Expert warns AI integration could slow down apps, frustrate users

A software engineer, Joseph Ajayi, has warned that the growing integration of artificial intelligence (AI) into mobile applications, while enabling smarter services, could also slow down performance and frustrate users. Ajayi, a React Native developer with experience in healthcare, fintech and e-commerce projects, said the rush to introduce features such as real-time recommendations, natural language […]

A software engineer, Joseph Ajayi, has warned that the growing integration of artificial intelligence (AI) into mobile applications, while enabling smarter services, could also slow down performance and frustrate users.

Ajayi, a React Native developer with experience in healthcare, fintech and e-commerce projects, said the rush to introduce features such as real-time recommendations, natural language processing and on-device machine learning often comes at the expense of speed and stability.

“Users don’t care how intelligent an app is if it lags or freezes. A half-second delay can mean the difference between a five-star review and an uninstall,” he said in a statement.

According to him, AI-driven features consume significant CPU and memory resources, increase battery usage, and raise crash rates, especially on mid-to-low-end devices. He cited an e-commerce project where a recommendation engine doubled engagement but pushed the crash rate from 0.5 per cent to over 2 per cent.

Ajayi noted that many teams fail to monitor AI components properly, adding that traditional performance metrics often do not capture the unpredictable resource demands of machine learning models. He advised developers to establish fallback mechanisms to ensure apps remain usable when AI services fail.

He explained that AI introduces complex dependency chains involving external APIs, real-time data pipelines and cloud-based services, which increase the risk of outages. “The apps that survived were those with intelligent circuit breakers and backup functionality,” he said, recalling a service outage during a Black Friday sales event.

The engineer also urged teams to rethink performance monitoring by tracking latency specific to AI operations and setting clear service level objectives. He recommended using feature flags for quick rollbacks and deploying AI models gradually to limit disruptions.

Ajayi further stressed that real-time features such as live order tracking and instant notifications amplify performance challenges. He said developers must plan capacity, implement intelligent caching and monitor persistent connections to prevent resource leaks.

He pointed to a healthcare project where AI-powered diagnostic tools slowed basic patient record loading to four seconds, forcing his team to overhaul the app’s architecture. Optimisation efforts later cut loading times by 60 per cent.

To avoid such pitfalls, Ajayi advised developers to test on low-end devices, apply lazy loading, use intelligent caching and conduct real-world performance checks. He also highlighted the need for robust monitoring of AI service dependencies and proper capacity planning, warning that AI workloads do not scale linearly with user growth.

“As AI becomes a standard feature in mobile apps, the challenge is not only to make them smarter but also to ensure they remain fast, stable and reliable,” Ajayi said, adding that developers should treat AI integration as a trade-off rather than a free upgrade.