The ongoing costs of an AI agent typically consist of three main categories: license and usage costs, maintenance and training costs, and integration costs for changes. After the go-live, you therefore do not only pay for the technology itself, but also for keeping the agent up to date and adapting to changing processes. Exactly how high those costs turn out to be depends heavily on the usage, the complexity, and the chosen approach. In this article, we answer the most frequently asked questions about the ongoing costs of an AI agent in customer contact.
What are the largest cost items after the go-live?
After the implementation of an AI agent, the largest ongoing cost items are license and usage costs, maintenance and knowledge management, and adjustments for process changes or system integrations. Many organizations underestimate these costs beforehand, because during the project, attention is mainly focused on the one-time implementation.
In practice, the ongoing costs of an AI agent are divided across the following categories:
- Licenses and platform usage: the fixed or variable fee for the use of the underlying technology
- Maintenance and updates: keeping the agent up to date in terms of knowledge, language models, and functionality
- Human supervision and management: the time employees spend monitoring, adjusting, and improving the agent
- Integration and adjustments: costs that arise when systems, processes, or policies change
The ratio between these items differs per organization, but maintenance and management are most often underestimated in practice. An AI agent that is not actively maintained quickly loses quality and effectiveness.
How are the license and usage costs of an AI agent calculated?
License costs for an AI agent are typically calculated based on usage, capacity, or a combination of both. Common models are a fixed monthly amount per environment or user, a variable rate per handled conversation or transaction, or a hybrid model with a base fee supplemented by usage-dependent costs.
Which model turns out to be most advantageous depends on the volume and spread of your customer contact. With a stable and predictable volume, a fixed rate offers clarity and manageability. With strongly fluctuating contact, such as seasonal peaks, a variable model can actually turn out to be more favorable because you only pay for what you actually use.
Also keep in mind that the costs of the underlying language model or AI platform may be part of the license, or charged separately. This is an important point to get clear with your supplier beforehand, so that you are not faced with surprises as usage grows.
What does it cost to keep an AI agent up to date and accurate?
Keeping an AI agent up to date requires an ongoing investment in knowledge management, quality monitoring, and periodic retraining of the model. These are not one-time activities: products, processes, regulations, and customer questions change continuously, and the agent must move with them to remain relevant and reliable.
Specifically, this involves activities such as:
- Updating knowledge bases and answer scripts for policy or product changes
- Analyzing conversation logs to identify errors, gaps, or outdated answers
- Periodically retraining or fine-tuning the model on new data
- Monitoring answer quality via samples or automated evaluation
The required effort varies, but in most organizations, count on a few hours per week for an actively managed agent. In more complex environments or with high quality requirements, this can increase. Organizations that underestimate this maintenance see the performance of their AI agent gradually deteriorate, which ultimately leads to higher costs for recovery or rebuilding.
When are extra integration or adjustment costs to be expected?
Extra integration or adjustment costs arise when the environment in which the AI agent operates changes. Think of new systems, adjusted processes, changed regulations, or expansion to extra channels. These costs are not always foreseeable, but they are manageable with the right architecture and agreements.
Situations that regularly lead to extra costs:
- Replacement or upgrade of a CRM, ERP, or contact center platform
- Addition of new channels such as WhatsApp, chat, or voice
- Changes in laws and regulations that affect the handling of customer contact
- Expansion to new departments, products, or target groups
- Organizational restructurings that change processes and responsibilities
A well-set-up AI agent works with modular, reusable building blocks. That makes adjustments simpler and cheaper than when everything is custom-built for one specific situation. Therefore, always ask your supplier about the flexibility of the architecture and what a common adjustment costs in practice.
How does the total cost of ownership relate to the savings?
The total cost of ownership of an AI agent, also called Total Cost of Ownership (TCO), must always be weighed against the demonstrable savings and returns. In customer contact, this involves lower handling costs per contact, reduced deployment of employees on repetitive questions, shorter waiting times, and less repeat contact.
An honest TCO calculation takes all costs into account: implementation, licenses, maintenance, management, and adjustments over the expected period of use. On the benefits side, direct savings on personnel costs count, but also indirect effects such as higher customer satisfaction, better accessibility, and lower employee turnover due to less burdensome work.
In most cases, a well-implemented AI agent pays for itself, but the payback period differs greatly per situation. Organizations with a high volume of repetitive questions typically see the fastest payback period. For a reliable estimate, a thorough business analysis is indispensable, in which you also take into account the hidden costs of the current situation, such as repeat contact, errors, and loss of employees.
What questions should you ask a supplier about ongoing costs?
Before you enter into a partnership with a supplier, it is essential to explicitly discuss ongoing costs. Many quotes focus on the one-time implementation costs, while the structural costs after go-live have at least as large an impact on your total budget.
Ask these questions to every supplier:
- How are the license costs structured? Fixed, variable, or hybrid, and what triggers a rate change?
- What is included in the maintenance? Are updates, bug fixes, and model improvements implemented automatically?
- Who is responsible for keeping the knowledge base up to date? Us as the customer, the supplier, or both parties?
- What does an adjustment cost in the event of a process change? Ask for a concrete example of a recent adjustment for a similar customer.
- Are there costs associated with scaling? What happens to the price if the volume doubles?
- Is there an SLA for availability and performance? And what are the consequences if these are not met?
A transparent supplier gives a concrete answer to all these questions. Vague responses about ongoing costs are a signal to ask further or to be critical about the collaboration.
How does Pegamento help in controlling the costs of an AI agent?
We at Pegamento help organizations not only to implement an AI agent, but also to keep the ongoing costs structurally manageable. We do this by working with a smart combination of proven modules instead of costly custom work, so that adjustments can be implemented quickly and efficiently. You have one point of contact for the entire process, from analysis to management and optimization.
Specifically, we offer:
- Transparent cost structure: no hidden rates, but clear agreements on licenses, maintenance, and adjustments
- Active management and monitoring: we monitor the quality of your AI agent and adjust where necessary, so that you do not have to organize that yourself
- Modular architecture: tailored solutions with standard building blocks that scale easily and adapt to changing needs
- Substantiated business case: we start with an analysis of your current situation and calculate the expected TCO and savings before you make a decision
- Everything under one roof: from contact center platform and AI to process automation and Agentic AI, without silos or complex supplier management
Do you want to know what an AI agent costs and delivers in your specific situation? Contact us and we will be happy to think along with you about an approach that fits your organization and budget.
Frequently Asked Questions
How large is the difference in ongoing costs between a simple and a complex AI agent?
The difference can be significant. A simple AI agent that handles a limited number of frequently asked questions requires relatively little maintenance and has lower license costs. A complex agent that integrates with multiple systems, serves multiple channels, and continuously learns from new data can bring two to five times higher ongoing costs. It therefore pays to start with a well-defined scope and only expand when the business case justifies it.
What are common mistakes when estimating the ongoing costs of an AI agent?
The most common mistake is underestimating the management and maintenance costs after the go-live, because the focus during the project is almost always on the one-time implementation. In addition, organizations often forget to take into account the internal hours that employees spend on monitoring, knowledge management, and quality control. Another common miss is not including adjustment costs for future system or process changes in the total budget.
Can I lower the ongoing costs of my AI agent as time passes?
Yes, in many cases, ongoing costs decrease relatively as the agent becomes more mature. A well-trained agent makes fewer mistakes, needs fewer corrections, and handles a larger share of the contact independently, which lowers the management effort. At the same time, expansions or new integrations can introduce new cost items. Actively steering for optimization, with regular evaluations and a supplier that proactively looks along, is the best way to structurally control costs.
How do I know if my organization is ready to take on the ongoing responsibilities of an AI agent internally?
A good indication is whether you have someone internally who can take ownership of the knowledge base, analyze conversation logs, and implement improvements. This does not have to be a technical profile, but it does require structural attention and a clear mandate. If that capacity is lacking internally, it is wise to make agreements with your supplier about active management as part of the collaboration, so that the quality of the agent remains guaranteed without you having to free up full-time capacity for it internally.
What is a realistic budget to take into account for the first year after the go-live?
This varies greatly per organization and implementation, but as a rule of thumb, you can count on ongoing annual costs of 20 to 40 percent of the initial implementation costs for licenses, maintenance, and management combined. In the first year, costs are often slightly higher because the agent is still being optimized and more adjustment is needed. A detailed business analysis beforehand gives a much more reliable picture of the specific costs in your situation.
How does the choice for a SaaS platform versus a custom approach affect the ongoing costs?
A SaaS platform typically brings predictable, fixed license costs and relieves you of technical maintenance on the underlying infrastructure, but offers less flexibility for specific adjustments. A custom solution gives more control, but requires a higher investment in maintenance, updates, and technical management. In most customer contact environments, a modular approach based on proven standard building blocks offers the best balance between flexibility and manageability of costs.
What happens to the costs if I want to expand the AI agent to a new channel or a new department?
Expansion to a new channel or department typically brings one-time adjustment costs for integration, knowledge building, and possible model adjustments, followed by an increase in ongoing license and management costs in proportion to the extra volume. How high those costs are depends heavily on the architecture of the existing solution. A modular setup makes expansions significantly cheaper than a monolithic approach, because existing building blocks can be reused instead of being developed anew.
