The Future of AI for Sales (And How to Prepare for It)

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Companies that prepare for and embrace the changes AI brings will thrive. Those that don’t — won’t.

It’s as simple as that.

Sales is traditionally a people-to-people business, but technologies like artificial intelligence are making expert sellers rethink the balance between human and machine.

In fact, automation is already impacting sales, and its influence will only continue to grow. Leaders looking for ways to transform their bottom line should look to artificial intelligence to provide solutions.

The good news is that you can begin to plan for those changes now.

So, what does the short-term future of AI for sales look like, and how can you begin using AI to beat the competition?

Let’s find out.

The Need for Automation in Sales

Sales has, generally speaking, been slow to adopt AI and automation.

Marketers have been much quicker to jump on the tech-adoption bandwagon. For instance, marketing language software — powered by machine learning — helped JPMorgan Chase increase headline clicks by as much as 450%.

The one notable exception to this has been conversational AI.

According to Salesforce’s recent State of Service report, more than half of service providers will be adding chatbots to their lineup in the next year and a half.

This is a start, but to stay ahead of the competition in today’s sales world, sales teams need to start utilizing AI much more than that.

The Salesforce State of Sales report notes that only 46% of sellers have access to client and prospect data insights (something that 85% of salespeople say helps them produce).

 

This difference in tech-friendliness between revenue-producing departments is not a surprise. Sales and marketing have always had different ways of approaching the same problems. This is what has led to the difficulty in coordinating account efforts in strategic ways.

However, with so many technologies transforming the landscape, it’s time for sales to jump headfirst into the fray.

Gartner research predicts that in the next three years, 70% of customer experiences will involve some kind of machine-learning component.

The first sales teams to adopt AI and pioneer how to use it will have a massive advantage in the near future.

The Potential Future of AI for Sales

The full potential of AI may be generations away, but there are already avenues to integrate the technology into modern sales operations today.

It’s already being used at many businesses, and it’s slowly being adopted by sales.

Nearly 90% of business people who use AI say they already are or are planning to use AI for sales forecasting and email marketing.

 

AI is predicted to grow a whopping 139% among sales teams in the next three years.

AI does have its limits though.

Salespeople, especially on the enterprise level, need to understand their company, their product, their market, and their buyer exceptionally well.

They need to be able to pull data from multiple sources, interpret it, intuit the needs of others, communicate abstract concepts intelligently, and make decisions on the fly — often with little information available.

That’s unlikely to be completely automated any time soon.

This brings us to the most common fear when talking about AI.

Will it take all our jobs?

In short, no, not for a long time. But not all jobs are completely safe.

Sales jobs that only require basic functional knowledge of the product, basic problem-solving skills, or basic written or verbal communication will likely be replaced by AI in the next decade or so.

All sales operations require some amount of basic communication, and basic communication has increasingly fallen to machines.

What began as door-to-door sales has moved to electronic communication. Websites and chatbots have begun to take over, and they will continue to take over— especially as the basic language capabilities of AI improve.

Consider how this will affect the marketplace in the coming years.

The sales jobs of the near future will shift to things that AI still struggles to do: strategic conversations, complex problem-solving, and human relations.

The majority of sales roles will focus on human relationships and connections. They will focus on situations where people are skeptical of technology, or where the sales apparatus is more complex than merely offering a widget.

If your sales job is largely based on easy-to-answer, back-and-forth conversations, or functional scheduling, expect it to change dramatically — or even go away entirely — within the next two decades.

Sales jobs of the future will require a much deeper understanding of customers and market dynamics than before.

We Must Place Greater Trust in Machines to Do the Job

In order to harness all the capabilities of AI, humans will have to place a basic measure of trust in it to do the reliable, public-facing work that people expect.

There’s a degree of unease that comes with the rapid rise of machine-learning. The notion of AI proliferation can be off-putting for many people.

Understandably so.

One thing that causes people to distrust AI is not understanding how it works, or how it arrives at its answers.

I was once asked by a client to explain how we were able to predict churn with higher accuracy. I could explain that we used AI, but I couldn’t do it in a more precise way.

I wasn’t being cagey; it’s simply a fundamental limitation of AI — specifically deep learning — as it exists today.

We don’t teach AI how to solve a problem, we teach AI how to teach itself to solve a problem. This means that the exact method it uses is often unknown, or too complex to understand.

This might sound problematic, but it’s not any different than what we experience when working with humans.

If you were asked,

“What did you have for breakfast this morning?”

You might respond by saying,

“A piece of toast.”

If you were pressed to answer why you ate that, you might say,

“I really like toast,” or “Toast was the only food I had at home.”

But that doesn’t explain why toast tastes good to you, why you chose it over going to the store, or how your brain even knows to classify it as food instead of some other inanimate object.

When we make decisions, it’s often because they feel correct. The true reason “why” is often well beyond our present grasp. Our brain simply does what it does. AI works in very much the same way.

Not understanding why a machine made a decision is usually only problematic when lives are at stake, like with self-driving cars, but for more mundane decisions, it’s not an issue.

If unsure, professionals can navigate this opacity by testing early and testing often to ensure the AI is making proper decisions.

It’s important to continually monitor what data is driving your AI’s decision-making, both to ensure the data sources are sufficient for driving the predictions and minimize the risk of undesirable bias due to limited data.

Distrust is something we need to get over if we’re going to effectively use AI in sales. Just because we don’t know how the AI solved a problem doesn’t mean we can’t trust it.

How You Can Use AI Today

Let’s get into the nitty-gritty of how you can stay on top of the game by using AI for sales to improve your efficiency, speed, and communication.

There are 3 main tasks that you can improve with AI in customer-facing sales right now.

  • Research
  • Communication
  • Training

We’ll look in-depth at each of these use cases to see how AI changes things, and how you can utilize it.

AI Use Case #1: Research

The struggle to find clients, research, and keep CRM data up-to-date is a task that sucks time and energy from a sales team’s day.

AI technology designed specifically for research can turn that job into a largely automated process.

Here’s the problem — AI cannot function without data, and it needs a lot of it to make high-quality predictions. Any manager who’s tried knows that getting sales teams to enter data consistently can be tougher than picking up the 800-pound phone to make a cold call.

Salespeople are busy.

When faced with winning or losing a commission check, they’re going to push forward and work directly with their accounts, not spend time entering data into a CRM system.

Luckily, AI and automation tools are also great for data entry.

RELATED: Sales Automation: 250+ Tools to Turbocharge Your Sales Process

Technology can’t take all the sting out of paperwork and data entry, but it can help.

Outreach and SalesLoft platforms track and manage email communication.

Chorus and Gong record and transcribe calls and other interactions.

Products like Yesware and Mixmax ensure inbox data finds its way into the corporate customer relationship management system.

Once solidly filed away, the data can then be curated by humans and bots. Salesforce notes that half of sales forecasts are driven by data alone, and that number should increase in the following years.

Now the only question is, how do you do use that data?

Finding the Perfect Client Fit

One of the best things you can do is put AI to work helping you find new prospects.

Figuring out the best prospects requires aggregating and coordinating data across multiple sources, looking for connections between each, and then qualifying the prospects.

This can be done by humans, but it takes a lot of time and effort.

Most sales groups rely on guesswork, buoyed by rudimentary data-clustering, to map out their strongest prospects.

Plenty of smart salespeople (and managers) operate under the belief that they should keep repeating their most successful sales efforts — with the same types of customers — until they no longer get hits.

Instincts and guesswork alone won’t cut it in the future. If you don’t use AI, then you’ll almost certainly miss out on new selling streams.

AI can help identify good fits and unlikely prospects that more traditional methods would miss.

For Instance, the data might suggest that the director or vice president of sales is your best bet at companies within a certain range of revenue. However, companies in that range might have dramatically different go-to-market models.

One may be a great fit, another may not be.

During my tenure as a sales leader at my company, we believed that our product was best-suited for a moderately sophisticated sales organization, typically companies in the $25 million to $250 million revenue range.

By using our own AI for account selection, we noticed that the companies’ go-to-market complexity formed a better indicator of their lead quality for us.

This meant that companies with as much as $5 billion in revenue suddenly became fair game if they presented a compatible structure.

If that doesn’t convince you, then this stat might — AI that can automate sales processes and recommendations is predicted to gain $2.7 billion in investment this year.

AI in Practice

I’ve seen this work firsthand. My company had a client that asked us to predict customer churn by looking at user engagement data.

With the help of AI, we were able to predict which customers churned with 45% higher accuracy than previous estimates. In the process, we found that 80% of the prediction improvement came from the structure and profile of the company using the product, not the engagement data.

None of those parameters were discernible by splitting up companies by industry, location, revenue, or other traditional classifiers.

Another time we asked our machine learning software to cull data for prospects related to the word “wine.”

Instead of concentrating only on wineries, the software returned unexpected potential customers:

  • Fine restaurants
  • Places in Napa Valley that had wine-oriented themes
  • Sommeliers who didn’t have “wine” explicitly stated in their bios

It didn’t matter that these suggestions contained no direct mention of “wine.” To the algorithms, those keywords were associated with wine.

Most humans wouldn’t immediately make all of these connections without some knowledge of the subject matter — and certainly not at the scale of millions of businesses — they would lose out on hundreds of potential new customers.

Allowing automation to take over is the key to uncovering revelations like these in the future. Nearly 62% of top sales pros say that this kind of guided-selling will become essential moving forward.

AI Use Case #2: Communication

Consider all the low-level conversations that go on in sales that AI could take care of.

Many sales teams have already started adopting chatbots to do just this — freeing up reps for deeper relationship building and strategic conversations.

RELATED: Do You Make These 13 Mistakes During Your Sales Conversations?

The possibilities for AI to improve communication will only grow as AI improves.

AI already reads at roughly the same level as humans.

  • It can comb through complicated documents more accurately — and at far greater speeds — than even the best associates
  • It can compose basic emails in ways that are strikingly similar to humans
  • It can respond to simple messages faster and more accurately than any human could

And the technology is only getting better.

Companies like Drift have found that people don’t mind interacting with a machine as long as they receive the help they need.

In fact, 55% of respondents to a recent survey by Drift said they would enjoy receiving a quick response to an easy-to-answer question from a chatbot.

Platforms such as Conversica — a conversational AI service for reaching consumers — are already taking the early communication steps out of the hands of business development representatives. The technology can answer basic product questions and schedule meetings without human intervention.

Drift’s bot (which my company uses) is another service that helps clients get answers to basic questions without human involvement.

Knowing What to Say and When to Say It

Taste, tact, and timing are crucial parts of relationship building in sales. Knowing what to say and when to say it is what separates the best salespeople from the rest. It involves making a judgment call about what prospects need and want, and understanding enough about their lives to appear at an opportune moment.

The underlying fuel for this skill is information. The more insight a sales representative has, the more empowered he or she will be to judge taste and timing.

Sales reps commonly spend between 6.5 and 8.8 hours every week gathering information about prospective clients.

That’s a lot of time they could spend actually talking with prospects.

AI can be instrumental in giving reps the tools to say the right thing at the right time.

Gong and Chorus are great resources for this. They’re always publishing research gathered from their call analytics platforms.

You can learn the most effective conversational tone, the best proportion of listening versus talking, maximum continual talk time, and endless other insights.

At my company, we’ve coached our sales reps based on this research, and we have seen an uptick in close rates as a result.

Deciding the Best Next Move

A big part of sales reps’ daily conundrum is trying to judge what they should do next.

They’ve made a connection with a contact, but what should they do now to keep warming that relationship without overstepping or being impatient? Should they:

  • Schedule a meeting?
  • Connect with another member of the team?
  • Send a piece of content or a reminder email?

These actions may seem similar or interchangeable, but the difference between the right connection and the almost right one can make or break an organization’s profitability in the long term.

Poorly judged communication can quickly weaken budding relationships.

AI for sales can help by collecting and inputting data without any leg-work from the sales rep. This leads to more accurate customer profiles that guide your reps to the most profitable course of action.

Platforms like Outreach and SalesLoft enable companies to experiment with different channels and cadences of communication to discover the most productive methods for different markets.

My company uses Outreach to find the perfect balance between phone calls and emails when prospecting.

We’ve found that a roughly equal number of calls and emails is optimal, with the addition of post-call email reminders providing an extra boost.

Without assistance from platforms like these — that utilize AI — we would be doing a lot more guesswork, and making a lot more mistakes.

AI Use Case #3: Training

Coaching salespeople might be one of the broadest and most transformational applications of AI for sales.

Reps need help understanding what mix of behaviors actually drive deals, and AI’s predictive capabilities can make this process speedy and accurate. In fact, the highest performing sales teams were 2.3X more likely to use AI guided selling.

RELATED: Coaching Salespeople into Sales Champions: 3 Times To Step In (& How)

AI can help direct training efforts, and in some cases can help predict where a deficiency may be before it appears.

Mobile devices work as great training devices because your sales reps all likely have some combination of phones, laptops, and tablets with them all the time.

For instance, the microlearning app Qstream can send training questions directly to the phones of sales reps. They answer the questions, and the managers review them.

This helps the reps grow their knowledge and confidence quickly and efficiently. Plus, it tells managers where sales deficiencies lie, and if more training is needed.

Of course, the best training pairs virtual training with in-person training by managers and peers.

Sales is all about relationships. But establishing good relationships takes time, technology, and training. Sales organizations that explore new technologies and training methods will see their reps improve faster and stand out in the sales world.

Dos and Don’ts of Making the Most of Sales Technologies

Sales teams that adopt AI stand to gain many benefits, just keep a few dos and don’ts in mind when you’re implementing high-tech solutions.

 

RELATED: In Search of the Perfect Sales Tech Stack (Here’s What’s Working Today)

All these forms of AI assistance will enable sales professionals to get back to doing what they do best — selling — something they currently spend just 34% of their time doing, according to Salesforce.

With so many benefits associated with technology adoption, salespeople should think in terms of how to use innovation, not should they use it. Smart companies will start embracing AI today.

This is a sponsored guest post from a Sales Hacker partner.

Greg McBeth is the head of revenue at Node.io, the first turnkey AI platform that enables businesses to embed artificial intelligence into their own products, customer databases, and external systems of record, all via a standard API.