By Dan Zaniewski, Chief Technology Officer, Auvik
As AI advances at a breakneck pace, excitement continues to grow around how the tech will evolve and automate IT management. AI tools are being integrated en masse to give long overburdened IT teams their time back. Yet many IT professionals are still waiting for these promised gains in productivity and outcomes to come their way.
Despite ongoing investment in AI solutions, only 5% of IT professionals currently report that AI is core to their operations. On top of this, MIT found that 95% of organizations studied are seeing zero return on investment in their generative AI initiatives. Many organizations now find themselves in the AI productivity paradox, marked by a clear disconnect between the technology’s rapid advancement and its realized value.
Still, 67% of IT professionals are optimistic about AI’s potential impact on IT. The challenge is translating that optimism into operational reality. Meeting that challenge requires going back to basics: prioritizing the right governance, visibility, and tool investment for AI.
AI Ambition vs. Reality
The gap between organizations’ vision for AI transformation and the operational hurdles teams are facing behind the scenes in implementing the tech is only growing. Budgets for AI tools continue to rise, yet spending isn’t translating into added efficiency or capacity to focus on higher-level strategy. Part of the problem is that IT teams are already stretched thin: 48% of corporate IT professionals surveyed by Auvik say they currently lack the time needed to move initiatives forward.
At the same time, many organizations are still working to establish the governance foundations required to implement AI effectively, with 61% of IT professionals discovering unauthorized SaaS applications at least monthly. This underscores the broader challenge of maintaining visibility into the very IT environment AI is meant to improve. There is also a disconnect in how AI policies are understood across the organization: 76% of IT leaders believe they have a formal AI policy in place, but only 42% of help desk staff agree.
Taken together, these findings highlight a clear AI maturity gap. While IT leadership is looking to automate processes as fast as possible, their teams are often still navigating the practical demands of putting those plans into action.
The Roadmap to AI Readiness
AI readiness is not a one-time technology decision. It is the cumulative result of how well an organization understands and manages its environment.
Key considerations for strengthening that readiness include:
- Establish Full Network Visibility: What might seem obvious to IT professionals may just be the thing holding AI initiatives back. You can't automate what you can't see. Any effort to automate IT operations should begin with a comprehensive audit of the organization’s network. Automating without a clear, current view of the IT environment leads to unreliable results, forcing teams to spend valuable time correcting AI systems or to abandon the tools as quickly as they are adopted.
- Address the Shadow IT Gap: Auditing the IT environment goes hand in hand with identifying unsanctioned IT and AI tools. Auvik's telemetry shows that customers discovered 102,939 Shadow AI applications in their networks in 2025 alone. While employees may adopt unauthorized LLMs or SaaS applications to improve productivity, these tools not only introduce security and compliance risks but also obscure how work is actually being performed. That lack of clarity makes it harder to develop an effective AI strategy.
- Make Governance Operational: An AI policy that hasn't reached the IT professionals using AI every day is merely a checkbox exercise. IT leaders should treat policy rollout as an ongoing conversation with regular check-ins, rather than as a one-time distribution of a static document. Governance must also extend to data management, with processes to identify and correct incomplete, inconsistent, duplicated, or outdated information before it shapes AI outputs.
- Make AI Failures Recoverable: Backing up network configurations is also a vital piece of building broader business continuity and disaster recovery (BCDR) strategy. This step is all too often overlooked in the rush to adopt new AI tools. From years of working on business continuity products, I know firsthand how quickly a single configuration error can snowball into extended downtime and outages. This risk only grows with AI in the mix, as one agent mistake could potentially wipe out critical business data and networks in seconds. With this, automated and consistently updated backups are crucial to ensure business and IT continuity, especially in the age of AI.
- Make New Tech Investments Wisely: Do new AI tools actually automate key processes and alleviate the burden of manual IT management, or do they simply add more to manage? Given the gap between rising IT budgets and realized ROI, organizations should first identify and address the operational barriers limiting returns. This should guide future investment decisions, whether that means consolidating the tool stack or choosing solutions that fit naturally into existing workflows.
Closing the Gap
Running basic troubleshooting commands with AI is rarely the end goal for IT leaders. Instead, they are building toward autonomous infrastructure that can monitor its environment, detect anomalies, and remediate issues in real time. The broader ambition is to create proactive IT systems that anticipate and prevent problems before they cause disruption.
But the move toward autonomous systems is not a shortcut to better outcomes, nor will it close the gap between AI spending and results on its own. Teams will see meaningful returns only if they first establish the conditions automation depends on: full visibility across the IT environment, data that's actually managed rather than just collected, and governance that gets revisited instead of filed away. Realizing AI’s full potential requires doing the less exciting operational work that makes the more exciting outcome possible.
About Dan Zaniewski
Dan Zaniewski serves as Chief Technology Officer at Auvik. With over 20 years of engineering experience and more than a decade in the MSP space, Zaniewski has played a pivotal role in driving product innovation and improving engineering processes at Auvik, joining in 2024 as Senior Vice President of Engineering. Prior to Auvik, Dan served as CTO at Cork, where he developed an industry-first security monitoring platform. Zaniewski also previously served as Senior Vice President of Engineering at Datto leading Engineering for Datto’s flagship backup product line.
