As businesses increasingly adopt artificial intelligence (AI), they encounter hurdles like poor data quality, integration with legacy systems, and data silos. Many of these issues can be effectively managed using “second-layer AI” tools, which enhance explainability and compliance, aiding organizations in overcoming deployment challenges. According to EY’s Responsible AI survey, 70% of companies plan to implement innovative AI technologies, despite fears around data quality and security. Companies should prioritize data normalization and preparation to maximize AI investments. Furthermore, with limited in-house expertise, organizations can leverage low-code platforms and large language models (LLMs) for employee training, ensuring proper AI utilization. Integration challenges can be mitigated through middleware solutions, while AI explainability tools maintain transparency and fairness. Organizations must also monitor AI performance to prevent model decay. As concerns rise about the risks of not adopting AI, leaders are increasingly recognizing the necessity of these transformative technologies.
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