AI Is Moving Fast, and Enterprise File Data Is Not Ready

Nick Burling
Nasuni

Enterprise AI Has Moved from Experimentation to Execution

Pilots are everywhere, stakeholders are seeking results, businesses are pushing for new tools, and IT teams are being asked to make AI secure, reliable, and useful at scale.

But as organizations move from testing AI to operationalizing it, many are discovering that the biggest barrier is not the model, the use case, or even the budget. It is the file data foundation within.

This is not a new challenge. Back in 2023, IDC research found that more than half of enterprise leaders say that unstructured data mostly remains siloed, while less than half of the information is shared between employees and systems. More recent industry research shows the problem is only getting larger, with Komprise finding that 74% of organizations now store more than 5PB of unstructured data.

Disconnected Data Is Holding Back AI Readiness

AI is only as efficient as the data it can access, understand, and operate on. For most enterprises, that data is not organized in one singular system. This data lives across documents, project files, emails, and more, scattered across the file ecosystem. It is the most valuable to an organization, yet the most fragmented, difficult to govern, and hardest to make AI-ready.

That disconnect is becoming more visible as AI ambitions grow. Nasuni's 2026 State of Enterprise File Data report found that 94% of organizations struggle to manage unstructured data, yet only 16% rank unstructured data management as a top IT investment priority. This means that enterprises know file data is a problem, but many still don't treat it as a strategic priority.

That Gap Will Become Harder to Ignore

Rolling out generative AI tools for employees is one thing. Scaling AI across workflows, functions, and locations is another. At enterprise scale, AI needs access to trusted, current, and permission-aware data. It needs context, governance policies, and consistent performance, without creating new security or operational risks.

This is why so many AI initiatives stall after the pilot phase. In fact, Nasuni's research found that 90% of organizations face barriers to scaling AI, including data security concerns, integration roadblocks, a lack of trust in data, data management challenges, and visibility issues. These are not just AI challenges; they are file data challenges.

The rise of AI agents makes this even more urgent. While 97% of organizations have deployed or are piloting AI agents, only 18% have reached extensive, enterprise-wide deployment. It is relatively easy to test agents in controlled environments; it is much harder to give them secure access to enterprise data across departments, regions, and systems.

For agentic AI to work safely, organizations need to know what data exists, where it lives, who has access to it, and whether an AI system should be allowed to act on it. Without that foundation, agents risk amplifying the very problems IT teams have spent years trying to contain: stale data, inconsistent permissions, duplicate files, fragmented visibility, and weak governance.

File Access Is Another Overlooked Piece of the AI Readiness Puzzle

If employees cannot reliably access and collaborate on files across locations, AI will not magically solve that problem; it will adopt it. Today, 79% of organizations report inconsistent file access and performance across locations. That is a productivity issue for employees, but it is also a scaling issue for AI.

The same is true for resilience; AI initiatives depend on data availability. If file data is unavailable, compromised, or slow to recover, AI-enabled workflows can quickly break down. Yet many enterprises still rely on fragmented storage, backup, and recovery environments, adding complexity for IT teams and slowing response when something goes wrong.

For IT Leaders, the Mandate Is Clear: Modernize the Operational File Layer

That means reducing fragmentation, improving visibility, consolidating where possible, and creating a trusted data foundation that can serve both people and AI. It also means moving beyond the idea that file infrastructure is simply a storage function. In the AI era, file data is an intelligence and resilience layer.

Enterprises that get this right will be better positioned to move from AI experimentation to AI impact. Those who do not will find themselves with plenty of pilots, tools, and very little measurable progress.

The next chapter of AI will not be trailblazed by the enterprises that implement the most pilots, agents, and tools; it will be mastered by those with the strongest data foundation and control. 

Nick Burling is Chief Product Officer at Nasuni

Related Links

Hot Topics

The Latest

When the Database Is the Problem, Is APM Enough?

Production incidents rarely announce themselves as database problems. They appear as slow transactions, timeouts, rising response times, or an application struggling under a workload it previously handled. APM provides an essential starting point. It can identify a slow transaction path, highlight an affected service, and show that a database dependency is consuming more time than expected. But identifying the database as part of the problem is not the same as explaining what is happening inside it ...

From Cleanup to Prevention: The Future of Cloud-Native Cost Optimization

Cloud teams are under constant pressure to reduce spend without slowing development or increasing operational risk. They are deploying autoscalers, rightsizing workloads, enforcing resource requests, reviewing utilization dashboards, and building FinOps processes around cloud-native environments. Yet the results often disappoint ...

The Real Enterprise AI Risk Isn't Hallucinations

Ask most IT leaders about their biggest concern with AI and you'll hear the same answer: hallucinations ... Today, however, the conversation has shifted ... As organizations move beyond chatbots and experiments, they are increasingly deploying AI agents that perform multi-step tasks. These systems retrieve documents, query databases, call APIs, generate reports, write code, and make recommendations. The issue is not whether the model can reason. The issue is whether the organization can see, verify, and govern the decisions being made along the way ...

Beyond Buy vs. Build: The New Playbook for Data Pipelines

While organizations want to take control of their telemetry, building telemetry pipelines from scratch can be a very daunting, complicated task, even when leveraging open-source standards like OpenTelemetry. It requires specialized knowledge across distributed systems, data engineering, and security. This fragmented approach across systems causes higher operational costs; it puts a strain on resources and reduces efficiency as teams have to work with different interfaces and processes ...

The Corporate Network Is Fading - Here's What Replaces It

For decades, enterprise networks were designed around a simple assumption: work happened inside the office. Applications lived in centralized data centers, employees connected through internal infrastructure, and security focused on protecting the perimeter that surrounded everything ... But the way organizations operate today bears little resemblance to that environment. Cloud platforms host critical applications, employees connect from homes and airports as often as they do from offices, and partners collaborate through shared systems that exist far beyond corporate walls. In short, the corporate network no longer resembles the environment it was designed to protect ...

IT Leaders Must Establish a Network Data Architecture Practice

As an analyst who researches how IT organizations design, build, and operate their networks, I find that network data is a constant source of pain. Network teams struggle with data quality, fragmentation, authority, access, and trust. And these issues undermine everything they try to do. Here are the numbers: Only 45% of network teams are completely confident in the accuracy of their network source of truth, which documents the intent of their network ...

Data Center Outages Are Decreasing but More Costly

The 2026 Global Data Center Survey from Uptime Institute reveals an industry navigating workforce constraints, escalating outage expenses, even as rising costs remain the top concern for management teams ...

Observability Has a Physical Layer Now: What AI Infrastructure Teaches IT Operations

The next observability gap may not be in the code. It may be under the rack. That sounds strange until you think about how AI incidents actually feel in the middle of an investigation ... The application dashboard may be accurate. It may also be stopping at the wrong boundary. AI systems depend on software, but they also depend on a dense physical stack: racks, power paths, thermal margin, maintenance activity and, in many environments, liquid cooling. Those physical dependencies can change slowly before they look like a software incident ...

TLS Certificate Expiration Is Becoming an Observability Problem

Certificate expiration is the rare outage you can see coming. Every TLS certificate carries the date it stops working, so the moment it will begin breaking connections is knowable in advance. That's what makes an expired certificate such a frustrating way to lose a service. What's changing now is how often that date comes around ...

Enterprise Cloud Observability: Choosing the Right Starting Point

Enterprises operate different combinations of workloads across cloud, hybrid and multicloud environments. For business-critical workloads, teams need to consider monitoring and observability early so they can detect health issues, investigate failures, and understand operational impact. Organizations place workloads on cloud platforms based on a combination of technical requirements, economics, existing dependencies, organizational standards, and business priorities. Their monitoring priorities therefore depend on what they operate and where those systems run. Those priorities will not look the same for every organization ...

AI Is Moving Fast, and Enterprise File Data Is Not Ready

Nick Burling
Nasuni

Enterprise AI Has Moved from Experimentation to Execution

Pilots are everywhere, stakeholders are seeking results, businesses are pushing for new tools, and IT teams are being asked to make AI secure, reliable, and useful at scale.

But as organizations move from testing AI to operationalizing it, many are discovering that the biggest barrier is not the model, the use case, or even the budget. It is the file data foundation within.

This is not a new challenge. Back in 2023, IDC research found that more than half of enterprise leaders say that unstructured data mostly remains siloed, while less than half of the information is shared between employees and systems. More recent industry research shows the problem is only getting larger, with Komprise finding that 74% of organizations now store more than 5PB of unstructured data.

Disconnected Data Is Holding Back AI Readiness

AI is only as efficient as the data it can access, understand, and operate on. For most enterprises, that data is not organized in one singular system. This data lives across documents, project files, emails, and more, scattered across the file ecosystem. It is the most valuable to an organization, yet the most fragmented, difficult to govern, and hardest to make AI-ready.

That disconnect is becoming more visible as AI ambitions grow. Nasuni's 2026 State of Enterprise File Data report found that 94% of organizations struggle to manage unstructured data, yet only 16% rank unstructured data management as a top IT investment priority. This means that enterprises know file data is a problem, but many still don't treat it as a strategic priority.

That Gap Will Become Harder to Ignore

Rolling out generative AI tools for employees is one thing. Scaling AI across workflows, functions, and locations is another. At enterprise scale, AI needs access to trusted, current, and permission-aware data. It needs context, governance policies, and consistent performance, without creating new security or operational risks.

This is why so many AI initiatives stall after the pilot phase. In fact, Nasuni's research found that 90% of organizations face barriers to scaling AI, including data security concerns, integration roadblocks, a lack of trust in data, data management challenges, and visibility issues. These are not just AI challenges; they are file data challenges.

The rise of AI agents makes this even more urgent. While 97% of organizations have deployed or are piloting AI agents, only 18% have reached extensive, enterprise-wide deployment. It is relatively easy to test agents in controlled environments; it is much harder to give them secure access to enterprise data across departments, regions, and systems.

For agentic AI to work safely, organizations need to know what data exists, where it lives, who has access to it, and whether an AI system should be allowed to act on it. Without that foundation, agents risk amplifying the very problems IT teams have spent years trying to contain: stale data, inconsistent permissions, duplicate files, fragmented visibility, and weak governance.

File Access Is Another Overlooked Piece of the AI Readiness Puzzle

If employees cannot reliably access and collaborate on files across locations, AI will not magically solve that problem; it will adopt it. Today, 79% of organizations report inconsistent file access and performance across locations. That is a productivity issue for employees, but it is also a scaling issue for AI.

The same is true for resilience; AI initiatives depend on data availability. If file data is unavailable, compromised, or slow to recover, AI-enabled workflows can quickly break down. Yet many enterprises still rely on fragmented storage, backup, and recovery environments, adding complexity for IT teams and slowing response when something goes wrong.

For IT Leaders, the Mandate Is Clear: Modernize the Operational File Layer

That means reducing fragmentation, improving visibility, consolidating where possible, and creating a trusted data foundation that can serve both people and AI. It also means moving beyond the idea that file infrastructure is simply a storage function. In the AI era, file data is an intelligence and resilience layer.

Enterprises that get this right will be better positioned to move from AI experimentation to AI impact. Those who do not will find themselves with plenty of pilots, tools, and very little measurable progress.

The next chapter of AI will not be trailblazed by the enterprises that implement the most pilots, agents, and tools; it will be mastered by those with the strongest data foundation and control. 

Nick Burling is Chief Product Officer at Nasuni

Related Links

Hot Topics

The Latest

When the Database Is the Problem, Is APM Enough?

Production incidents rarely announce themselves as database problems. They appear as slow transactions, timeouts, rising response times, or an application struggling under a workload it previously handled. APM provides an essential starting point. It can identify a slow transaction path, highlight an affected service, and show that a database dependency is consuming more time than expected. But identifying the database as part of the problem is not the same as explaining what is happening inside it ...

From Cleanup to Prevention: The Future of Cloud-Native Cost Optimization

Cloud teams are under constant pressure to reduce spend without slowing development or increasing operational risk. They are deploying autoscalers, rightsizing workloads, enforcing resource requests, reviewing utilization dashboards, and building FinOps processes around cloud-native environments. Yet the results often disappoint ...

The Real Enterprise AI Risk Isn't Hallucinations

Ask most IT leaders about their biggest concern with AI and you'll hear the same answer: hallucinations ... Today, however, the conversation has shifted ... As organizations move beyond chatbots and experiments, they are increasingly deploying AI agents that perform multi-step tasks. These systems retrieve documents, query databases, call APIs, generate reports, write code, and make recommendations. The issue is not whether the model can reason. The issue is whether the organization can see, verify, and govern the decisions being made along the way ...

Beyond Buy vs. Build: The New Playbook for Data Pipelines

While organizations want to take control of their telemetry, building telemetry pipelines from scratch can be a very daunting, complicated task, even when leveraging open-source standards like OpenTelemetry. It requires specialized knowledge across distributed systems, data engineering, and security. This fragmented approach across systems causes higher operational costs; it puts a strain on resources and reduces efficiency as teams have to work with different interfaces and processes ...

The Corporate Network Is Fading - Here's What Replaces It

For decades, enterprise networks were designed around a simple assumption: work happened inside the office. Applications lived in centralized data centers, employees connected through internal infrastructure, and security focused on protecting the perimeter that surrounded everything ... But the way organizations operate today bears little resemblance to that environment. Cloud platforms host critical applications, employees connect from homes and airports as often as they do from offices, and partners collaborate through shared systems that exist far beyond corporate walls. In short, the corporate network no longer resembles the environment it was designed to protect ...

IT Leaders Must Establish a Network Data Architecture Practice

As an analyst who researches how IT organizations design, build, and operate their networks, I find that network data is a constant source of pain. Network teams struggle with data quality, fragmentation, authority, access, and trust. And these issues undermine everything they try to do. Here are the numbers: Only 45% of network teams are completely confident in the accuracy of their network source of truth, which documents the intent of their network ...

Data Center Outages Are Decreasing but More Costly

The 2026 Global Data Center Survey from Uptime Institute reveals an industry navigating workforce constraints, escalating outage expenses, even as rising costs remain the top concern for management teams ...

Observability Has a Physical Layer Now: What AI Infrastructure Teaches IT Operations

The next observability gap may not be in the code. It may be under the rack. That sounds strange until you think about how AI incidents actually feel in the middle of an investigation ... The application dashboard may be accurate. It may also be stopping at the wrong boundary. AI systems depend on software, but they also depend on a dense physical stack: racks, power paths, thermal margin, maintenance activity and, in many environments, liquid cooling. Those physical dependencies can change slowly before they look like a software incident ...

TLS Certificate Expiration Is Becoming an Observability Problem

Certificate expiration is the rare outage you can see coming. Every TLS certificate carries the date it stops working, so the moment it will begin breaking connections is knowable in advance. That's what makes an expired certificate such a frustrating way to lose a service. What's changing now is how often that date comes around ...

Enterprise Cloud Observability: Choosing the Right Starting Point

Enterprises operate different combinations of workloads across cloud, hybrid and multicloud environments. For business-critical workloads, teams need to consider monitoring and observability early so they can detect health issues, investigate failures, and understand operational impact. Organizations place workloads on cloud platforms based on a combination of technical requirements, economics, existing dependencies, organizational standards, and business priorities. Their monitoring priorities therefore depend on what they operate and where those systems run. Those priorities will not look the same for every organization ...