By Jason Balser, Guest Author

Ask people what they think about AI, and they often sound as if they are choosing sides. This choice often centers on how much control they are comfortable giving to a small number of powerful technology companies. But capable AI can now run locally on our own devices, within our organizations, and in more controlled cloud environments, giving us more options than simply embracing AI or rejecting it.

Like most technologies, AI can produce both benefits and harm. Our job, as citizens and leaders, is to maximize the benefits while minimizing the risks and consequences. Local and more controlled AI environments can give us greater control over where our information goes and who has access to it.

The greatest adoption of AI will come when it stops being a destination. Instead of stopping what we are doing, going to an AI tool, and bringing the result back, AI will increasingly become part of the tools and workflows we already use.

That shift is becoming more practical. Newer phones and computers include hardware designed specifically for AI processing. Smaller models are becoming capable enough to handle useful tasks without relying on the largest cloud-based systems. Software companies are also building AI directly into familiar applications, making it less visible and easier to use.

Local AI may be especially useful when a task involves sensitive information, occurs frequently, or simply does not require the power (and cost) of the largest available model. In those situations, keeping the processing closer to the user may provide enough capability while offering greater privacy, control, and predictability.

Of course, locally installed software must still be secured, updated, and checked for unexpected data collection. Local AI reduces certain risks, but it does not eliminate the need for good security practices.

At the consumer level, newer phones and computers increasingly use a hybrid model. Some AI tasks can be processed directly on the device, while more complex requests may be handled in a more controlled cloud environment. I use dictation software that runs locally to clean up notes and voice memos. The raw audio never has to leave my device. It’s a small example, but it’s a low-stakes task that doesn’t need a large cloud model.

For enterprise and government use cases, cloud providers now offer options to run AI models inside a company’s own private environment, choosing which model handles the work while keeping the data inside the organization’s control, rather than sending it out to another system.

To get started yourself, identify one recurring task that you would like to run locally. Look for an existing local app that supports it or, for a larger business process, determine whether it could run on a company-controlled server or cloud environment. Remember, it does not have to be perfect right away. The objective is to start small and see what you can do. I believe much of the future of AI will be local. Deciding how to use AI will be less about picking a side and more about choosing the right type of AI for the task, balancing the need to get things done with security, capability, and cost.