced agents tomorrow, or of better adapted models, due to failure to anticipate modularity.
In other words: we risk a false start which would return AI to the status of a buzzword, even though it is becoming a structuring layer of work in organizations.
Gain height: think about work, not tools
To avoid this false start, you must agree to change your angle. The central question is no longer: “Which AI tool will we deploy?”
But: “How do we want work to be redistributed between humans, models and AI agents in the next 6 to 18 months?”
This change of perspective requires:
- Clarify the types of tasks that are intended to be handled by AI (generation, structuring, control, orchestration),
- Define the acceptable level of human supervision according to the risks,
- Identify the professions and positions where time savings will really be converted into value (decision, customer relations, creation, innovation),
- Design an architecture that allows AI agents to evolve, replace each other, and combine without breaking everything.
It’s not a luxury. This is a condition so that current investments do not turn into fixed costs without return.
Simplify stacks, prepare agents
The good news is that we can act very quickly, without stopping everything. A realistic six/twelve month trajectory can be based on three principles.
– Voluntarily limit the proliferation of entry points: rather than activating AI everywhere, choose a limited number of access points (for example a company chat, one or two business tools) and concentrate the learning and governance effort there. We reduce friction, we increase readability, we create deep rather than superficial uses.
– Separate the reasoning infrastructure from the tools: consider AI models and agents as a separate layer, which can be controlled and evolved independently of the applications. Tools become interfaces; the “brain” can change. This separation is what will allow tomorrow to introduce new agents, remove old ones, and adjust models without redesigning the entire stack.
– Authorize agents… but under control: introducing AI agents capable of carrying out several work steps – particularly for repetitive and well-defined tasks – is logical. But this must be done with a clear framework: what data, what actions, what safeguards, what human recovery. The objective is not to automate at all costs: it is to identify where an agent brings positive net value, without transforming a productivity gain into operational risk.
Without enlightened governance, failure is likely. We must state it clearly: without enlightened governance, many AI initiatives will fail in the next six months. Not for lack of technology. Lack of framework.
- Budgets absorbed by rarely used licenses.
- Teams tired of experiments without follow-up.
- Managements which, as a reflex of caution, will choose to slow down rather than adjust.
- Rigid architectures which will make the evolution of models or agents costly and risky.
This scenario is avoidable, but it requires a change of attitude: moving from a logic of “rapid response to fashion” to a logic of mid-term design, where today’s choices are made to remain compatible with tomorrow’s needs.
What there is still time to do
Three concrete actions can be decided immediately.
1) Lay down what already exists: map current AI uses, direct costs, supervision costs, technological dependencies. This work provides a common language to talk about what happens next.
2) Appoint clear governance: designate a manager or a small collective capable of linking technological, economic and human issues. Not a purely technical “AI committee”, but a body which has the legitimacy to arbitrate and prioritize.
3) Define a simplified scalability trajectory: accept that everything will not be perfect in the short term, but establish principles: modularity, limitation of entry points, agents introduced where the value is clear, targeted training of key professions. It is this trajectory which will make the next arbitrations understandable and legible for the teams.
We don’t have six years to structure this new layer of work. We have, at best, six to eighteen months before the decisions made today become difficult to correct.
Gaining height now does not mean slowing down the AI. It means giving ourselves a chance to bring it into organizations sustainably, without explosion of costs, without unnecessary fatigue, and with the capacity to adjust stacks and organizations to the rhythm of real progress of models and agents.
