Implementing an AI agent in a company is not only a technological challenge but also a strategic one. As more businesses consider using artificial intelligence in their daily operations—from customer service to document analysis—successful implementation requires careful planning. This article explains what to focus on before deploying an AI agent, which areas of the business need to be well-prepared, and how to avoid common mistakes.
There are many areas where AI can be helpful. From automating routine tasks, supporting customer service and data analysis, to streamlining decision-making processes and creating intelligent assistants that support team workflows. The potential is enormous—but the key lies in properly preparing the organization for this change.
Stages of AI Assistant Implementation
The process of implementing an AI assistant in an organization can be divided into several stages, each requiring specific actions. From analyzing business needs, selecting the right language model, and preparing the infrastructure, to integrating with existing systems and testing—each step impacts the overall effectiveness of the solution.
The key stages are:
- Needs analysis and readiness assessment
- Data and content preparation
- Solution design
- Assistant development and configuration
- Testing and pilot phase
- Deployment and maintenance
Needs analysis and readiness assessment
To ensure the best results from implementing an AI agent, start by asking yourself: which tasks and areas have the most potential for optimization through the use of artificial intelligence?
When looking for an answer to this question, it’s worth carefully analyzing your company’s current structure, processes, and employee responsibilities. This will help identify so-called “bottlenecks” that may affect the quality of services provided. These might include, for example:
long response times to quote requests
teams overloaded with routine tasks
lack of consistency in customer communication
manual processing of documents and data
difficulties in quickly accessing internal company knowledge
Based on this analysis, you’ll be able to identify areas for improvement as well as the people who will directly benefit from the support of AI assistants.
The second area that should be reviewed is the existing infrastructure. Implementing an AI assistant doesn’t require a large amount of hardware. If the company doesn’t want to invest in new machines, it can opt to use cloud services such as Azure, AWS, or Google Cloud.
Data is a crucial part of the preparation process. To fully leverage the potential of dedicated AI solutions, it’s important to understand that training the model behind the assistant requires datasets stored in digital form. These should be well-organized and kept in a central repository or database. The less structured the data, the higher the cost of implementing the assistant—and the greater the risk that the solution won’t meet expectations.
Data and content preparation
At this stage, it’s essential to gather all materials that contain important company knowledge—this may include PDF, Word, and Excel documents, website content, FAQ sections, emails, or data from databases.
Next, the collected information needs to be properly prepared—organized, cleaned of unnecessary content (e.g., unreadable PDFs), standardized where possible, and exported to CSV or JSON files (e.g., emails).
In some cases, such as when planning further model customization (fine-tuning), it will also be necessary to label the data or prepare a dedicated training set in the form of instructions and expected responses, for example:
{"prompt": "What documents are required to sign an OCS agreement?", "response": "The following documents are required to sign an OCS agreement: ..."}Solution design
At this stage, decisions are made about the technical design of the solution. It’s important to define what type of assistant will best meet the company’s needs—whether it’s a simple chatbot answering questions, a more advanced assistant with access to company knowledge (so-called RAG – Retrieval-Augmented Generation), or an agent capable of independently performing specific tasks such as making bookings, generating reports, or sending emails.
The next step is selecting the appropriate technologies, including the large language model (LLM) that will power the assistant—such as GPT-4, Claude, Mistral, LLaMA, or Gemini—depending on specific needs and requirements related to privacy, cost, and integration capabilities.
Finally, it’s worth preparing a list of functions the assistant should perform and planning integration with other systems used in the company—such as the CRM, knowledge base, or email.
Assistant development and configuration
At this stage, both the technical backend and the user-facing part of the assistant (frontend) are developed. This could be, for example, a chat interface on the website, a button that launches the assistant in an application, or a widget integrated with tools like Slack. You can read more about how AI agent integration with the Slack communication platform can look here >>LINK
In parallel, the selected language model is deployed—via services such as Azure OpenAI, OpenAI API, Anthropic (Claude), Google Vertex AI (Gemini), or locally using open-source models like LLaMA, Mistral, or Mixtral.
If the assistant is meant to use internal company knowledge, a RAG (Retrieval-Augmented Generation) mechanism needs to be configured—enabling it to search and match relevant documents to user queries.
Finally, integrations with other systems—such as CRM, ticketing systems, or email—are implemented, allowing the assistant to meaningfully support the team’s day-to-day work.
Testing and pilot phase
After implementation, thorough testing of the solution is essential. The first step is functional testing—checking whether the assistant correctly understands user intent, responds in line with company documentation, and handles different types of queries appropriately.
The next phase is testing with end users (UAT – User Acceptance Testing), which helps assess how well the assistant performs in real-world scenarios and whether it meets employees’ expectations.
Based on feedback and observations, iterative improvements are made—such as adjusting responses, adding new documents to the knowledge base, or refining prompts and the agent’s logic. This phase is often repeated several times until a satisfactory level of quality is achieved.
Deployment and maintenance
After completing the testing phase, the assistant is deployed to the target infrastructure—this may be a public cloud (e.g., Azure, AWS, GCP), on-premise servers, or a hybrid solution, depending on security and availability requirements. More about this is covered later in the article.
It’s also necessary to set up monitoring, which allows you to track things like token usage, query frequency, error rates, and the quality of generated responses. This enables quick issue resolution and cost optimization.
In daily use, it’s important to keep the data up to date—adding new documents, removing outdated information, and updating the knowledge base the assistant relies on.
Over time, as business needs evolve, it may be worth considering retraining or fine-tuning the model—e.g., every few months—to better align it with the organization’s specific context.
Finally, it’s important to provide technical support and user assistance to ensure the solution is not only technically reliable but also convenient and intuitive for everyday use.
Data privacy
In the “Deployment and maintenance” section, we discussed the available options for choosing the infrastructure on which the AI agent will be deployed.
Each solution has its pros and cons. Choosing an on-premise setup gives you full control over the data, but it requires a dedicated machine with specific parameters.
Another option is using a public cloud service, such as Azure. Microsoft clearly states that data submitted to the Azure OpenAI service is not used to train or improve OpenAI or Microsoft models (source).
According to Microsoft, prompts and responses are not shared with other customers or OpenAI. Azure operates in full isolation mode: when using GPT-4 on Azure, no information from your conversations is shared with OpenAI LLC. Microsoft has confirmed this in a Data Processing Addendum (DPA).
AI decision accountability
It’s important to remember that formal and legal responsibility for the outcomes of an AI agent’s actions and the data it processes lies with the entity that implemented and oversees the solution—most often.
- the organization (e.g., the company that deployed the assistant),
- the system administrator,
- the individual making decisions based on AI suggestions (e.g., a customer service representative, recruiter, or doctor).
How to reduce risk?
- Human-in-the-loop (HITL) – A human must approve important decisions, while AI only supports the process (e.g., the assistant drafts a response, but a person approves it).
- Clear disclaimers and warnings – The AI should inform users: “I am an AI assistant – please verify my responses before making a decision.”
- Source verification – The AI assistant should, where possible, cite sources for its answers or indicate when it doesn’t know rather than guessing. Using RAG enables precise control over the knowledge base.
Summary
The process of implementing an AI agent must be well-planned and carefully considered. It may seem challenging at first, but with proper preparation, it can deliver long-term benefits. If you need support, feel free to contact us.