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Data Leaders Doubt That Their Data Is AI-Ready

While nearly all data leaders surveyed are building generative AI applications, most don't believe their data estate is actually prepared to support them, according to the State of Reliable AI report from Monte Carlo Data.


Source: Monte Carlo Data

Findings include:

■ 100% of data professionals feel pressure from their leadership to implement a GenAI strategy and/or build GenAI products.

■ 91% of data leaders (VP or above) have built or are currently building a GenAI product.

■ 82% of respondents rated the potential usefulness of GenAI at least an 8 on a scale of 1-10, but 90% believe their leaders do not have realistic expectations for its technical feasibility or ability to drive business value.

■ 84% of respondents indicate that it is the data team's responsibility to implement a GenAI strategy, versus 12% whose organizations have built dedicated GenAI teams.

While AI is widely expected to be among the most transformative technologies of the last decade, these findings suggest a troubling disconnect between data teams and business stakeholders.

Data leaders clearly feel the pressure and responsibility to participate in the GenAI revolution, but some may be forging ahead in spite of more primordial priorities — and in some cases, against their better judgment.

The State of Reliable AI Infrastructure

Even before the advent of GenAI, organizations were dealing with an exponentially greater volume of data than in decades past. Since adopting GenAI programs, 91% of data leaders report that both applications and the number of critical data sources has increased even further — deepening the complexity and scale of their data estates in the process.

"Data is the lifeblood of all AI — without secure, compliant, and reliable data, enterprise AI initiatives will fail before they get off the ground. Data quality is a critical but often overlooked component of ensuring ethical and accurate models, and the fact that 68% of data leaders surveyed did not feel completely confident that their data reflects the unsung importance of this puzzle piece," said Lior Solomon, VP of Data, Drata. "The most advanced AI projects will prioritize data reliability at each stage of the model development life cycle, from ingestion in the database to fine-tuning or RAG."

What's more, the survey revealed that data teams are using a myriad of approaches to tackle GenAI, suggesting that not only is the volume and complexity of data increasing, but that there's no one-size-fits-most method for getting these AI models customer-ready.

How data teams are approaching AI:

■ 49% building their own LLM

■ 49% using model-as-a-service providers like OpenAI or Anthropic

■ 48% implementing a retrieval-augmented generation (RAG) architecture

■ 48% fine-tuning models-as-a-service or their own LLMs

As the complexity of the AI's architecture — and the data that powers it — continues to expand, one perennial problem expands with it: data quality issues.

The Key Question: Is Your Data GenAI Ready?

Data quality has always been a challenge for data teams. However, survey results reveal that the introduction of GenAI has exacerbated both the scope and severity of this problem.

Our findings suggest that while the data estate has evolved rapidly over the last few years to accommodate AI and other novel use cases, data quality management has not. In fact, many respondents still rely on tedious and unscalable data quality methods, such as testing and monitoring, with more than half (54%) of data professionals surveyed depending exclusively on manual testing.

This lack of automated, resolution-focused solutions is reflected in the data, with two-thirds of respondents experiencing a data incident in the past 6 months that cost their organization $100,000 or more. This is a shocking figure when you consider that 70% of data leaders surveyed reported that it takes longer than 4 hours to find a data incident. What's worse, previous surveys commissioned by Monte Carlo reveal that data teams face, on average, 67 data incidents per month.

"In 2024, data leaders are tasked with not only shepherding their companies' GenAI initiatives from experimentation to production, but also ensuring that the data itself is AI-ready, in other words, secure, compliant, and most of all, trusted," said Barr Moses, Co-Founder and CEO of Monte Carlo. "As validated by our survey, organizations will fail without treating data trust with the diligence it deserves. Prioritizing automatic, resolution-focused data quality approaches like data observability will empower data teams to achieve enterprise-grade AI at scale."

Methodology: The Wakefield Research survey — which polled 200 data leaders and professionals — was commissioned by Monte Carlo in April 2024.

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Data Leaders Doubt That Their Data Is AI-Ready

While nearly all data leaders surveyed are building generative AI applications, most don't believe their data estate is actually prepared to support them, according to the State of Reliable AI report from Monte Carlo Data.


Source: Monte Carlo Data

Findings include:

■ 100% of data professionals feel pressure from their leadership to implement a GenAI strategy and/or build GenAI products.

■ 91% of data leaders (VP or above) have built or are currently building a GenAI product.

■ 82% of respondents rated the potential usefulness of GenAI at least an 8 on a scale of 1-10, but 90% believe their leaders do not have realistic expectations for its technical feasibility or ability to drive business value.

■ 84% of respondents indicate that it is the data team's responsibility to implement a GenAI strategy, versus 12% whose organizations have built dedicated GenAI teams.

While AI is widely expected to be among the most transformative technologies of the last decade, these findings suggest a troubling disconnect between data teams and business stakeholders.

Data leaders clearly feel the pressure and responsibility to participate in the GenAI revolution, but some may be forging ahead in spite of more primordial priorities — and in some cases, against their better judgment.

The State of Reliable AI Infrastructure

Even before the advent of GenAI, organizations were dealing with an exponentially greater volume of data than in decades past. Since adopting GenAI programs, 91% of data leaders report that both applications and the number of critical data sources has increased even further — deepening the complexity and scale of their data estates in the process.

"Data is the lifeblood of all AI — without secure, compliant, and reliable data, enterprise AI initiatives will fail before they get off the ground. Data quality is a critical but often overlooked component of ensuring ethical and accurate models, and the fact that 68% of data leaders surveyed did not feel completely confident that their data reflects the unsung importance of this puzzle piece," said Lior Solomon, VP of Data, Drata. "The most advanced AI projects will prioritize data reliability at each stage of the model development life cycle, from ingestion in the database to fine-tuning or RAG."

What's more, the survey revealed that data teams are using a myriad of approaches to tackle GenAI, suggesting that not only is the volume and complexity of data increasing, but that there's no one-size-fits-most method for getting these AI models customer-ready.

How data teams are approaching AI:

■ 49% building their own LLM

■ 49% using model-as-a-service providers like OpenAI or Anthropic

■ 48% implementing a retrieval-augmented generation (RAG) architecture

■ 48% fine-tuning models-as-a-service or their own LLMs

As the complexity of the AI's architecture — and the data that powers it — continues to expand, one perennial problem expands with it: data quality issues.

The Key Question: Is Your Data GenAI Ready?

Data quality has always been a challenge for data teams. However, survey results reveal that the introduction of GenAI has exacerbated both the scope and severity of this problem.

Our findings suggest that while the data estate has evolved rapidly over the last few years to accommodate AI and other novel use cases, data quality management has not. In fact, many respondents still rely on tedious and unscalable data quality methods, such as testing and monitoring, with more than half (54%) of data professionals surveyed depending exclusively on manual testing.

This lack of automated, resolution-focused solutions is reflected in the data, with two-thirds of respondents experiencing a data incident in the past 6 months that cost their organization $100,000 or more. This is a shocking figure when you consider that 70% of data leaders surveyed reported that it takes longer than 4 hours to find a data incident. What's worse, previous surveys commissioned by Monte Carlo reveal that data teams face, on average, 67 data incidents per month.

"In 2024, data leaders are tasked with not only shepherding their companies' GenAI initiatives from experimentation to production, but also ensuring that the data itself is AI-ready, in other words, secure, compliant, and most of all, trusted," said Barr Moses, Co-Founder and CEO of Monte Carlo. "As validated by our survey, organizations will fail without treating data trust with the diligence it deserves. Prioritizing automatic, resolution-focused data quality approaches like data observability will empower data teams to achieve enterprise-grade AI at scale."

Methodology: The Wakefield Research survey — which polled 200 data leaders and professionals — was commissioned by Monte Carlo in April 2024.

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According to Auvik's 2025 IT Trends Report, 60% of IT professionals feel at least moderately burned out on the job, with 43% stating that their workload is contributing to work stress. At the same time, many IT professionals are naming AI and machine learning as key areas they'd most like to upskill ...

Businesses that face downtime or outages risk financial and reputational damage, as well as reducing partner, shareholder, and customer trust. One of the major challenges that enterprises face is implementing a robust business continuity plan. What's the solution? The answer may lie in disaster recovery tactics such as truly immutable storage and regular disaster recovery testing ...

IT spending is expected to jump nearly 10% in 2025, and organizations are now facing pressure to manage costs without slowing down critical functions like observability. To meet the challenge, leaders are turning to smarter, more cost effective business strategies. Enter stage right: OpenTelemetry, the missing piece of the puzzle that is no longer just an option but rather a strategic advantage ...

Amidst the threat of cyberhacks and data breaches, companies install several security measures to keep their business safely afloat. These measures aim to protect businesses, employees, and crucial data. Yet, employees perceive them as burdensome. Frustrated with complex logins, slow access, and constant security checks, workers decide to completely bypass all security set-ups ...

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Cloudbrink's Personal SASE services provide last-mile acceleration and reduction in latency

In MEAN TIME TO INSIGHT Episode 13, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses hybrid multi-cloud networking strategy ... 

In high-traffic environments, the sheer volume and unpredictable nature of network incidents can quickly overwhelm even the most skilled teams, hindering their ability to react swiftly and effectively, potentially impacting service availability and overall business performance. This is where closed-loop remediation comes into the picture: an IT management concept designed to address the escalating complexity of modern networks ...

In 2025, enterprise workflows are undergoing a seismic shift. Propelled by breakthroughs in generative AI (GenAI), large language models (LLMs), and natural language processing (NLP), a new paradigm is emerging — agentic AI. This technology is not just automating tasks; it's reimagining how organizations make decisions, engage customers, and operate at scale ...

In the early days of the cloud revolution, business leaders perceived cloud services as a means of sidelining IT organizations. IT was too slow, too expensive, or incapable of supporting new technologies. With a team of developers, line of business managers could deploy new applications and services in the cloud. IT has been fighting to retake control ever since. Today, IT is back in the driver's seat, according to new research by Enterprise Management Associates (EMA) ...

In today's fast-paced and increasingly complex network environments, Network Operations Centers (NOCs) are the backbone of ensuring continuous uptime, smooth service delivery, and rapid issue resolution. However, the challenges faced by NOC teams are only growing. In a recent study, 78% state network complexity has grown significantly over the last few years while 84% regularly learn about network issues from users. It is imperative we adopt a new approach to managing today's network experiences ...

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