Los principales desafíos de la inteligencia artificial para empresas.

Artificial Intelligence has become one of the most disruptive technologies of the 21st century so far, and we still do not know its full potential. More and more companies are incorporating different AI tools into their business models and value propositions to improve their processes, carry out more thorough controls and make better-informed, more accurate decisions.  At the same time, according to a recent ManpowerGroup report*, 93% of Spanish companies have difficulty adopting AI in their processes. The figure confirms something many companies already experience day to day: moving from the idea to real implementation runs into artificial intelligence problems that are worth anticipating.  In this article we review the most relevant AI challenges for companies and how to tackle them with a clear strategy, through practical cases we have carried out at Imascono.

Before integrating any AI tool into the company, you need to understand where the real risk lies in order to gauge the extent of its impact. Generally speaking, there are three areas that come up again and again.

Problemas éticos de la IA

One of the most critical ethical challenges of AI is algorithmic bias. Models learn from historical data and, if that data carries discrimination, they perpetuate it in processes such as hiring, credit scoring or healthcare.  Data privacy is another sensitive point. LLMs need large volumes of information to train on, and in Spain and the EU that has clear limits set by the GDPR. An AI that processes data locally is not the same as one that sends it to external APIs: the difference determines your company's level of exposure.  Added to this is transparency, or rather the lack of it, one of the biggest challenges of AI. The “black box” problem makes it hard to understand how an AI model reaches a specific conclusion, which complicates accountability to customers, employees and regulators.

Technical and adoption challenges of AI

Many AI problems are practical in nature. The first, almost always, is data quality. Many companies work with fragmented, unstructured or outdated information.  The second technical challenge is integration. Incorporating AI into an existing technology infrastructure (with legacy systems, different vendors and well-established workflows) requires being clear about the right tool, and that calls for AI consulting to help with planning and implementation.  Finally, the shortage of specialized profiles capable of implementing and maintaining these projects remains one of the artificial intelligence problems that holds organizations back the most. This point can be reversed with a trusted technology partner.

Desafíos legales y normativa

Otro desafío de la IA que puede traer muchos problemas a las organizaciones es la aplicación de la ley. El Reglamento Europeo de IA ya está en aplicación progresiva, y las empresas deben identificar qué categorías de riesgo afectan a sus sistemas concretos. Esta clasificación determina las obligaciones que tendrá que cumplir cada herramienta. Entre nuestros servicios de inteligencia artificial están completamente regulados y nuestro equipo puede explicarte cómo integrar la IA en tu empresa de forma segura y acorde a la legalidad.

Knowing the challenges of artificial intelligence is the first step. The second is to build an implementation process that takes them into account from the start, rather than correcting course along the way.  Before investing in any tool, it is worth carrying out an honest diagnosis of the real state of the organization's data. Through technology consulting, you can identify which information is ready to feed a model and which processes need to be put in order before making the leap.  With that diagnosis on the table, implementation is approached in four phases:

1. Identify needs and objectives: define what you want to achieve with AI and what specific problem it should solve, without losing sight of budget, deadlines and available resources.

1. Select the most suitable solution: choose the AI tool that best fits the company's size, sector and digital maturity.

1. Prepare the data strategy: organize storage and analytics systems, and train the model with clean, representative data.

1. Integrate, test and measure: incorporate the solution into the value chain, monitor results and apply improvements continuously.

Following this order reduces a good portion of the risks we saw earlier. When implementation is done right, the benefits of artificial intelligence translate into real results and lower costs for the company. If you want to know how to apply AI in business, click here: Artificial Intelligence Services for companies

Here are some examples of how we at Imascono have tackled artificial intelligence problems.  With Iria Iveco, the challenge was one of data quality and integration: centralizing information scattered across more than 100 documents from ten different departments. The result is an AI avatar that gives 4,570 professionals across the group secure access to that training, with 90% of interactions coming from the dealer network.  With Fabiola Saphir, the challenge was adoption by the end customer: getting a user to trust an AI-generated purchase recommendation. Caravan Fragancias' virtual personal shopper solves this by offering personalized recommendations from more than 125 products, reinforcing trust in the process and in the brand.  The Shin project, developed for VML Health and Santer, faced a challenge of transparency and rigor: an AI cannot be a “black box” in a medical-scientific environment. Trained on bibliography specific to the event, Shin became a moderator and a reliable source of information for speakers and attendees.  AI challenges are solved by designing each project around the specific risk that needs to be covered. That is the approach we apply at Imascono.  Contact us if you need more information.