Artificial intelligence (AI) is one of today’s leading investment priorities, yet research reveals a striking paradox at the heart of current AI ambitions.
According to a recent survey, an overwhelming 74% of organizations plan significant investments in AI initiatives for 2025, but only 46% of executives trust the quality of their organization’s data. This glaring mismatch poses a fundamental investment risk because 98% of enterprises acknowledge poor data quality as a critical barrier holding back AI adoption.
The disconnect is concerning at a time when significant financial commitments rest on the promise of AI’s transformative potential: More than half (52%) of organizations now allocate over 10% of their technology budgets to AI projects. Despite the high stakes, many companies find themselves unguided, lacking comprehensive governance frameworks and necessary cross-functional alignment to ensure trustworthy data.
Success in AI hinges not only on the sophistication of algorithms or the sheer volume of data but also on a foundation of data trust. AI promises transformative leaps in business insight and efficiency; however, without addressing data quality and governance at source, organizations risk costly AI projects falling short of expectations.
Continue reading on Informs Analytics
Share this post
Featured Resources
No featured post selected.