Since late 2022, artificial intelligence (AI) has gone from being a technological promise to becoming a real driver of transformation in businesses. Its integration into corporate processes is no longer optional: more and more organizations are using it to automate tasks, generate content, improve decision-making, and increase their competitiveness.
However, as with any emerging technology, along with enthusiasm comes growing doubts and risks. At Mottum Analytica, we believe that to move forward, it is essential to speak transparently about the uncomfortable truths about AI. Not to discourage its adoption, but to promote strategic, conscious, and safe use.
Inconvenient truths about Artificial Intelligence that no one wants to talk about:
- AI isn't cheap
- There are risks of data breaches
- There are risks of hallucinations
- Difficulty introducing AI into internal processes
- Loss of critical thinking
- AI has an environmental impact, consuming water and energy
Let's review each of these points and how they can be effectively managed in the context of an SME.
1. AI isn't cheap
Why does this perception exist? Free or low-cost tools are often advertised, creating the impression that implementing AI in the company is simple and inexpensive.
The reality: developing custom AI solutions for a company's internal processes involves the costs of qualified personnel, infrastructure, and consumption. Furthermore, a successful deployment isn't achieved in days: it requires design, testing, and adaptation to business processes.
How can it be managed? With a strategic approach that focuses on:
- Prioritizing use cases with a measurable impact on productivity or profitability.
- Adopt scalable solutions in phases.
- Have a specialized partner help select technologies and optimize costs from the start.
2. Risk of data breaches
Why is this common belief? Many public models are hosted in the cloud and operated by third parties, creating uncertainty about where and how the input data is stored.
The reality: If appropriate measures are not taken, it is possible to expose sensitive or confidential information when interacting with AI models.
How can this be mitigated?
- By defining an appropriate technological architecture that hosts the models in a proprietary or controlled environment.
- By using solutions with privacy guarantees and data control.
- By training staff in good practices for interacting with AI.
3. Risk of hallucinations
Generative language models can produce answers that sound plausible but are incorrect or fabricated (hallucinations). This risk exists, especially in complex or specific topics. If left unchecked, it can lead to errors in decision-making.
How can it be avoided?
- By precisely defining use cases.
- By implementing human validation in critical processes.
- By training models on reliable internal databases or using approaches such as Retrieval Augmented Generation (RAG), which reduce the risk of hallucinations.
4. Difficulty introducing AI into internal processes
Why does this perception exist?
Many management teams lack a clear vision of where and how AI can add value to their company. This leads to paralysis or ineffective initiatives.
In reality, successful AI adoption requires a thorough understanding of the business and its processes. It's not enough to simply "test" generic tools.
How can this be mitigated?
- By analyzing internal processes to identify areas for improvement.
- By designing an AI adoption plan aligned with strategic objectives.
- By training teams in the intelligent use of these tools.
5. Risk of dependency and loss of critical thinking
Why does this perception exist?
The excessive use of AI for tasks that require judgment can reduce the critical capacity of teams. If AI is abused to generate content or make decisions without supervision, there is a risk of creating a passive and uncritical organizational culture.
How can this be mitigated?
- By promoting the responsible use of AI: as a complement to human intelligence, not as a substitute.
- By establishing good practices that encourage critical thinking in the use of these tools.
6. Environmental impact: water and energy consumption
There are increasing studies warning about the high water and energy consumption associated with the training and operation of large AI models. The data centers that support the models require intensive cooling systems, with a real environmental impact.
How can this be mitigated?
- By opting for leaner and more efficient models.
- By using sustainable infrastructure.
- By choosing use cases with a clear and justifiable return in terms of resource consumption.
In conclusion
Adopting AI in an SME requires an informed and strategic approach. It's not enough to follow trends or integrate tools thoughtlessly: it's essential to understand the risks, manage them consciously, and design an adoption that delivers real value. At Mottum Analytica, we help our clients navigate this path: from identifying opportunities to implementing secure, sustainable solutions aligned with their business.
Want to know how your company could effectively integrate AI? Contact us, and together we'll design the most appropriate strategy for your organization.