Why engineers are sounding the AI alarm (www.techradar.com)

Engineers are sounding the alarm as enterprises rush to embed AI across stacks without first fixing the foundations. AND Digital’s report finds 56% of business leaders plan AI investment despite knowing their data may be inaccurate, and 77% of senior engineers say integrating AI into existing apps is a major pain point. The crux: many companies still run legacy systems and siloed data architectures that weren’t designed to interface with modern ML tooling, making integrations expensive, fragile and strategically risky even as the AI application market grows (currently valued at about $5.2B). For the AI/ML community this matters technically and operationally. Poor, inconsistent or inaccessible data directly undermines model reliability, fuels bias, and increases maintenance and privacy workload; plug‑and‑play platforms aren’t a silver bullet and can worsen problems if adopted without the right architecture and governance. Engineers emphasize practical needs—data discovery, clean pipelines, modernization of legacy interfaces, and broad upskilling so developers understand model lifecycle, ethics and system-level impacts. Long‑term competitive advantage will go to organizations that treat data readiness and workforce enablement as non‑negotiable foundations, not optional afterthoughts.