AI has captured the attention of technology leaders worldwide through the rapid rise and adoption of large language models. Yet, many enterprises are not fully equipped to capitalize on AI’s full potential. Based on an AI Data Readiness Report, only 8.6% of organizations are fully AI-ready, while nearly 70% say poor or fragmented data still limits their ability to make informed decisions. These organizations often lack the data infrastructure, governance frameworks, skilled personnel, and computing resources needed to train or operate modern AI systems effectively. And as Agentic AI systems emerge, capable of reasoning, self-orchestration, and multi-agent collaboration, the technical and operational complexity only deepens. For smaller businesses, this gap reinforces why it’s often smarter to focus on Intelligent Automation (IA) first, allowing their AI readiness to mature over time. IA represents structured, rule-based systems that automate repetitive tasks and augment human workflows, delivering measurable efficiency …