Organizations face significant challenges in scaling AI proofs of concept (POCs) into production, with nearly 40% remaining stuck at the pilot stage. The FOREST framework outlines six dimensions of AI readiness—foundational architecture, operating model, data readiness, human-AI experiences, strategic alignment, and trustworthy AI—to help organizations overcome barriers and successfully implement AI initiatives.

Financial institutions are eager to adopt AI for analytics but often overlook the necessary infrastructure and data quality improvements required for successful implementation. Many fail to realize that AI needs ongoing management and compliance considerations, leading to costly mistakes. Successful AI adoption in finance focuses on specific outcomes, gradual scaling, and investing in talent development to bridge the gap between business and technology.

The article provides strategies for minimizing AI hallucinations, which occur when artificial intelligence generates false or misleading information. It discusses techniques such as improving training data quality, fine-tuning models, and implementing better validation processes to enhance the reliability of AI outputs.