AI loses trust when it is given the wrong job

TechRadar contributor Luis Blando argues that AI earns trust only when assigned work suited to its strengths and supervised by people who understand its limits. Overconfident deployments can leave employees managing agents without enough experience, while a single visible mistake can quickly undermine confidence.

AI works through probabilities, so it can be useful with unclear or complicated information but is a poor fit where a wrong answer could have serious consequences. Blando identifies three areas where it can help: processing documents, decision support and personalization.

In document-heavy work, AI can extract, classify and summarize material, compare contracts with standard clauses, or organize customer emails by issue. For decisions, it can combine past performance, delivery expectations and discounts to present options, but a human usually makes the final choice.

Personalization can also benefit from probabilistic output when content only needs to feel relevant to an individual. The risk is allowing an unvalidated answer to trigger action: a small contract error can spread into records, external messages or new software.

Blando compares AI with a super-intelligent, hard-working intern: access to sensitive information should be limited, control should increase only after the system proves itself, and humans should continue monitoring its work. In software development, fast AI-generated code can create quality and management problems. Gartner has reported that rising token consumption and consumption-based pricing are increasing AI coding costs.

Blando says the riskiest place for AI to reason is deep in the implementation layer. A safer design keeps AI closer to business ideas and rules, where people can review its suggestions before a deterministic platform turns a validated model into executable software. Abstraction can limit the impact of small errors.