The real change isn’t just faster reporting — it’s a broader surface for asking questions. TL;DR Using AI in data analytics adds a new capability layer: natural language queries, automated analysis, and generative AI on top of your current analytics stack, taking on work that used to require specialists. This shift expands who can actually use your data, not only how quickly they get answers. AI removes both the access gate and the interpretation barrier that once kept data work limited to experts. 62% of teams say the #1 workflow change they want is making data more accessible to non-technical users. AI is the first technology that can truly do this without giving up accuracy. Six core capabilities make this shift tangible: natural language querying, AI-written performance summaries, automated data cleaning, anomaly detection, prescriptive analytics, and agentic AI workflows. But AI only works well on top of clean, connected, consistently defined data. Skip that foundation and you simply scale inconsistency across the organization. Introduction How many people on your team can get a solid answer to a question about business performance without submitting a ticket, digging through documentation, or tracking down the right analyst? At most companies, the honest answer is: very few. Everyone else either guesses, waits, or stops asking altogether. In Databox’s Time to Insight research, 62% of teams say improving data access for non-technical users is the single workflow change they’d most like to make. The desire is widespread. The capability is not: most business data is still locked behind layers of permissions, technical skills, and interpretation that only…