By Thornton Craig
As AI scales, the true constraint is not models but data integration, quietly driving cost, complexity and ultimately determining whether organisations realise measurable value.
Artificial intelligence (AI) has rapidly shifted from experimentation to execution, but many organisations are encountering a critical barrier: not the model, but the underlying data infrastructure. Data integration, long treated as a technical afterthought, is emerging as a major and poorly understood driver of AI cost. As pressure to demonstrate ROI intensifies, this once invisible layer is becoming a strategic issue that increasingly demands board-level scrutiny.
The cost problem hiding in plain sight
For decades, data integration has been treated as a necessary but low-visibility back-office function. Pipelines move data, systems connect, dashboards populate, and if everything works, it remains largely invisible. That invisibility is now part of the problem.
Gartner’s Chief Data and Analytics Officer Agenda Survey for 2026 shows that 64% of data and analytics (D&A) leaders are responsible for demonstrating business value and ROI, yet data integration is still difficult to articulate beyond operational necessity. This creates a structural blind spot. Organisations are investing heavily in AI, while the cost base that enables it remains fragmented, poorly understood and often inefficient.
As AI workloads scale, particularly with real-time analytics and agentic AI systems, these inefficiencies are amplified. What was once manageable technical debt becomes a material financial burden. Duplicate pipelines proliferate, legacy architectures struggle to keep pace, and cloud consumption rises without clear linkage to business outcomes.
The result is a paradox: organisations are investing more in AI than ever, yet many cannot clearly explain where value is being created, or why costs are accelerating.
Why AI is exposing data integration weaknesses
AI does not create data integration challenges; it exposes and accelerates them.
Traditional architectures were designed for batch processing and periodic reporting, not continuous, real-time, multi-agent workloads. AI fundamentally shifts these requirements, demanding faster data delivery, greater flexibility and significantly higher volumes of data movement.
This shift increases workload frequency, drives higher compute consumption and introduces architectural complexity as multiple pipelines are created to support different use cases. At the same time, consumption-based pricing models mean inefficiencies translate directly into rising costs. Tool sprawl compounds the issue, as different teams deploy overlapping solutions, creating duplication and technical debt.
Gartner highlights that workload frequency, execution time and vendor pricing models are key drivers of data integration cost. Yet many organisations still lack a clear way to connect these technical variables to business value.
Without that linkage, optimisation efforts are often misdirected, reducing costs in visible areas while inefficiencies persist elsewhere.
From cost centre to value driver
Addressing this challenge requires a fundamental shift in perspective.
Rather than treating data integration purely as a cost, leading organisations are reframing it as a value driver that underpins revenue, efficiency and innovation. This means moving beyond technical metrics toward a clearer understanding of how data flows support business outcomes.
A practical starting point is linking each data integration pipeline to a defined business purpose. Gartner research shows that pipelines vary significantly in the value they deliver, from those directly tied to revenue-generating use cases to those providing indirect operational support.
Some pipelines enable real-time decision-making or AI-driven optimisation, with clear and measurable impact. Others support foundational processes such as master data management or internal reporting. Many, however, operate at the margins, supporting niche or redundant use cases with limited value.
This distinction allows organisations to prioritise investment more effectively, shifting away from blanket cost reduction toward targeted optimisation. It also provides a language that resonates at board level, translating technical infrastructure into business impact.
Simplifying cost without oversimplifying reality
A common barrier to progress is the belief that data integration costs are too complex to model effectively. This often leads to inaction or overly detailed approaches that fail to deliver insight.
A more effective approach is to adopt a simplified cost framework focused on key drivers such as connectivity, data volume, workload frequency, execution time and vendor pricing models. The goal is not precision, but clarity.
This avoids analysis paralysis and enables organisations to identify where costs are concentrated, which pipelines are most expensive, and how those costs relate to business value. That level of visibility is sufficient to inform meaningful optimisation decisions.
Eliminating redundancy without losing capability
Once costs are understood in context, rationalisation becomes possible.
Most data integration environments evolve organically, with teams building pipelines independently to meet immediate needs. Over time, this leads to duplication, fragmentation and unnecessary complexity.
Consolidating pipelines is one of the most effective ways to reduce cost, but it must be approached carefully. Each pipeline supports a specific requirement and removing them without understanding dependencies can disrupt critical processes.
Metadata and data lineage are essential here. They provide visibility into how data flows across systems, enabling organisations to identify truly redundant pipelines while preserving those that are essential. This reduces the risk of unintended disruption while supporting more informed decision-making.
At the same time, organisations should reassess their tooling landscape. Overlapping platforms often create unnecessary cost and complexity. Rationalising tools, while ensuring they remain complementary, can significantly reduce technical debt and improve efficiency.
Designing for scale in the age of AI
Cost optimisation alone is not enough. The objective is to build a data integration capability that can scale with AI.
This requires more mature architectures and operating models. Hybrid approaches are becoming increasingly common, combining central governance with decentralised execution to balance control and agility. Metadata-driven automation is also playing a growing role, enabling pipelines to be optimised dynamically and reducing manual effort.
Modern architectural patterns, including lakehouses and data products, are helping to minimise unnecessary data movement and improve reusability. At the same time, automation and AI augmentation are enabling more adaptive, responsive integration environments.
These developments reflect a broader progression in maturity, from reactive maintenance to proactive value delivery. At higher levels of maturity, data integration becomes increasingly automated, scalable and aligned with business needs.
This is critical for emerging use cases such as agentic AI, which depend on continuous, flexible data flows. Without a modern integration foundation, these use cases remain difficult and costly to scale.
Why this is now a board-level issue
The growing importance of data integration reflects a fundamental shift in how organisations approach AI. It is no longer a standalone technology initiative, but a cross-functional investment shaping cost structures, operating models and competitive positioning. As a result, core enablers such as data quality, integration and governance have become strategic concerns.
Boards are increasingly focused on AI ROI, scalability and risk. To meet these expectations, business and IT executives must explain not only what AI delivers, but what it costs to operate and scale. Data integration sits at the centre of that discussion. It determines how efficiently data is delivered, how quickly use cases can be deployed, and how costs evolve over time. When poorly managed, it becomes a hidden tax on AI investment; when optimised, it becomes a source of competitive advantage.
The next phase of AI adoption will be defined not by access to models, but by the ability to operationalise data at scale. Organisations that continue to treat data integration as a back-office function will struggle to control costs and demonstrate value. Those that elevate it to a strategic priority will be best positioned to unlock AI’s full potential.
Gartner analysts will further explore how organisations can optimise data integration for AI scale, reduce AI cost and accelerate AI value at the Gartner Data & Analytics Summit 2026 in London from 11–13 May.
