By Eva Feng

As Apple Intelligence rolls out writing tools, photo editing features and a supercharged Siri, the most significant aspect of the company’s new “personal intelligence system” is running behind the scenes. Now, without actually using the term, Apple has delivered the most consumer-friendly implementation of edge AI we’ve seen so far. And it’s a great model for any enterprise to consider for its edge AI strategy.

Eva Feng, VP of product management at Zededa

The simple explanation is that each iPhone, iPad and Mac runs an AI model locally. For more intensive AI tasks, it enters a mode Apple calls “Private Cloud Compute,” which sends only the relevant data to a secure cloud environment for processing and then returns the results to the device.

Apple’s approach is a textbook example of edge computing. By processing data as close to the source as possible (on the device itself) and only reaching out to the cloud when necessary, Apple has provided the most high-profile example of the power and flexibility of edge computing architecture. And it’s particularly useful for data-intensive AI use cases.

Private cloud compute: A model for edge AI

While the on-device AI processing is technologically impressive (and aligns well with Apple’s emphasis on keeping its users’ data private), the real example enterprises looking to scale their AI efforts should follow is the Private Cloud Compute system.

Certainly, the on-device AI model is noteworthy, and the models themselves will continue to evolve. But the real example enterprises looking to extend AI to the edge should follow is the relationship between the on-device processing with the Private Cloud Compute system. There’s a reason that private is the first word in its name — privacy and security at the edge are among the most important and difficult problems for distributed companies to solve. This is why our company builds edge orchestration and management solutions with security as the starting point.

It’s worth emphasizing how Private Cloud Compute echoes some key edge computing principles and what enterprises can learn from this approach.

1. Process data close to the source: This reduces the delay between data collection and action. Teams can make decisions confidently, equipped with the latest data, and significantly improve response times.

2. Extend into the cloud when necessary: Sometimes the computational power needed to complete a task can’t be done locally. Particularly with AI, many requests will need help from the large, complex model available in the cloud.

3. Minimize data transfer: Send only the data needed to fulfill the user’s request, then delete it when you’re done. This keeps valuable information from being collected unnecessarily.

4. Put privacy at the heart of everything: When end-to-end encryption isn’t available, companies often have to create their own solutions to ensure that data remains safe.

We’re seeing a dramatic acceleration of edge computing and AI rollouts as organizations realize that any company with distributed operations will need those two technologies to work together. Edge computing has become essential across major industries — from automotive manufacturers optimizing their production lines to shipping companies orchestrating complex logistics networks. Edge technology is equally vital for utilities and energy providers as they modernize their grids and integrate renewable energy sources into their operations.

With that in mind, I’m excited that Apple’s innovations with Apple Intelligence and Private Cloud Compute are not just advancements in consumer technology — they represent a paradigm shift in how we approach AI and data processing at scale. By bringing edge computing principles to the forefront, Apple is paving the way for both consumers and enterprises to reimagine their AI strategies.

The future of AI isn’t just about powerful algorithms — it’s about creating secure, efficient and privacy-preserving systems that can scale to meet the demands of modern life and business.

As Apple is demonstrating, the future is a hybrid model that takes advantage of the particular strengths of both cloud and edge without compromising security.

Eva Feng, a seasoned product management executive, is currently vice president of product management at Zededa. Previously, she held leadership roles at Twilio, Amazon Web Services, ServiceNow, HPE and several startups. She drives product innovation and expands Zededa’s market presence, including entering new segments and industries and addressing customer use cases.

Illustration: Dom Guzman