Troops Archive

This post originally appeared on TechCrunch.

One of the first computers required punch cards. I repeat, punch cards. Yes, you would take a piece of paper with tiny holes and use it to interact with the device.

Now we have computers the size of soda cans that sit in your house and control your lights, provide weather updates, solve math equations and tell jokes, all by simply speaking to them… and some of them have better jokes than my actual friends.

In many ways, we all should have seen this coming — we can thank our Hollywood friends for that.

We had C-3PO and R2-D2 running around the galaxy with Luke trying to help him save the universe from his dad.

“Artoo says that the chances of survival are 725 to 1. Actually Artoo has been known to make mistakes… from time to time… Oh dear…”

More recently, we’ve had others as full-fledged assistants that are smarter than most humans, like TARS from Interstellar and Jarvis from Iron Man.

As you’re reading this, you’re probably doing some kind of work. It’s a thing we spend one-third of our lives doing, after all. (Sleep and Netflix supposedly make up the other two-thirds.)

Given the massive chunk of our lives spent at work, shouldn’t we enjoy the tools we need to use for our jobs? Shouldn’t they feel more human and delightful, like Amazon’s Alexa or some of the other consumer-facing applications we rely on daily?

I think so.

And how much more effective and productive could you be if you had something like TARS or Jarvis helping you with your job?

I think the answer is… a lot!

How do we get there?

Many of the consumer-facing AI solutions we see today are built on the backs of generic APIs.

Let’s take something like Siri, for example. If you wanted to know the weather, you would simply ask: “Siri, what’s the weather?”

Siri could then transcribe your question and reach out to weather.com or another weather service for the answer using your location as a proxy.

Based on the answer, you’d have the immediate information you need to determine whether you should take an umbrella to work or not.

However, introducing a similar, frictionless AI assistant in the enterprise is a bit more challenging. Things are a bit more complex because each organization uses varying degrees of tools and workflows to run their business.

Borrowing from the weather example above, let’s say you wanted to know how much revenue was booked for the business in the first quarter. You might ask: “Siri, how much revenue did we book in Q1?”

If this “Siri for work” existed, it might give you an answer along the lines of “$100mm.”

From here you might want to drill deeper into revenue generated from each product line. If you were the Chief Revenue Officer of Microsoft, you might want to know how that revenue breaks out between Office 365, Windows and Xbox… and you might want the answer to be in top-line revenue because that’s how you like looking at the forecast.

Shouldn’t we enjoy the tools we need to use for our jobs?
Do you see how nuanced this can become? As we start to account for organizational preferences, things get complicated very quickly.

It’s easy to see how replicating “Siri for work” is a much heavier problem to solve because of the variance amongst organizational processes, systems and preferences. For consumer applications, there isn’t nearly as much divergence in the answers users expect (see above); this does not hold true for businesses.

This same issue applies in the context of scheduling. There are companies like x.ai and Clara Labs trying to take the simplicity of Alexa or Siri and apply it to the tedious task of scheduling meetings.

It’s one thing to say: “Siri, book me a meeting with Jon for some time next week.”

But all of a sudden you realize there are a handful of non-trivial variables this “scheduling Siri” would need to take into account. Things like the location of the meeting, preferences of the person taking the meeting, the availability and coordination of both parties instead of just one and so on.

And let’s take one more vertical application similar to “Jarvis for Work.” Within the legal industry, an AI-powered lawyer called ROSS has emerged. Firms can ask ROSS questions like they would their colleagues on important data, like citation resources, and it returns an answer. Their secret sauce is based on using natural language processing (NLP) to query publicly available law documents.

But can ROSS adopt to the style of the firm and specificity of a given case? Maybe some firms have found that very recent court rulings tend to be the best support, while others rank searches based on credibility and prominence.

In all the instances, there is nuance, which means some level of unique configuration and intelligence is required. This should comfort those fearful of waking up one day and having their job completely replaced by a robot. More realistically, the robot will allow them to be 10x more productive and allocate more time to higher-leverage tasks.

We’ve seen this story before; each time we experience new technological breakthroughs, we learn that people’s jobs are changed but not altogether replaced.

From a 1928 issue of The New York Times:

Different, yet the same

In all these different instances, the end result and goal for a user remains the same.

A perfect “Siri for work” would help reduce complexity and guide the end user to more quickly arrive at the information they need to make a decision or take an action. In the enterprise, even slight improvements can mean huge revenue increases and significant cost savings.

But, let’s take it a step further and explore how this artificially intelligent assistant at work evolves and becomes more intelligent over time.

The previous example highlighted the ability to look up information. What about having the AI suggest and take actions for you?

As we start to account for organizational preferences, things get complicated very quickly.

Say the VP of Sales at Microsoft needs to forecast her revenue for the quarter. We’ll call her Samantha. To do that, Samantha would need to have accurate close dates of when she thinks her deals will close. In this hypothetical example, she has five deals that are supposed to close in one week, but the AI knows there has been no communication with those accounts for more than four months because it understands your email, social media and phone communications.

Is it likely those deals will close? Probably not.

Therefore, the AI would know to automatically change the close dates for forecasting purposes, or make a suggestion like, “Hey Samantha, I noticed a discrepancy between your sales activity and your proposed close dates. Would you like me to change the close date for you?”

Voilà. The dates are closed and Samantha doesn’t look like a slouch at the next forecast meeting.

It’s easy to see how facilitating this level of workflow is entirely too complex for an out-of-the-box plug-and-play solution like Amazon’s Echo or Apple’s Siri. It requires a greater degree of configuration that is specific to the organization and which becomes smarter over time based on user input and data.

To facilitate this there needs to be a middle layer or conversational run-time between the various systems and data sets in an organization so an end user can quickly and easily do their job without having to open a new app or piece of software.

As Satya Nadella, CEO of Microsoft puts it: “In software development terms A.I. is becoming a third ‘run time ’— the next platform.”

I couldn’t agree more.

Toward the future

So what does this all mean?

The next frontier of software development and technological breakthrough will happen in a conversational run-time. I call it “conversational CRM.” It is the inevitable evolution of the technology stack for the enterprise.

This next era will occur on top of conversational interfaces because it is where work is already getting done and everyone already knows how to use them. This is why we are building on messaging platforms like Slack, which will serve as the conduit to facilitate enhanced intelligence at work.

Moreover, there will be even more companies, big and small, that crop up to help power some of the underlying technology that makes this intelligence and conversational workflow happen.

For example, Google recently unveiled TensorFlow, which is an “open source software library for numerical computation using data flow graphs.” To break that down in English, this sort of technology enables computers to do computations that more closely mirror the way human brains think and make decisions. Some people call this “deep neural networks.”

There’s also IBM Watson, which provided the backbone for ROSS mentioned above.

Within the realm of smaller startups, you have companies like API.ai and Wit.ai, which was recently acquired by Facebook, that have built a simple natural language processing API that helps developers turn speech and text into actionable data. This sort of technology will help bring that “Siri-like” experience to many other applications and experiences.

So as computers continue to shrink, and eventually shift from robots the size of soda cans to no interface at all, the next area of innovation will live in the messaging context (voice, text, email). Interactions between humans and machines will occur in the same place, side by side, all working toward a common goal of driving businesses forward.

The lines will get blurry, and, just like the movies, we, too, will have our own R2-D2 or Jarvis at work — no matter where “work” may be.

There was once a vision to put a personal computer in every home. Many companies today have a similar vision, which is putting a personal AI assistant for work in everyone’s hands. Think of it as a “Jarvis for Work” of sorts, except Jarvis will have cousins that each specialize in their own, unique vertical.

It wasn’t too long ago that my partners and I found ourselves obsessing over an idea for a product that we’ve always wanted. It would help us be more effective at what we’ve been doing our entire lives: hustling –or in a more traditional sense, “selling.”

In the world of technology that support sales and account management teams, the most commonly used software is a broad category commonly known as Customer Relationship Management (CRM). If you’re not familiar, maybe you’ve heard salespeople at your company complain about having to use it.

It seemed crazy to us that despite the fact that there are now self-driving cars, the advancements in a category that pervades such a large portion of the workforce has largely not evolved in 30 years. We talk about the applications we use in our personal lives as “delightful,” but not the ones we use at work. There had to be a better way to arm organizations and sellers to succeed at their jobs.

1. It had to be mobile first.
2. It had to be intelligent and predictive
3. It had to be turnkey and work with the tools we were already using
4. It had to be easy to use and dare I say fun to use

In order to imagine the future, sometimes the best place to start is by looking at the past.

In 1977, Oracle launched with the goal of building a relational database. This database needed to be installed on-premise at a specific physical location, and could be managed using a bunch of fields, forms, buttons and boxes. And then Marc Benioff, founder of Salesforce, very astutely realized that this database should live in the cloud and not have to be installed on-site, so that people anywhere could access these same fields and forms whether they were in a hotel room or at the office.

As time went on, our computers became smaller and so software developers all over the world tried to pack a ton of functionality into a little screen by simply making those same field and forms smaller.

If you ask most people today who rely on this software to do their jobs, they’ll tell you the experience isn’t exactly enjoyable.

Why?

To date, enterprise software companies have been trying to get humans to adapt to software, when instead, the software should adapt to humans.

We think this is the wrong approach. Instead these companies should be striving to build solutions that mirror behaviors people already know and enjoy.

That brings us to our mission and what we’ve been quietly building at Troops. Today we’re excited to share more publicly what we’re up to.

At Troops, we fundamentally believe that the future of “getting things done” is going to look a lot less like static fields and forms, and more like a conversation with a human being… an intelligent one for that matter!

Furthermore, we believe that messaging, or conversation, will be the defacto UI for a growing number of software applications. The “bots”or human-like inhabitants of these interfaces will not only be highly responsive to real-time requests, seamlessly cutting out a myriad of clicks, but also be able to intelligently suggest actions based on my process and workflow across all the tools I already use.

Everyone already knows how to message. We believe so strongly in messaging and conversational interfaces because these are already behaviors we’re accustomed to in our everyday lives.

Six of the top ten apps in the world are messaging apps, and it’s no surprise that the apps we use for work are looking more and more like the ones we use in our personal lives. Ten years ago, would you expect cat giffy’s to be shared in the workplace? The consumerization of the enterprise has arrived and it’s here to stay.

Whether it’s Slack or Facebook and their new announcements at F8 to make messenger the place where businesses and customers interact, it’s clear to us that messaging is beginning to permeate the work environment in a very different way than we’ve seen before…and we think it’s the first inning of this trend.

As Tomasz Tunguz says from Redpoint Ventures:

“At work, we’re thirsty for data to guide and inform decisions and we bring with us similar expectations of technology’s ability to answer questions instantly. As chat becomes an increasingly important user interface in the workplace, there’s a massive opportunity for startups to enrich conversations with answers to questions that pop-up in Slack and elsewhere.”

We agree.

So back to Troops and what we are announcing today.

Troops is a Slackbot for sales teams. It makes it easy to use CRM data to do your job — no more trudging through Salesforce. Troops lets you instantly push and pull CRM data in and out of Slack, turning it into the sales hub for both you and your team.

Over time, this Slackbot will evolve and become smarter. It will live on all conversational platforms that are either controlled by text or voice. It will become your artificially intelligent assistant for work — completely agnostic to medium.

We’re starting with the roles and functions we know best: people who interface directly with customers — sales, business development and customer success teams.

We’ve been fortunate enough to work with many leading sales organizations and customer success teams. These teams are trailblazing their respective industries, and we are excited that Troops is playing a small part of their success.

For those of you who are excited about our mission of making it easier to be successful in the workplace, drop us a line. We’d love to hear from you. And if you’re interested in giving Troops a try, sign up for the beta at Troops.AI.

– Dan

PS. And some press on our launch here – 3 founders spent a year building a Slackbot that makes sales jobs easier, and they raised $2.6 million from a who’s who list of investors

This post originally appeared on Forbes.com.

I’ve been thinking about R2D2 a lot lately and it’s not because I’m a nerd or a big Star Wars fans. Although on some days I’m probably both. I’ve also been thinking about the other sci-fi movie robots out there like TARS from Interstellar, Samantha from HER, or even Arnold from The Terminator.

I think Jeff Bezos and Elon Musk have been thinking a lot about this, too.

Once upon a time artificially intelligent machines, talking robots and self driving cars were just some figments of imagination but now we have Siri, Amazon Echo and Tesla’s driverless cars.

Almost ready to release highway autosteer and parallel autopark software update

— Elon Musk (@elonmusk) July 31, 2015

And these are just the pre-school version of what is yet to come. You can be sure these technology juggernauts like Apple, Facebook, Tesla and Google are cooking up some next level, world changing solutions. I mean, Google just created an entire holding company called Alphabet to do precisely that.

Our society has finally crossed a threshold with technology, and there is a shift underway.

Invisible apps, zero UI, artificial intelligence, messaging-as-software. These are all phrases I’ve heard over the past few months that are meant to convey that shift which is: We’re at a moment in time where software can adapt to humans whereas before humans were the ones adapting to software.

Microsoft calls it “Productivity Future Vision.” I call it, “just getting stuff done.”

The software, computing power, hardware, interoperability of platforms, and APIs are all there for this shift to finally happen and for highly complex systems to be built and deployed at scale.

I saw a tweet the other day that said API’s were the unsung hero of technology. There is certainly some truth to that. They allow us to move beyond stand alone solutions to a world where solutions can work with other another.

And to date, those stand alone solutions, at least in terms of software have mostly been about drop down menus, check boxes, field and forms, and some mediocre UI and UX. It’s been about making screens look pretty. Making buttons click so that you can turn to yet another pretty page.

But the next 10 years will be about machine learning, artificial intelligence, natural language processing and technology that is contextually aware to human beings and their preferences. It will happen through interfaces that are intuitive and commonplace to human behavior.

Ben Evans says messaging is the software and not the other way around.

Old: all software expands until it includes messaging New: all messaging expands until it includes software

— Benedict Evans (@BenedictEvans) March 13, 2015

I agree.

I’m personally interested to see how this plays out in an enterprise environment and so I recently started a company with some amazing people to put these thoughts to paper and ideas to bits and code.

Someone once told me that we spend a third of our lives sleeping, a third working and a third watching TV. And it seems like an incredible time to try to make that “working third” a little better for everyone and to do so with entirely new engineering and software development concepts.

And the new company?

It’s called Troops.