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Will AI Solve the Growing Data Divide?

IT and line-of-business teams are increasingly aligned in their efforts to close the data gap and drive greater collaboration to alleviate IT bottlenecks and offload growing demands on IT teams, according to The 2025 Automation Benchmark Report: Insights from IT Leaders on Enterprise Automation & the Future of AI-Driven Businesses from Jitterbit.

"The path to success is clear: businesses must break down data silos and automate workflows to thrive in the age of AI," said Jitterbit President and CEO Bill Conner. "While many organizations still struggle to find the resources across IT, IS, and line-of-business teams to bridge this 'data divide,' the opportunity for those who can is immense. We're on the cusp of a new era of efficiency and innovation, driven by true end-to-end AI automation."

Key findings include:

A Growing Data Divide

67% of enterprises today deploy over 500 applications, creating significant data silos.

70% of resource demand for enterprise automation falls to IT teams.

99% of IT leaders acknowledge the need for seamless integration and automation, yet 71% still lack a unified platform to achieve it. 

Increasing Importance of Self-Sufficiency for Line-of-Business Leaders

97% of IT leaders recognize the importance of empowering non-technical users to build, deploy, and maintain applications and integrations, ensuring faster time to value.

Agentic AI on the Horizon

99% of enterprises have integrated AI into their operations; early-adopter organizations increasingly see agentic AI as the next frontier.

31% of enterprises are already planning for agentic AI, signaling the next wave of autonomous decision-making enterprise AI solutions, which require end-to-end AI.

IT's Biggest Challenges

Cybersecurity, data privacy, scaling, resources and compliance remain the top concerns for IT leaders navigating the AI-powered automation landscape.

50% of IT leaders cite vulnerabilities in AI-powered, third-party integrations as their top data security concern. This underscores the urgent need for robust AI security protocols, platform security controls and accountability processes.

"Legacy automation, designed to execute isolated tasks, is no longer sufficient enough to keep up with modern business demands," said Jitterbit CTO Manoj Chaudhary. "Agentic AI is driving a fundamental shift — moving from task-based automation to intelligent automation with adaptive workflows that drive real business outcomes. By leveraging AI-driven decision-making, enterprises can break free from data silos and IT bottlenecks, enabling seamless end-to-end automation."

Methodology: The survey, conducted by Censuswide Research, gathered insights from 1,000 IT decision-makers in the US and UK.

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Will AI Solve the Growing Data Divide?

IT and line-of-business teams are increasingly aligned in their efforts to close the data gap and drive greater collaboration to alleviate IT bottlenecks and offload growing demands on IT teams, according to The 2025 Automation Benchmark Report: Insights from IT Leaders on Enterprise Automation & the Future of AI-Driven Businesses from Jitterbit.

"The path to success is clear: businesses must break down data silos and automate workflows to thrive in the age of AI," said Jitterbit President and CEO Bill Conner. "While many organizations still struggle to find the resources across IT, IS, and line-of-business teams to bridge this 'data divide,' the opportunity for those who can is immense. We're on the cusp of a new era of efficiency and innovation, driven by true end-to-end AI automation."

Key findings include:

A Growing Data Divide

67% of enterprises today deploy over 500 applications, creating significant data silos.

70% of resource demand for enterprise automation falls to IT teams.

99% of IT leaders acknowledge the need for seamless integration and automation, yet 71% still lack a unified platform to achieve it. 

Increasing Importance of Self-Sufficiency for Line-of-Business Leaders

97% of IT leaders recognize the importance of empowering non-technical users to build, deploy, and maintain applications and integrations, ensuring faster time to value.

Agentic AI on the Horizon

99% of enterprises have integrated AI into their operations; early-adopter organizations increasingly see agentic AI as the next frontier.

31% of enterprises are already planning for agentic AI, signaling the next wave of autonomous decision-making enterprise AI solutions, which require end-to-end AI.

IT's Biggest Challenges

Cybersecurity, data privacy, scaling, resources and compliance remain the top concerns for IT leaders navigating the AI-powered automation landscape.

50% of IT leaders cite vulnerabilities in AI-powered, third-party integrations as their top data security concern. This underscores the urgent need for robust AI security protocols, platform security controls and accountability processes.

"Legacy automation, designed to execute isolated tasks, is no longer sufficient enough to keep up with modern business demands," said Jitterbit CTO Manoj Chaudhary. "Agentic AI is driving a fundamental shift — moving from task-based automation to intelligent automation with adaptive workflows that drive real business outcomes. By leveraging AI-driven decision-making, enterprises can break free from data silos and IT bottlenecks, enabling seamless end-to-end automation."

Methodology: The survey, conducted by Censuswide Research, gathered insights from 1,000 IT decision-makers in the US and UK.

Hot Topics

The Latest

Across the enterprise technology landscape, a quiet crisis is playing out. Organizations have run hundreds, sometimes thousands, of generative AI pilots. Leadership has celebrated the proof of concept (POCs) ... Industry experience points to a sobering reality: only 5-10% of AI POCs that progress to the pilot stage successfully reach scaled production. The remaining 90% fail because the enterprise environment around them was never ready to absorb them, not the AI models ...

Today's modern systems are not what they once were. Organizations now rely on distributed systems, event-driven workflows, hybrid and multi-cloud environments and continuous delivery pipelines. While each adds flexibility, it also introduces new, often invisible failures. Development speed is no longer the primary bottleneck of innovation. Reliability is ...

Seeing is believing, or in this case, seeing is understanding, according to New Relic's 2025 Observability Forecast for Retail and eCommerce report. Retailers who want to provide exceptional customer experiences while improving IT operations efficiency are leaning on observability ... Here are five key takeaways from the report ...

Technology leaders across the federal landscape are facing, and will continue to face, an uphill battle when it comes to fortifying their digital environments against hostile and persistent threat actors. On one hand, they are being asked to push digital transformation ... On the other hand, they are facing the fiscal uncertainty of continuing resolutions (CR) and government shutdowns looming near and far. In the face of these challenges, CIOs, CTOs, and CISOs must figure out how to modernize legacy systems and infrastructure while doing more with less and still defending against external and internal threats ...

Reliability is no longer proven by uptime alone, according to the The SRE Report 2026 from LogicMonitor. In the AI era, it is experienced through speed, consistency, and user trust, and increasingly judged by business impact. As digital services grow more complex and AI systems move into production, traditional monitoring approaches are struggling to keep pace, increasing the need for AI-first observability that spans applications, infrastructure, and the Internet ...

If AI is the engine of a modern organization, then data engineering is the road system beneath it. You can build the most powerful engine in the world, but without paved roads, traffic signals, and bridges that can support its weight, it will stall. In many enterprises, the engine is ready. The roads are not ...

In the world of digital-first business, there is no tolerance for service outages. Businesses know that outages are the quickest way to lose money and customers. For smaller organizations, unplanned downtime could even force the business to close ... A new study from PagerDuty, The State of AI-First Operations, reveals that companies actively incorporating AI into operations now view operational resilience as a growth driver rather than a cost center. But how are they achieving it? ...

In live financial environments, capital markets software cannot pause for rebuilds. New capabilities are introduced as stacked technology layers to meet evolving demands while systems remain active, data keeps moving, and controls stay intact. AI is no exception, and its opportunities are significant: accelerated decision cycles, compressed manual workflows, and more effective operations across complex environments. The constraint isn't the models themselves, but the architectural environments they enter ...

Like most digital transformation shifts, organizations often prioritize productivity and leave security and observability to keep pace. This usually translates to both the mass implementation of new technology and fragmented monitoring and observability (M&O) tooling. In the era of AI and varied cloud architecture, a disparate observability function can be dangerous. IT teams will lack a complete picture of their IT environment, making it harder to diagnose issues while slowing down mean time to resolve (MTTR). In fact, according to recent data from the SolarWinds State of Monitoring & Observability Report, 77% of IT personnel said the lack of visibility across their on-prem and cloud architecture was an issue ...

In MEAN TIME TO INSIGHT Episode 23, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses the NetOps labor shortage ...