Eranda Maldeniya, Director of Product at Enterprise Analytics, states that organizations often incorrectly start AI adoption by asking which model to adopt rather than which business process to improve. ^[1] A January 2026 McKinsey analysis states that using AI systems to automate authorizations and claims could reduce hospital billing costs by 30% to 60%. ^[2] According to a 2026 Deloitte report, healthcare systems could lose up to $54.5 billion over the next decade if they fail to implement virtual care options. ^[3] McKinsey reports that for a healthcare system with $6 billion in annual revenue, automating administrative cycles with AI can save between $60 million and $120 million annually in operational expenses and reprocessing. ^[4] The Peruvian Ministry of Health reported in early 2026 that the country has a national network of 3,072 interconnected facilities and has conducted over 9 million telehealth consultations in the last three years. ^[5] Maldeniya observes that teams are currently repeating a pattern with agentic AI where they search for deployment places before defining the business problem, leading to unclear outcomes and wasted time. ^[6] The article recommends that organizations assess AI by operational impact, specifically asking if it reduced data entry time, error rates, or resolved exceptions faster. ^[7] The author asserts that the workflow is the real unit of value in enterprise AI, noting that extracting invoice data alone does not guarantee a better business outcome without the full accounts payable workflow. ^[8]