AI built into the product or AI as a buzzword: how to tell the difference before you buy
How to evaluate AI vendors before you buy: signs of real AI, signs of buzzword, and the questions every manager should ask before signing the contract.
In short
- Real AI works on your operation's data, integrates with the systems your company already uses, and shows measurable results before you buy.
- The signs of AI as a buzzword are a pitch focused on the technology, no case study with a measurable KPI, and an isolated tool with no ERP or CRM integration.
- Before signing, ask the AI vendor for a proof of concept with your company's real data, a baseline measurement and success criteria defined in writing.
- A poorly evaluated AI purchase costs more than the contract: it eats up team time and builds internal resistance to future AI initiatives.
Only 5% of Brazilian SMBs truly use SaaS.
The rest bought a tool, not a solution.
And a significant share of those tools have 'AI' in the name or the pitch, but not in what they deliver.
The problem with AI hype
It has never been easier to put AI in a sales presentation. It has never been harder to know whether the AI you're buying will change anything in your operation.
The market is saturated with products that use AI as a sales pitch, not as a real feature. And buyers, without clear criteria to evaluate, end up paying for a promise.
B2B Stack analyzed 19,000 enterprise software reviews in 2025 and reached a direct conclusion: the 2026 buyer is more discerning, less driven by promises and more focused on real day-to-day impact.
The question is no longer 'does this tool have AI?' It's 'does this AI solve my specific problem?'
Three signs the AI in a tool is real
1. The AI works on your data, not on generic data
AI that delivers real value is trained on or fed with your operation's data. It learns from your history, identifies patterns in your business and generates contextualized insight.
Generic AI answers generic questions. It can be useful for individual productivity. It doesn't transform operations.
Question to ask the vendor: 'How will the AI learn from my operation's data specifically?'
2. You can measure the result before you buy
A vendor that offers real AI can show results in a context close to yours: a case study with a measurable KPI, a demonstrable MVP, a proof of concept with real data.
A vendor selling a buzzword shows a generic demo, talks a lot about 'potential' and avoids showing concrete results.
Question to ask the vendor: 'Can you show me a real result, with numbers, in an operation similar to mine?'
3. Integration with your systems is clear
AI that lives in a silo doesn't transform operations. To deliver value, it needs to connect with the systems your company already has: ERP, CRM, tax, billing and operations systems.
If the vendor has no clear answer on how the tool will integrate with your ecosystem, the AI will operate in a vacuum.
Question to ask the vendor: 'How does this tool connect with the systems I already use?'
Three signs the AI is a buzzword
• The pitch focuses on what the technology is, not on what it solves for your specific business
• There's no case study with a measurable KPI available for review
• The implementation doesn't include integration with your systems: it's a separate tool you open when you feel like it
Other questions the AI vendor needs to answer
The three signs above separate real AI from buzzword. But real AI can still be a bad purchase if questions about data, cost and maintenance go unanswered. Take these questions to your meeting with the vendor:
- Where is the company's data stored, and who has access to it?
- Is my operation's data used to train models for other clients?
- How does the cost grow with usage: per user, per query volume or per processing?
- Who maintains the solution after deployment, and what happens when the process changes?
- If I end the contract, can I take my data and the history it generated?
Whenever the AI processes personal data from customers, employees or suppliers, the LGPD (Brazil's data protection law) comes into play. The vendor needs to explain clearly how it handles that data, and the company's legal team should review the contract.
A vague answer does not rule out the vendor right away. But it shows where the risk is and what needs to be in writing before you sign.
Why does this matter for operations?
Buying AI that doesn't deliver creates two problems beyond the financial one.
First: the team wastes time trying to use a tool that doesn't fit the real operation.
Second: the manager is left feeling that 'we already tried AI and it didn't work', which creates resistance to the next attempt, even if the next one is the right one.
The cost of a poorly evaluated adoption is higher than it looks in the contract.
How to test an AI solution before signing the contract
The safest way to tell real AI from buzzword is to test it. A good proof of concept is short, uses real data and has success criteria agreed on before it starts. The process is simple:
- Pick a specific, frequent problem in the operation, such as classifying tickets, checking documents or predicting delays.
- Measure the current scenario: how long the process takes, how many errors happen and how much it costs.
- Run the AI on a sample of the company's real data, not on the vendor's demo dataset.
- Compare the result with the baseline and decide based on the number: scale, adjust or stop.
Define the success criteria in writing before the test. Without that, any result becomes an argument for buying.
Involve the people who will use the tool day to day from the start. The AI can get it right in the test and still not be adopted if it does not fit the team's routine.
How Wolkee approaches this
Wolkee doesn't sell AI as an off-the-shelf product. It develops solutions that integrate with the data ecosystem the company already has, solve a specific problem with measurable results and start with an MVP so you can see results before scaling.
No client scales a Wolkee solution without first seeing concrete results. That's the difference between AI that transforms and AI that shows up in a presentation.
Before you sign your next AI contract: do you know what you're buying?
Wolkee assesses what your operation really needs before proposing any solution. No rigid scope, no vague promises.
Tell us about your situation. Wolkee knows how to unlock it.
Frequently asked questions
How can you tell if a tool really has AI?
Ask to see the AI working with your operation's data, not in a generic demo. A tool with real AI shows what changes in a specific process, with a measurable indicator, and explains how it connects to your systems. If the vendor only talks about potential and shows no result with a number, the AI is probably in the pitch, not in the delivery.
What should you ask an AI vendor before hiring them?
Ask how the AI will use your operation's data, what results the vendor has already shown in a scenario similar to yours, and how the tool connects to the systems you already use. Then ask where the data is stored, how the cost grows with usage and who maintains the solution after deployment.
What is the difference between a proof of concept and an MVP in AI projects?
A proof of concept tests whether the AI solves the problem, with real data and a small scope. An MVP is the first version used day to day, already integrated into the process and the team. In practice, the proof of concept answers whether it works, and the MVP answers whether the operation adopts it. Both need a KPI defined before they start.
Can generic AI like ChatGPT solve operational problems?
It helps with individual productivity, but on its own it rarely changes an operational process. For that, the AI needs to be fed with company data and connected to the systems where the work happens, such as ERP and CRM. Language models like OpenAI's can be the foundation of the solution, as long as they are integrated into the real workflow, with access rules and measured results.
