AI Challenges for Businesses
Artificial Intelligence has become one of the most disruptive technologies of the 21st century, 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, perform more exhaustive controls and make more informed and accurate decisions.
On the other hand, according to a recent report by ManpowerGroup*, 93% of Spanish companies are having difficulty adopting AI into their processes. The figure confirms something that many companies are already experiencing on a daily basis: moving from the idea to actual implementation runs into artificial intelligence challenges that should be anticipated.
In this article, we review the most significant AI challenges for businesses and how to address them with a clear strategy, using case studies we’ve carried out at Imascono.
The Challenges of AI That Need to Be Addressed Urgently
Before integrating any AI tool into the company, it is necessary to understand where the real risk lies in order to assess the extent of the impact. There are generally three areas that come up repeatedly.
Ethical Issues in AI
One of the One of the most critical ethical challenges of AI is the algorithmic bias. Models learn from historical data, and if that data contains bias, they perpetuate it in processes such as hiring, credit scoring, and health care.
La Data privacy is another sensitive issue. LLMs require large volumes of data to be trained, and in Spain and the EU, there are clear limits set by the GDPR. An AI that processes data locally is not the same as one that sends it to external APIs: this difference determines your company’s level of exposure.
In addition to this, Transparency—or rather, the lack thereof—is one of the major challenges facing AI. The «black box» problem makes it difficult to understand how an AI model reaches a specific conclusion, which complicates accountability to customers, employees, or regulators.
Technical and Adoption Challenges of AI
Many of the problems with AI are practical in nature. The first one is almost always the data quality. Many companies work with fragmented, unstructured, or outdated information.
The second technical challenge is integration. Integrating AI into an existing technology infrastructure (with legacy systems, multiple vendors, and established workflows) requires a clear understanding of the right tool, and to achieve this, it is necessary to have a AI consulting that will assist with planning and implementation.
Ultimately, the shortage of specialized professionals capable of implementing and maintaining these projects remains one of the biggest obstacles to artificial intelligence adoption that holds organizations back. This issue can be addressed with a trusted technology partner.
Legal and Regulatory Challenges
Another challenge posed by AI that could cause significant problems for organizations is law enforcement. The European AI Regulation is already being phased in, and companies must identify which risk categories apply to their specific systems. This classification determines the obligations that each tool will have to meet.
Between our artificial intelligence services They are fully regulated, and our team can explain how to integrate AI into your business safely and in compliance with the law.
Would you like to learn more about Imascono and how it uses AI?
How to Implement AI in Businesses Safely and Efficiently
Understanding the challenges of artificial intelligence is the first step. The second is to develop an implementation process that takes these challenges into account from the outset, rather than making adjustments as you go.
Before investing in any tool, it’s a good idea to conduct an honest assessment of the actual state of the organization’s data. A technological consultancy It allows you to identify which information is ready to be fed into a model and which processes need to be sorted out before taking the plunge.
With that assessment on the table, implementation is planned in four phases:
- Identify needs and objectives: Define what you hope to achieve with AI and what specific problem it should solve, while keeping in mind the budget, deadlines, and available resources.
- Select the most appropriate solution: Choose the AI tool that best fits the company's size, industry, and level of digital maturity.
- Develop a data strategy: Organize the data storage and analysis systems, and train the model using clean, representative data.
- Integrate, test, and measure: Integrate the solution into the value chain, monitor results, and implement continuous improvements.
Follow this order reduces many of the risks we've discussed earlier. When implemented correctly, the benefits of artificial intelligence translate into tangible results and lower costs for the company.
If you'd like to learn more about how to apply AI in businesses, click here: How to Implement AI: Steps and Success Stories You Should Know
Examples of how Imascono has overcome AI challenges in real-world projects.
Here are some examples of how we at Imascono have addressed challenges in artificial intelligence.
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 provides secure access to this training for 4,570 professionals within the group, with 90% of interactions coming from the dealer network.
At Fabiola Saphir:, the challenge was getting end customers to adopt the technology: getting users to trust a purchase recommendation generated by AI. Caravan Fragancias’ virtual personal shopper addresses this by offering personalized recommendations from among more than 125 products, thereby building trust in both the process and the brand.
The project Shin, developed for VML Health and Santer, faced a challenge regarding transparency and rigor: AI cannot be a «black box» in a medical-scientific setting. Trained using literature 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 based on the specific risk it is intended to address. This is the approach we take at Imascono.
Contact with us If you need more information.
