AI is currently the hottest topic in tech. But how far have enterprises progressed towards realizing the potential, and how should they now focus their efforts to ensure success? A survey of 400 data professionals provides some insights.

Key takeaways

• Enterprise AI expectations are high, with activity unfolding across the business, but most mainstream organizations indicate early-stage experimentation and adoption rather than broad established deployment.
• User-centric AI solutions like copilots and embedded features in business applications see strong acceptance, while autonomous AI agents face more skepticism, especially for complex decision-making.
• People-related challenges feature prominently when it comes to implementation concerns, with legacy mindsets and conventions cited as major impediments to strategic AI adoption.
• Data related issues represent the most critical barriers, however, with two thirds reporting data quality challenges and almost as many citing data availability problems as progress limiting factors.
• On-premises AI platforms are gaining traction alongside cloud services, driven by data sovereignty, a reluctance to move on-premises data unnecessarily, along with concerns over cloud costs and complexity.
• Fewer than a third of organizations have started to lay coherent infrastructure foundations to support their AI strategy, but those further along already show signs of being able to accelerate progress and time to value.