While I am often amazed at the functionality provided by my smartphone, I’m also surprised at how dumb the device can be. Every night I turn my work phone to “do not disturb,” yet despite its vaunted usability, my iPhone isn’t smart enough to perform this fairly simple task automatically. Even new “intelligent” apps like Google Now lack fairly basic intelligence. Google Now is supposed to proactively alert you to delays in your usual commute, but wasn’t smart enough to realize I was on vacation recently and did not need a commute time from the Cayman Islands to a restaurant that Google somehow had concluded was my workplace.
Intelligence versus parochialism
Our smartphones have all the tools to act in a more intelligent manner. They know our location, intimate details about our lives ranging from who we communicate with to how active we are, and what our schedule looks like months in advance.
Contextual knowledge, intelligence about what activity we’re performing and where we’re performing it, has long been cited as the next innovation in smartphones. Our devices already have all the information to do seemingly intelligent things like pull up the PowerPoint for the meeting that we’re heading to, or informing our spouse that we’ll be home a few minutes later from work due to an accident or a long-running meeting.
Despite having this information available, one of the main obstacles to more intelligent smartphones is that our data is often segregated among multiple applications and the underlying operating system, each of which jealously guards its knowledge about us. Furthermore, most of these applications have been created by companies that are likely more concerned with leveraging our data to sell to other companies than providing a superior user experience.
The enterprise “customer” advantage
One of the main challenges to smarter mobile devices is determining who the customer of many mobile applications actually is. The preponderance of “free” consumer applications often harvest data about their users, selling those data to the “true” customers: advertisers and other companies that pay dearly for these data.
Enterprises generally don’t have these concerns and, in an interesting twist to the trend toward consumer-driven innovation, can actually deliver contextual intelligence to end users rather than selling it to marketers. For example, a warehouse application might act differently when a mobile device is out on the shop floor versus when it’s in the office and might be used for reporting rather than data gathering. A smartphone might sense a worker’s movements in a high-risk environment and, rather than “phoning home” to sell marketing data, might report movements that indicate the worker has fallen or passed out and needs help.
Furthermore, rather than being focused on gathering individual data, enterprises are likely to find aggregate data more helpful. In which buildings or sections of a plant are employees spending most of their time? Which routes do workers take to service a piece of equipment that’s out in the field? How long is it taking to service the average sales call? Gathering aggregate data can be likened to key performance metrics, and the effect of initiatives meant to improve those metrics can be quickly and accurately measured.
As with advertisers, it’s tempting to gather all manner of information from employee devices and have those devices track, scold, and monitor employees around the clock in what amounts to a creepy effort at best, and an unethical or illegal manner at worst. While courts have historically sided with employers on questions of monitoring use of employer-provided technology, these lines have grown blurry with increasingly personal data available from smart, mobile devices. Rather than immediately running to the lawyers to see what you can get away with, assemble a working group of employees who might be impacted by “smarter” devices and gauge their reactions to your plans.
While commercial interests have driven much of the location- and contextually-aware functionality of today’s mobile devices, enterprises might just be in a unique position to commercially exploit the functionality without exploiting the user generating the data.
Patrick Gray works for a global Fortune 500 consulting and IT services company and is the author of Breakthrough IT: Supercharging Organizational Value through Technology as well as the companion e-book The Breakthrough CIO's Companion. He has spent over a decade providing strategy consulting services to Fortune 500 and 1000 companies. Patrick can be reached at firstname.lastname@example.org, and you can follow his blog at www.itbswatch.com. All opinions are his and may not represent those of his employer.