A new Qlik study highlights a major disconnect between AI investment and execution. While 94% of businesses are increasing spending on AI-related data readiness, just 21% have successfully embedded AI into their operations. The research shows a critical gap in strategic planning, governance, and compliance—essential for maximising AI’s impact.
“Companies are rushing to adopt AI, investing heavily without a cohesive strategy,” said Drew Clarke, EVP & GM of data business unit at Qlik. “AI isn’t a temporary solution—it’s a permanent transformation that requires structure, governance, and transparency. Without a clear plan and solid data foundations, businesses are magnifying risks instead of driving value.”
The explosion of data collection further complicates AI adoption. As many as 64% of organisations pull data from 100 to 499 sources daily, showing the immense challenge of data complexity. Without a clear framework to process, clean, and integrate this data, businesses struggle to make it usable for AI-driven insights and decision-making.
When evaluating AI’s success, most companies focus on operational efficiency, with 57% using this as their primary performance metric. However, fewer organisations track AI’s broader strategic impact, such as revenue growth, competitive advantage, or innovation. This narrow view may limit AI’s true potential, keeping it as a tool for automation rather than transformation.
What are the biggest concerns?
Concerns around AI bias, governance, and compliance remain major hurdles. While 48% of organizations attempt to mitigate bias by increasing transparency in their AI models and data sources, many still lack robust safeguards. Without proper oversight, AI systems may reinforce existing biases and lead to unintended consequences in decision-making.
Lastly, data governance remains a weak point. Only 47% of companies strongly agree that their governance policies are consistently enforced, leaving room for inconsistencies and compliance risks. Organizations may struggle with inaccurate insights and unreliable AI outputs without stringent data quality measures, undermining the technology’s effectiveness.
Companies must focus on investment, building the necessary safeguards, ensuring data governance, and developing strategies for long-term AI integration. Many organizations risk failing to realize AI’s transformative promise without these measures.