This is the Electi Review. Today, why a polished AI chart is still an unverified claim, and why automation and data visualization shifts the human job from building graphs to auditing the trail behind them. AI data visualization tools now do the first pass automatically. Instead of waiting for someone to pick fields and chart types, the software scans series for anomalies, selects an adaptive view, runs queries from plain language questions, and drafts a written summary of what moved. That speed is real and it is also the trap. A clean revenue line and a confident paragraph underneath do not prove that Friday's upload finished, that marketplace returns were excluded, or that a regional sales dip came from weak demand rather than two stores running out of stock. The evidence backs up the skepticism. In a 2025 Cap Gemini survey, 93% of organizations were exploring or enabling generative AI, yet 71% said they could not fully trust autonomous AI agents for enterprise use. Technical benchmarks show why. Chart question-answering evaluations found that charts generated by large language models failed to match the accuracy of original charts. And on the Chart QA human benchmark, GPT-4V scored 0.26 below human performance when counting valid responses. Even when the numbers are right, default templates often pick the wrong visual for the decision. A NORC comprehension study of 3,176 panelists across three rounds found that stacked and diverging stacked bar charts produced the highest average share of correct interpretations, while line charts worked better for estimating specific values across periods. Separate experiments showed graphs beat tables for estimating differences, but tables beat graphs when finance needs exact sums or equality checks. And three-dimensional styling actively reduced pie chart comprehension. A workable workflow treats AI as an overnight junior analyst, never the final sign-off. Connect fresh sources, write a concrete decision question, generate three to five candidate views, and drop duplicates. Then run the hard gate, check every narrative number against the source table, recompute whether the aggregation is a sum, average, or distinct count, and expose the filters, date range, and data timestamp right beside the chart. Before scaling any tool, test it on one recurring report alongside your manual version for 30 days. Keep it only if corrections drop and every flagged spike traces back to its rows. That's the review.