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In the new film Ex Machina, a reclusive billionaire invents a robotic artificial intelligence. To test whether his invention is indistinguishable from a human being, he helicopters-in a young engineer to see if he falls in love with the robot.

Today, making machines and humans indistinguishable from each other is no longer science fiction, it’s good business. In fact, a wave of startups are part of a new trend that promises to radically simplify our lives by making it harder to determine whether we’re communicating with a person or computer code.

In my last post I discussed how I use some of these services and in this post, I’ll go deeper into what this trend is all about. I’ll look into how pairing new technologies with human assistants will result in tremendous new products, which promise to enhance our lives — that is, until the robots completely take over and destroy us all. *insert nervous laugh here.*

Messaging is the Medium

On a typical day, I’ll chat with colleagues on Slack. Later, I’m sure to receive a message from a friend on WhatsApp or Facebook Messenger. Then, on my way home, I’ll use good old SMS to let my wife know I’m on my way.

What’s been absent from these conversations is commerce. Although messaging is the way users communicate with each other, it’s not how they interact with businesses. That is, until now.

Of all the ways humans communicate, texting might be the most direct. Text carries less superfluous information than other ways of sending information. With text, there are no voice intonations to decipher or accents to understand, no facial gestures to interpret, and no body language to translate. Text is something computers can understand and process quickly and it’s why messaging is a great place for humans and A.I. to work together to serve customer needs.

Although terms like “conversational commerce” and “invisible apps” have floated around the web recently, neither is quite right for describing what I’m seeing.

Instead, I’ve proposed “assistant-as-app” to mean: an interface designed to enable users to accomplish complex tasks through a natural dialogue with an assistant.

Note that the assistant does not have to be a real person, at least not all the time. The assistant could be a human using automated script to send messages or reminders at appropriate times. It could also be a group of people interacting with customers through an online persona. Or, it could be an A.I. that occasionally calls in human help when it’s stuck.

For example, Mindy is there for me when I use Vida Health to track what I eat, Tim helps me book travel on Native, and Amy schedules my meetings through However, when I get a response from Mindy, Tim, or Amy, I have no idea if it was a human or machine that sent the message on their behalf. With an assistants-as-app, the distinction between who’s doing the messaging isn’t always clear — sometimes is a bot and sometimes it’s not.

Better Than Bots

The messaging interface used by assistant-as-app services is an effective way to help people get more out of the Internet. Since it looks like a chat, interactions feel familiar. As I mentioned in a previous post, people don’t want something truly new, they want the familiar done differently (see the California Roll Rule).

The power of the conversational interface is that it shields the end user from having to learn anything new. We already know how to chat, so making requests is easy. An assistant-as-app leverages well-trained humans to use complex technology behind the scenes. The assistant can process requests that would otherwise require several steps, time-consuming analysis, or pro tools the layperson is unlikely to have the knowledge or patience to use.

What is it Good For?

An assistant-as-app is suited for certain scenarios. Here are three cases where I expect assistant-as-app services to excel:

1. When There’s One Goal and Too Many Options

According to a recent survey by Carat, a global media agency, “41% of people feel overwhelmed by the wealth of choices on the web, making it hard for them to make purchase decisions.”

When booking a flight for example, customers have only one objective — to find the best deal. They don’t need so many dizzying options, they just need one, as long as it’s the right one. Before travelers started booking by themselves online, a good travel agent could help narrow down the choices. But today, we’re on our own.

When we stopped calling travel agents, we implicitly chose cheaper tickets over better service. However, with an assistants-as-app, the consumer no longer has to compromise.

With an app like Native, the traveler can text instructions to an assistant such as, “I have a workshop that starts at 9am and ends at 5pm in Midtown Manhattan. Book me the cheapest trip to and from San Francisco on the 29th. Red eye is ok.” The human Native assistant can then quickly cull the options using sophisticated tools and return the best two or three options.

It should be noted that an assistant-as-app is not ideal for situations when the user enjoys browsing or where the best option is subjective. For example, for many people, clothes shopping itself is part of the fun. Being told the best choice may not be all that helpful.

However, there are plenty of opportunities to use an assistant-as-app where the user has to weed through too many choices, particularly in enterprise applications. I envision a future where complex tasks, like running a marketing campaign to increase site traffic or launching a coupon offer, are executed by an assistant. Instead of asking the busy user to navigate a complicated web interface, an assistant-as-app will do the heavy lifting and offer up just a few of the best options.

2. When Data Collection is Easy but Analysis is Hard

An assistant-as-app is particularly good at off-loading the burden of analysis. For example, the Vida app leverages dietitians using backend tools to help diagnose food sensitivities and allergies. With Vida, the user simply has to take pictures of their food before each meal. Then the assistant compares what was eaten to how the user felt, looking for what may be causing the adverse reaction. This sort of analysis is a burden for the user but is easy for a skilled agent using pro-tools.

The assistant becomes even more powerful when she, he, or it, can access disparate sources of information. Processing data is a headache for users but is an opportunity for assistant-as-app companies.

For example, when I book my travel on Native, my assistant already knows the balance of my various frequent flyer programs and can suggest flying one airline over another so I can claim a free flight on my next trip. If a traveler grants access to his or her calendar, the assistant can book travel around meetings as well as account for car travel times to and from the airport. My message back from Native read, “I’ve found these two flights on United that will give you plenty of time to make your last meeting of the day. Which should I book for you?”

For enterprises swimming in data, an assistant-as-app could be a godsend. Imagine an assistant continually optimizing your website. Instead of hiring someone with these rare talents, specially trained assistants could use the latest tools to continually run tests to increase conversion rates. Interestingly, these sorts of enterprise tools don’t require much new technology or any A.I. All the testing and number crunching would get handled by a well-trained human while requests and updates would be handed through the messaging interface. The site owner would just need to provide access, approve the tests, and ok subsequent roll-outs of successful changes.

Imagine the collective sigh of relief from busy workers who no longer need to learn how to use yet another vendor’s complicated online tools or manage yet another dashboard. With an assistant-as-app, just ask and ye shall receive. 

In addition to complex interfaces, assistant-as-app services are particularly well-suited for small screens. Customers have enough difficulty poking around drop-down menus on mobile phones and doing so on a smart watch is impossible. However, reciting requests to an assistant-as-app in plain English is easy on web, phone, or watch.

3. When it Feels Like a Friend

Working with an assistant through a conversational interface should feel like interacting with a friend. These apps work best when the user trusts the assistant’s unbiased recommendations.

However, if a friend started chatting you up to make a buck, you’d quickly see through the scheme. Similarly, an assistant-as-app is best suited for subscription models where the value lies in being an objective filter. If Native started recommending specific hotels over others based on earning a commission or if Vida began hawking vitamins, I’d quickly lose trust.

Another friend-like characteristic of an assistant-as-app has to do with the pace of interactions. When sending a friend a text, you’d expect to wait a while before they respond. Likewise, assistant-as-apps are for when you need something soon but not immediately. Culling the right options, running tests, or analyzing data takes time and assistants are not well suited to provide instant feedback quite yet. 

Who Needs Humans?

But isn’t voice recognition powered by artificial intelligence enough? Not really and not yet.

Though Google and Apple are working on perfecting virtual assistants like Siri, made of 100% computer code, such fully automated technologies are only good for specific scenarios — namely, when the user needs immediate information for simple queries. In contrast, an assistant-as-app excels when a request takes several steps and is done better with a human touch.

Just last week I found myself desperately trying to talk to a human when calling my credit card company. After several failed attempts to get through the automated voice response, I called out my request in a mechanical, over-articulated, robot-sounding voice. “REP-REE-ZENT-A-TIV,” I said, sounding like Robby the Robot.

“OK, let me get someone to help you with that,” the automated voice finally responded despite several attempts, although I wasn’t sure if by “that” she meant my credit card problem or my weirdo-who-speaks-like-a-robot problem. Although talking in a robot voice is perhaps a humorous example, it makes the point that users still have to structure their requests in a way machines can understand.

Today, there are primarily three solutions. Either send users through fully automated prompts (like an annoying call routing robot), send them to human helpers, or — the most common and perhaps most burdensome option for the user — ask them to fend for themselves on the company’s website.

However, as A.I. becomes more capable, assistant-as-app services will provide a better alternative. As A.I improves, each human assistant will be able to serve more users. These services, leveraging highly adept humans working with increasingly sophisticated technology, will be the way we interact with an array of businesses in the years to come. If this new breed of start-ups is successful, we’ll all fall in love with our robots.

Here’s the Gist:

– An assistant-as-app is an interface designed to enable users to accomplish complex tasks through a natural dialogue with an assistant.

– Since they look like chats, conversational interfaces feel familiar (see the California Roll Rule).

– Expect to see assistant-as-apps expand throughout consumer and enterprise applications.

-An assistant-as-app works best in certain situations:

  1. When a user wants to accomplish a singular goal but has too many options.
  2. When a user does not enjoy browsing through the options.
  3. When data entry is easy but processing and analysis is hard.
  4. When the traditional screen interface is too complicated or small.
  5. When a trusted relationship helps.
  6. When a request does not have to be completed immediately.

What Do You Think?

Do you think the assistant-as-app trend will catch-on? Do you use any assistant-as-apps? What yet-untapped opportunities do you see for assistant-as-app in the years to come?

Nir’s Note: Since first writing this article, I have become an investor in Native. Also, thank you to Jonathan Libov for commenting on previous versions of this essay.

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