Customizing strategies for industry specific goals is essential for maximizing ROI and avoiding generic, underperforming implementations. The key is to start with your core business objectives – whether it’s reducing patient readmissions in healthcare, minimizing fraud in finance, or minimizing waste in construction fabrication – then engineering the AI approach around those outcomes. In regulated sectors like healthcare and finance prioritize strict data governance. In manufacturing focus on predictive maintenance and quality control. A “one size fits all” AI tool rarely delivers transformative value. Instead, companies should select the right modality (predictive vs. generative vs. agentic), tailor data pipelines, fine tune models on domain specific datasets, and build industry appropriate KPIs. Those that succeed treat AI s a strategic differentiator rather than a technology experiment. This targeted approach typically yields a 3-5x higher business impact than broad spectrum, off the shelf AI deployments.