Nia Christair is a leading voice in the evolution of mobile technology, bringing a wealth of experience that spans from the intricacies of device hardware design to the complex demands of enterprise-level mobile solutions. Having navigated the shift from traditional app development to the current AI-driven landscape, she possesses a rare perspective on how infrastructure must adapt to support modern workforces. Our conversation explores the intersection of open-source flexibility, the rising prominence of mixed-OS environments, and the critical need to eliminate technical debt to make room for the next generation of automation.
Open-source models are increasingly used alongside partner programs for VARs and MSPs. How does this collaborative approach specifically address the need for custom enterprise configurations, and what role do these partners play in bridging the gap between standard management tools and unique organizational workflows?
Enterprise needs are incredibly particular and vary wildly from one organization to the next, which is why a rigid, one-size-fits-all approach often fails. By leveraging an open-source core, we allow partners like MSPs and resellers to step in and actively customize solutions for specific requirements, such as the numerous ways a company might automate VPN or Wi-Fi access on an employee’s first day. These partners serve as the essential bridge, translating a standard management tool into a bespoke workflow that fits the unique culture of a business. This collaborative model ensures that we can balance the strict demands of information security with the practical, day-to-day needs of IT departments. Ultimately, it allows us to optimize for small, documented iterations rather than forcing disruptive, massive overhauls on a company.
While Linux and Apple adoption is rising, some IT departments still struggle with enrolling new hardware into traditional management systems. In mixed-OS environments, how can teams improve their diagnostics to identify software bugs, and what steps prevent the mistaken assumption that new hardware is enterprise-unready?
We recently saw a case where an IT director at a casino company nearly gave up on the MacBook Neo because they couldn’t get it to enroll in their traditional MDM, assuming the hardware wasn’t ready for the enterprise. In reality, the issue was a bug within their management software, not the laptop itself, which highlights the danger of poor diagnostics in mixed-OS environments. To prevent these mistaken assumptions, teams must move away from ecosystem-locked tools—like Jamf, which is Apple-only—and embrace platforms that offer deep visibility across Linux, Mac, and Windows. With desktop Linux reaching 3.16% and Mac at 9.52% market share, having a single source of truth for diagnostics allows you to see that if a device fails to enroll, the fault likely lies in the management layer. Providing clear, transparent data helps IT teams realize that new hardware, like the Neo, is often perfectly capable if the software supporting it is up to par.
Many IT departments face decades of technical debt while shifting budgets toward AI innovation. How can transitioning to infrastructure-as-code models accelerate patching cycles from months to hours, and which “lights-on” tasks should be the first to go to free up resources for modern automation?
The reality for 2026 is that every company is being asked to do more with less, even as some IT estates carry 30 or 35 years of technical debt. Transitioning to an infrastructure-as-code model is the only way to break the cycle of manual maintenance, allowing teams to go from patching monthly to patching in just a matter of hours. The first “lights-on” tasks to go should be those repetitive, manual configurations and compliance checks that currently require entire job roles just to maintain the status quo. By automating these foundational elements, leaders can finally extract real value from their existing infrastructure and redirect those reclaimed hours toward AI-driven workflows. It is about shifting the focus from mere survival and control to the speed and automation required by modern teams.
Software often satisfies high-level stakeholders while failing the end-users who interact with it daily. How do you identify and strip away unnecessary complexity in management tools, and what specific design principles ensure that strict security requirements do not compromise the actual usability of a platform?
There is a phenomenon I call the “Concur effect,” where a product is designed to check boxes for stakeholders but becomes a nightmare for the actual users, and I have a personal vendetta against that kind of complexity. Identifying unnecessary friction starts with looking at the end-user experience—if a security protocol makes a device feel “broken” or slow, it’s a failure of design. We prioritize “baby steps” and small iterations, ensuring every new feature is clearly documented and explained so it doesn’t overwhelm the person on the ground. The goal is to build tools that satisfy both the infosec team’s need for compliance and the employee’s need for a device that just works. When you design for usability first, you actually improve security because users are less likely to seek workarounds for cumbersome systems.
Organizations are currently navigating the rise of “shadow AI” tools and headless agents like OpenClaw. What strategies can IT teams use to track these unauthorized installs or underutilized expensive software licenses, and how does centralizing this visibility within a management platform impact the company’s bottom line?
The rise of “shadow AI” is a major challenge, with some organizations now seeing tens of thousands of computers running headless agents based on open-source software like OpenClaw. IT teams need a management platform that provides granular visibility into every application install and license usage across the entire fleet. This allows you to pinpoint exactly who is using unauthorized apps like Claude or, conversely, identify who isn’t using that $25,000-a-year Bloomberg terminal license you’re paying for. Centralizing this data directly impacts the bottom line by eliminating wasted spend on underutilized software and mitigating the security risks associated with unmanaged AI tools. It turns the MDM from a simple control tool into a powerful financial and security auditing asset.
The enterprise landscape is shifting toward a future where Apple and Microsoft may compete equally while Linux dominates AI services. As workers move between these ecosystems, how will chat-based interfaces and integrated productivity tools fundamentally reshape the daily routines and responsibilities of IT administrators?
By 2030, we expect to see a world where IT administrators no longer toggle between separate platforms for every different operating system or security function. Instead, they will use centralized, chat-based interfaces to kick off complex projects and manage a truly heterogeneous environment where Microsoft and Apple are neck and neck. While human workers will likely continue to favor Mac and Windows for their daily tasks, Linux will be the invisible powerhouse running the AI services those workers rely on. This shift means the daily routine of an administrator will move away from manual “box-ticking” and toward high-level orchestration through integrated tools. The job will become less about managing machines and more about managing the seamless flow of data and productivity across various ecosystems.
What is your forecast for the MDM industry?
I believe the MDM industry is entering a post-silo era where the traditional boundaries between Apple-only or Windows-only management will completely dissolve. In the next five years, we will see Microsoft and Apple competing on equal footing for the enterprise desktop, while Linux becomes the undisputed standard for the underlying AI infrastructure. Organizations will demand a single, open-standard platform that can handle this diversity while offering the speed of infrastructure-as-code. The focus will shift from “controlling” devices to “enabling” them through AI-driven workflows and chat-integrated management. Ultimately, the winners in this space will be the ones who can strip away the complexity of the past 30 years and provide a unified, transparent view of every asset in the company.
