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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

For many B2B and B2C enterprise brands, technology isn't a core strength. Relying on overly complex architectures (like those that follow a pure MACH doctrine) has been flagged by industry leaders as a source of operational slowdown, creating bottlenecks that limit agility in volatile market conditions ...

FinOps champions crucial cross-departmental collaboration, uniting business, finance, technology and engineering leaders to demystify cloud expenses. Yet, too often, critical cost issues are softened into mere "recommendations" or "insights" — easy to ignore. But what if we adopted security's battle-tested strategy and reframed these as the urgent risks they truly are, demanding immediate action? ...

Two in three IT professionals now cite growing complexity as their top challenge — an urgent signal that the modernization curve may be getting too steep, according to the Rising to the Challenge survey from Checkmk ...

While IT leaders are becoming more comfortable and adept at balancing workloads across on-premises, colocation data centers and the public cloud, there's a key component missing: connectivity, according to the 2025 State of the Data Center Report from CoreSite ...

A perfect storm is brewing in cybersecurity — certificate lifespans shrinking to just 47 days while quantum computing threatens today's encryption. Organizations must embrace ephemeral trust and crypto-agility to survive this dual challenge ...

In MEAN TIME TO INSIGHT Episode 14, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses hybrid multi-cloud network observability... 

While companies adopt AI at a record pace, they also face the challenge of finding a smart and scalable way to manage its rapidly growing costs. This requires balancing the massive possibilities inherent in AI with the need to control cloud costs, aim for long-term profitability and optimize spending ...

Telecommunications is expanding at an unprecedented pace ... But progress brings complexity. As WanAware's 2025 Telecom Observability Benchmark Report reveals, many operators are discovering that modernization requires more than physical build outs and CapEx — it also demands the tools and insights to manage, secure, and optimize this fast-growing infrastructure in real time ...

As businesses increasingly rely on high-performance applications to deliver seamless user experiences, the demand for fast, reliable, and scalable data storage systems has never been greater. Redis — an open-source, in-memory data structure store — has emerged as a popular choice for use cases ranging from caching to real-time analytics. But with great performance comes the need for vigilant monitoring ...

Kubernetes was not initially designed with AI's vast resource variability in mind, and the rapid rise of AI has exposed Kubernetes limitations, particularly when it comes to cost and resource efficiency. Indeed, AI workloads differ from traditional applications in that they require a staggering amount and variety of compute resources, and their consumption is far less consistent than traditional workloads ... Considering the speed of AI innovation, teams cannot afford to be bogged down by these constant infrastructure concerns. A solution is needed ...