Most people are trying AI assistants—but few rely on them for high-stakes advice

A growing share of consumers are testing AI assistants for everyday tasks. But most still hesitate to use them for high-stakes topics like health or finance. This split makes sense: low-risk chores reward speed and convenience, while consequential decisions still demand human judgment, vetted sources, and clear accountability.

Many say they use assistants for quick summaries, email drafts, meeting notes, or brainstorming alternative phrasings. They’ll ask for trip checklists, recipe ideas from what’s in the pantry, or gentle rewrites to match a friendlier tone. Fewer say they trust them for decisions that affect money, medical care, or legal issues, where a wrong suggestion can carry real costs—missed deductions, medication conflicts, or contract pitfalls. . Younger users report higher experimentation rates than older ones. Even so, the majority in every age group say they double-check important information elsewhere. That “two-source rule” shows up across contexts: a model might outline the steps, but people still confirm with a government page, a bank portal, a patient leaflet, or a lawyer’s note before acting.

Confidence often depends on context. People are more comfortable asking for trip ideas or coding snippets than for tax guidance or diagnoses. They’ll accept a rough restaurant itinerary or a sample regex, but expect citations and disclaimers when stakes rise. Tools that adapt to intent—lightweight in casual chat, rigorous in expert modes—earn more trust over time.

Accuracy is only part of the story. Perceived bias, missing sources, and unclear training data also make users cautious. If an answer can’t show where it came from, or leans heavily on a single perspective, people hesitate. Transparent source lists, date stamps, and explicit limitations (“this is general info, not advice”) help users calibrate their reliance level.

Risk management features matter, too. Guardrails that refuse unsafe requests, structured checklists for sensitive workflows, and escalation paths to humans signal responsibility. When models summarize, then link to original documents, users can spot-check quickly. When they handle personal files locally, they expect on-device encryption and obvious controls to delete or export data.

As assistants improve, expectations will rise too. Clear sourcing, on-device privacy options, stronger retrieval against trusted corpora, and domain-specific guardrails could nudge more people from casual use to regular reliance. Think: a health mode that cites guidelines, a finance mode that pulls bank statements securely, or a legal mode that flags jurisdiction and effective dates.

For now, AI assistants remain helpful co-pilots—not primary advisors—for most consumers. The sweet spot is partnership: let the model draft, outline, or compare options; let humans decide, verify, and sign. As capabilities mature and transparency deepens, that boundary may shift—but caution today is a feature, not a bug.