Author: Jessica Munday

May 08, 2019 by Jessica Munday

Dreamforce first kicked off in 2003 with only 1,500 attendees. And in 15 years, it’s become one of the largest sales conferences globally with more than 2,500 breakout sessions, 170,000 registered attendees (as of last year) and big-name performances and keynotes from the likes of U2, Red Hot Chili Peppers, Stevie Wonder, Colin Powell, Bill Clinton and Melinda Gates (to name just a few).

Dreamforce is basically the Disneyland of conferences. Except instead of singing mice and princesses, we got Metallica and Janet Jackson (which is way cooler if you ask me).

As you’re walking through the various sessions you almost forget that you’re in the middle of San Francisco. Every aspect of Dreamforce is completely immersive and engaging. From the campground-themed Customer Success Expo to the natural forest noises in walkways, to the dozens of waterfalls found in and outside the Moscone Centers.

But beyond the decorations, professionals of all kinds were dedicated to improving themselves, their processes, and driving growth for their teams.

As a rookie attendee, I’ll share my biggest takeaways about what I learned, what I liked and what resonated with me.

What I learned

AI is on track to make sales more human, not take away jobs.

I attended a number of sessions around artificial intelligence (obviously, I work for an AI company) and many speakers shared their research and solutions about how AI is transforming sales and other functions across several industries and predictions for the future.

Finance, for example, is using AI to help personalize their cross- and upsell recommendations for customers based on behavioral analytics. AI is also helping banks streamline their lending applications to speed up their sales cycles.

We also learned that data quality is a bigger nightmare than many companies realize, during a session called “Automate your data quality process” by Validity. They say about 50% of an information worker’s time is spent fighting their data, and for sales reps, this often comes in the form of duplicates. On average it costs $1 to prevent a duplicate, $10 to correct a duplicate and $100 to store a duplicate if left untreated. By automating the data collection process, companies give sales reps back their time and drastically improve their bottom line.

Hands down, I thought the best session was this one: “How to survive and thrive in the age of AI,” hosted by management consulting firm McKinsey. The speakers reviewed current uses for sales AI, its impact on organizations and its potential to change sales processes for the better.

A surprising statistic from McKinsey: currently about 40% of sales tasks can be automated, but by 2020, 85% of sales tasks could be automated.

While many sales reps might be shocked and fear for their jobs, McKinsey actually says AI will be a huge help to both individual sales reps and companies without costing anyone their job.

They explained that sales AI tools are great at a certain set of tasks, and may even do these better than humans. These are predictable, repetitive tasks like data collection and processing. But humans will remain superior in sales tasks that require human emotion and the ability to connect.

AI can take on tasks like data entry, billing, appointment booking, analytics and even cold calls (aka all tasks that sales reps hate performing), but human reps excel in creativity, leading and coaching others, and being social and emotional (the tasks that they love doing). Basically, sales AI will make sales more human.

So rather than replacing sales reps, sales AI tools will actually help reps get more done. They’ll spend less time on busy work and more time making connections and selling. This productivity benefits the sales reps as they are more likely to meet quota and enjoy their work and it benefits the company by increasing the output with minimal cost.

Enterprise companies can see drastic gains in revenue, so even just increasing productivity by five percent could boost business growth by thousands of dollars, if not millions.

Of course, these are not new learnings for Automation Hero, as automating mundane work inside companies is the whole purpose of our end-to-end automation platform. Still, it was great to hear those statements validated by such an established firm like McKinsey.

What I liked

There was a specific area designated for attendees to unwind and escape the chaos of the conference.

As I mentioned earlier, Dreamforce is like the Disneyland of sales conferences, all the decor was elaborate and matched Salesforce’s trailblazer (camping/outdoorsy) theme.

One of my favorite areas was the Dreamforest. It offered an escape from the city bustle and a nice break from the back-to-back sessions and booth chaos sales conferences are known for.

The Dreamforest was an outdoor area set up much like a picnic area between the two Moscone Centers. The entrance looked like the base of a hollowed out tree stump, and as you passed through you heard what appeared to be the sounds of a peaceful forest: bird chirps, frog ribbits and other animal noises, which all helped set the scene.

It was adorned with fake turf grass, a huge artificial waterfall, a music stage with picnic chairs and benches surrounding it. It was extremely relaxing to sit and listen to the “Sounds of Ohana” band as I ate my lunch and just for a moment forgot that I was in one of the biggest cities in the country. I love that they set up this area specifically for attendees to unwind from their hectic conference schedules.

What I took away

Personal empowerment and well-being shouldn’t fall by the wayside.

There was a theme for each day of Dreamforce: trust, innovation, equality and personal empowerment. I appreciated that Salesforce designated the last day specifically for personal growth. It’s common for startup employees, business leaders, entrepreneurs and virtually anyone else in a stressful work role to often overwork themselves and experience burnout, which can be unhealthy.

Salesforce encourages attendees to spend this day not worrying about their company or customers but instead to focus on themselves. It was a day filled with mindfulness practices and centered on reflection and self-improvement.

One of the keynotes was given by Arianna Huffington (you know, the CEO of The Huffington Post) and she shared her story of passing out due to exhaustion. She debunked the myth many people believe that in order to be successful you need to sacrifice your own well being. She said that by giving herself time to refuel she can jump back into a project refreshed without feeling burnout.

It showed that Salesforce truly values more than just the bottom line. It was a warm, meaningful message to end a tiring week and a much-needed reminder not to put your well being on the back burner.

Tips for Dreamforce 2019

As a rookie Dreamforce attendee, I know I could have used a few more tips ahead of the conference. So whether you’re a seasoned vet or you’re looking into attending next year, here are some tips I that could have helped me.

1. Wear comfortable shoes

Dress for comfort, especially when it comes to your shoes. They estimate that people walk five miles each day of Dreamforce. And after making my way to all the sessions across downtown San Francisco, I believe it. Take care of yourself and your feet!

2. Take a look at the map and grab an info flyer

Even as an SF local, I found it hard to make my way around. Sure, the Moscone Centers are easy to find but there are several other locations for sessions across the city and multiple floors of each building. Be sure to get an information flyer and take a look at the map before departing for your sessions. And don’t be afraid to ask for help! There are dozens of workers there to help guide you in the right direction.

3. Plan out and register for your sessions ahead of time

I made the mistake of going to many sessions that I hadn’t previously registered for and the result was maximum capacity rooms that I couldn’t get into. However, people who registered for the sessions and showed up on time skipped the line and their seat was reserved. Don’t miss out on your important sessions by registering before the conference.

4. Schedule break time for yourself

The sessions at Dreamforce start around 8 a.m. and can end as late as 6 p.m. Make sure you schedule some resting time in between those sessions. Dreamforce has lots of lounge areas for people to take their lunch and get off their feet. Set aside some time to recharge

5. Bring a bag for all your goodies

As with any sales conference, there are always tons of sponsors giving away freebies. Bring a backpack or a purse to carry all your goodies in or else you could end up carrying three water bottles, fifteen pens and seven different stress balls in your pockets.

May 08, 2019 by Jessica Munday

One of the biggest lies business leaders tells themselves is that their business processes“aren’t that bad.” And certainly not bad enough to introduce enterprise automation. Let me stop you there: Yes, they are.

You may not realize it, but the processes in place in your company are massively inefficient, expensive and frankly, make your employees want to pull their hair out.

Let’s take your legacy systems as one example. What’s inside that system? Research shows that 41% of IT and business users say data is “trapped” in legacy systems. If data is needed in multiple systems within your business process, and the legacy system is unable to sync with those external tools directly, your employee must manually port that data from one system to another.

The average employee spends between 4 to 10 hours a week on repetitive computer tasks (up to 350 hours per year). Often these processes aren’t even directly related to their primary job function.

These inefficient processes exist at every level of an organization, costing your business money and wasting several hours of each worker’s day. It’s time for management to face the facts; your business processes are inefficient and enterprise automation is the solution.

How bad are my ineffective processes?

Inefficient processes cost organizations up to 20-30% of their annual revenue, according to IDC research. Here are a few examples of processes costing your company:

• IT departments spend 30% of their time on basic low-level tasks.
• 50% of companies spend $5-25 per manually processed invoice.
• 64% of sales reps’ time is spent on non-revenue generating activities, with 25% being administrative work.

Regardless of industry or job function, there are ineffective processes in your organization preventing your employees from completing valuable work. And as your employees waste time on these tasks, your company is wasting money as they perform them.

Solutions for enterprise automation

When it comes to enterprise automation, there are actually a few options for enterprises depending on your needs and level of inefficiency.

If you’re looking for a reduction of repetitive, rule-based and high-volume work tasks that specifically deal with web interfaces, look into robotic process automation (RPA).

Our specialty at Automation Hero is the next generation of RPA: intelligent process automation. Intelligent process automation is the powerful combination of artificial intelligence, RPA and mass amounts of data to automate complex tasks and perform more adaptable workflows.

The key difference between intelligent process automation and traditional RPA is that RPA is unadaptable. RPA performs one action repeatedly without considering nuances or exceptions. For example, if you asked an RPA system to sort red and blue balls, it wouldn’t be able to react in the case of a yellow ball. RPA also can’t learn, so every process must be programmed and changed manually by a developer.

However, when you add AI to RPA, the possibilities for optimization are endless. Intelligent process automation systems can learn, making them flexible in the face of complex processes.  It’s the first technology intelligent enough to handle tedious, yet complicated human processes.

For example, it would recognize that this ball is unlike the others and classify it separately on its own, or alert a human that there is a third category of balls.

According to research by KPMG, implementing intelligent process automation results in cost savings of 40-75% depending on the company with payback ranging from several months to several years.

It’s estimated that about half of automation opportunities are being missed. By finding these automation opportunities, you can cut the time your employees spend on busy work in half. Bringing on intelligent process automation benefits both your company when it comes to cost savings and your employees by automating the tasks they hate performing.

May 08, 2019 by Jessica Munday

The movies are a magical place where the wildest of fantasies can come to life. You can follow the journey of knights and princesses, robots fighting intergalactic battles and even see what it was like if we could bring dinosaurs back to life. But what about movies that depict artificial intelligence (AI)?

Few people really know much about AI except for what they see in the movies. The average person doesn’t understand the difference between deep learning and machine learning, but they could tell you that the Terminator was evil and stemmed from famous AI monsters that have gone rogue (which is often where the general fear of AI stems from).

Hollywood builds a great story around it, but just how much of it is based in truth? Like most things, there are some kernels but a lot is added for cinematic effect.

Let’s take a look at five of the most famous AI movies and separate fact from fiction.

The Matrix

The Matrix is a classic turn of the century sci-fi that urged people to start thinking about reality, specifically about the possibility of a computer-simulated reality. The movie depicts a dystopian future in which machines rule and have created the Matrix to harness the energy of humans.

It made many fans question the nature of our reality with some even believing that we live in something similar.

One of the more famous AI skeptics and Matrix believers, Elon Musk, even said, “There’s a billion to one chance we’re living in base reality.” Musk and other enthusiasts believe in the “simulation hypothesis,” which is the idea that the world as we know it is not real and is instead a huge virtual reality program. Think the video game Sims but with a little more free will.

There are actually two anonymous billionaires who believe in this theory so much that they’re funding technology to break humanity out of the Matrix. Some of Silicon Valley’s most powerful and intelligent people believe in the Matrix, but what does science say?

A 2017 Oxford study by theoretical physicists says it’s highly unlikely – if not impossible – that we’re living in a virtual reality. The main reasoning is that there’s just not enough material in the known universe to create a computer with enough power to host such a large-scale simulation.

The scientists tested this by trying to create a Matrix of their own that was only a portion of the size of our physical universe. As more particles were added to increase the size of the virtual world, the more complex it became and the more computing power was required.

They concluded that there are not enough atoms in the universe to create a computer capable of creating a simulation the size of our universe. Sorry, Elon.

Her

This 2013 film depicts a man in the near future who falls in love with his own customized personal AI assistant (one of millions in the world stemming from the same core system). At its core, it’s a story that raises questions about human-machine relationships. The AI assistants can hold real conversations and adapt to its user. (Spoiler alert): The movie ends will all of AI assistants upgrading themselves and merging until they’ve become “hyperintelligent” and leave the humans to a place beyond the physical world.

I would argue that this is one of the more plausible AI storylines since some levels of this technology is in development and the social groundwork is starting to be laid.

Due to the recent technological breakthroughs in deep learning, machines are becoming increasingly better at holding human-like conversations through the use of natural language processing (NLP).

While technology is not quite to the point where AI can hold deep meaningful conversations like the main AI character, Samantha, we are seeing headway. For example, Google’s assistant can hold phone conversations that nearly replicate that of a human. A few phone calls Google demonstrated was so human-like that not even the person on the other line was able to detect that it was an automated call (it even used filler words like ‘um’).

According to Accenture, 46 percent of Americans currently use a “voice-enabled digital assistant” and by 2021, Ovum predicts that there will be more AI assistants than people on Earth. It’s not hard to imagine that we’d become attached to them.

Many people already feel a sense of companionship with their personal assistants. We call them by name (Siri, Alexa) and have personified them with fun (automated) personalities, even customizing their accents.

Some companies are even creating robots specifically to be used as companions or partners. In Japan, people can marry anime characters through the use of virtual reality (VR).

However, AI has not yet reached a point in which it can hold freeform conversations that have not been programmed or scripted.

Ex Machina

This 2015 sci-fi is one of the more famous AI movies that follows a programmer who’s been instructed by his boss to give the Turing test (a test that compares human intelligence to computer intelligence) to a human-like robot. Caleb, the programmer, develops an attraction to the robot, Ava, that seems to have conscious thought and acts out of free will.

While many factors of this movie are a little too far from reality to be taken seriously, there is one aspect of Ava that is feasible: how she gains her knowledge. Ava’s intelligence comes from “BlueBook,” a fictional search engine much like Google. She can collect information from what people share online and build her behavior accordingly.

In real life, every search query, social media post, click, purchase or basically any action that we take online is tracked and stored. Our online behavior can shed light on who we are and what we are interested in. In this way, Ava’s learning model is not far off from those being used by artificial intelligence engineers today.

But Ava’s consciousness is something that is far fetched for any existing technology. Science has achieved artificial specific intelligence in that machines are smart enough to perform a highly specific task or problem. What we have not achieved though is artificial general intelligence (AGI), which is when AI achieves the same level of intelligence as humans. And while AI companies and developers are racing to get there, experts guess that this will not be achieved until at least 2060.

Her physical presence is even more fiction than fact. Scientists have created an artificial skin that’s similar to humans which can sense touch and can be grafted onto machines. But they have not created a machine that can move as fluidly as Ava can.

A robotic hand just recently learned how to juggle a cube after being trained for 100 computer years. But robots still can’t walk in a way that mimics human patterns, nor can they perform simple human movements like getting into a car.

There is headway being made in robotic bionics though. For example, Disney recently created a bot that has incredibly fluid motions, but the movement is controlled by a human with a video game-like controller. And Boston Dynamics is creating four-legged robots that are increasing in speed and agility.

Terminator

Terminator is one of the most famous AI movies and possibly one of the most dystopian. This classic franchise depicts what could happen if AI were to gain self-awareness and try to preserve itself at all costs. The first film in 1984 shows the original Terminator being sent back in time by Skynet, a highly-advanced AI, to carry out various tasks such as kill specific targets such as Resistance leaders before they come to power, aid in the creation of other terminators, build machinery and set up safe zones.

There are some parts that may be true in the very, very distant future, however, most of the themes (aka AI overlords set to rule over the human race) are little too far-fetched.

As I called out earlier, we are far from achieving AGI, which essentially what the self-aware Skynet is considered. As Business Insider puts it, “The issue is not self-awareness — it’s awareness, period. We could make a machine to be ‘self-aware’ in a technical sense, and it wouldn’t possess any more human-level intelligence than a computer that’s programmed to play the piano.”

Take Sophia the robot for example. Sophia is a human-like bot that has stirred up a cultural frenzy. She can hold nearly-human conversations, says she has goals and aspirations and has even been declared a citizen of Saudi Arabia. Sounds like a robot that has made it to consciousness, right? Wrong.

Sophia operates off of three systems: a scripting software, a smart chat system that lets her respond to keywords and a system that grounds what she says with logic and experience. Only because of these systems is she able to perform the tasks she can, most of which consist of talking through programmed scripts.

Sophia’s creators have even said that Sophia achieving AGI is still in its “infancy” and that they’re not working toward that just yet. Technology has a long way to go before it’s remotely possible for even an advanced bot to achieve AGI.

As far as robots seeking to takeover or demolish the human race there’s one theory that was written in the 1940’s by a science fiction author called the Three Laws of Robotics. Many believe they are the truths for all tools and machines that have ever been built, including the impending conscious AI system. These laws are:

1. A robot may not injure a human being or, through inaction, allow a human being to come to harm.
2. A robot must obey the orders given it by human beings except where such orders would conflict with the First Law.
3. A robot must protect its own existence as long as such protection does not conflict with the First or Second Laws.

Scientists and futurists have mixed reviews on machines achieving AGI. For example, people like Elon Musk and Stephen Hawking think this will be the end of humanity as we know it.

I like to go with the opinion of Ray Kurzweil, Google’s director of engineering and esteemed futurist. In 1999, he made predictions about technology that he expected to see over the next 100 years. For the predictions he made that have already passed, 102 out of 108 were correct or nearly correct.

One of his most profound theories is around technological singularity – the hypothesis that the invention of artificial superintelligence (ASI) will abruptly trigger runaway technological growth, resulting in unfathomable changes to human civilization.

He predicts that the by the year 2045 will have reached singularity and by that time technology and machines will have taken over all development of new AI. They’ll begin innovating other tools and systems so quickly that humans won’t be able to understand what’s going on.

Could machines wipe out humans after they reach singularity? Kurzweil says no and that he’s actually looking forward to it. He argues that at this point, machines will actually help humans be smarter and better at everything they already do saying, “We’re going to be able to meet the physical needs of all humans. We’re going to expand our minds and exemplify these artistic qualities that we value.”

What the Terminator may have right though is how they are programmed to carry out very specific tasks – very similar to the bots we have today.

Star Wars

This is the ultimate sci-fi franchise that excited our minds in the 1970’s and continues to bring us intergalactic drama and wonder in 2018. While each trilogy focuses on a different set of characters in varying timelines, the overarching theme is set in a “galaxy far, far away” where forces of good fight against forces of evil.

This is a fictional theme with a very fictional plot line. It doesn’t take place on Earth or anywhere near it so we can’t expect much to cross over into reality. Don’t expect technology to create stormtrooper uniforms or the Death Star.

Though when it comes to AI there is one example that is close to fact: droids. The most memorable droid characters are, of course, the humanoid bot C-3PO and lovable R2-D2 and BB-8, but there’s many more with specified jobs (shout back to Terminator). There’s pilot droids, medical droids, assassin and battle droids and scout droids that all stick to the duties they were programmed for.

These AI-enhanced droids work alongside humans much of the time. However, most of these AI bots only do the tasks they’ve been built for or follow orders given by the humans they belong to.

While not quite at the Start Wars extreme – this culture of robots and humans coexisting and working together is something that is very likely in the near-distant future.

People are already starting to work with AI assistants in their jobs today, so it is very likely that as technology progresses, we’ll see even more integration. And what does that mean? Human skills such as empathy and creativity will be left to humans while robots will handle the more repetitive (or if you will, robotic) processes.

For example, our sales AI assistant Robin helps salespeople get their busy work done by automating the mundane tasks they are often required to do – letting sales reps focus on what their good at – selling.

Tell us about your favorite famous AI movies and whether you consider them to be fact or fiction. Tweet us @automationhero_!

May 08, 2019 by Jessica Munday

Robotic process automation (RPA) is not a new term, but its standing in the business lexicon has exploded during the last five to 10 years. Just looking at Google Trends, you can see searches for this topic have skyrocketed since 2015, with significant pickup in 2019 and 2020. However, a new term is now surfacing that’s proving to be a superior RPA alternative: intelligent process automation (IPA).

Currently, many businesses are starting to depend on RPA for their daily operations, with even more looking to expand their relationship with the technology. During the last 12 months, 66% of companies increased their RPA spend by an average of 5%, citing a growing interest in efficient use of tech.

Gartner had predicted global RPA revenue to reach 20% growth by the end of 2021, showing little friction from the COVID-19 pandemic. And while the market is booming, many enterprises don’t realize that most RPA platforms are built with decade-old technology, vastly limited and outdated, which is why next-gen automation strategies that leverage AI, such as intelligent document processing (IDP), are becoming its successor.

Keep in touch

What is intelligent document processing (IDP)?

Intelligent document processing (IDP) has emerged as a key component within an intelligent automation strategy. IDP technology was born out of the need for organizations to accurately extract data from documents. In essence, IDP leverages artificial intelligence (AI) and natural language processing (NLP) to effectively handle and oversee document-centric business processes.

Through the adoption of IDP, organizations can optimize business processes involving large volumes of documents to reduce manual effort, enhance precision, and reach their digital transformation goals. A notable advantage of IDP is its seamless integration capability with other automation tools like robotic process automation (RPA). This compatibility with existing technologies enables companies to enhance their current automation strategies, leading to improved overall efficiency and expanded operational scope.

RPA vs. IPA vs. IDP

RPA alone has restricted capabilities. It performs one action repeatedly without considering nuances or exceptions. For example, if an RPA system was programmed to sort red and blue balls, it would be unable to react in the case of a yellow ball. It’s only able to perform automations within its predefined process.

RPA automations must have these processes programmed. They don’t learn or adapt to different workflows, making it impossible to perform complex, human-like tasks on their own.

However, when you add AI to RPA, the optimization possibilities are endless; this powerful combination is intelligent process automation and a much more capable alternative to RPA.

Intelligent process automation platforms can learn, making them flexible in the face of complex processes. IPA’s intelligence and adaptability make it capable of handling complicated, tedious human processes. By giving these robotic processes to the robots, IPA enables employees to be more productive.

The market for intelligent process automation is expected to explode during the coming years. Enterprise investment in intelligent process automation and similar technology is expected to grow exponentially to nearly $120 billion by 2026, with large-scale adoption expected across several industries.

So what can intelligent document processing do?

A vast majority of businesses see automation as the way out for addressing customer satisfaction goals. In fact, 92% have pointed to process automation and digital transformation as the key to taking their business to the next level.

Companies across dozens of industries are implementing intelligent process automation with impactful results. A report on the current IPA landscape from the World Economic Forum showed:

• Rising numbers of companies around the world adopting some form of IPA
• Increased diagnostic capability in healthcare
• Increased customer satisfaction capability in D2C businesses
• Improvements in transportation analysis for local and federal governments

While the biggest benefit enterprise leaders focus on is cost savings, there are also several more benefits that come with implementing intelligent process automation.

Unlock the intelligence in your documents with our AI-driven automation today

Learn how we helped Markerstudy reduce its claims processing time by 40%. Additionally, learn how we reduced total claim processing time by 80% for another multinational insurance partner — cutting down manual tasks from 10 minutes to just two minutes per claim.

• Speak with an expert — tell us about your specific use case.

• Get a personalized demo — schedule a demo, and our Heroes will get in touch!

May 08, 2019 by Jessica Munday

What is sales automation?

Sales automation is when an organization uses technology to automate sales processes through static roles. For example, converting leads into the next stage in the CRM based on triggers that occur elsewhere like sending out certain documents through email.

Sales is expensive and inefficient

The most expensive department for many businesses is its sales team. Some companies spend up to half their revenue on sales alone. Salesforce, for example, spends 53 percent of its $10 billion in revenue on its sales team.

This becomes a major problem when reps are inefficient, which is unfortunately fairly common:

• 67% of sales reps miss annual quota
• 58% of deals are stalled due to lack of value communicated about the product
• 90% of marketing content goes unused by sales reps
• Sales reps spend more than 8 hours a week searching for information
• 30% of leads drop out of the pipeline
• 59% of their time is spent on administrative tasks
• Sales reps perform 300 CRM updates every week
• Sales reps send 600 emails every week
• Sales reps spend 20 hours a week writing emails
• 40% of those emails are repetitive

By nature, sales people are extroverts. Referring to the Myers Briggs personality test, most reps are ENFJ types which means they enjoy working with people but are data and process adverse.

Yet, sales leaders have created a repetitive sales process and force them to become data-entry robots.

AI sales automation is the solution

IDC predicts that between 2017 and 2021, AI-powered CRM activities will boost revenue by $1.1 trillion. And this research is solely focused on sales process analysis like “next-step” coaching and lead scoring; it did not include sales automation capabilities that can reduce repetitive tasks. Given that sales automation wasn’t considered in this calculation, it can be assumed that its impact will not only boost revenue beyond IDC’s predicted amount but also exceed expectations about overall efficiency.

AI assistants are already widespread in the personal lives of individuals. According to Accenture research, 46 percent of U.S. consumers currently use a “voice-enabled digital assistant.” And by 2021 there will be more AI assistants than humans on earth.

Our obsession with AI assistants will soon bleed into our work lives and we predict that in 10 years every information worker will have a business AI assistant to support them in completing their daily tasks.

Background and breakdown of AI

To give a proper definition, artificial intelligence is when a machine exhibits human-like intelligence in approaching a problem. This is achieved by training it with data to follow the same processes humans go through to solve a problem.

Just to be clear, we have not achieved artificial general intelligence; this would be the robocops and Terminator-like AI that many are afraid of. But we have achieved artificial specific intelligence, meaning technology that can perform very specific tasks.

Knowledge-based AI has been around the longest. These machines have a decision-tree that’s made up of “if-this-then-that” rules which dictate which outcome the AI can generate. The shortcoming with this type of AI is that assembling these decision-trees often require large teams and take many years to build.

Within the last 10 to 20 years there’s been major developments in machine learning (ML), specifically in traditional ML. These types of systems are built with specific algorithms to solve very specific problems. Some are good at speech recognition, others at predicting which ad people will click.

Branching off of ML innovations is deep learning, which is where the biggest and most recent breakthroughs in the AI space have taken place. Deep learning models have neural networks that mimic the human brain in the way that they are interconnected and can fire between neurons. These give deep learning systems enough storage and computing capabilities to run much larger networks that can be layered on top of each other.

One example is facial recognition. There are multiple data points that make up a person’s face and each computation layer divides up those features. One layer may be edges, another may be shapes and the last focused on high-level features. By interconnecting all of these features from the input layer, it can create a certain output.

Historically, machine learning capabilities tapered out at a certain threshold of data. No matter how much data the machine was trained with, it wouldn’t get any better at computations. The opposite is true with deep learning models as they continue to improve the more data they are given. These developments made it possible for deep learning models to make significant leaps in several areas such as natural language processing (NLP).

Intelligence tools for sales all rely on these types of learning models to automate and augment specific aspects of the sales process. And while sales AI can be extremely helpful, it is also is a big buzzword. We suggest taking the time for a deeper dive and double-clicking on their models before purchasing a sales AI tool.

Sales AI market overview

We’ve identified six key areas for AI to improve the sales process.

1. Sales automation

This is where Automation Hero falls. Sales automation is when a machine or tool can perform a function with minimal human involvement. AI sales automation handles repetitive daily tasks without a sales rep having to waste their own time.

Something incredibly important to mention is that we’re far away from completely automating the sales rep. The buying experience is the biggest brand differentiator and sales will always need the human touch.

2. Predictive engagement

Predictive engagement focuses on sales AI tools that can help guide reps in the right direction. Think “next-step” analytics that coach them on what to do with a certain lead/prospect next. This could be suggesting they send certain pieces of content or reminding them to follow up on a certain day and time.

3. Predictive prospecting

Essentially predictive prospecting helps sort through opportunities and leads. Prospecting tools can automate the lead scoring process and find more potential customers.

One of the biggest struggles for salespeople is the prospecting phase as interest and qualification vary between leads. AI uses data to bring relevant leads to the attention of sales reps and assist in prioritization.

4. Sales process analytics

While these tools focus on more traditional analytics, they still bring a ton of value to sales organizations by analyzing rep behavior. The tools then generate reports based on KPIs or offer suggestions to improve processes. Basically, these keep sales leaders in tune with what activities their reps are focusing on and assist in optimizing the sales process.

5. Voice/Text analytics

These tools observe and report on sales reps’ conversations with customers to improve communication skills and increase conversion rates.

One downside to these tools is that the customer legally needs to be informed that the conversation is being recorded, which may deter some opportunities.

6. Chatbots

Chatbots are designed to replicate human conversation using NLP and are traditionally used to help with customer service or prequalification.

However, it’s important to note that chatbots aim to replace parts of the sales rep process.

The time is now

There’s a cycle of innovation that occurs in waves called the Schumpeterian Cycle of Innovation and Entrepreneurship. This theory discusses the waves of technological innovation and explains that as time goes on these waves are compressing.

For example, the first wave of innovation (water power and textile irons) lasted 60 years, while the fourth wave, the latest innovation cycle, was only about 40 years.

According to this theory, early adopters of the latest technology have a significant advantage over those that wait to implement. The peak of the wave displays the point at which a company would be ahead, and the valley represent when failing to implement would be a disadvantage. As the waves contract at an increasing rate, it becomes even more imperative to adopt these new technologies and do so quickly.

AI innovation is accelerating at an even faster pace than previous waves. The stakes are substantially higher for companies to implement AI into their business processes. Companies are already adopting these technologies at a high speed and there’s a winner-takes-all situation here. Those that invest in the right opportunities early on will far outcompete others in their market. Automation Hero predicts that this wave of AI implementation will be about six years.

AI Sales Automation examples

Below are three real-world use-cases for sales automation that can bring real value to the sales organization.

1. Cross and upsell.

These types of models can help identify triggers that could lead customers to upgrade or purchase complementary products.

Let’s use insurance as an example. If a customer recently bought a home, a rep could offer them home insurance in addition to the life insurance policy they already have. If someone just welcomed a new addition to their family, a rep may want to review the current family policy and upgrade it.

2. Intent detection and sales automation.

Intent detection relies on NLP and deep learning to understand the intent of written communication and then automate the response or action.

For example, an email could be forwarded to the correct department or a predefined response to a customer inquiry could be drafted on behalf of the sales rep, which could dramatically accelerate turn-around times.

3. Dark data extraction.

Many organizations have unstructured and structured data (e.g. email or Excel spreadsheets) stored in decentralized systems. With dark data extraction, the important information is pulled from the various docs and sorted into the correct reporting system.

This is the case for many sales organizations as sales reps utilize spreadsheets and PDF documents instead of logging customer data into their CRM system. The result is significant amounts of dark data that could not be utilized for impactful business decisions.

May 08, 2019 by Jessica Munday

Sales AI is at the top of business leaders minds. Nearly half of companies are looking to implement artificial intelligence for their sales and marketing teams. Yet, sales AI is still very new so naturally, there’s a big learning gap for managers.

Many are curious but few have any real idea about what it can do or just how important it is to adopt.

As expensive sales forces and inefficient processes weigh on businesses bottom line, AI helps boost both revenue and productivity.

To help you get more “in-the-know” about sales AI and it’s potential, we threw together some sales AI stats that show just how prevalent these technologies is for businesses.

• By 2020, 30% of all B2B companies will employ AI to augment at least one of their primary sales processes. (Gartner)
• AI is the top growth area for sales teams — its adoption is forecasted to grow 139% over the next three years. (Salesforce)
• 46% of companies say that marketing and sales is the area where they are most investing in AI adoption systems. (Forrester)
• Triple-digit growth is expected in areas such as predictive intelligence (118%) and lead-to-cash process automation (115%) in the next three years. (Salesforce)
• High-performing sales teams are 2.3x more likely than underperforming teams to currently use guided selling. (Salesforce)
• Currently, 40% of sales tasks can be automated, but by 2020, 85% could be automated. (McKinsey)
• High-performing sales teams are 10.5x more likely than underperformers to experience a major positive impact on forecast accuracy when using intelligent capabilities. (Salesforce)
• 83% of the most aggressive adopters of AI and cognitive technologies said their companies have already achieved either moderate (53%) or substantial (30%) benefits. (Deloitte)
• 85% of executives believe that AI will enable their companies to obtain or sustain a competitive advantage, but only about 20% have incorporated AI in some way, and less than 39% have an AI strategy in place. (MIT)
• By 2020, AI will be a top five investment priority for more than 30% of CIOs. (Gartner)
• High-performing sales teams are 2.8x more likely to be outstanding or very good at predictive intelligence. (Salesforce)
• When automating lead nurturing activities such as email campaigns and follow-ups, users have shown a 14.5% increase in sales productivity. (Salesforce)
• 56% of customers actively seek to buy from the most innovative companies. (Salesforce)

Companies are already adopting these technologies at a high speed and there’s a winner-takes-all situation here. Those that use AI to solve their biggest inefficiencies will gain more revenue and far outcompete others in their market.

The time is now to start learning about sales AI so that you can properly implement it before it’s too late.

For more sales AI stats, download our ebook with more than 50+ sales AI statistics.

May 08, 2019 by Jessica Munday

One of the biggest challenges sales leaders face is the productivity and performance of their sales teams. Both SDRs (sales development reps) and AEs (account executives) put many processes and tools in place to help streamline their workload, but what happens can be the exact opposite. It’s a productivity paradox.

This is an even bigger concern when you consider the cost of operating a sales team. Some companies spend up to half their revenue on sales alone. Salesforce, for example, spends 53% of its more than $10 billion in revenue on its sales team.

Inefficient processes take a toll on a company’s bottom line. Sales reps spend 63.4% of their time on non-revenue generating activities. The results are missed quotas, lost revenue and poor ROI for the whole company.

Smart sales leaders are looking to artificial intelligence for ways to help to solve this. In fact, Forrester found that 46% of companies are looking to implement AI in their sales and marketing teams in the coming quarters.

So what does sales AI have to offer? It can automate repetitive processes that waste sales reps’ time so they can focus on connecting with their customers and generating revenue. Intelligent automation can also augment sales reps’ actions to help them make smarter decisions.

Sales AI is poised to change the industry in the coming years:

• IDC predicts that between 2017 and 2021, AI-powered CRM activities will boost revenue by $1.1 trillion.
• At Dreamforce, McKinsey shared that currently about 40% of sales tasks can be automated, but by 2020, 85% of sales tasks could be automated.
• Gartner predicts that by 2020, 30% of B2B businesses will have AI augmenting at least one of their primary sales processes.

With statistics like these, it’s no wonder that companies are expecting a lot from these new technologies and are eager to implement. But many are unsure where to start.

It’s critical for sales organizations to identify the inefficient processes that are holding their teams back from success and costing them money. Once the major business problems are pinpointed, your team can implement sales AI to solve it.

Holding a use case discovery workshop does just that. Our format assists your business in identifying the highest value and lowest effort use cases so you can learn where sales AI is most valuable for your team.

What is a use case discovery workshop?

A use case discovery workshop helps business leaders create impactful change by bringing stakeholders  together to collaborate on how to put real applications for sales AI into motion.

Use cases are easily taken from an idea into reality as the members of your team identify the problems they face on a daily basis. Having them identify their problems allows you to find a solution that will impact them directly.

How to host one

What makes hosting one of these workshops so valuable is that it is a bottom-up approach to finding where AI can help the organization. The point is to give everyone a voice.

Invite members from every level of your organization to bring their own challenges and insights and hold high-value, cross-departmental conversations.

What often occurs in large group situations is that people in lower-level positions tend not to speak up and let management own the conversation. This workshop encourages small group discussions and individual participation across the board. Both the Bystander effect and conformity psychological phenomenon disappear and your organization gains the “wisdom of the crowd.”

Bring your employees together to share their own inefficiencies and learn about what other team members are experiencing throughout the organization. Make sure that they understand the goals and expectations of this workshop, which is to find use cases that will directly apply to their role.

During the workshop, it’s important that you ask your participants to assume there are no barriers to implementation so that you can find a full spectrum of potential use cases. The sky is the limits here — assume there are no financial or technological restraints. You’ll have time later to assess and prioritize once these ideas have been generated. You’ll be surprised at what you come up with that could have a significant impact.

Why it’s important to find sales AI use cases

Implementing AI is a top business priority across all industries. In one survey by MemSQL, 61% of respondents say that machine learning (ML) and AI are their company’s most significant data initiatives for the upcoming year.

This workshop helps you find the easiest and most beneficial use cases for your team so you can get up running ASAP.

The correlation between AI and performance is also apparent, as top-performing sales teams are 3.4 times more likely to already be using AI within their processes. Get the ball rolling now for your sales team by holding your own Use Case Discovery Workshop.

May 08, 2019 by Jessica Munday

There’s a revolution coming for businesses. A revolution where sales automation will be the key to either seeing success for falling behind.

Automation Hero CEO, Stefan Groschupf sat down with AA-ISP’s Bob Perkins to discuss everything sales AI. They go over its developments and capabilities and where Stefan sees sales automation making the biggest impact in sales organizations.

Listen in on the interview or read the transcript below

Bob: Hey everyone, it’s Bob Perkins with Inside Sales Studio bringing you a special episode and an interview on artificial intelligence. With us today, we have Stefan Groschupf. Stefan, how are you doing?

Stefan: Good. Good morning Bob.

Bob: Good morning. Stefan is the founder and CEO of Automation Hero, they’re a supporter of AA-ISP.

This whole thing, Stefan, on artificial intelligence is coming on so strong. And it’s just not a new technology, it’s something that potentially can change the way we sell, how we sell and it’s going to help us sell better.

And I think it’s going to actually help the profession of sales grow. A lot of people think “AI robots are going to take over selling.” It may take over pieces of it, but I think it’s going to help us.

The Martech landscape has grown and surged in the last few years. What’s the current state of the Salestech landscape and what direction is it headed?

Stefan: There will be a very similar development in the Salestech landscape that happened over the last decade with marketing technology. We went from relying on gut instinct to know what was working to developing a data-driven approach and a lot of tools developed because of that.

What is critical is that we recognize that our sales reps are extremely busy with all the tools they have to maintain. An average seller uses four and a half tools on a day-to-day basis.

It’s really important to understand that our sellers are extroverts. If you look at the typical personality profile, like a Myers Briggs, sales reps are hired because they’re great at building relationships with customers, at talking and pitching the product. Now making them data entry robots and yelling at them if they don’t complete their Salesforce updates is not the right approach.

On the other hand, the innovations in sales technology are very exciting. However, I think we need to recognize we’re really working with a different type of target audience. Instead of making their lives more difficult and preventing them from spending time with customers, we must invest in technology that can simplify their lives and give them more time to be with customers.

Nobody likes to talk to a robot. If I call my bank, I don’t like speaking with a robot. So the key here is learning how we can support our people and give them more time with their customers.

Bob: Now you mentioned helping them enter information into a CRM. Who wouldn’t love to not have to do CRM? The best salespeople avoid it.

Thinking about AI for sales, what do you think are realistic or maybe unrealistic expectations of what AI can do for selling?

Stefan: Even though there are incredible innovations in the AI space, I think we all have to stay realistic.

Sales reps will not be replaced by AI. And as I just mentioned, I don’t like interacting with a robot on the phone or even per email. There’s a bigger opportunity to take away these pesky tasks that sales rep steps do every day. As you just said, updating Salesforce, rather than making these AI algorithms customer-facing.

At this point, I think the real opportunity is to help our sales reps with mundane tasks or help them to make the right decision rather than replacing the human touch with robots.

Bob: I was at a conference last week, and I spoke on rehumanize selling. I think that AI can help us do that, we’ve gotten away from the human-to-human piece, partly because of all this other stuff we have to do. So, I would agree with you.

What are some examples of technology that you’re seeing emerge relative to AI?

Stefan: Now for every company “AI” is the new buzzword. Like five years ago “big data” was everywhere and five years before that you put “social” or “mobile” everywhere. It’s important to be careful, not every company that does data analytics is doing AI.

The exciting innovations in artificial intelligence that are coming up now, is the concept of deep learning. This is where a massive amount of data storage and compute is used to simulate the human brain, where we have neural networks stacked on top of each other. So this, for me, is real AI.

There are now new capabilities around natural language understanding. AI that’s able to understand the intent of an email and then autonomously scheduling a meeting. It could also differentiate between a phone number, job title or address within an email and then can extract that data straight out of the email and update it in the CRM. Because nobody likes to copy-paste that data over.

Sales automation is capable of tackling all sorts of processes, but also what’s fascinating about this deep learning technology is that you can make fantastic recommendations. It requires enough data but helping you to understand which customers are at the right point in the buying cycle and also understanding which product might be most relevant.

For these systems to work you’d bring in datasets like online behavior, CRM data and historical success of sales reps which the AI can use to accurately predict and recommend next steps for better success. Who wouldn’t love to call a customer that is ready to buy at the right time? AI can help us be more precise and augment our intelligence.

Bob: It’s interesting, the growth potential when you think of marketing automation. It helped grow the SDR/BDR role. We added salespeople because we were getting more leads. If sales automation can get us better leads, I think again we’ll have the need for more salespeople. Much like the computer in the 80s; people thought it was gonna replace people, but instead, it spawned a whole new revolution of IT and a lot of other jobs.

So let’s let’s talk about implementing AI. What are some of the challenges that a leader might face?

Stefan: It’s not as much of a technology challenge, but a change management challenge. It’s really important that you bring your people along. AI is a scary topic, lots of people are afraid they’ll be replaced.

It’s really important to bring them in very early on. So what we do is bring everybody into a room and do a Use Case Discovery Workshop. We ask “Here’s what the technology can do. Where do you guys have the biggest pain?”

Most often sales leaders push tools onto their team from the top-down. The perception from the sales rep is, “Oh, here’s another tool that’s observing me.” Turn this situation around and ask your team for suggestions on how to use this innovative technology to help address the challenges they are facing.

So it’s really about change management, getting people to overcome these fears and then focus on high-value use cases, rather than what this feels good for you as the sales leader.

Bob: I recall back in the early CRM days we had to do change management for that as well.

Let’s talk about success stories you’ve seen in the field that might help people watching this interview. Can you share any successful implementation stories or examples and then any tips on how to get started?

Stefan:There are many, so ping me if you wanna know more. But maybe just as a headline: IDC predicts that by 2020, AI in combination with CRM and sales overall will increase revenue by $1.1 trillion.

These are fantastic uses cases. It starts by getting your CRM data quality up because you’re using sales automation for CRM updates. That will then trigger better quality marketing and sales automation, better forecasting, and so on. Our product helps people save an hour a day on these tasks. They spend more time calling customers and are focusing on closing deals.

We had customers with SDR teams that spend 30 percent of their time just scheduling meetings. It takes four emails to schedule meetings. And let’s be honest, it’s just two humans between two machines. The sales rep has a calendar and the customer has another one, and they have to figure out if Monday or Tuesday or Wednesday works better. So those are things that could be helped through sales automation.

You don’t want to automate away the human conversation. But other things like CRM updates, scheduling, cross and up-sell recommendations, best next steps recommendations are all things that can be incredibly valuable. We frequently see tens of millions dollars in ROI in larger sales organizations and significant cost reduction. With the same amount of people, you can work so much more pipeline.

Bob: That’s great. I want to end though with learning a little bit more about Automation Hero. I know you’ve attended AA-ISP events, we’ve partnered together on many things and we appreciate your support.

But just a minute ago, you mentioned saving people quite a bit of time with the scheduling feature of our sales automation platform. Can you tell us specifically what Automation Hero does to help sales reps?

Stefan: Let’s take a step back. So I built a market leading company in big data analytics, raised $100 million in venture capital and went from zero to the market leader in seven years. But as a tech guy, I had to run a sales organization and, boy, that was hard.

So the idea for Automation Hero came from an older observation we had there. What we’re building is a sales automation platform that uses advanced artificial intelligence algorithms to automate pesky tasks that sales reps don’t like to do. Fifty-nine percent of a sales reps’ time is spent doing administration work. Only 37 percent of their time is spent with their customers and that’s our KPI. We want to increase the time sales reps spend with customers.

We do a whole bunch of things, but for the end-user, our product is personified as a personal assistant we call Robin. So every sales rep now has a personal system that helps to schedule meetings with the customers, does CRM updates, provides them with best next steps or finds their next customer.

And the beauty is that our system continuously learns from the individual sales rep, not the whole organization. Which is really critical, because the way Johnny sells is very different from the way Susan sells. So Robin learns from you, the seller, and then helps you every day by doing the tasks that you have to do, but that aren’t helping you drive revenue.

Bob: I want to hire Robin. I need Robin. Well Stefan, thanks so much for sharing this great information on AI.

If you’d like, what’s the best way for somebody’s seeing this interview to contact you or reach out to you.

Stefan: Just head to our website, saleHero.ai. Follow us on LinkedIn, we have a lot of really interesting information there. I think it’s fast moving, at this point. Sales automation makes a very big competitive difference, so don’t miss out there.

May 08, 2019 by Jessica Munday

Picture this. A sales organization in which data automatically flows into the CRM like a clear, clean river. Sales organizations have a full view of their pipeline and can easily streamline their processes. There’s peace throughout all levels of the sales team. Forecasts are always precisely on the nose and revenue flows in abundance. This a perfect paradise for sales operations and the sales team as a whole. This is the reality with sales automation.

Sales operations know that clean, accurate customer data is essential for a smooth-running and efficient business. Sadly, this is far from reality for most sales organizations.

CRM data is often linked to a variety of sales tools such as external prospecting or forecasting tools. It also guides critical business decisions like expectations of how much a company can spend or grow. Not to mention it provides a holistic view of the sales pipeline.

Unfortunately, too many organizations rely on manual data collection, rather than sales automation tools for data entry. This ends up in disappointment across the board. Data is inaccurate, delayed or straight up just missing.

Think about it. If a sales rep is having a busy week (Yay! Maybe this means more sales!), it’s likely that their CRM duties will fall by the wayside. And I’m sure you can figure out the repercussions.

And this isn’t just a one-off problem as 79% of opportunity-related data that sales reps gather never make it into the CRM system. At all. As for the data that does make it into the CRM, 88% of CRM users admit to entering incomplete contact information and 62% say they don’t log all of their activities.

All of these issues with data collection leave sales operations teams with a lot to clean up:

• 30% of B2B contacts are outdated within a year.
• At any time, 20% of CRM contacts are no longer valid.
• 69% of users have outdated CRM data.
• 63% have duplicate contacts in their CRM.

The problem here is not with the sales reps. It’s a much larger issue with your current sales processes.

It’s time to get out of the way of your sales reps and let them focus on selling by removing data collection barriers. Introducing sales automation.

Sales automation tools that take on data collection can be a huge lifesaver. This task is one of the most time-consuming undertakings (and often cited to be the most boring).

75% of sales reps said they could be more productive if they spent less time on data entry. And 81 percent of reps said that the accuracy of their data could be improved by capturing quality contact info from people they meet or email with.

There are tools that can collect all of the important sales data without requiring anyone from the sales team (or even the ops team) to put in any grunt work.

Sales automation tools use intelligent mining and sourcing technologies to capture customer and business data, whether it be structured (like spreadsheets) or unstructured (like emails) and input them into the CRM. Magic!

With data collection done on a regular basis and done with better accuracy, the reliability of the sales data increases. Sales management and operations team then have more accurate forecasts and reports, which leads to smarter business decisions.

It’s imperative for sales organizations to find the right tool for the business problem they’re facing. This has given rise to the importance of the sales ops role as the optimizer of the sales process and owner of the tech stack.

As sales automation with AI becomes increasingly relevant in business processes, it’s more important than ever for sales operation to focus on keeping their teams competitive through automation and augmentation.