AI is making a big impact on how sales teams function. It won’t replace your sales manager, but the use of artificial intelligence in sales can automate some of the menial tasks involved in successfully closing a deal. Eliminate data entry? Check. Source leads? Check. Spend less time crafting an email? Check.

The problem is that small businesses struggle with incorporating AI into their sales processes on a tight budget. Knowing which software to invest in—and what gives the biggest ROI—is daunting for small businesses that don’t know enough about AI to comfortably navigate the landscape.

The easiest way to get started is by using sales tools with built-in AI capabilities. Many CRMs and sales software tools have incorporated AI and machine learning into their offerings, allowing for an accessible introduction for small businesses looking to use artificial intelligence in their sales processes.

And it’s wise to get started sooner rather than later.

Predictions from Statista show steady year-over-year growth in revenue from AI, with an estimated total $396 billion gained from AI-related CRM activities by 2021. If you want a slice of this AI pie, you need to start incorporating artificial intelligence into your CRM and sales recipe now.

In this piece, I’ll go through the:

  • Different types of AI used in sales
  • Tasks that can be automated using AI
  • Role of a sales manager in championing AI

2 types of AI for sales

Gartner defines artificial intelligence as technology that “appears to emulate human performance.” (Full report available to clients.)

Gartner further explains:

“It learns, comes to its own conclusions, appears to understand complex content, engages in natural dialogue with people, enhances human cognitive performance or replaces people in the execution of nonroutine tasks.”

Although the use of AI is broad and seemingly limitless across industries, its use in software technologies such as CRM generally takes two different forms:

  • Machine learning (ML): As a branch of AI, machine learning uses tons of data to learn from previous interactions to help complete tasks or make predictions. It relies on the amount and quality of data that you have in order to learn from; the better the data, the smarter your AI will be.
  • Natural language processing (NLP): This involves using human interactions to help make your AI smarter. Take customer service chatbots as an example. The more questions that you ask it, the larger variety of answers it’ll be able to provide.

It’s more than just automation: AI is able to use data to make predictions and draw conclusions that would otherwise be difficult to source.

Below are four sales tasks that AI can make a lot easier.

Sales forecasting

Sales forecasting relies on historical data to make educated assumptions about what sales figures will look like in the future. It takes into account monthly sales figures and fluctuations due to seasonality to help sales managers better assign resources.

Traditionally, this has been a manual process that involves the sales manager interpreting data from previous periods to plan for upcoming sales cycles. For non-savvy sales managers, this is a daunting and time-consuming task.

 THE AI UPGRADE: 

Artificial intelligence takes a lot of the grunt work out of forecasting by mining CRM data and making predictions based on complex algorithms that are much more accurate than a sales manager could hope to be. It’s known as predictive analytics, and it can provide a dynamic picture of predicted sales figures based on even the most recent sales data from the previous day. This allows for much better planning and resource allocation than without the power of AI.

Clari uses AI to help make predictions based on historical sales data

Clari uses AI to help make predictions based on historical sales data (Source)

Writing emails

Emails are an important part of the sales process as an ideal way to connect with prospects and follow up on deals. Unfortunately, sales reps spend far too much time sending them.

According to research from Forbes, sales reps spend 33 percent of their time writing emails. Compare that with the 35 percent of their time spent actually selling, and you can see where there’s a huge opportunity for improving the balance between revenue-generating and non-revenue generating activities.

Automation has added the ability to send emails using triggers based on prospect activity (or inactivity), but it still requires a lot of prep time to set up properly.

 THE AI UPGRADE: 

Personalization is a huge part of how AI-powered emails can help sales reps better target prospects with more accuracy and better messaging. Pulling data from previous interactions logged in your CRM will add individualized context to the email. Some solutions can even incorporate industry research including trends and statistics into email communication to add further personalization. Many solutions also offer smart suggestions for how and when to contact prospects based on strategic and personalized touch points.

Nova uses AI to help craft personalized emails and track their success

Nova uses AI to help craft personalized emails and track their success (Source)

Lead management

Generating highly qualified leads is still a fairly manual task for sales teams. It involves scouring social media, setting up Google alerts, and deciphering which companies and contacts are good opportunities to reach out to.

Once there are enough leads in the pipeline, sales reps need to keep track of where leads are in the sales cycle, which ones need following up with, and how likely they are to convert. Known as lead scoring, this means continuously going through a CRM to find hidden gems among a huge pool of potential customers and clients.

 THE AI UPGRADE: 

AI-powered tools can scour the web for lead opportunities, removing the manual effort involved in the hunt for potential customers while also providing a list of highly valuable prospects likely to convert. After setting up parameters for the type of leads that you’re looking for, the process can be automated.

To score leads, a CRM will use predictive analytics by pulling relevant data from the software (as well as pulling data from your marketing tools) to be able to bucket leads into those most and least likely to convert. Data includes previous interactions, social media activity, and company or contact status to identify the most promising opportunities. It can also help find the best opportunities for upselling and cross-selling based on relevant data.

Cognism uses AI to identify the most promising leads

Cognism uses AI to identify the most promising leads (Source)

Performance management

Performance management is one of the least likely tasks to be automated. It relies on a good sales manager to coach and mentor sales staff to grow within the company. This involves looking at past and current sales numbers for individual reps, seeing what’s available in the pipeline, and monitoring the progress of deals to see where there’s opportunity for team members to close.

 THE AI UPGRADE: 

Predictive analytics strikes again! To help sales managers spend less time crunching numbers and monitoring sales calls, AI-powered features in sales tools can do it for them.

Not only will AI-powered tools be able to listen in on sales calls, they can also help draw conclusions. Based on learned cues from past performance, previous trends related to specific companies, and historical deal progression, AI features can spot opportunities for improvement and ping sales managers to help sales reps take the next steps to close the deal. Depending on which stage of the sales cycle the customer is in, this can be sending an email, making another phone call, or scheduling a meeting.

It will still be up to sales manager to coach staff, but being able to monitor and identify performance improvements can safely go in the hands of AI.

Chorus uses AI to record sales calls and find patterns that can be used to improve sales activities

Chorus uses AI to record sales calls and find patterns that can be used to improve sales activities (Source)

Turn your sales manager into an AI champion

Despite recent advances in AI, the technology is still largely in its infancy. As Gartner notes (research available to clients), AI for CRM is at the top of the hype cycle, and there are still a lot of kinks to work out before it’ll be a ubiquitous part of every CRM and sales solution. Those kinks have caused some skepticism among decision-makers when it comes to adopting or using a CRM with AI capabilities.

It’s important to have a champion within the organization that can promote the benefits of incorporating AI into a CRM strategy. Sales managers are a natural fit: They can use their knowledge, experience, and judgment to hammer home the potential benefits of early AI adoption.

Sales managers will play a crucial role in driving adoption among:

  • Stakeholders: Stakeholders are wary about investing in a technology that’s not fully fleshed out. Sales managers should stress the high ROI potential of early AI adoption and the accessibility of AI through affordable software tools like a CRM.
  • Management: Management is wary about the implementation challenges involved with adopting new technology. Sales managers should highlight the seamless functionality of AI in CRM systems and calm fears about the broad-sweeping technological barriers to entry associated with AI.
  • Employees: Employees are wary about the threat of AI taking over their jobs. Sales managers should stress the importance of sales reps using AI to improve upon sales processes, as opposed to worrying about it taking over their roles.

Moving forward, AI will only play a bigger role in sales processes. It’s important that sales managers become champions for early adoption to get ahead of the curve and become familiar with the technology and its potential.