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AI implementation company: how to choose well
A reliable AI implementation company starts with your process, not the tool, says in writing what it will do with your data and hands over control of what it builds. To choose, compare method, data clauses, ownership of results, human oversight and what happens when the contract ends.
The choice matters because almost no one does it alone. According to the executive summary of the ICT Enterprises 2025 survey by Cetic.br, released in August 2026, 54% of Brazilian companies that used AI in 2025 hired external vendors to develop or modify their AI systems. In other words, most of the AI in companies passes through a third party's hands.
This guide is for those who have already decided to hire and need to tell a real vendor from a tool seller. The project stages are in the article on AI implementation in companies. Here the subject is the choice, with the questions Wama considers fair to hear from any client before signing.
What does an AI implementation company need to show before the proposal?
Before the proposal, the AI implementation company needs to show that it understood the process you want to improve. If the first meeting already ends with a tool name and a license price, the conversation started at the end. A serious vendor asks how long the task takes today, how often it repeats and where the necessary data is stored.
That care has a basis. Microsoft's AI adoption planning guide, updated in October 2026, asks you to turn each business problem into a clear use case and confirm whether the activity happens often enough to justify the investment. The same guide recommends adding 20% to 30% contingency to timeline estimates.
Another good sign is the vendor saying when AI isn't the answer. Many processes are solved with integration and a well-written rule, and the article on AI process automation shows how to separate those cases. Whoever never recommends less than they sell is selling, not diagnosing.
What should you ask in the first meeting?
The questions that best separate vendors are the ones that force them to explain their reasoning. The exact answer matters less than the clarity: whoever works with a method explains each step without beating around the bush.
How do you find out where AI fits before proposing a solution?
What number from today will we use to measure before and after?
What company data will the AI read, where is it stored and who else can access it?
What access will the AI have to our systems, and can we revoke that access?
Who reviews what the AI produces before it reaches the end customer?
Who owns what gets built: code, configurations and results?
What happens to the data and the system if we switch vendors?
Questions 3 and 4 usually get the vaguest answers. It's worth bringing to the meeting what the guide on the LGPD, Brazil's data protection law, and artificial intelligence says, and what to require for access in MCP for businesses, the standard that connects AI to in-house systems.
What should you ask for in writing in the AI contract?
Ask in writing for everything involving data, liability and exit. A promise made in a meeting protects no one when the system makes a mistake or when the contract ends.
The good practice guide for contracting AI systems from ANBIMA, the Brazilian financial and capital markets association, from December 2025, was written for the financial market, but its checklist works for any company. It says to check whether the contracting company's data can be used to train other models, in which countries the data is processed, whether there's secure return or destruction at the end of the contract and who owns the results generated. It also recommends avoiding contracts that authorize broad use of data with vague justifications, such as "system improvement."
In the law, each party's role is already written. Brazil's General Data Protection Law, in Article 39, requires the processor to handle data according to the controller's instructions, and Article 37 obliges both to keep records of the operations. In practice, the implementation company works under your instructions, and the contract is where those instructions live. Anyone who doesn't have internal rules yet can start with the AI use policy.
How Wama handles these points
Wama starts every AI client with the AI Assessment, a small, paid first step that shows where AI fits into the company's slow, time-consuming and error-prone processes. It produces the custom Implementation Plan and the baseline for measuring before and after, which answers the first two questions on the list.
There's no boxed solution: each company gets its own plan, and only after it comes execution. If the client moves on to the plan, the diagnostic fee is credited against the first installment. This setup removes the rush to sell the next stage, because the diagnostic has already been paid for and can recommend less than the client imagined.
As for the fifth question, Wama's rule is simple: every AI output goes through human review before reaching the client. The difference between this work and standalone artificial intelligence consulting is that diagnostic, plan and execution stay on the same journey.
Common mistakes
The most expensive mistakes in choosing an AI implementation company happen before signing, when they can still be avoided.
Choosing based on the demo. A pretty demo runs on prepared data. Ask to see the flow with a real sample from your operation, even a small one.
Accepting standard terms without reading the data section. The ANBIMA guide warns that standard contracts from global platforms often include broad liability waivers and generic permission to use data.
Comparing only the project price. Part of the cost comes after the project, in model usage and maintenance, and the article on how much it costs to implement AI shows how to do that math.
Leaving the knowledge only with the vendor. Documents, rules and knowledge bases that feed the AI need to remain accessible to the company, as in a well-built AI agent with a knowledge base.
Skipping the pilot. Starting with an internal process limits the damage of a mistake and shows whether the vendor delivers what it promised.
Frequently asked questions
How do you know if an AI implementation company is reliable?
Ask it to explain its method before talking about tools, to show how it measures results and to answer in writing about data and system access. Be wary of anyone who promises full automation without human review or can't say where the data is stored. Clarity in these answers is worth more than a portfolio.
Do I need a specific contract for an AI project?
Yes. A standard software contract usually leaves out the use of data to train models, ownership of the generated results, decision logs and return of data at the end. These points define who is liable when the AI makes a mistake and what the company takes with it if it switches vendors.
AI implementation company or in-house team?
It depends on what the company already has. An in-house team makes sense when there are people with time and experience in data and integration. Without that, an external vendor speeds things up, as long as it transfers knowledge and leaves documentation. In many cases a combination works best: the vendor designs and implements, the in-house team operates and adjusts.
What changes in the choice for a company in a regulated industry?
In a regulated industry, the contract needs to provide for the company's and the regulator's access to data and logs, incident notification and continuity of operation if the vendor leaves. The ANBIMA guide lists these points for the financial market, and the article on AI for fintechs shows what changes in that sector.
How long does it take to choose an AI vendor?
There's no fixed timeline, but the choice should rarely be the bottleneck. With the priority process defined and the right questions, a few meetings are enough to compare the answers side by side. At Wama, that conversation leads to the AI Assessment, which works as a small test of the vendor before any bigger commitment.
If your company is making this choice, start with the process that takes up the most of the team's time and bring these questions to the next meeting. Talk to Wama about the AI Assessment and see how it answers each of them.
