FEATURE
How do pipelines help control AI’ s escalating costs?
How will AI pipelines evolve in the next few years?
AI is resource-hungry and therefore expensive. Running large models across petabytes of uncurated data consumes massive compute and storage resources. If the same raw data is processed repeatedly, costs multiply.
Pipelines act as a cost-control mechanism by curating only the relevant subsets of data for a given AI task. This reduces redundant processing and accelerates performance. Models train and run faster on smaller, better-targeted datasets. For enterprises under budget pressure, pipelines are essential to scale AI sustainably.
How do pipelines keep AI applications current and relevant?
One of the most overlooked challenges in AI is keeping models updated. A chatbot trained on last quarter’ s catalogue risks frustrating customers with outdated information.
Pipelines will grow smarter, more adaptive and more strategic. We’ ll see advances in intelligent indexing, local preprocessing and governance controls at massive scale. Pipelines will also learn from usage patterns, dynamically optimising how data is curated and moved.
Compliance will become embedded by default, with auditability built into workflows. Over time, pipelines won’ t just support AI – they’ ll enable data monetisation as a core business strategy. By unlocking the value of unstructured data, they’ ll turn what was once a liability into a powerful competitive asset.
The bottom line
Traditional ETL cannot handle the scale and complexity of unstructured data. Automated pipelines provide the curation, governance and cost control necessary to transform this messy resource into a strategic advantage.
AI pipelines solve this by continuously feeding fresh data into AI systems. In healthcare, that might mean delivering the latest imaging files to diagnostic models. In retail, it could mean ensuring assistants reflect real-time inventory. Keeping AI applications current is a defining advantage of automated pipelines.
As Kansal notes, the organisations that master unstructured data pipelines won’ t just build better AI – they’ ll reshape their competitive position by monetising data in ways laggards will struggle to match. x
What obstacles do enterprises face in building pipelines for unstructured data?
Three main challenges stand out.
1. Scale: Many industries – healthcare, media, financial services – manage tens of petabytes of unstructured data. Moving it all is slow and costly. A better approach is to build a global index and metadatabase. 2. Diversity: File types vary enormously and metadata is inconsistent or absent. Automation is needed to harvest metadata from filesystems, headers, application layers and even external systems like CRMs and ERPs. 3. Governance: Sensitive data often lurks deep within storage systems, beyond the reach of conventional tools. Pipelines must include intelligent indexing, policy-based classification and automated detection of sensitive data.
Without these specialised capabilities, enterprises risk spending heavily on AI with little to show for it.
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