AI for Sales Enablement

What is AI For Sales Enablement?

AI for sales enablement integrates artificial intelligence to enhance and support the sales process. While sales enablement provides sales teams with tools, resources, and support to sell effectively, AI revolutionizes this by automating tasks, delivering real-time information, and improving efficiency.

Examples of AI in sales enablement include:

• Notetaker apps that record and transcribe conversations during sales calls
• Virtual assistants that schedule meetings and send reminders
• Sales analytics tools that track prospect behavior to identify and score potential leads
• Chatbots that interact with customers to answer inquiries and assist in purchases

While sales enablement software gives sellers tools that help them personalize the buying experience and close more deals, AI takes it to the next level. Thanks to AI, today’s sales reps have unprecedented access to company information, high-quality customer data, and granular analytics.

Synonyms

• AI sales enablement software
• AI-powered sales tools
• AI-driven sales enablement platforms

Significance of Sales Enablement in Modern Business

As of December 2023, 87% of companies on Forbes’ Cloud 100 list had a sales enablement function. And, over the years prior, corporate interest in the function (and technology surrounding it) has been on a consistent rise.

There are a few glaring reasons for this:

• The ever-growing need for personalization and a frictionless buying experience
• The inherent complexity of sales and customer data
• The abundance of sales and marketing collateral, most of which goes unused

In the past, companies relied on generic sales enablement programs. They provided one-size-fits-all content to their sales force. Customization for individual reps was limited, and firms expected all sales personnel to absorb the same fundamental material at the same pace. There was little acknowledgment of the hurdles reps faced individually.

Corporations were ready to invest considerably in these initiatives, but they struggled to gauge the effectiveness of their expenditure. The success of these programs was judged merely by whether sales staff completed the training, not by their ability to implement what they learned to clinch deals.

It’s hardly surprising, then, that sales professionals often viewed these enablement efforts as mere formalities, rather than opportunities for advancement. However, with the advent of AI for sales, this perception has radically transformed.

How AI Helps Sales Enablement

One of the key ways AI contributes to sales enablement is by automating time-consuming, manual tasks that traditionally occupied a significant portion of the sales team’s workweek. It handles tasks like drafting and sending documents, creating sales reports and forecasts, scheduling calls, and running lead gen campaigns.

AI also plays a crucial role in prospect and lead management. It automates data entry, which reduces human errors and saves valuable time reps can spend on more strategic sales activities. AI tools can enhance voice search capabilities, update lead records with additional information, and improve lead scoring by analyzing past deals to identify prospects with the highest conversion potential​​.

Training sales teams is another area where AI can have a significant impact. AI tools can record, transcribe, and analyze sales calls, helping leaders and team members identify strategies and areas for improvement. This information is used to develop better sales scripts and training programs, and to address buyer concerns more effectively.

In terms of refining your sales enablement strategy, AI can keep prospect lists clean and up-to-date, enrich data with the latest information, and improve lead scoring by identifying the leads most likely to convert. AI can also assist in creating training programs tailored to the needs of new sales team members, helping them become productive more quickly​​.

Core Concepts in AI For Sales Enablement

The exact application of sales enablement tools, of course, depends entirely on (a) the ones you’re using and (b) how you integrate them into your workflows. Today’s AI sales software is built for scalability, extensibility, and customizability.

Let’s take a look at a few core concepts in AI for sales enablement and how they can enhance the sales process:

Machine Learning

Machine learning is a subset of AI that focuses on algorithms that enable computers to perform tasks without explicit instructions. Instead, ML relies on patterns and inference to learn from data, make predictions, and decisions.

In the context of sales enablement, machine learning is used in tools like:

• CRM systems (to analyze data and predict customer behavior)
• Predictive sales analytics software (to automate forecasting and identify sales trends)
• Lead scoring tools (to identify high-value leads)
• Chatbots (to interact with customers, present relevant information, and gather data)

Fundamentally, machine learning works by continuously gathering large sets of data relevant to the task at hand, such as past sales interactions, customer profiles, and market trends. From there, the system develops an understanding of what success looks like and uses that to make predictions, drive decisions, or automate workflows.

Natural Language Processing

Natural language processing (NLP) is a branch of AI that enables computers to understand and process human language. Sales enablement tools that use NLP can analyze the intent behind customer inquiries and assist in purchases. A perfect example is chatbots that recognize keywords and phrases to provide personalized responses.

NLP is also used for voice search, which can significantly improve the accuracy and speed of sales reps’ searches for information. Reps can use voice commands to pull up relevant data or find answers to customer questions, making meetings and calls more efficient.

Generative AI

Although personalization is more important than ever, there are still elements of sales outreach, engagement, and negotiations your team can automate.

• The bulk of your DM/cold email
• Initial replies to inbound leads
• Email follow-ups
• Appointment scheduling
• Contract generation (with contract AI)

Generative AI is built on GPT (generative pre-trained transformer) — a deep learning algorithm that understands and generates human-like written text. It’s trained on millions of words from the internet, so it understands context and can generate text that follows along with your brand’s or seller’s voice.

It isn’t a replacement for human touch, of course. But you can use it to auto-draft a sales outreach message based on a prospect’s potential use case for your product, respond contextually to a lead inquiry, or fill in the blanks of a follow-up email template based on a previous interaction.

Predictive Analytics

Predictive analytics assess large volumes of your sales data, then make inferences based on patterns, macro and micro trends, and input from external sources. They’ll normally give you an upper limit (best-case scenario), lower limit (worst-case scenario), and an expected value (reasonable prediction), which you can use to conduct a cost-benefit analysis.

You can use it to:

• Forecast (and communicate) pipeline growth
• Decide where to invest in or cut resources
• Understand product performance on a granular level
• Set realistic sales goals for each quarter, month, and year

Enterprise companies with complicated webs of data will still need to visualize and make sense of the data with a dedicated team and sophisticated tools. But most predictive modeling software can visualize certain data for you (e.g., a sales matrix or revenue growth chart).

Conversational Intelligence

This involves tools that analyze sales calls to extract vital insights, such as identifying trends, objections, and key moments within conversations. An AI-powered tool can:

• Recognize the context of a conversation
• Note key terms like pain points and competitor mentions
• Assess a buyer’s tone, body language, and engagement levels
• Pinpoint areas for improvement in sales performance

By understanding the “why” behind sales outcomes, sales teams can work with their mentors or leaders to adapt their techniques and improve their skills​​​​.

The Components of AI-Powered Sales Enablement

Sales Analytics and Insights

Sales analytics is perhaps the broadest application of AI sales enablement tools. It’s essential for gathering data-driven insights, optimizing productivity and workflow processes, and identifying key areas of improvement.

It includes:

• Predictive analytics
• Forecasting software
• Performance measurement tools
• Data visualization software

Predictive Lead Scoring

Lead scoring is the process of assigning a numerical value to prospective customers based on their likelihood to convert, potential value, and readiness to buy. AI-powered lead scoring tools analyze historical data on the characteristics of your most valuable customers. They assess that alongside buyer engagement data to determine each lead’s potential value.

Content Personalization and Recommendations

AI-powered tools can analyze customer data and behaviors, then use that information to create personalized content tailored to their specific needs.

• Automated email campaigns
• Dynamic content creation (for websites, social media posts, etc.)
• Content recommendations for sales reps based on the lead’s stage in the buyer’s journey
• Personalized product or service recommendations based on a customer’s interests and previous purchases

Content personalization and recommendations help sales reps deliver more targeted messaging, which ultimately improve the customer experience and increase your conversion rate.

Sales Coaching and Training

Whether you’re onboarding new reps, going over qualification calls at weekly sales meetings, or doing a deal review for a high-value prospect, AI-powered software can guide the process.

You can use AI to:

• Deliver personalized learning experiences
• Analyze sales calls to provide feedback
• Use predictive analytics to identify skill and communication gaps
• Create automated role-play scenarios
• Give real-time guidance during customer interactions
• Track performance to tailor your coaching efforts

Let’s say you have an SDR who’s struggling to hit their quota. With a sales AI tool, you could pull up some of their qualification calls during a 1:1 to see how well they execute your sales methodology. You could also pinpoint issues in their overall delivery.

You might find they didn’t do well at getting all the information from the prospect, or their sales mirroring was too obvious. These are the types of insights you can use to improve each team member’s abilities at the individual level.

CRM Integration and Data Management

Integrating sales enablement AI tools with your CRM software gives them access to all your customer information and sales pipeline data. This integration improves the accuracy of AI-assisted sales tasks, recommendations, and insights.

One critical area where this can really help is when it comes to managing your prospects’ contact data, which changes from time to time. AI can enrich customer profiles with the most recent information. It can also flag dead leads and stale opportunities, which you can handle by either eliminating them from your pipeline or re-engaging them with a new approach.

Benefits of AI-Driven Sales Enablement Platforms

Improved Sales Performance and Efficiency

The 4th edition of Salesforce’s State of Sales Report found that high-performing sales teams were 2.8x more likely to use AI sales software than their underperforming counterparts.

Really, this comes down to two things:

• Automation
• Data integration

Artificial intelligence algorithms are constantly gathering data, so sales team members have the most valuable and timely information for each prospect. And, since AI automates most of the clerical work associated with lead management, they can spend more time selling. Plus, they have the bandwidth for more leads in their pipeline.

Enhanced Customer Engagement and Personalization

Two-thirds of B2B buyers say they expect the same level of personalization in their professional lives as their personal ones.

Frankly, that isn’t a demand you can deliver on without the help of AI. There are just too many variables. And reps are juggling too many deals at once to constantly recall each buyer’s specific needs.

When AI does the heavy lifting for them by…

• Scoring deals based on engagement and potential value
• Presenting relevant content during and after sales conversations
• Analyzing customer data to inform personalized outreach

…sales reps can make every interaction count. They can respond faster to every customer, and they can do so with the content and information that drives the deal forward.

More Accurate Sales Forecasting

When you have a clearer understanding of the factors that drive customer decisions, and how those differ from one deal to another, it becomes a lot easier to forecast your team’s performance.

AI-powered sales forecasting tools analyze historical data, trends, and patterns to project future sales numbers. Sales leaders use this info to make informed decisions about resource allocation, setting goals, and identifying areas for improvement. Plus, it helps you communicate with your stakeholders and, if you’re fundraising, potential investors.

Streamlined Sales Processes and Workflows

You can use artificial intelligence to streamline your whole sales motion.

• Onboarding new reps
• Prepping for sales demos
• Responding to prospects in real-time, on calls
• Driving deals forward with automated playbooks
• Nurturing and re-engaging leads
• Reviewing deals in the pipeline

Every step of the way, AI makes it easier for you to stay on course and deliver a seamless selling experience. The end result is a faster sales velocity, a highly engaged sales force, and more top-line revenue generated.

Better Use of Sales and Marketing Collateral

Forrester found that only a small minority (35%) of marketing content is actually used. According to the market research firm, there are two reasons behind this:

• It’s unfindable (to users and your sales team)
• It’s unusable (irrelevant, low quality)

The latter is a marketing problem (though AI’s insights can help fix it). But the former is easily solvable with AI sales enablement software. It indexes all your sales content and tags it with relevant metadata, so reps can find what they need quickly (or, it can present it automatically). Plus, you can use AI to analyze how effective each piece of collateral is at moving deals forward.

Better Sales Coaching and Training Opportunities

Sales training, done properly, nets a 353% ROI for the average company. Realistically, personalized training that hits the nail right on the head can probably deliver far better results. 

Sales AI tools can help you identify…

• The skills reps lack
• Where they’re struggling to close deals (and why)
• What coaching will be most effective at improving specific KPIs

It can also assist in automating training processes and providing personalized modules for each rep’s unique areas for improvement.

Best of all, these insights are based on hard data, so the coaching and training you provide have a much better chance of working effectively. And if they aren’t, you can use those insights to refine them.

Challenges and Considerations in Implementing AI For Sales Enablement

Data Privacy and Security Concerns

As far as data privacy is concerned, the core issue revolves around handling sensitive customer information, which, if compromised, could lead to breaches of confidentiality and trust. That erodes customer relationships and corporate reputations.

It’s also a double-edged sword — AI can be both a defender and a potential tool for attackers. Robust security measures play a vital role in safeguarding against cyber threats and manipulation. AI enhances cybersecurity by detecting anomalies, but it can also be exploited for sophisticated attacks.

Integration with Existing Systems and Processes

Integration capabilities are the most important consideration when investing in AI sales enablement tools. AI algorithms must be able to link, analyze, and distribute data between your different systems.

If they can’t share information, you’ve just created another data silo. And you’ve failed to automate anything.

Ideally, look for tools that take a modular approach (like DealHub), so you can keep as much functionality within the same UI and codebase. For outside tools, like a data enrichment app or sales coaching platform, look for native integration with the system they affect most directly (normally your CRM).

Adoption and Training Challenges

Even if your software works well together, adoption isn’t guaranteed. You need a system your sales team finds easy to use.

Again, this is why you want to look for tools with additional modules, like Salesforce’s Sales Cloud or DealHub’s CPQ, Billing, and DealRoom. It allows you to keep everything within the same user interface, so your sales reps don’t need to adjust to multiple systems.

During the procurement process, get opinions from your sales team. And before you implement a new AI system, provide hands-on training to your employees.

Ensuring Ethical Use of AI in Sales Enablement

Ethics are, of course, a concern when it comes to anything surrounding customer data. To avoid the pitfalls of unethical AI use, follow these best practices:

• Be transparent about how you gather and use data to train your algorithms
• Let customers know when calls and meetings are being recorded
• Evaluate AI tools for ethical considerations before purchasing them
• Continuously evaluate your AI tools’ outputs for accuracy, fairness, and potential bias

Remember that in addition to legal concerns, ethical missteps can erode trust and damage your reputation.

Examples of AI-Powered Sales Enablement Tools

Gong + DealHub: Sales Intelligence and Predictive Insights

Gong, an AI-driven revenue intelligence platform, is a perfect example of this. It helps sales enablement teams improve sales productivity and onboard new hires faster by recording, transcribing, and analyzing every sales call. 

Integrating Gong with DealHub, gives your sales team has a holistic view of buyer intent and behavior for every deal they’re working, while they move through the quoting process. Predictive algorithms use the data to generate a consolidated view of pipeline risk and next-best actions your sales rep can take to increase their chances of a conversion.

McKinsey Periscope: Advanced Lead Scoring

McKinsey Periscope’s Dynamic Deal Scoring platform is an example of how AI can take the concept of lead scoring to the next level for complex enterprise deals.

The system recognizes key factors like deal size, deal stage, product configuration, and the sales channels utilized. From there, advanced analytics models like K-means clustering and Chi-square Automatic Interaction Detector (CHAID) decision trees, group similar deals.

After the comparison, it generates a score based on quality, value, and price optimization insights with color-coded indicators. Deal scores are used in sales processes to direct deals for approval and adjust incentives based on quality.

Aircover: Real-Time, In-Meeting Sales Coaching

Aircover is a software you can use to integrate Zoom with your sales playbook. Based on each unique sales conversation, it surfaces the best sales content, responses, and next steps in real time.

On the backend, it takes notes, summarizes the meeting, and provides insights you can use to tailor the rest of your approach to that deal.

ChatSpot: CRM Data Enrichment

Data enrichment enhances data quality by importing information directly from a third party into your CRM. This helps you gain a deeper understanding of prospects, leads, and existing clients.

Traditionally, sales reps manually input supplemental info to profiles. Now, AI tools like ChatSpot can import spreadsheets, recognize customers or leads, and link them to the correct CRM profile.

In addition to learning more about existing data categories, you can also discover new contexts. For instance, import discovery call transcriptions into CRM to enrich customer profiles with qualitative data, including sentiment.

Future Trends and Outlook for AI in Sales Enablement

Emerging Technologies and Innovations

While we already have the primary technologies driving AI for sales enablement, the future holds massive potential for advancement and extending capabilities. Some key areas AI will continue to improve your sales workflows include:

• Lead identification and scoring
• Personalization and customer-centric selling
• Sales planning and reviews
• Sales playbook optimization
• Sentiment analysis and social listening
• Customer advocacy and social proof in the sales cycle
• Risk assessment
• Sales forecasting

Potential Impact of AI Advancements on Sales Strategies

Gartner predicts that by 2025, 80% of all B2B sales interactions will use AI in some way to improve productivity and enhance the customer experience.

Tools like Microsoft’s Sales Copilot and Salesforce’s Einstein GPT represent a paradigm shift in sales technology integration. However, building personal connections and trust between sales professionals and customers remains crucial for success.

With tech handling all the clerical work (and arguably removing some of the “personal” elements of sales), modern-day sales excellence is defined by two things:

• Your ability to deliver value
• The quality of customer interactions

Every day, sales is becoming less and less of a “numbers game” — it is not defined by the quantity of sales outreach.

In the future, three core fundamentals will be key to success:

• Maintaining the human element through consultative selling and value delivery
• Finding the right AI tools for your specific sales process and using them in a way that aligns with ethical practices and company values
• Developing new sales skills and adapting to new AI technologies as they emerge

Predictions for the Future of AI-Powered Sales Enablement Platforms

Artificial intelligence will continue to advance across industries completely unrelated to sales. But sales departments will be the beneficiaries of its newfound applications as time goes on.

As an example: The facial recognition software market, which is projected to grow at a CAGR of 12.62 and reach $11.53 billion by 2030, is innovating for all sorts of reasons. Multi-factor authentication, retail sector applications, and law enforcement use cases are a few of the most well-known.

But sales enablement platforms can incorporate this software to help reps make emotion-based decisions by analyzing body language, tonality, and facial expressions during sales calls.

Another critical result of advancing technology is increasing levels of accessibility. In the early days of sales enablement AI, it was the large-scale enterprise companies that could develop their own tech that enjoyed a competitive advantage.

Now, lower costs and more software options mean SMBs are starting to adopt AI sales enablement platforms in larger numbers. Leveling the playing field in this way will force companies to pursue sales innovation through human personalization and custom sales methodologies.

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