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Home/Guides/AI Integration in Your Company: 6 Steps

AI Integration in Your Company: 6 Steps

A hands-on AI integration path: pick the process, audit data and APIs, choose a model, pilot with CRM/ERP, test, roll out and monitor.

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Introduction

What is this guide about

Deciding that your company should use AI is the easy part. The real work starts when a language model has to read customer data from your CRM, create a ticket in your helpdesk or answer a call coming through your VoIP system. This guide covers exactly that stage: the technical path from picking a process to running a production system.

If you are still working out where AI fits in your organisation and how to prepare your team, start with our guide on how to implement AI in your business. Here we assume the direction is set and the question is practical: how to build it, connect it to the systems you already run and keep it reliable.

We walk through six steps in the order we follow on projects: defining a success metric, auditing data and APIs, choosing the architecture, piloting on real systems, testing quality and security, and rolling out with cost monitoring. The final section covers typical budgets and timelines.

Chapters

Key topics

01

Step 1: Which process should you choose for AI integration?

Pick a repetitive, high-volume process with a clearly measurable outcome, such as triaging tickets, answering common customer questions or handling inbound calls. Before writing any code, define the success metric, for example handling time, share of cases closed without a human or cost per case, and record its current baseline value.

A good first candidate has three traits: it happens every day, it is based on text or speech, and there is a clear way to tell a right answer from a wrong one. Classifying inbound email, drafting helpdesk replies, extracting fields from invoices and booking appointments by phone all qualify. Rare, judgement-heavy or high-stakes decisions are better left for later.

The metric has to be measurable before launch, otherwise you cannot prove any effect. Track the current state for two to four weeks: how many cases come in, how long they take, how many end in an error. Also set an acceptance threshold, such as a minimum share of correct answers below which the pilot does not go to production. It is a short document, but it anchors every technical decision that follows.

02

Step 2: How do you audit data and systems before integrating AI?

Find out where the data the model needs lives, what format it is in and who owns it. Then list the systems AI must talk to, such as CRM, ERP, helpdesk and VoIP, and check their APIs: available endpoints, authentication, rate limits and write access. Missing or weak APIs are the most common source of delays.

The data audit answers practical questions. Is your product documentation current, or scattered across drives and inboxes? Does your ticket history have categories you can reuse for testing? Does the data contain personal information that must be masked or excluded? The answers decide whether a well-written prompt is enough or whether you need a searchable knowledge base.

The systems audit matters just as much. An older ERP without a REST API may need a middleware layer, and a CRM with tight rate limits will force you to queue requests. Check webhooks, sandbox environments and the permissions of technical accounts. If your systems do not exchange data at all today, fix that first, because AI will not repair broken integrations. The output is a data flow map and a list of risks.

03

Step 3: Which AI model and architecture should you choose?

For simple tasks with short context, a direct LLM API call is enough. When answers must be grounded in company documents, use RAG, which retrieves relevant passages from a knowledge base. An agent with tool access makes sense when AI must take actions in your systems. For personal data, choose EU hosting and a data processing agreement.

Keep the architecture as simple as the process allows. A model call with a carefully designed prompt and structured JSON output handles classification, data extraction and summaries. If the model has to answer from policies, price lists or procedures, you need RAG and an AI knowledge base: documents are split into chunks, indexed in a vector database, and only the relevant ones are passed to the model. An agent that calls functions such as check availability or create order is the most capable option, but also needs the tightest permission control.

Model choice is a trade-off between quality, speed and token cost. Design the integration so you can switch providers without rewriting the application. For GDPR, check where data is physically processed, whether the provider offers EU data residency, whether it signs a data processing agreement and whether your prompts are excluded from model training. For the most sensitive data, consider an open-source model hosted on your own infrastructure.

04

Step 4: How do you build an AI pilot connected to real systems?

Run the pilot on real data and real integrations, but in a narrow scope: one process, one channel, a selected group of users. Connect the model to your CRM, ERP, helpdesk or VoIP system through APIs, log every request and response, and keep a human approving the model's actions at the start.

A demo on sample data shows that a model can do something. A pilot has to show that the solution works in your environment, with your permissions, data formats and traffic. That is why AI integration with your business systems belongs in the pilot, not after a prototype is signed off. A typical scope is reading customer context from the CRM, writing the result back as a note or ticket, and notifying the right person.

In a voice channel, the pilot usually covers connecting to the phone system, real-time speech transcription and handing the call to an agent when AI does not know the answer. Decide early what context reaches the human agent with a transferred call, so the caller does not have to repeat everything. Whatever the channel, make sure you have full logging with personal data masked, a feature flag that switches AI off instantly and a test environment where problem cases can be replayed safely.

05

Step 5: How do you test the quality and security of an AI integration?

Build an evaluation set from real cases with expected answers and run it after every prompt or model change. Test for hallucinations, prompt injection and data leaks. Decide which actions require human approval, and tell users they are dealing with an AI system, in line with the transparency obligations in the EU AI Act.

Testing an LLM-based system differs from classic QA because the same request can produce different answers. The foundation is an evaluation set: from a few dozen to a few hundred historical cases, each with a correct answer or a scoring rule. You measure accuracy, completeness and whether the model admits it does not know instead of guessing. With RAG, you also check separately that retrieval returns the right documents.

Security testing includes prompt injection attempts, meaning instructions hidden in a customer message or document that try to make the model break its rules. An agent should never have broader permissions than the task requires, and irreversible operations such as refunds or contract changes should go through human approval. The AI Act introduces transparency obligations, so users should know they are interacting with AI. For use cases in higher-risk areas, review the requirements with legal counsel.

06

Step 6: How do you roll out, monitor and scale an AI integration?

Roll out gradually: a share of traffic or a single location first, then full scale. Monitor token cost per case, response time, escalation rate to humans and evaluation set scores on an ongoing basis. Expand to new processes only once the first one consistently hits the success metric defined in step one.

In production the job changes from building to guarding quality and cost. Your dashboard should show cases handled, model cost per day and per case, latency, integration errors and responses flagged as wrong by users. Set spending caps and alerts, because a prompt change or a traffic spike can raise your API bill quickly.

Providers update models regularly and retire old versions, so every version change should pass through the evaluation set. Add new processes on the same foundation: logging, the CRM integration layer and the knowledge base. That way the second and third use cases ship faster and cheaper than the first. For a production example, see our AI voice agent case study for a medical clinic.

07

How much does AI integration cost and how long does it take?

Cost depends on the architecture and the number of integrations. A chatbot or AI pilot typically costs EUR 1,900 - 6,000, a voice agent or AI agent integrated with your systems EUR 6,000 - 19,000, and full AI process automation EUR 19,000 - 47,000+. Ongoing model API fees and maintenance come on top. A pilot usually takes a few weeks.

The biggest budget drivers are the number and quality of APIs AI has to connect to, preparing data for the knowledge base, and security requirements. A simple integration with one modern CRM is a different project from connecting an undocumented ERP and a phone system. Running costs include model tokens, vector database hosting and monitoring, and maintenance starts from EUR 200.

A typical timeline looks like this: one or two weeks for the audit and architecture, a few weeks for the pilot, then a testing period on limited traffic followed by a gradual rollout. Detailed ranges for each option are in our breakdown of AI integration cost.

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