May 16, 2024

AI for Sales Teams: How It Works, and How to Get Started

The average sales representative spends less than a third of their week actually selling

The rest of their valuable time is eaten up by planning, forecasting, researching prospects, and sitting in meetings. And while these tasks are important, they detract from the time reps can spend closing deals.

Fortunately, artificial intelligence (AI) is now changing that. Comprehensive ai sales assistant tools like AI Sales OS by Copy.ai can be used to automate repetitive tasks, research and predict best-fit prospects, hyper-personalize outreach, and dramatically lower the amount of time reps spend doing manual busywork.

Whether sales teams are using chatbots and AI sales assistants to scale their self-serve functions, or tools like Copy.ai to improve go-to-market velocity through workflows and tools that increase efficiency and lower bloat, AI sales automation is where the world is placing its bets for 2024 and onwards.

In this guide, we will:

  • Compare the sales process before and after AI automation
  • Look at the benefits of adding AI to your sales process
  • Explore the common (as well as unexpected) use cases of AI for sales
  • Suggest 6 steps to implement best practices in your sales organization

Let's dive in!

What is AI for sales?

Artificial intelligence (AI) is a type of technology that includes processes like machine learning, natural language processing, and deep learning. In AI for sales, these capabilities are used to make sales professionals’ jobs easier — by automating tasks, crunching numbers, surfacing opportunities, and suggesting sales copy.

But how does AI work?

Just like a sales development rep (SDR) might learn to write great cold pitch emails by studying a ton of examples and learning from a mentor, AI tools learn by being fed a tremendous amount of data. The platform then learns from these inputs to create outputs of its own, often detecting patterns that humans might miss — which can lead to the discovery of new opportunities, improved sales communication, and the highlighting of helpful customer data.

But AI and machine learning models don’t just produce new outputs — they’re specifically trained so that they continually improve their results. When these algorithms are being trained, they’re not just fed existing SDR pitches.

They’re also given the results of each of these pitches, along with huge data sets on the customers being pitched, so that they learn which types of content work best. They then tailor the pitches they write based on best results, the type of customer being pitched, the platform used for messaging, and the product being sold.

This same learning model is also used for lead generation and scoring, sales forecasting, and conversational AI.

Of course, SDRs and sales reps may modify their approaches based on their past experiences and the customer persona they’re pitching. But, far too often, these choices are based solely on hunches, which can be wrong. AI apps make decisions based solely on data, which is far more likely to lead to successful results.

Since computers have incredibly strong processing power, they can also churn out these outputs in a matter of seconds compared to your average SDR or sales rep. By completing routine tasks at a phenomenal speed in real time, AI can help salespeople accomplish more in less time.

The sales process before AI

Before the great AI revolution, salespeople were buried in tedious tasks that took up the majority of their time:

  • Tediously updating the CRM with research about prospects, companies, and market information
  • Continuously checking and updating battle cards with pertinent news and market insights
  • Manually researching and tracking prospects across the company
  • Rushing to prepare for calls with notes, outcomes, expectations, and talking points
  • Taking detailed notes during calls (while attempting to pay attention) and writing customized follow-up messages
  • Finding small pockets of time to keep your sanity

This left little time to focus on the key quality sales people are hired for: relationship-building for the long-term. However, according to Salesforce’s 2022 State of Sales report, high-performing sales reps were nearly two times more likely to use AI than underperformers.

This shouldn’t be surprising, considering the ever-growing list of opportunities that arise when adding AI to your workflow. 

How does AI for sales actually work?

Artificial intelligence (AI) is a type of technology that includes processes like machine learning, natural language processing, and deep learning. In AI for sales, these capabilities are used to make sales professionals’ jobs easier — by automating tasks, crunching numbers, surfacing opportunities, and making recommendations for copy, insights, and ideation.

What makes AI feel like magic in the sales process? Its capacity to “learn on the job.”

Just like a sales development rep (SDR) might learn to write great cold outreach emails through studying examples or learning from a mentor, AI systems learn from:

  • Growing online databases and data points (trillions, last we checked)
  • Company information about prospects, pitches, and more
  • Analytics and outcomes of deals

Because AI is continuously trained on massive databanks, they tend to excel with things like pattern-finding and statistics (ie. win/lost), and are able to compute all of this within seconds.

So, all SDRs have to do is ask the right questions, set up the most efficient automations, and make use of the most valuable workflows for them. 

By using the power of AI sales assistants for automating repetitive tasks (like call transcription, analysis, and summaries), reps free up their time do more of the “human” part of their own sales jobs: building rapport with customers and prospects over the long-term, so they’re viewed more as consultants than pushy salespeople.

The Benefits of AI for Your Sales Team

1. Increased productivity and efficiency

AI technology can significantly enhance the productivity and efficiency of sales teams by automating manual tasks.

With AI, sales representatives can focus their expertise on closing deals while mundane tasks like data entry, lead enrichment, and scheduling are handled seamlessly. By freeing up time, sales teams can cover more ground, further customer relationships, engage with more prospects, and increase their chances of achieving revenue targets.

2. Improved analytics and insights

AI-generated analytics can help sales teams gain a deep understanding of customer preferences, pain points, and buying signals.

With this information, sales representatives can tailor their approach and offer personalized solutions to customers.

AI can also help sales teams make data-driven decisions (thanks to its processing power and strength in recognizing patterns in mass amounts of data), optimize sales strategies, and identify trends and patterns that would otherwise go unnoticed. 

3. Higher conversion rates

AI can help sales teams achieve higher conversion rates by identifying the most promising leads and providing sales representatives with valuable insights on how to engage and convert these prospects effectively.

Alternatively, AI-powered chatbots and virtual assistants can also assist sales managers in nurturing leads, answering inquiries, and guiding customers towards purchase decisions.

4. Faster processes

AI automation streamlines time-consuming tasks like manual data entry, report generation, and follow-up emails.

With this newfound efficiency, sales teams can operate at a faster pace with greater accuracy and consistency across tasks. This allows sales representatives to respond promptly to customer inquiries, capitalize on time-sensitive opportunities, and stay ahead of competitors.

5. 24/7 availability

AI-powered chatbots and virtual assistants can provide 24/7 availability, enhancing customer satisfaction by offering prompt and personalized service regardless of the time or day.

This availability allows sales teams to capture leads and address customer needs at any hour, establishing a reputation for exceptional service and providing a competitive edge in the B2B sales landscape. 

Also, AI sales assistants (like AI Sales OS by Copy.ai) run workflows automatically in the background 24/7: transcribing, assessing, analyzing, and providing insights while you sleep, so that when you get to your desk, you have valuable data to take action on with prospects.

Common Use Cases and Applications of AI on Sales Teams

AI technology is a powerful tool for sales teams, and it can help with just about any part of the sales process. With the help of AI, teams can now use data to make smarter decisions about their outreach and communication, sales forecasting, customer segmentation, and lead scoring — and these days, AI can even engage in conversation and make product recommendations for customers.

1. Prospecting

AI can analyze data from various sources to identify potential leads that are more likely to convert. This helps sales teams focus their efforts on high-value prospects and improves their prospecting efficiency.

AI Sales OS has prospecting workflows that help you:

  • Find detailed information on a lead or a company via a LinkedIn URL 
  • Find cross-sell expansion leads from an existing customer
  • Enrich prospect information from their email address
  • Find decision makers at particular companies

2. Competitive analysis

AI-powered sales tools can gather and analyze data about competitors, such as pricing, product features, customer reviews, and social media presence. This information enables sales teams to identify unique selling points, develop effective strategies, and update battlecards to stay ahead of the competition.

AI Sales OS has competitive analysis workflows that help you:

  • Automatically create a competitor analysis report using their URL and G2 reviews, including any recent news
  • Analyze competitor blog posts, landing pages, and marketing strategies

3. Personalized outreach

AI can help sales teams personalize their outreach efforts by analyzing customer data and generating insights from customer interactions. This enables sales representatives to tailor their messaging, recommendations, and offers to individual customers, increasing the chances of success.

AI Sales OS has personalized outreach workflows that help you:

  • Customize both cold outbound emails and inbound lead follow-ups with unique data pulled from LinkedIn profiles and email addresses
  • Tailor messages based on LinkedIn profile, posts, and interactions with your website

4. Lead scoring and segmentation

AI algorithms can assess the likelihood of a lead converting into a customer based on various factors, such as behavior, demographics, and engagement. This helps sales teams prioritize leads and tailor their approach accordingly.

AI Sales OS has lead scoring workflows that help you:

  • Verify the potential value of your leads with lead scoring
  • Extract MEDDIC (Metrics, Economic Buyer, Decision Criteria, Decision Process, Identify Pain, and Champion) from sales call transcripts
  • Get a quick analysis of lead score and segmentation as well as insights on nurture suggestions sent to Slack (this workflow has changed the mind of our Head of Growth about pursuing certain accounts!)

5. CRM management and hygiene

AI can automate tasks related to CRM (customer relationship management), such as data entry and updating customer records. This streamlines the process, reduces manual effort, and improves data accuracy and hygiene.

AI Sales OS has CRM management workflows that help you:

  • Transcribe sales calls and pull out pertinent insights
  • Update customer and account records and notes after sales calls
  • Extract product feedback from sales transcripts to siphon to your product team

6. Content personalization

AI can analyze customer preferences and behavior to recommend relevant content, such as articles, blog posts, or product recommendations. This helps sales teams engage customers with personalized content at various stages of the sales process.

AI Sales OS has content personalization workflows that help you:

  • Create sales enablement documents that are catered to the unique pain points mentioned during sales calls
  • Suggest content from your website that might be relevant for customers

7. Sales coaching

AI-powered tools can analyze sales calls, emails, and meetings to provide feedback and suggestions to sales representatives. This helps improve their sales techniques, communication skills, and overall sales performance.

AI Sales OS has sales coaching workflows that help you:

  • Analyze data from earnings calls
  • Pull key insights about product feedback, pain points, and sales techniques from sales calls
  • Provide feedback on transcribed sales calls based on your company’s sales scripts and best practices

Best Practices for Implementing AI in Sales

Integrating AI and automation into your sales processes can deliver significant benefits, but it's important to approach implementation strategically. Here are some step-by-step suggestions to help you add AI to your sales function effectively:

Step 1: Assessing Current Sales Processes and Metrics

Start by assessing your current sales processes and metrics to identify any weaknesses or bottlenecks that could benefit from AI and automation.

Where is your team spending the most time? Where are your results lagging? What are the current bottlenecks in your sales process? What are the time-consuming, repetitive tasks that are most prone to errors?

Based on your findings, explore where sales AI could potentially be of use. For instance, if you find that SDRs’ cold emails aren’t getting the results they should and that your account managers are spending too much time answering emails, an AI content generator could hit two birds with one stone. 

Each company’s assessment and opportunities for automation will be different, but here are some examples of sales tasks that are primed for AI:

  • Researching prospects, companies, and identifying good leads
  • Customizing cold emails and LinkedIn outreach to make an impact
  • Transcribing and summarizing sales calls
  • Updating CRM with insights and follow-up notes from calls

Step 2: Researching AI Tools and Platforms

Thoroughly research different AI sales tools and platforms available in the market. Evaluate their features, integrations, ease of use, and scalability to find the solution that aligns best with your team's needs and goals. Consider factors such as data security, customer support and training, and compatibility with your existing systems.

Here are some AI solutions you could consider for your organization:

CRM with AI capabilities: CRM platforms that leverage AI to optimize sales processes and provide predictive insights can automate data entry, provide lead scoring, offer personalized recommendations, and optimize sales workflows. Examples include: Salesforce Einstein, Microsoft Dynamics 365 AI for Sales, and HubSpot Sales Hub.

Sales Engagement Platforms: Sales engagement platforms leverage AI to improve communication and engagement with prospects and customers, and offer features such as email tracking, cadence automation, call coaching, and AI-powered analytics to optimize outreach efforts. Examples of sales technology used include: Outreach, SalesLoft, and Groove.

Conversation Intelligence: Leveraging AI to analyze sales calls and meetings helps you automatically provide insights into sales conversations, identify successful sales tactics, and offer coaching opportunities based on data-driven sentiment analysis. Top picks include: Gong.io and Chorus.ai.

Lead Generation: These tools help sales teams identify and target potential leads more effectively using AI algorithms to analyze data and provide accurate prospecting information. Examples include: LinkedIn Sales Navigator and DiscoverOrg.

Sales Analytics and Forecasting: These advanced tools help sales teams gain deeper insights into their pipeline, track performance metrics, and forecast sales outcomes through historical data analysis and predictive sales cycle forecasts. Clari, InsightSquared, and Tableau are top examples in this category.

Sales Chatbots: AI-powered chatbots engage in real-time conversations with customers and assist with lead generation, customer support, and nurturing. These chatbots leverage natural language processing and machine learning to understand and respond to customer inquiries. Some examples include Drift, Intercom, and HubSpot Service Hub.

AI Sales OS: Alternatively, you could use an AI-powered Sales Operating System like the one from Copy.ai to apply automation to all of your go-to-market sales functions.

AI Sales OS by Copy.ai offers workflows for the full sales process — in one place:

  • Generating pipeline through automated company and persona research, lead enrichment, and personalized cold outreach
  • Powering operations with AI-powered lead scoring, meeting summaries, and opportunity analysis
  • Closing business with post-meeting action items, win probability scoring, and sales forecasting
  • Enabling reps through marketing content repurposing, battle card recommendation, and competitor analysis

Step 3: Starting with a Pilot Program

If the myriad use cases for AI in sales sound overwhelming, don’t worry.

Most sales teams don’t integrate every AI sales tool into their tech stack all at once. Instead, you can take a methodical approach to decide which area to invest in and start with a pilot program in which you implement AI in a specific area or process. 

This allows you to test the effectiveness of the chosen AI tool in a controlled environment and gather feedback from your team.

Make necessary adjustments and improvements before scaling up to minimize disruption to sales operations and ensure a smooth transition.

Step 4: Training Employees on New Processes

Provide comprehensive training to your sales reps on how to use the new AI tools and incorporate them into their workflow.

Offer hands-on training sessions, resources, and documentation to ensure everyone understands and feels comfortable using the AI technology. Encourage open communication and address any concerns or questions that arise during the training process.

Step 5: Setting Goals and Metrics to Track Success

Clearly define your goals and metrics for implementing AI in sales. Establish key performance indicators (KPIs) to measure the impact and success of the AI implementation.

This could include metrics such as increased lead conversion rates, reduced response time, improved accuracy in forecasting, or enhanced customer satisfaction. Regularly track and analyze these metrics to gauge ROI and make data-driven decisions.

Step 6: Developing a Rollout Roadmap

Create a rollout roadmap to guide the phased implementation of AI in your sales team processes.

Determine the order and timeline for implementing AI in different areas, considering dependencies and resource availability. Communicate the roadmap to all stakeholders and ensure alignment across the organization.

Regularly assess progress and make any necessary adjustments to the roadmap based on feedback and evolving business needs.

By following these best practices, you can implement AI across your sales team smoothly and maximize the benefits for your sales leaders and entire organization. Remember to continuously monitor, evaluate, and optimize the use of AI to stay ahead in the competitive sales landscape.

How AI is Revolutionizing Sales Teams

While AI may not be able to convert a prospect on an hour-long call just yet, it can and does lighten the administrative load, automate the busywork, and set you up to deliver value in every sales call you make.

This is your invitation to not only witness but lead the revolution for sales excellence: where every sales challenge is met with an intelligent, data-driven solution. 

Embrace the future with AI Sales OS, where cutting-edge artificial intelligence empowers your sales team like never before. Try it for free today or join our forward-thinking community to redefine the boundaries of what's possible in sales with AI.

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