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AI often fails to meet expectations due to a misalignment between business goals and the AI&#8217;s implementation. Many companies adopt AI without a clear vision or strategy, expecting quick wins, but AI requires deep integration into the business to generate tangible results. It’s not a plug-and-play solution.

One major misconception is that AI is a magic bullet that can solve business problems without much effort. In reality, AI is a powerful tool, but it requires precise application, thorough data preparation, and a clear business use case to be effective.

AI hallucinations occur when AI models generate incorrect or nonsensical outputs. These can be harmful, especially in critical industries like finance or law, where mistakes can lead to legal issues or loss of trust. To prevent this, it’s essential to build AI systems with robust safeguards and ensure accurate data input.

Data readiness is crucial. Without well-structured and organized data, even the most advanced AI systems will struggle to deliver results. Many companies overlook this, leading to underperforming AI projects. Investing in proper data management and infrastructure is key to success.

The demand for AI talent far exceeds the supply. This shortage of skilled professionals makes it difficult for many companies to implement AI projects effectively. Without the right expertise, businesses may find it challenging to move beyond the pilot phase of AI adoption.

Yes, the costs of AI can be underestimated. While some off-the-shelf solutions offer a quick start, they often require customization and ongoing operational support, such as updates, security, and integration. These additional layers can drive up costs and complicate the overall process.

AI regulations are rapidly changing, with new rules focusing on data privacy, intellectual property, and ethical AI use. Companies need to stay informed and compliant to avoid risks. As AI becomes more integrated into business operations, regulatory compliance becomes increasingly critical.

It depends on your business needs. Off-the-shelf solutions are quicker and easier to implement but may lack the flexibility required for complex tasks. On the other hand, building a custom AI solution offers more control and precision but requires more time, expertise, and resources. Some companies opt for a hybrid approach, customizing off-the-shelf tools to meet their specific needs.

AI must be aligned with your strategic goals from the start. It’s essential to identify clear business problems that AI can solve, invest in data readiness, and continuously refine AI models to ensure they generate measurable ROI. Without this alignment, AI projects risk underperformance.

Start by asking questions. Identify your business challenges, evaluate your data infrastructure, and determine if AI can genuinely help. At Sombra, we have over 10 years of experience integrating AI into real-world business solutions. Contact us, and we’ll help you assess whether AI can bring value to your organization.

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AI and ML in Asset Management: 3 Use Cases You Need to Know

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FAQ

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How is the regulatory landscape around AI evolving?

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