How AI on Apple Silicon will help the enterprise

Machine learning will transform every part of the Apple experience in the coming years.

Apple, iOS, Mac, Apple Silicon, AI, industry 4.0, AI, Machine Learning, CoreML

Apple’s senior vice president for AI and Machine Learning, John Giannandrea, has said he thinks machine learning will transform every part of the Apple experience in the coming years. How might this impact your business?

What about on-device Grammarly?

Grammarly is invaluable, but the one thing I don’t like about it (and a good reason for enterprises to forbid its use) is suspicion concerning what happens to what is written as it passes through the system.

In business, privacy is essential – particularly in heavily regulated industries. Imagine an on-device grammar checker that keeps what you write tight without your work ever leaving your device, unless you choose to share it.

That’s the kind of AI-based service Apple could provide – an enhanced version of what it offers already, but one that's private and secure enough for business use.

Effective, relevant on-device grammar checking could become invaluable – though it needs to be more user configurable than autocorrect.

How about augmenting workflow tasks?

Of course, once you think about one augmentation solution, it becomes easier to consider others.

Think about the repetitive tasks that form part of your workflow. You may have already automated elements of these (I like text snippets and text replacement, for example), but the promise of on-device intelligence points to more effective articulations in which your device becomes your productivity boosting "digital twin."

I am convinced Apple is already moving in this direction: Siri recommendations are the poster child for this, but imagine if this predictive intelligence were applied across other business processes. Imagine a Shortcuts app automatically curated to provide the relevant productivity skills used within your particular enterprise?

Help for business process management

The focus so far has been on empowering individuals to focus less on repetitive tasks in favor of more complex challenges. That’s great in terms of individual accomplishment, but most enterprises rely on teams.

How can on-device machine intelligence boost team workflows and improve process management?

One model that may work: Apple’s focus on privacy means no individual is marked out, but business process data can be shared within a group in an anonymized form.

The AI could, for example, follow the path of decision trees, analyze the time taken between communication and resolution and work to identify bottle necks in business processes that might not otherwise be visible.

This would inevitably support useful things such as automated shared appointments and business target calendars and system-generated automations to help speed certain tasks. 

And the simple stuff

What if your computer populated your appointments book for you (subject to your chosen preferences)? What if your Mac and iPhone worked together to dig out and follow-up on project delivery deadlines and SLAs? The data detectors you already use in emails, Messages and elsewhere on Apple’s platforms show that your technology is quite capable of picking up such information, but to what extent can increasing automation of such tasks boost your enterprise?

Further out, why wouldn’t on-device AI become capable of monitoring its own condition, warning you when your Mac, iPhone, iPad or third-party connected equipment were about to fail – and letting you know why?

System level monitoring of third-party connected equipment potentially positions Apple's machine learning systems running on Apple Silicon at the center of Industry 4.0 infrastructure.

I see it this way:

Those iPads on the factory floor will monitor production machinery to identify the likelihood of service, automatically rerouting manufacturing capacity and flow to pre-emptively mitigate potential equipment failure. In theory, the first time a factory manager might know their machine is about to break down could be when a service technician arrives to fix a fault that hasn’t emerged yet.

What about platform integration?

Staying with the smart manufacturing example, here’s a sequence that shows how on-device machine intelligence could support smart machinery:

  • The iPad in Section 7 of the manufacturing facility has alerted of a likely fault in an essential riveting machine.
  • Working with automated management systems and human overseers, it has rerouted work scheduled for that machine for the next 48 hours and summoned a service technician with a notification to their Apple Watch.
  • On arrival at the section, the tech dons his or her AR glasses, which guide them across the factory floor to the machine, providing analytic data and making field service manuals available.
  • Components can be automatically requested, and accurate delivery and repair outcome data made available in real time as the fix takes place.
  • This information automatically feeds into the company’s systems, enabling other sections of the company – and its clients/partners – to make accurate promises for delivery.
  • Transparency and fast resolution leads to much improved client liaison.

Such tools are already in use across manufacturing – but Apple’s move to field machine intelligence across its platforms means iPhones, iPads and Macs will inherently share the processing and OS powers they need to take their place at the heart of this AI-augmented future. Siri’s not a patch on what’s to come.

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Copyright © 2020 IDG Communications, Inc.

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