This is the Electe Review. Today, connecting systems is not the same as deciding anything. The article's core claim is blunt. A dashboard can display isolated facts, but it cannot resolve a stock discrepancy. Explain a customer's risk, or tell a maintenance team which machine needs attention next. So the roadmap starts with a decision, not a platform. Pick one action that matters. Replenish stock, review a supplier, escalate a risk signal, approve a financial variance, schedule maintenance, then map the systems that inform it, the owner of each field, the refresh requirement, and the named person who responds. Five use cases carry that argument. Real-time sales and inventory synchronization across POS, e-commerce, marketplaces, and warehouse systems where the cited outcome is stockouts down 30 to 40%. Unified customer profiles for marketing and risk, where identity resolution comes first, because duplicate profiles produce contradictory recommendations. Marketing ROI, gains of 25 to 40% are cited. Supplier and procurement integration, with three-way matching between purchase orders, invoices and receipts, and procurement cost reduction of 8 to 15%. Financial consolidation across entities, currencies, and charts of accounts, where a close of 30 to 40 days can drop to 5 to 10. And equipment monitoring, sensors joined to work orders, downtime and repair costs, with unplanned downtime down 30 to 50%. Now the skepticism, which the article supplies itself. Integration does not guarantee a result. Financial and compliance outputs require professional review. A finance professional approves material adjustments and keeps the audit trail. Automation should rank signals and show its evidence so people can check the reasoning. Every use case needs clear ownership of data quality. Connected systems cannot govern an invoice field nobody owns. If operators log inconsistent descriptions, the model learns administrative noise instead of equipment behavior. The scale is real. The data integration market was $13.7 billion in 2022, projected at $29.9 billion by 2030. One retail bank use case analyzed trillions of records and produced about a terabyte of basal three reports monthly. But 98% of businesses reported AI related data quality issues. 21% traced them to inefficient integration, and 42% named a skills or staff shortage as the biggest barrier to data quality. Start narrow measure decision quality. Expand only when people and process are ready. That's the review.