Opinion
Why the AI era demands stable integration infrastructure more than ever
"Before we can rely on AI-driven insights, we must ensure that systems communicate with one another in a unified, consistent, and controlled language," writes Li-Mor Navon, CEO of iConduct.
In an era where organizations rush to adopt Artificial Intelligence solutions, it often seems that the primary challenge is the race for the latest model. In practice, long before selecting a model and developing applications, serious consideration must be given to a deeper layer: data quality, freshness, and the integration between existing organizational systems.
This is not a marginal technical task or mere “behind-the-scenes plumbing,” but a complex, sensitive, and critical process where failure undermines the organization's very ability to function. As AI moves into the heart of decision-making processes, it is becoming clear that a stable integration infrastructure is not a luxury, but a fundamental prerequisite.
Organizations today operate in multi-system environments more than ever before. Research shows that large organizations operate, on average, anywhere from dozens to over a hundred different information systems. In the public sector, these numbers are even higher due to various legacy core systems operating alongside a new generation of digital services.
Furthermore, data flows between systems at different speeds, using different formats, and often without coordination. Consequently, without an adequate synchronization layer, the information reaching both decision-makers and AI systems remains incomplete.
Artificial Intelligence was supposedly meant to solve this problem; it learns, analyzes, predicts, and assists in decision-making. However, the truth is that algorithms do not fix incorrect or missing data, but simply learn from it and amplify existing patterns. When data is partial, inconsistent, or outdated, even the most sophisticated models will produce results that appear impressive but are business-incorrect.
Industry research and surveys indicate that 60-70% of organizations report that the primary challenge in embedding AI into the organization isn’t model selection itself, but rather data quality and the connectivity between various organizational systems.
In industry and manufacturing, cost is measured in hard currency. Even a one-hour delay caused by unsynchronized data can amount to tens of thousands of shekels. In certain factories, the cost of downtime is measured in thousands of shekels per minute. In the public sector, the implication is service continuity. In Israel, hundreds of national information systems operate across education, healthcare, welfare, and infrastructure. A delay in data synchronization is not a minor technical glitch; it can directly impact millions of citizens and erode public trust.
Despite the differences between sectors, the common denominator is clear: integration is a strategic infrastructure. It is not just another IT project, but an organizational layer that enables continuity, reliability, and data-driven decision-making. It prevents redundancies, creates a "single source of truth," and allows systems to operate in harmony.
From Reactive Infrastructure to a Smart Mechanism
The modern approach to integration includes several key principles: the transition to Low-Code/No-Code platforms that enable rapid interface deployment even with limited IT teams; the adoption of Observability capabilities that provide full, real-time visibility into cross-system activities; and the combination of cloud and on-premises deployments in accordance with the hybrid reality of most organizations.
This is also where the next generation of systems comes in, systems that embed AI capabilities directly into the integration layer itself. These capabilities allow for real-time anomaly detection, load forecasting, process optimization, and even the automated remediation of common faults. Integration thus evolves from merely a reactive infrastructure into a smart, proactive, and managed mechanism.
Ultimately, the challenge in the AI era is not just technological, but infrastructural. Before we can rely on AI-driven insights, we must ensure that systems communicate with one another in a unified, consistent, and controlled language. Proper integration is the foundation upon which all advanced capabilities rest. It is what ultimately determines whether AI will become a genuinely functional tool or remain nothing more than an impressive demonstration in a presentation.
Li-Mor Navon is the CEO of iConduct, part of the Unitask Group, the software and digital arm of the EMET Group.
