Amazon's Alexa and Google Assistant killed automation but saved the smart home

Tyler Lizenby/CNET

When the Nest Learning Thermostat burst into the smart home market nearly a decade ago, it seemed poised to change how we saw smart home tech: Lindsey Turrentine, CNET's editor-in-chief at the time, decided to buy the $250 (!) thermostat within a week of trying it out. The sell? Nest would learn over time to set itself according to your comfort and budget. It was the promise of automation realized -- a home that truly takes care of itself, and you.

But a few years later, another device called the Amazon Echo strolled onto the scene. At first the smart speaker couldn't do much, other than house the digital assistant Alexa. Even its developers didn't fully grasp Alexa's potential at the time of its release, Amazon's VP of Software for Devices and Services Robert Williams recently told me. Yet by 2020, voice control has become almost a prerequisite for smart home gadgets, and Alexa boasts integration with over 100,000 devices.

These two approaches to the smart home space are necessarily in conflict: automation aims to cut out your daily around-the-house tasks, while voice assistance changes the apparatus for accomplishing them, instantiates your active role in them. Alexa and Google Assistant are winning this battle, and for good reason: they offer a more inspiring vision of the future.

The promise (and problems) with automation

I like automation in theory: a house that does everything you'd expect, but cuts out many of the small touch points that take up your time. It sounds like a no-brainer. I hate running through rooms to switch off all the lights while my 2- and 3-year-olds whine at the front door because they want to get out of the house. I hate coming back to a cold home when I've turned down the thermostat for a weekend out of town. I hate bumbling around a dark house to get my sons their sippy cups in the middle of the night.

Sensors can help automate smart lights, but is that as useful inside as out?

Chris Monroe/CNET

While a handful of devices help mitigate these particular pain points -- most directly, the Nest smoke detector's motion-sensing night light and the Thermostat's learning features -- true automation that goes beyond those problems seems far off. I've tested motion-sensing smart lights and Bluetooth-enabled smart plugs, but home life is often too fluid for devices like these. More importantly, someone still needs to do the automating.

When we buy our phones and laptops, we expect them to already be programmed for us. Yet if we want automated homes, we have to do much of the "programming" ourselves. Nest's Thermostat stood apart from the crowd by "learning" from how you used it, but the majority of automation on the market today requires you to take the time to set up your own routines: "When I open my garage, turn on the lights and open the blinds," or, "At 11 p.m., turn off all lights and lock all doors."

These routines really are helpful, but setting them up and keeping track of them is a pain. Plus, exceptions to the rule always turn up, and getting locked out because you took the garbage to the street late one night isn't fun (yes, I've been locked out by smart locks before, along with half my office mates). And those annoyances don't even take into account the frustrating idiosyncrasies native to many of the platforms for setting such routines. (Works with Google Assistant, for instance, doesn't even let you delete routines.)

Most fundamentally, though, home automation faces a problem of philosophy. Automation looks to all the things we've done in our homes in the past -- opening shades, toggling lights, locking the door -- then asks how to make those things effortless. Alexa and Google Assistant have always been less concerned with what we already do than with what we could be doing. It's a more forward-looking approach, and that resonates more with people chasing the future.

Automation is cooler when you have dozens of shades and lights all integrated. But maintaining that setup can also become a full-time job.

Tyler Lizenby/CNET Welcome home, Alexa

At a private happy hour at CES 2020, while Guy Fieri prepped to come out, tell some groaners and cook with Alexa, I got the chance to chat with Amazon developers. There, VP of Smart Home Daniel Rausch told me they wanted Alexa to become a utility in the home, no different from electricity or water -- available in nearly every room, optimized for convenience and accessibility.

It sounds like a lofty goal, but Amazon has been pursuing it aggressively. Between its growing line of speakers and displays -- plus supercheap, modular devices like the Echo Flex and Echo Input -- Alexa could feasibly find its way into every room of your house soon. And if it controls your kitchen appliances, your washer and dryer, your TV, your music and your alarms, then it could actually be useful in each of those rooms, too.

Google Nest seems to have a similar approach, particularly by investing in devices like the thermostat, security cameras, doorbells and smoke detectors -- now all integrated with their Works with Google Assistant program, and slightly less focused on the "learning" aspects of their technology than the voice-integrated aspects.

By prioritizing ubiquity above all else for the moment, Amazon and Google have drowned out older automation-centric devices. But in the same move, paradoxically, they've breathed new life into home automation as an idea.

Salvaging the promise of automation

Alexa Guard was a simple software upgrade, but when it hit every nearly Echo device in 2018, it also provided a crucial insight into the future of the voice assistant in the home: The feature simultaneously listened for break-ins, automated connected lights to simulate an occupied house and communicated with security systems such as ADT. It wasn't a feature any great number of customers were begging for, in part, because few of us had imagined such a thing.

Smart speakers can do more every day, from telling time to monitoring your house for break-ins. What began as a gimmicky gadget is now positioning itself at the center of the smart home.

Tyler Lizenby/CNET

Automation has the potential to be banal (switching on lights when you enter a room) or to be visionary (attempting to diminish the chance of break-ins and improve their outcomes). The same imagination that produced a voice assistant, which felt like science fiction a decade ago, is starting to infect how companies think about automation.

The kitchen space offers plenty of examples: a new breed of smart kitchen appliances, for instance, is working to automate multiple steps of cooking -- comparing the contents of your fridge with online recipes to select ones you can cook, preheating the oven automatically once you start prepping, or even using cameras to tell you when your meat is fully cooked.

These more recent examples of automation give me hope: that as our homes become more intraconnected, and more connected to us, developers in all areas -- not just voice assistance -- keep their eyes forward, less concerned with optimizing how we live than redefining it.

How New Human-Machine Collaborations Could Make Government Organizations More Efficient

Most of today’s jobs will not be here tomorrow. The World Economic Forum predicts that 65 percent of children entering primary school today will ultimately end up working in completely new job types that don’t exist today.

This represents an opportunity for government organizations and employees to intentionally redesign work and jobs to not only accommodate the role of technology and machines, but also to design for broader economic, workforce, and societal shifts.

For example, a government HR manager who now only hires full-time employees may need to start tapping into a pool of crowd workers or gig workers for certain types of work. A procurement department may now need talent with blockchain expertise to manage secure supply chains. Or given the increased use of algorithms in government systems, agencies now need to prevent algorithmic bias from creeping into public programs.

In a recent Deloitte survey of more than 11,000 business leaders, 61 percent of respondents said they were actively redesigning jobs around artificial intelligence (AI), robotics, and new business models.

The prevalence of automation, as well as machines working alongside humans, is increasing in government too. According to the US Office of Personnel Management, almost one-half of government agencies’ workloads could be automated and close to two-thirds of federal employees could see their workloads reduced by as much as 30 percent.

That makes it an imperative to focus not only on how humans and machines can best collaborate at work, but also how that collaboration can enable better work processes and create more value.

Although there are many ways in which humans and machines can work together, we typically identify the human as the supervisor or the primary worker. This view can be limiting. Machines work for us, with us, and sometimes they even help guide us.

To harness the real potential of the human-machine partnership in the workplace, we should consider the full spectrum of possibilities. Machines are already taking on a wide range of routine, manual tasks. When this lower-value, tedious work is automated, opportunities are created to reduce cost and redeploy staff to more valuable activities. For instance, the Food and Drug Administration’s Center for Drug Evaluation and Research (CDER) uses robotic process automation (RPA) in its drug application intake process. This slashed application processing time by 93 percent, eliminated 5,200 hours of manual labor, and saved $500,000 annually.

Automation also allows jobs to be split up between humans and machines. When a job is broken into steps or pieces, automating as many as possible, humans are left to do the rest and, when needed, supervise the automated work. U.S. Citizenship and Immigration Services uses chatbots to answer basic questions. This frees up time for employees to respond to more complicated inquiries.

There are many lenses to use when thinking about these partnerships:

Shepherd: A human manages a group of machines, amplifying their productivity. In this instance, a human might manage a fleet of autonomous buses. Or a nurse manager could oversee a group of hospital robots.

Extend: A machine augments human work, combines their strengths to achieve faster and better results, often doing what humans simply couldn’t do before. For example: A department of human services could use cognitive technology to help predict which child welfare cases are likely to lead to child fatalities. Once high-risk cases get flagged, they are carefully reviewed and the results are shared with frontline staff, who then choose remedies designed to lower risk and improve outcomes.

Guide: A machine prompts a human to help them adopt knowledge. Machines help humans learn new knowledge and skills; or adopt desirable attitudes and behaviors. For instance, a researcher can set up a custom digital assistant that not only knows what current research a person is doing but can also crawl the web for old and new research relevant to the topic that the researcher might not be aware of.

Redesigning the WorkWork redesign is fundamentally about making sure that government agencies—their work, workforces, and workplaces—keep pace with shifting opportunities and needs and prepared for the future. But architecting jobs can feel overwhelming without a clear idea of where to start. We recommend these three steps:

Step One: What Will the Future Look Like for Your Organization?The first step is to think long-term. Imagine what the future could look like, determine how this could impact your organization, and plot out the course. There are a host of external forces—technological advancement, automation, changing customer demands and behaviors, or the rise of new business models—that could impact how your organization will deliver on its mission in the future.Imagine you’re looking out to 2030. People are living longer and staying in the workforce longer. The composition of the workforce has changed too. Digital natives have joined the workforce, as well as more freelancers and contingent workers. Technology is omnipresent and AI, augmented reality, the Internet of Things (IoT), and robotics are integral parts of the workplace. Organizations may have new analytical capabilities, thanks to unprecedented volumes of data and increased computing power.

Think about what all these factors and the changes could mean for your organization. By zooming out, you will explore beyond what technology and other disrupters can allow you to improve on what you’re already doing. And envision how these changes could enable you to unlock entirely new outcomes.

Step Two: What Work Should Be Done Differently?Given the vision you’ve imagined for your organization and the role disrupters can play, think about the current state of work across the organization. Ask: “What might we do differently across different jobs and work units to achieve greater impact and desired outcomes in the future?”

The process of deconstructing (and then reconstructing) work, and then defining the new roles that can support the new disciplines, can be broken down into four focused pieces with a simple framework: start, stop, change, and continue (S2C2). Some work will be catalyzed by technology and other disrupters and start anew. Meanwhile, some dull, dirty, or dangerous work may be automated or stopped entirely. Some work will be still critical to mission and business outcomes but will be changed by the application of technology. And, lastly, some work will continue relatively unchanged.

For example, a government agency might start hiring gig workers for certain skills such as data science. A transportation department might stop installing and maintaining traffic lights when autonomous vehicles become the norm. Given the dangerous nature of their work, firefighters might change how they extinguish fires and use drones and robots instead to assist them. Social workers will continue to visit their clients in their homes and build a personal connection.

The S2C2 lens can be applied to different levels of an organization—to a single role, across stages of a program, or at a high level within an organization’s department. Agency leaders can use this information to gauge the downstream impacts on jobs. Starting new activities might require leaders to create new roles while changing and stopping certain activities might require reskilling and redirecting talent.

In the future, the “matching” of evolving skills to evolving work within the context of a redesigned job should be very intentional. This can help ensure that you build a job that is a logical, holistic combination for a single person to have, rather than a somewhat haphazard mix as it often is today.

Think of it like this: What if by 2030, your current role no longer existed? How would the work get done? How would you rethink doing the work?

Step Three: Who Should Do the Work?While reconstructing work, asking the question, “Who should do the work?” can help organizations explore new talent options such as crowd workers, gig workers, or digital labor. Depending on factors such as how specialized the task is, or whether the desired capability requires a security clearance, some talent options might be more suitable than others.

To engage outside perspectives, leaders may want to develop or use a new crowdsourcing platform. Working with digital labor or AI may require leaders to select the most appropriate form of human-machine collaboration to answer the previously asked question, “Should an AI technology augment the human worker or relieve them?” All these considerations feed into work redesign.

Looking to The FutureBy reconstructing work, government organizations can not only capture efficiency gains through human-machine collaboration; more importantly, they can find new avenues to create value that may not have been possible before.

Future work scenarios don’t simply feature the human as supervisor of the machine. Instead, they consider the full spectrum of possibilities for human-machine pairings. And as humans and intelligent machines working together becomes the norm in the workplace, organizations have an opportunity to maximize the potential of both. This effort, when realized, can fundamentally help create better work processes for everyone and more value to taxpayers.

Read the full article here for a step-by-step guide to optimizing human-machine collaboration.

How voice assistants killed automation and saved the smart home

Tyler Lizenby/CNET

The promise (and problems) with automation

Sensors can help automate smart lights, but is that as useful inside as out?

Chris Monroe/CNET

Automation is cooler when you have dozens of shades and lights all integrated. But maintaining that setup can also become a full-time job.

Tyler Lizenby/CNET Welcome home, Alexa

Salvaging the promise of automation

Smart speakers can do more every day, from telling time to monitoring your house for break-ins. What began as a gimmicky gadget is now positioning itself at the center of the smart home.

Tyler Lizenby/CNET

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