Deep learning, one of the most effective approaches to artificial intelligence, continues to gain traction in the business world. And AI is booming: Salesforce’s “2018 State of Sales” report found that sales leaders predict that the use of AI will grow 155 percent by 2020.

Gartner predicted that AI and deep learning would be commonplace by 2023, mainly as a means of accelerating data science. This will have enormous consequences for sales, so it’s critical for sales leaders to have a basic familiarity with these approaches.

Get to Know Deep Learning

Deep learning is an approach by which machines can analyze large amounts of data to find patterns that are difficult for humans to find unassisted. Deep knowledge is a subcategory of machine learning that looks for patterns in data by applying several layers of analysis, which can deliver hidden insights that can help humans make better decisions.

One strength of deep learning is that it doesn’t require anticipation of every contingency. Instead of trying to program specific attributes to look for, you allow the learning software to extract features from the data automatically.

For example, if you feed the system images of cats, deep learning software will, over time, decompose the images into particular components, such as color, edges, and contrasts. In the end, it makes a determination: Is this an image of a cat? Before approaches such as deep learning, this was nearly impossible for even our most influential computer programs.

While an image of a cat is not relevant for a sales organization, finding patterns in data is. For example, you could feed the deep learning algorithms all of the factors leading to a sale: leads that are converting, deal sizes, or the typical interactions of your sales team. From these data points, deep learning can extract key insights, such as recognizing a potentially good customer or salesperson.

Such insights matter to sales outcomes. Salesforce’s 2018 report showed that high performers prioritize leads based on data analysis 1.6 times more than less successful salespeople.

3 Considerations for Deep Learning in Sales

Although deep learning holds a lot of promise for sales, it is new enough that it seems difficult to implement and to know what issues to keep in mind. So let’s take a look at a few key points to consider when looking to achieve deep learning for your sales teams.

1. Build a predictable and value-driven pipeline.

Deep learning is most helpful when there are specific, measurable insights you are hoping to extract. An important one is the expected value of a customer, which is related to how likely a client — individual or institutional — is to become a customer and the revenue that will result. For example, clients who are 20 percent likely to make a $50,000 purchase would have an expected value of $10,000.

The challenge with traditional systems is that it’s often difficult to calculate expected value because there’s too much information to factor in an old-school heuristics-based approaches don’t cope well. Factors such as location, season, revenue, number of employees, company structure, purchase history, and other variables can play a role in determining expected value — often in complex ways.

But this kind of data set is where deep learning can succeed. It allows users to build models that take all these factors into account and calculate expected value much more accurately. With these actionable models, sales leaders can assign appropriate sales team members to the right clients and maximize revenue.

2. Create equitable, data-driven territories.

Deep learning is particularly good at building data-driven sales territories. Standard, geographic-based sales territories are convenient but are rarely well-informed by the data. But deep learning models can help sculpt a sales territory that makes the most sense in terms of expected value.

That’s because it’s much easier to apply your resources more effectively if you know how much a particular territory is likely to be worth to your business. You might find that your sales territory map doesn’t make as much sense geographically but has the highest value to your sales team. Knowing that insight is backed by in-depth learning analysis can also empower your salespeople and give them the confidence that they are seeking sales in the best locations.

3. Deliver ROI from your sales and martech stacks.

Many companies that are interested in deep learning are hesitant because it’s challenging to figure out the best ways to implement it. Fortunately, several vendors are offering AI-as-a-service solutions that can plug into your existing sales and marketing automation systems. This will save your data team the difficulties that come with building deep learning systems from scratch. Those include not only years spent in development and debugging, but also the high costs that go with paying for experienced deep learning engineers and managing these systems as your business grows. For these reasons, third-party options can be attractive to many companies.

In order to pick an appropriate solution, it’s important to understand what your data sets look like and how you are going to leverage that data effectively.

Deep learning works best with large data sets, and it’s essential that you have the resources to “clean” your data of any extraneous data points or significant inaccuracies.

To ensure success, you should expect some preparation before you begin to work with vendor solutions to develop your deep learning models.

Deep learning has the potential to transform sales, which can lead to more revenue, more satisfied customers, and an empowered sales team. But to get the full value from deep learning solutions, it’s important to prepare carefully and keep an open mind when the data points to necessary changes in how your sales team operates.

Companies that make this transition will be well-positioned to compete in tomorrow’s marketplace. And as your sales grow and your company scales in response, deep learning will continue to be highly adaptive and accelerate your trajectory.

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Deep learning is one of the most compelling new technologies available to businesses and is poised to be a game changer for many. Now is an ideal time for companies to consider leveraging deep learning because it is quickly moving from the realm of academic research to one in which new B2B software platforms can leverage the technology effectively.

Gartner recently reported that the use of artificial intelligence (of which deep learning is a specific category) among enterprises tripled in the past year, with 37 percent of organizations reporting that they use it.

Deep learning stands as one of AI’s most promising lines of research.

It’s a specific type of machine learning in which software algorithms ingest large amounts of data and then extract insights from patterns within it. That data can be customer behavior, images, sound files, video, maps, sales figures — provided the data is structured appropriately; the possibilities are endless.

Deep learning’s fundamental value — and particularly that of unsupervised deep learning — is that it’s flexible, it’s powerful, and it doesn’t require step-by-step algorithms or heuristics to tell it how to arrive at its conclusions.

Designed properly, it can reach human-level or better performance across a wide range of tasks without any specific programming instructions about how to do so. Knowing how to navigate the system itself, allows it to better manage novel situations that would confound traditional programs, such as when Google’s AlphaGo system used deep learning to beat world Go master Lee Sedol.

Getting Deep Learning Underway — What Does It Take?

Integrating deep learning into business isn’t as tricky as it sounds, but it’s helpful to have an overview of what it does and why it’s valuable before diving in.

The most common deterrent from implementing deep learning is the assumption that you need in-house expertise to leverage it. And that expertise isn’t cheap. Experienced deep learning engineers can command salaries upward of $1 million per year at the most competitive companies.

The good news, though, is that there is now technology that allows businesses to leverage the power of deep learning without in-house experts. Platforms like People Data Labs and BigML are helping users who are not experts in the field take advantage of the technology.

Another common deterrent is a lack of sufficient data. While deep learning does require a large amount of data to be effective, nowadays even smaller companies are tracking most interactions both internally and externally. So while they won’t have as many opportunities as larger companies to leverage deep learning tech, there are plenty of chances to do that as they scale up.

Given these two facts, there is now no legitimate excuse for not employing deep learning at your business.

3 Departments Where Deep Learning Can Make a Big Difference

To get some ideas for where you can deploy deep learning, let’s review a few critical areas in which deep learning is likely to shine.

1. Marketing

Because modern marketing manages customer interactions at every step in the buying funnel, marketing departments typically have large data sets and are positioned to benefit from deep learning.

In the simplest case, deep learning can replace traditional heuristics-based lead scoring. But that’s just the start. Because it can consider many interconnected factors, deep learning can deliver significant returns across a variety of data points that are unique to your marketing needs.

I recently worked with a large technology firm that leveraged deep learning to create a “likelihood to convert into pipeline” model. After tuning, it was able to drive an 81 percent increase in performance over traditional technology. That kind of leap forward is consistent with predictions: Accenture foresees a 40 percent jump in labor productivity for companies that leverage AI effectively.

2. Sales

Sales teams can also exploit deep learning’s power for customer predictions. Because it can make use of unstructured data across a variety of sources, sales leaders can not only identify a good-fit potential customer, but also predict the possible deal size, deal cycles, and other insights.

Traditionally, unsupported human judgment — and some guesswork — was needed to decide how to manage customer interactions. Now, through deep learning, your team can match representatives to the deals they’re most likely to close; determine the time, day, or season that drives the most success; and evaluate the customer and seller interactions that are most likely to lead to a close. Overall, AI technology has the potential to increase worldwide sales by up to $2.6 trillion in value, according to one report.

3. Finance

Financial firms that take significant risks (e.g., credit card companies) build comprehensive models to determine how likely a person or company is to default on payment, how much they’re likely to spend, etc. While credit scores might be drivers at the consumer level, many factors determine how much of a credit line to extend that cannot be adequately captured by typical data analytics.

Because deep learning is designed to analyze complex multifactor scenarios effectively, it is a natural solution for creating highly predictive risk models. For this reason, big firms are beginning to employ large data science teams staffed with experts who leverage deep learning.

For instance, machine learning is being used by multinational insurance giant AXA to predict major traffic accidents with 78 percent accuracy. The accuracy of the predictions allows them to price optimally based on factors like each driver’s age, his or her address, and the car’s age.

These are just a few of the areas in which deep learning will be making strides in the near future, but we’re just beginning to explore the myriad uses for this technology. That’s why forward-thinking companies aren’t missing the opportunity to get their approach in place now.

Indeed, in a marketplace racing toward a future in which deep learning is critical to the success of all significant business operations, there will be few second chances for companies that don’t play ball.

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Technology that changes the way we live has always been met with suspicion — especially information technology. When new tech supplants less efficient ways to engage with the world, it usually attracts trumpets of doomsday proclamations about how tech will negatively impact humanity. Even the printing press was said to be a harbinger of our cultural demise. In the long term, though, many information technologies have positively contributed to the modern world.

Artificial intelligence is in its infancy, so in one sense, the distrust it’s received is no different than that of any other game-changing technology. In the next 25 to 50 years, AI is likely to mirror the trajectories of the printing press, early computers, or cars: Initial applications will become increasingly powerful. Pundits on both sides will warn of both incredible potential and potential catastrophe. Once the undulations smooth out, AI will likely contribute a net positive gain to society.

To be clear, that’s the short-term view of narrow AI, which is good at specific tasks. Today’s AI is still rudimentary and is generally only good at performing one thing at a time. I believe narrow AI’s capacity has been overestimated in the next decade or so, but I do think we will see valuable benefits in the near future.

General AI is where things become much more interesting — and potentially perilous. That’s when we’ll see machine learning that is truly human-level or beyond, a program that can perform a range of cognitive tasks as well as or better than a human. The advent of general AI is when the comparisons of production-enhancing previous technology is no longer relevant because we simply can’t predict what will come from it.

Even though AI in the short term will be beneficial to society, it will quickly outgrow its obvious positive impact. We will face security, informational, and existential threats within our lifetime as AI becomes smarter unless we get serious about the risks as well as the benefits.

On the Near Horizon

To date, AI’s practical impact has primarily been felt in natural language and image processing, which are difficult for traditional computers to accomplish. Narrow AI has brought efficiency to tasks that would otherwise slow down processes or bore humans. When done correctly, this has an overall positive impact on business.

More importantly, AI will bring two major advances in the near future that will save millions of lives: self-driving cars and AI-driven medicine.

Every year, more than 1 million people die in auto accidents worldwide, the vast majority of which are due to human error, including intoxication. Self-driving cars could cut the mortality rate of driving by a factor of 10. We’ve seen companies like Uber struggle to get road-ready self-driving cars off the ground (despite billions invested), but even the most pessimistic projections put self-driving cars on the road for private use within the next 20 years.

The potential upside for AI-driven medicine is even more incredible. AI’s proven utility in medical triage aside, it will play a revolutionary role in the pharmaceutical industry. Currently, the cost to bring a drug to the U.S. market is well over a billion dollars. This forces drug companies to prioritize mass-market drugs and so-called drugs of desperation, which consumers will likely pay for by any means necessary. AI can predict which drugs are likely to be effective against a particular disease at much lower costs and risks to companies, and it could reduce the need for testing on humans and animals. Furthermore, AI-led DNA analysis might usher in an era of personalized drug treatments.

Resentment and a Crisis of Misinformation

As the technology develops, so will challenges. The first sticking point will be a growing resentment against AI for taking jobs currently performed by humans — driving trucks or intaking patients, for example. Robots taking jobs from humans is, again, not a new concern. The level of blowback will be determined by society’s ability to reallocate resources and adjust for job shifts.

The second challenge is a misinformation ecosystem that will be impossible for humans to make qualified judgements against. As machines learn how to create better and more nuanced information, humans will want digital verification that the information is somehow real — or at least not false. But it’s almost certain that it’ll become a game of cat and mouse; as the verification gets better, so, too, will the fakery.

I believe there’s potential for an AI-driven misinformation crisis in our lifetime. AI can already convincingly manipulate images and video. Actresses’ faces have been superimposed on pornographic photos and videos. World leaders are being made to say ridiculous or inflammatory statements.

Fake news is just the half of it. Individuals will face risks to their reputations (fabricated naked photos sent to co-workers or fake revenge porn posted online), finances (forged bank documents that impact credit), and legal standing (phony audio, video, or other evidence of a crime). This is not a worst-case scenario; this will probably be the norm.

If people don’t know what’s real or what’s fake, personal responsibility could go out the window. Even now, strategic people use AI-created media as a way to dodge the court of public opinion. If a person is caught on tape saying sexist comments, for example, he can argue that the tape is fake. When the AI is good enough, it will be hard to prove otherwise. At a certain point, the simple existence of advanced technology will be enough to cast doubt on nearly any information.

Beyond the risks to individuals and their families, AI poses global security hazards. AI-created intelligence or media could be used to create a political firestorm, spark riots, or even start World War III.

Long-Term Look: Super AI

By far the biggest threat is posed by general AI. There are questions as to whether human-level AI (or beyond) is even possible. But unless we find evidence that human intelligence is driven by processes that humans simply cannot tap into, it’s only a matter of time before a super AI is developed. However, despite what Ray Kurzweil says, I don’t believe superhuman AI will be created in our lifetime.

A reasonable timeline for AI to achieve human-level or better performance in most tasks is about 250 years. Anything that historically required our intelligence — building machines, solving problems, making important decisions — will be handled more efficiently by machines. Perhaps there will be a few preeminent mathematicians working out the equations of the universe, but the rest of us will have little to offer society.

In this world, most folks will probably live on universal basic income. It’s possible that we would have the freedom to simply learn and enjoy time with ourselves, our friends, and our families. But I think it’s more likely that we become lazy, unmotivated, and irrational people — a shell of society, much like in “Brave New World.” Ongoing long-term studies on UBI might shed some light on how this would affect us.

I agree with the late Stephen Hawking, who believed the birth of AI could be “the worst event in the history of our civilization.” Because we simply don’t know the outcome of creating a super AI, the possibilities demand an abundance of caution for what could be one of the best or worst events in human history.

It remains to be seen whether the benefits will outweigh the ensuing negatives from AI and machine learning. Already, AI has opened the door to an era potentially ruled by misinformation. From forged bank statements to world leaders declaring false wars, AI-created media will cast doubt on what we read, see, and hear. Once we dig deeper into the possibilities of the technology, humans will become displaced by machines. These will be massive problems to solve, and the companies that solve them will be worth billions. I don’t see any other solution beyond a technical one.

The most important thing we can do is start the conversation about how to deal with general AI now. We need to understand whether we can build effective safeguards — such as Asimov’s three laws of robotics — that could control any conceivable superintelligence.

I hope I’m wrong, but I’m skeptical.

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