The primary obstacle for companies in leveraging AI effectively is their outdated data systems, which are ill-equipped to manage the large, complex datasets AI requires. This often leads to poor AI outcomes, with experts attributing most AI failures to inadequate data management rather than algorithmic issues. Modernizing data pipelines with robust quality and security measures is crucial, as top-tier AI performance is unattainable without contemporary data infrastructure.
What is the biggest obstacle to achieving AI ROI in enterprises?
The main obstacle to realizing AI ROI is outdated data pipelines. Legacy ETL processes cannot handle the rapid growth of unstructured data, leading to unreliable AI outcomes. Modernizing data pipelines with robust quality controls and security-first design is essential for successful enterprise AI adoption.
Organizations worldwide are pouring billions into AI, yet fewer than 30 % report any measurable return. Behind the headlines of breakthrough models lies a silent blocker: brittle, 1990s-era data pipelines. In 2025, when unstructured data is expanding at 60 % year-over-year, legacy ETL jobs that still run nightly cannot keep pace, and the AI stack stalls before it ever reaches production.
Matheus Dellagnelo, CEO and co-founder of data consultancy Indicium, told a recent webinar audience that “outdated data pipelines are the single biggest reason AI adoption fails.” His observation aligns with McKinsey’s finding that 75 % of AI programs fall short because of data management issues—not algorithmic complexity or talent shortages. Even the flashiest large language model is only as reliable as the freshness and fidelity of the data it ingests.
Security is no longer an afterthought. With 55 % of firms acknowledging that AI has widened their attack surface, NSA’s Artificial Intelligence Security Center recommends end-to-end encryption, digital signatures on training data, and immutable provenance logs. Hybrid architectures are also surging: Flexential’s 2025 report shows 80 % of enterprises expect to repatriate some AI workloads for compliance and cost reasons, making flexible, multi-cloud pipelines a strategic necessity.
The takeaway is straightforward. AI budgets that ignore data infrastructure resemble race cars fueled by garden hoses. Modern pipelines, rigorous quality controls, and security-first design turn raw data into reliable decisions at scale.
