Most conversations about business AI start with software: which model to use, which platform to build on, which use case to tackle first. Fewer start with a more basic question: can the devices your teams already carry actually run this technology well?

That question matters more than it seems. AI features, whether built into a laptop, a phone, or a line-of-business app, place new demands on processing power, memory, storage, and network connectivity. A device that handles email and spreadsheets without issue can struggle the moment it is asked to run AI-assisted workflows at scale. And because AI often touches sensitive data, from customer records to internal documents, security has to be part of the readiness conversation from day one, not an afterthought once the technology is already deployed.

Before rolling out AI tools across a team or an organization, it helps to walk through a short checklist. Here is what we recommend teams look at first.

Do our devices have enough processing power for AI?

Many AI features, especially those that run directly on a device rather than in the cloud, depend on a dedicated processor built for that kind of work (often called a neural processing unit, or NPU). Older devices, even ones that otherwise still perform well, may lack this hardware entirely, which means AI features either run poorly or do not run at all.

Before rolling out new AI tools, take stock of the processors already in the field. A device inventory that flags chip generation and AI-specific hardware gives IT teams a clear, factual starting point instead of guesswork.

As AI-driven workflows expand, many organizations are considering higher processing power devices like iPhone^® and iPad^® because of their capabilities.

Do our devices have enough memory and storage?

AI workloads, particularly ones that run locally, tend to use more memory than the applications teams are used to. A device that has been running comfortably at eight gigabytes of RAM may need sixteen or more to handle AI features without slowing everything else down. Storage matters too: on-device AI models and their supporting files can take up meaningful space, especially on devices that are already close to capacity.

This is a good moment to check average device age and specifications across the fleet, rather than assuming today’s devices will simply be fine.

Where will AI processing actually happen?

Some AI features run entirely on the device. Others send data to the cloud for processing. Some use a hybrid of both. This distinction affects everything from performance to cost and data privacy, and it is worth understanding for any tool under consideration.

On-device processing generally means less data leaving the organization’s control and less dependence on network connectivity, but it also requires more capable hardware.

Cloud-based processing can run on lighter devices but raises questions about cost and where data travels and how it is handled once it leaves the building.

As many businesses that have embraced cloud-based AI have learned, the cost of tokens quickly adds up.

That’s why the concept of Private AI is quickly gaining traction because of its security and cost advantages.

No approach is automatically right or wrong. The point is to understand how data moves before adopting a solution.

Is our network ready to support these tools?

Cloud-connected AI features depend on reliable, adequately provisioned network access. Locations with older wireless infrastructure or inconsistent coverage may see AI tools perform unevenly, which can undermine confidence in the technology before it has a fair chance to prove itself. Reviewing network capacity, particularly in field locations, warehouses, or retail environments, is a practical step many organizations skip until problems surface.

What happens to our data when AI is involved?

AI tools often need access to organizational data to be useful, whether that is documents, customer information, or operational records. That access needs a clear policy behind it: what data a tool can see, where it is processed, how long it is retained, and who can review that activity.

This is also a moment to revisit device-level security. Encryption, access controls, and mobile device management should already be in place before AI is layered on top, not treated as a follow-up step. If your MDM policies were built before AI was part of the picture, they are worth a second look.

Are our devices protected against new categories of risk?

AI introduces a few risk categories worth planning for specifically. Devices should be able to detect and respond to unusual data access patterns, since AI tools can sometimes surface or move data in ways a traditional application would not. Endpoint protection, patch management, and device compliance monitoring all become more important, not less, once AI is part of daily workflows.

Can our support and lifecycle processes handle this?

AI-capable devices are often newer, more specialized, and in some cases more expensive to replace than the devices they succeed. That has implications for mobile lifecycle planning: how devices are deployed, maintained, repaired, and eventually retired. Teams that already have a defined device lifecycle process will have an easier time absorbing AI-capable hardware than teams building that process for the first time under pressure.

It is also worth asking whether your support model can handle AI-specific troubleshooting. New categories of tools tend to bring new categories of support requests.

Do we have a plan for training and change management?

Even well-suited hardware will not deliver much value if the people using it are not confident with the new tools. A short rollout plan, paired with realistic expectations about what AI can and cannot do, tends to matter as much as the technology itself.

Where this leaves you

None of these questions require a perfect answer before getting started. Most organizations will find gaps somewhere on this list, and that is normal. What matters is knowing where those gaps are before AI tools are deployed at scale, rather than discovering them afterward through slow performance, security concerns, or frustrated teams.

A device and network assessment is often the fastest way to get real answers instead of assumptions. If you are weighing an AI rollout and want a clearer picture of where your fleet stands, our team can help you work through it.

Ready to see where your devices stand? Connect with a Stratix expert to talk through a device and technology assessment.

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