#USA #San Francisco #AI Automation #Infrastructure as Code #Fleet Device Management

New Insights on AI Foundations and IT Leadership

In the ever-evolving landscape of technology, artificial intelligence (AI) has emerged as a pivotal force for innovation and efficiency. However, recent research conducted by Fleet Device Management reveals a concerning trend within the IT sector: a staggering 70% of IT leaders are neglecting the critical foundations necessary for safe AI implementation. This oversight poses significant risks to both operational efficiency and security in enterprises.

Key Findings from the Fleet Survey

The "Road to AI in IT" report, which surveyed over 500 enterprise IT leaders, sheds light on the disparity between investment priorities and foundational infrastructure. While nearly half of the respondents—46.5%—identified AI-driven automation as their top investment priority for the next 12 to 24 months, only 29.6% acknowledged the importance of infrastructure as code (IaC). This gap suggests that a majority of organizations are pursuing AI outcomes without establishing the necessary groundwork for effective governance, auditability, and control.

According to Allen Houchins, CIO of Fleet, the lack of infrastructure as code limits organizations' ability to deploy AI confidently. "You can turn an AI agent loose, you probably shouldn't," he cautions, emphasizing that without machine-readable, version-controlled infrastructure, organizations risk operating haphazardly in their AI initiatives. Proper IaC allows for safe deployment, human review, and the ability to roll back changes if necessary, ensuring better control over AI operations.

The Implications of Skipping AI Foundations

Despite the growing appetite for AI investments, the research indicates a troubling reality—only 13% of organizations report their endpoint management as "fully autonomous." The remaining 87% still lean on manual or partially automated workflows, highlighting a significant lag in their operational capabilities. Many leaders express concerns about maintaining pace with the rapid evolution of technology, particularly in areas such as patching critical vulnerabilities. The survey underscores this worry, tracking the increasing complexity of devices in use as well as the potential fallout from not swiftly addressing security flaws.

A staggering 79% of surveyed organizations take more than a day to deploy critical security patches, even as cyber attackers increasingly exploit vulnerabilities within mere hours. Additionally, an alarming 60% of organizations lack comprehensive visibility across their device fleets, further complicating their ability to respond to threats effectively. The Fleet report also found that 59% of organizations take longer than 24 hours to provision a new employee device, which can hinder productivity and operational efficiency.

The Shadow AI Dilemma

Amidst this backdrop of operational struggles, another concern is emerging: shadow AI. With the average enterprise running 14 AI applications while IT departments only have visibility into four, the prevalence of unmonitored AI tools heightens the risk of security breaches. This phenomenon expands the attack surface and can lead to significant financial repercussions. According to IBM's Cost of a Data Breach report, incidents involving shadow AI result in an average cost of $670,000 more than those without.

Mike McNeil, CEO and co-founder of Fleet, highlights the advantages of adopting infrastructure as code. IaC transforms AI from a basic chatbot into a powerful ally for IT teams, where every change is rigorously reviewed, version-controlled, and reversible—allowing organizations to automate routine operations while retaining human oversight.

The Path Forward

To address these challenges, organizations must prioritize the modernization of their operational foundations that underpin endpoint management. The opposition between AI ambitions and the necessary infrastructure can hinder effective governance and pose serious challenges moving forward.

In conclusion, as organizations race toward AI implementation, they must not overlook the importance of establishing a solid foundation for safe and controlled operations. By adopting infrastructure as code, IT leaders can turn their AI tools into effective resources that enhance operational efficiency without compromising security. The future of AI in organizations depends on how well they balance their aspirations with the necessary groundwork.

To explore the full findings of the Fleet report, visit Fleet's website.