Enterprise AI should solve problems, not chase hype

Businesses should choose AI tools to meet specific needs, rather than adopting more powerful models or agents simply because they are available, argues TechRadar contributor Alberto Ocampo. The article says success depends on solving real business problems and justifying the cost and complexity.

Traditional machine learning has long supported recommendations, forecasting, fraud detection and estimates such as customer lifetime value. For straightforward predictions, more sophisticated models may cost more and add unnecessary variability. For processes that follow known rules and need consistent results, conventional automation can be reliable, fast and inexpensive.

Ocampo says generative AI is useful when a task requires creativity or interpretation, such as understanding language or images. Agents can use that understanding to interact with systems and take actions. He expects businesses to combine conventional automation, traditional machine learning, generative AI and agents, rather than relying on one approach for everything.

Moving from experimentation to production brings running costs, security, monitoring and the challenge of controlling autonomous systems. Ocampo compares agent development to pruning a tree: unexpected behaviors can emerge in real-world use, making evaluation and safeguards important. He says control should cover system behavior, data access, authorized actions, retained information and opportunities for human intervention.

He also argues that AI will change engineering work without removing the need for engineers. Experienced staff may work faster, and junior engineers may become productive sooner, but organizations still need people with deeper technical knowledge to build, control and repair the systems.