Effective device management is a critical operational challenge
You can’t scale AI transformation on a shaky IT foundation. A lot of mid-sized companies are eager to integrate AI. They see its ROI. They see the headlines. But most of them are not dealing with their most basic operational issues, like managing the laptops, phones, and hardware their people actually use each day. The result is a disorganized baseline that lacks control, efficiency, or security.
In global firms with 500 to 1,000 employees, IT is rarely front and center in day-to-day operations. Their tech teams are small. They’re using spreadsheets to track assets across countries, time zones, and customs processes. This is not a scalable situation. You can’t run autonomous systems when human error is baked into every device assignment or retrieval.
AI relies on clean data, stable infrastructure, and disciplined controls. That starts with knowing where your company’s machines are, who’s using them, and whether they’re secure. If you’re not doing that, investing in AI is like investing in speed without knowing if the brakes work. And that’s not how you build long-term value.
The numbers back this up. According to Salesforce, AI budgets have nearly doubled in the past few years. Around 30% of CIO budgets are now going into Agentic AI. But if you’re one of the firms that still can’t get a laptop to your new hire without a delay, take a step back. Solidify the basics. Then accelerate.
Small and mid-sized businesses encounter significant logistical complexity
Remote teams add velocity. They also introduce complexity. For small to mid-sized firms, managing IT hardware across emerging markets is a task they’re not built for, yet. Most of these companies are in high-growth mode. They’ve got talent spread across India, Nigeria, Brazil, markets where delivery infrastructure might not be as predictable. Procurement and payments aren’t unified either. And logistics don’t follow a standard playbook; they shift from country to country.
Here’s what the data tells us. Rayda analyzed 20,000 remote-first tech firms based in the U.S. or Europe. Almost 80% of them had remote staff in Asia, Latin America, or Africa, that’s over 1.1 million employees. Among businesses with 100 to 500 people, 47% had at least five team members working in emerging markets. That’s no longer an edge case. It’s the new norm.
The pressure this puts on IT is real. In many startups, device management isn’t even handled by IT because IT doesn’t exist yet. It’s a job for the CEO, HR, or whoever has time. When the company finally hires IT, the team inherits a mess, device spreadsheets, missing hardware, untracked shipping. Makes scaling harder. Adds friction when speed should be the focus.
As you’re hiring in new markets, onboarding lag caused by delayed devices sends the wrong signal to high-value recruits. Worse, bad logistics don’t just impact perception, they burn budget when mismatched or lost equipment piles up.
If you’re serious about a globally distributed team, and you should be, you can’t treat hardware logistics as an afterthought. Build systems that are decentralized but controlled. Make it easy to issue, track, and recover devices anywhere. Treat it with the same urgency you’d give to scaling sales ops or product throughput. You’ll move faster across all functions when the basics don’t get in the way.
Inadequate device management fosters the emergence of shadow IT
If your teams don’t have the right tools, they’ll find their own. That’s not speculation, it’s already happening. In companies without reliable, centrally managed device provisioning, employees are defaulting to personal devices and accounts to get work done. It seems efficient. It’s actually a major problem.
This is what’s referred to as shadow IT. Employees operate outside of official systems, using unvetted hardware and apps that IT doesn’t authorize, monitor, or secure. That uncontrolled surface area exposes customer data, intellectual property, and access credentials in ways that won’t meet regulatory requirements. Especially for anyone pursuing SOC 2 or ISO 27001, this directly threatens audit readiness.
There’s hard data behind it. Gartner estimates that by 2027, 75% of employees will create or modify tech tools without formal IT involvement. The scale of invisibility here is enormous. Meanwhile, a 2023 Cyberhaven report showed that 11% of the data employees paste into ChatGPT is classified as confidential. That means people are treating consumer-grade AI models like trusted enterprise partners, even when policies say otherwise.
When teams don’t receive secure, pre-approved devices, they substitute in whatever they have. That includes using personal Google accounts to sign up for work platforms, putting company assets in ecosystems that IT lacks authority over. Once that happens, you can’t control what you can’t see.
If security matters, and it always should, this is where enforcement starts: get work devices into the hands of people who need them. Ensure they’re configured, monitored, and easy to replace or recall. Implementing strong device policies isn’t just IT process, it’s a strategic move to maintain control in an increasingly automated and AI-integrated environment.
Poor device logistics negatively impact productivity and inflate operational costs
When you look at the typical onboarding process for remote employees, the first 48 hours count. If device delivery is late or the specs are wrong, that employee can’t contribute. That’s downtime. Worse, it reflects a lack of execution. At scale, these repeated inefficiencies kill momentum and slow the company’s progression across departments.
The impact isn’t limited to perception, it’s operational. Delays in provisioning reduce the effectiveness of onboarding. Productivity drops because job readiness drops. And if the first touchpoints are slow and error-prone, high-performing hires are more likely to leave early. According to data cited in the source, almost 38% of first-year employees leave within 12 months. Two-thirds of those exits happen in the first six months. Logistics is not a support function. It sets the tone.
The reverse scenario, offboarding, carries real costs too. Without effective recovery flows, assets go missing or are never returned. Many companies choose to ignore the problem entirely, especially with remote staff, calculating (incorrectly) that the cost to retrieve is greater than the value of what’s lost.
A Capterra survey showed that 71% of HR professionals had experienced lost equipment due to employee non-return. Remote and hybrid workers were 17% more likely to not return hardware. These numbers compound quickly across departments and quarters.
And here’s the operational challenge: many small IT teams are using spreadsheets to track everything. That makes recall plans manual, fragmented, and unreliable. This should not be acceptable to any leadership team focused on risk, cost, or efficiency. Automating lifecycle processes, from procurement to recovery, frees bandwidth and builds resilience.
Device logistics isn’t just about keeping track of expensive gear. It protects data, keeps teams productive, and plugs budget leaks that grow quietly in the background. If those problems persist, scaling becomes harder, without adding any real complexity. It’s just avoidable chaos.
Prioritizing IT logistics and device management is essential for establishing a secure foundation
AI implementation doesn’t begin with models, prompts, or dashboards. It begins with structure. If your operating infrastructure isn’t stable, secure, and consistent, your ability to apply AI meaningfully will be limited by internal issues. Most companies overlook this. They push budgets into AI development while ignoring the logistical bottlenecks that still exist in how devices are issued, tracked, and recovered.
This misalignment creates real friction. Workflows meant to power modern systems are grounded by outdated inventory tracking, manual onboarding, and fragmented compliance checks. Meanwhile, the regulatory burden is increasing. Your business needs better observability and tighter controls, not just for AI, but also for protecting broader business continuity.
This is being reflected in strategic moves by market players. Deel, known for global payroll solutions, recently acquired Hofy, a device lifecycle management firm. Rippling, a major HR and IT integration platform, has also launched its own in-house device management capabilities. These aren’t brand experiments. These are fundamental infrastructure investments. They signal that device logistics is no longer being treated as backend support, it’s being integrated as a core product feature for companies operating globally and preparing for deeper automation.
With the direction the market is accelerating toward, having a fragmented device management process is a liability. It creates blind spots in security, delays onboarding, complicates compliance, and stifles agility. In contrast, companies that solve this challenge early build the operational discipline required for AI systems to function properly, without introducing unnecessary risk.
The broader IT leadership landscape confirms the tension. In the 2025 State of the CIO Survey, 38% of CIOs said monetizing data is their top strategic priority, a clear signal of the shift toward AI integration. Yet 35% of those same CIOs also highlighted meeting compliance requirements as a top concern. You cannot prioritize one and ignore the other. Success with AI requires both.
Treating device management as a “basic IT task” misses the long-term opportunity. It’s not just operational cleanup, it’s readiness. The companies that get this in place now are the ones who’ll be able to scale AI without being pulled back by foundational instability.
Key takeaways for decision-makers
- Prioritize device management before scaling AI: Many mid-sized companies pour budget into AI while struggling to handle basic IT logistics. Leaders should address device provisioning, tracking, and retrieval to build the operational foundation AI requires.
- Address logistical risks in emerging markets: Managing hardware across geographies like Asia, Latin America, and Africa requires more than generic processes. Executives should invest in scalable, region-aware systems to streamline onboarding and reduce IT friction globally.
- Eliminate shadow IT to protect data and compliance: When companies fail to issue secure devices, employees turn to unapproved tools, exposing sensitive information and undermining compliance. Leadership must enforce device policies to maintain security and audit readiness.
- Fix onboarding and offboarding inefficiencies: Delays in asset delivery hurt productivity and increase attrition while poor offboarding raises the risk of asset loss and data exposure. Standardizing and automating these workflows can reduce cost and improve employee experience.
- Build IT discipline to unlock AI scalability: A fragmented device management process blocks long-term automation and introduces risk. Leaders should treat IT logistics as strategic infrastructure to ensure AI systems are secure, compliant, and ready to scale.
