Every few weeks a new language model appears that "challenges OpenAI and Anthropic." The latest to make headlines is Kimi K3, an artificial intelligence model of Chinese origin that the tech press discusses as an emerging alternative to the industry’s major players
What Kimi K3 Is and Why People Are Talking About It
A language model (or LLM, for its acronym in English) is an AI system capable of understanding and generating text: answering questions, writing, summarizing, classifying, or programming. The best-known names are GPT (OpenAI), Claude (Anthropic), or Gemini (Google). On that map, Kimi K3 presents itself as one more option, coming from the Chinese technology ecosystem, aiming to compete in quality and cost
What’s interesting about the phenomenon isn’t a particular model, but the underlying trend: the supply of generative AI is growing, diversifying, and becoming cheaper. Players from different countries are emerging, some with open models and others closed, and competition pushes prices down and capabilities up. For the market, that’s good news. For the person in charge of an SME trying to decide what to do, it can also be a source of confusion.
What a Headline Doesn’t Tell You
Comparing models by their position in a ranking is entertaining, but misleading for making business decisions. A model can perform spectacularly in academic tests and still not fit your company for very down-to-earth reasons:
- Where your data is processed. A model hosted in the European Union is not the same as one whose data processing is unclear. Privacy and regulatory compliance weigh as much as the quality of the responses.
- Real integration. A brilliant model that doesn’t connect with your ERP, your email, or your day-to-day tools provides little operational value.
- Total cost, not price per token. The API price is only one part. You have to add integration, maintenance, training, and supervision.
- Reliability and support. Who responds when something fails? Is there continuity and mid-term guarantees?
That’s why the emergence of Kimi K3 or any other model shouldn’t lead you to change course impulsively. The model is an interchangeable piece; what really makes the difference is how you apply it to a specific problem.
From the Model Race to the Use Case That Really Matters
At Tisa we’re clear about it: AI provides value when it’s applied to a measurable use case, not when it’s adopted "just to have AI." A use case is a specific business task that can be improved and measured. For example:
- Classifying and responding to customer service emails with a draft reviewed by a person.
- Summarizing technical documentation or contracts to speed up decisions.
- Extracting data from invoices or delivery notes and dumping them into the ERP without typing them by hand.
- Helping the sales team prepare proposals based on the company’s history.
In all these examples, the underlying model —whether GPT, Claude, Gemini, or Kimi K3— is secondary. What’s decisive is the workflow design, the quality of the data, and the controls that ensure reliable results.
The Role of Data and Governance
An AI doesn’t perform without organized data. If your company’s information is scattered, duplicated, or without criteria, no model will work magic. Before choosing technology, it’s advisable to put things in order: identify which data is relevant, who accesses it, and under what rules. That is data governance, and it’s the foundation on which any AI project rests.
In addition, there’s a well-known risk: hallucination, that is, the model generating incorrect information with an appearance of truth. It is mitigated with techniques such as RAG (giving the model access to your own knowledge base so it responds with verifiable information) and, above all, with human supervision at critical points.
What If I Already Work with Microsoft?
Many companies already operate within the Microsoft ecosystem: email, Office, Teams and, in many cases, the ERP Business Central. For them, the most direct way to incorporate AI is usually Copilot and Power Platform, because the AI is embedded in tools the team already uses, with integration and the security framework already resolved.
This doesn’t mean closing yourself off from anything. It means starting where the return is fastest and the risk is lowest. And, when a specific use case justifies it, evaluating other models —including open or alternative ones like the one Kimi K3 represents— with business criteria: fit, cost, privacy, and integration.
How We Approach It at Tisa
We are a Microsoft partner and ISV with more than 30 years helping private companies go digital
- Advisory: we identify relevant data, evaluate tools according to your investment capacity, and define governance protocols.
- Use case: we develop and execute a specific, measurable application for your business.
- Training: we train your team to use AI strategically and with sound judgment.
This way you avoid the most common mistake: chasing every new model that appears in the news instead of building something that provides real and lasting value.
Conclusion
Kimi K3 is a useful reminder that the AI market moves fast and that no provider has a monopoly on innovation. But the best strategy for a company isn’t to guess which model will win, but to rely on organized data, measurable use cases, and a partner that translates technology into results.
Do you want to explore how to apply AI to a specific case in your business? Write to us at info@grupotisa.com, call us at (+34) 971 305 885, or visit grupotisa.com and we’ll help you with a no-obligation assessment.