AI platforms must move beyond generic market data and provide insights that are specifically tailored to an organisation’s unique internal context, according to technology intelligence firm IDC.

While current artificial intelligence platforms can rapidly process broad information regarding market trends and vendor landscapes, they often struggle when faced with specific queries about a company’s internal roadmap, staffing models or unique operational constraints.

The firm’s latest research indicates that a failure to address these specific needs often leads to generic responses, which can hinder the strategic decision-making process for businesses.

According to the IDC Future Enterprise Resiliency and Spending Survey, more than 75 per cent of AI projects fail to transition from the proof of concept stage to full-scale production.

The most frequently cited barriers are not technical but are instead rooted in a lack of trust, with 27 per cent of organisations citing challenges in protecting against sensitive data exposure and 23 per cent reporting inadequate data governance as a primary obstacle.

Security, privacy and governance concerns were ranked as the most significant challenges for buyers adopting AI-driven intelligence solutions, consistently placing ahead of budget constraints, skill shortages and technical integration issues.