Chloe Maraina is a powerhouse in the world of Business Intelligence, known for her ability to transform complex data sets into vivid narratives that drive corporate strategy. As an expert who bridges the gap between high-level data science and practical infrastructure management, she understands that the current rush toward Artificial Intelligence is only as strong as the data feeding it. Today, she joins us to discuss the “dirty secret” of modern IT operations—fragmented data—and how organizations can build a foundation of trust before turning over the keys to automation.
Our conversation explores the persistent and risky reliance on outdated tracking methods like spreadsheets, the dangers of scaling errors through AI, and the critical importance of visibility in infrastructure. We also delve into the massive efficiency gains and improved employee morale possible when organizations finally consolidate their records into a single, trusted source of truth.
How does the continued reliance on manual tracking methods like spreadsheets fundamentally undermine the potential for AI integration within modern organizations?
It is truly striking that as we approach 2026, roughly 34% of organizations are still tethered to manual spreadsheets to manage their sprawling digital environments. When you have procurement data living in one silo and active device logs in another, the lack of communication creates a thick fog that no AI algorithm can see through. This fragmentation means IT teams are often working with “ghost” assets or outdated information, which turns simple maintenance into a frantic, time-consuming scavenger hunt. Without a unified system of record, AI does not solve problems; it simply speeds up the process of making massive, systemic mistakes based on those disconnected rows and columns.
What are the hidden operational and security risks that surface when an organization lacks a complete, holistic view of its infrastructure?
The reality is that what you cannot see will eventually hurt your bottom line and your security posture in ways that are hard to recover from. I have seen cases where a deep assessment revealed a customer had 30% more devices than their leadership even knew existed, creating massive, unpatched vulnerabilities that were ripe for exploitation. This lack of visibility acts as a sensory blackout; if a critical server goes down, the team might not even know which services it supports or how to prioritize the fix in the heat of the moment. These gaps lead to a reactive “firefighting” culture where IT staff are constantly stressed and overwhelmed, chasing blue screens and zero-day exposures instead of focusing on innovation.
In your experience with transitioning to autonomous management, what kind of tangible impact does a single source of truth have on employee productivity and morale?
Shifting to a data-driven model transforms the daily rhythm of an IT department from one of constant chaos to one of calm, automated precision. For example, by adopting an autonomous endpoint strategy, organizations can reclaim a staggering 56,000 employee hours per year through simplified compliance reporting and automated remediation. In the healthcare sector, we have observed that modernizing these fragmented operations leads to a significant drop in support ticket volumes and a visible boost in overall job satisfaction. When professionals are not bogged down by the soul-crushing task of reconciling contradictory logs, they can actually use their expertise to improve the end-user experience, which feels far more rewarding than manual data entry.
How can leaders move away from fragmented visibility toward a unified data foundation that empowers AI to deliver truly actionable insights?
Leaders must first break down the internal walls where ownership is split across different, uncommunicative teams, as this fragmentation is the enemy of automation. By implementing a platform like Ivanti Neurons, organizations can finally stop the endless, frustrating cycle of reconciling disconnected systems and start building a trusted context for their AI tools to operate within. This transition allows for proactive patch remediation and endpoint security that happens seamlessly in the background, rather than as a panicked response to an audit or a breach. Once that unified foundation is in place, AI becomes a powerful enabler that solves old pain points with a level of speed and efficiency that was previously unimaginable.
What is your forecast for the future of IT data management?
I believe we are rapidly approaching a tipping point where the reactive support model will become entirely obsolete for any business that wants to remain competitive. As we see more companies successfully integrate autonomous management, the gap between data-mature organizations and those still clinging to spreadsheets will widen into a canyon. My forecast is that by the end of the decade, the role of the IT professional will shift entirely away from the manual labor of finding data to the strategic work of interpreting insights, as unified platforms handle the heavy lifting of visibility and remediation. Trusting your data will not just be a technical goal; it will be the primary metric for organizational resilience, security, and long-term survival in an AI-driven market.
