Can AI Really Help You Sell?

Though more and more companies are applying sophisticated technology to sales processes, research suggests that most aren’t using it effectively (and some don’t even use it at all). Even customer-relationship-management systems, which digitally savvy sales organizations have had in place for decades, aren’t being fully taken advantage of. In Sales Mastery’s 2022 Sales Performance Scorecard survey of 332 sales managers, 15% of respondents reported that their companies were not actively using CRM, and 42% stated that they were using it only for storing information about customers and prospects.

No wonder salespeople have been struggling. According to global surveys of nearly 1,000 sales leaders by CSO Insights, the percentage of salespeople meeting their annual quotas fell from 63% to 57% from 2012 to 2019. When the leaders were asked to evaluate their teams’ performance in 16 distinct sales activities, they said their teams were less effective at 15 of those tasks than they had been five years earlier. And according to the sales executives we talk to, lately the performance of salespeople has gotten even worse.

Part of the problem is that buying processes have been evolving faster than selling processes. Buyers are better informed than ever, with access to a wide range of online resources that help them evaluate products before ever meeting with a salesperson. Another factor may be that sales reps spend too much time doing things that don’t directly involve selling. The Sales Mastery 2022 survey found that on average salespeople devoted only 32% of their time to selling—and 68% to non-revenue-producing activities.

For more than two decades, two of the authors of this article (Barry and Jim) have studied processes and relationships in sales organizations. A third author (Ben) has been teaching classes on that subject since the 1970s. The fourth author (Boris) has researched the organizational implications of AI and data analytics for many years. Collectively we’ve published dozens of articles and white papers on those topics. In 2021 we also surveyed more than 500 sales organizations to assess the role that AI plays in improving sales performance. In our research and consulting work, we’ve observed a virtuous cycle: The more AI tools are applied to a process, the more data is generated. Better data leads to better algorithms. Better algorithms lead to better service and greater success. Those, in turn, lead to more usage, continuing the cycle. So we believe that the sooner an organization implements AI solutions and the more broadly they’re applied, the better they work. Success grows exponentially. And the competitive risks of not adopting AI tools grow as well.

Unfortunately for sales leaders, implementing AI-based sales processes isn’t as simple as downloading new software.

In this article we’ll examine the ways in which AI is already used to facilitate selling—and how it can be used to do more. We’ll detail how sales leaders who have successfully adopted AI optimize its performance. And we’ll provide sales leaders with a self-assessment tool that is designed to help them start or improve their AI-for-sales journeys.

Heightening Customer Engagement

Let’s begin by looking at an instance where the application of AI in sales was a real game changer—one that McAfee Enterprise, a leader in computer security solutions that later became Trellix, shared with us in May 2021. AI in the company’s internally developed platform analyzes a billion sensors across its customers’ systems and identifies and prioritizes security threats. The platform predicts the impact each threat could have, alerts the customer, and then prescribes corrective actions. In addition to increasing the effectiveness of the security teams, the AI is a valuable tool for the sales organization. According to Pilar Schenk, the company’s former vice president of global sales strategy and operations, its sales professionals harness the AI to understand potential risks for noncustomers and for current customers who aren’t yet using the platform. Aggregating the sensor data, the AI gives salespeople targeted recommendations about which firms in their territories they should proactively contact and why. The salespeople then follow playbooks on the sales organization’s High Velocity Sales (HVS) platform, which describe how they should engage prospects and provide supporting materials they need to do that.

This approach has altered the dynamic between salespeople and buyers. Instead of asking potential customers to share information from their systems, salespeople offer to share the risks that the AI’s analysis has surfaced for their companies, as well as advice on how to address them. Since incorporating sensor-generated insights into HVS, in January 2020, the company has been tracking the performance of the salespeople who use them and has found a 10-fold improvement in their ability to start conversations with prospects. The number of initial conversations that they’ve converted into sales opportunities has also increased three-fold. In addition, they’ve had a 5% increase in renewal rates. Their managers have benefited too: Before HVS, the managers were spending 9% to 10% of their time coaching their team members. But since the platform now gives them a continuous analysis of their salespeople’s activities, revealing who needs what type of help on what type of opportunity, the managers no longer have to spend hours trying to figure that out—and the percentage of time they devote to coaching has jumped to about 30%.

The Sales Success Matrix

To help companies determine what kinds of AI solutions they’re ready to implement, we’ve developed a tool that we call the Sales Success Matrix. It has two axes: relationship level and process level. Sales organizations can identify their position on it, which will point them to the kinds of AI tools that would best boost their sales now and what steps they might take next. For most, the ultimate goal will be to move up to the highest levels of relationships and processes, where customer loyalty and competitive advantage are the strongest.

Relationship Level

The matrix maps out five types of relationships that selling organizations can have with customers: transactional vendor, preferred supplier, solution consultant, strategic collaborator, and trusted co-creator. AI can be useful with all five kinds, but in different ways.

Transactional vendor.

This is the lowest level of relationships. The customer’s transactions are rapid, repetitive, and routine, and usually involve self-service or online shopping. To remain profitable and competitive at this level, companies need to squeeze out costs, leverage automation, and minimize buyer-seller interactions, and AI algorithms can help them do this. A common example of AI at this level is e-commerce site recommendations: Customers who bought this item also bought these items. Recommendations can be based not only on the activity of similar buyers but also on past purchasing history or imported data, such as web searches, buyer demographics, and paid placements.

Preferred supplier.

At this level the organization has managed to differentiate its offerings enough to create a measurable customer preference. That differentiation gives sellers an opportunity to gain customer information, which can then be used to win more business from the customer, generate referrals, cross-sell other products and services, and obtain still more information about emerging needs or competitive activity. For example, many preferred suppliers offer managed services (such as monitoring printer ink levels and automatically sending new supplies), which minimize outages and downtime for their customers while increasing their own revenue and profits. AI can help sellers at this level anticipate customers’ needs by analyzing historical usage patterns, comparative user volumes, and maintenance records.

Solution consultant.

At the third level sellers offer a complicated set of products and services that are integrated into one system. To be competitive, a seller must get buyers to believe that the integrated solution provides more value than assembling the components on their own would. Typically, the seller makes a profit on both the components and the integration. Software-as-a-service companies fall into this category. Their sales teams usually include someone in a customer success role who is responsible for monitoring usage and encouraging the adoption of other capabilities. AI applications can assist solution consultants by offering suggestions, based on customer records and on “like population” usage data, about how to increase customer “stickiness” and minimize churn. Trellix’s use of AI to improve relationships with customers and prospects is a perfect example of a technology for solution consultants.

Strategic collaborator.

At this level the connections between buyer and seller are stronger and more numerous and intricate. Relationships are usually regional or even global. And as both the size of transactions and the duration of the relationship increase, higher levels of management get involved. The sales approach required is totally different from the traditional one salespeople take with purchasing agents. Account management is truly enterprise-wide and cross-functional and involves orchestrating multiple varied conversations and marshaling both internal and external resources. With interactions between buyer and seller taking place on so many dimensions, complexity grows exponentially. “Back of the envelope” or spreadsheet tracking of opportunities is no longer adequate. Companies at this level can harness AI to analyze a customer and compare its financial performance with that of its closest competitors, identify and prioritize gaps, and recommend possible solutions tailored to both the customer’s needs and the supplier’s capabilities.

Trusted co-creator.

At this level sellers go beyond helping customers execute strategies and collaborate on formulating them. Traditionally, this has been the crème de la crème of sales approaches. But not every customer wants or will pay for the very best. Furthermore, many companies lack the skills to be this type of supplier. Because these relationships are so complicated, take so many resources, and demand so much top-management involvement, in the past companies could have only a small number of them. They often require what’s known as “extraprise” account management, in which multiple levels and functions on both sides of the buy-sell equation communicate directly. CFOs from the two parties, for instance, may talk about supply chain issues and contingency planning. AI sales tools here would involve multiple parties inside and outside the company. Engineers at an engine manufacturer, for instance, might work with the engineers at an aircraft maker to create a “digital twin” of a jet engine that can predict maintenance needs, drawing into the discussion a maintenance representative from an airline. Interactions at this level are varied, intense, and forward-looking—and exclusive.

Historically two-thirds of sellers have occupied the lowest three relationship levels (transactional vendor, preferred supplier, and solution consultant), while only 9% have reached the top level, trusted co-creator. In general, the objective at the transactional vendor and preferred supplier levels is to use AI to improve efficiency and decrease costs while maintaining or improving customer service. At the next levels, solution consultant and strategic collaborator, the primary objective is more-effective sales efforts, and AI tends to support more-sophisticated sales professionals. At the trusted co-creator level, the goal is deep, intensive collaboration with customers.

In addition to helping companies sell more, thoughtfully applied AI can help move customer relationships to a higher level. But at every level—and amid all the rapid changes in business, technology, and how and where people work—some things remain constant: Customers still will ask, What do you know about me? What do you know about our business? What value do you (singular and plural) add? Those are the questions sellers need to answer to establish and elevate their relationships. AI does not supplant that need or those questions; rather, it enables better, richer answers.

Process Level

Advances in technology and access to new classes of information and data have profoundly changed the way businesses need to think about their sales processes. AI tools can improve performance across the spectrum, which runs from ad hoc process (at the low end) to informal process, formal process, agile process, and customized process.

Ad hoc process.

At this level each sales rep is allowed to do his or her own thing. Reps receive relatively little or no sales training beyond product or service information. Their feedback from the field is not sought and often not even welcome. Sales support means that the top marketing or sales executive or the CEO goes out with the salesperson to close larger deals. Efficiency is the watchword with this kind of process, and sales organizations can improve it with simple AI that, say, scans emails and adds prospects’ contact information into a CRM system.

Informal process.

Here the company suggests a defined sales approach to its reps and encourages them to use it but doesn’t monitor whether they do or measure the results. In many cases there is little sales training. More-experienced sales managers and salespeople might even disparage the suggested process and discourage its use: “That’s headquarters stuff that doesn’t work here!” But CRM systems powered by AI can help salespeople work more quickly, improve their client insights, measure results, and ultimately better understand and refine their processes.

Formal process.

At this level a company regularly enforces the use of a defined sales process (sometimes religiously) and conducts periodic reviews to see how effective it is. These companies can see when win/loss rates and lead conversion ratios begin to change, analyze the causes, and react. AI can help firms do all this more accurately and with exponentially greater speed, providing sales teams with immediate opportunities for improvement.

Agile process.

Companies at this level not only have a formal sales process in place but also have CRM systems that continually generate metrics on what’s happening in the marketplace. That helps them react to external changes quickly. They can increase their agility even more by leveraging analytics and business intelligence and tapping the full capabilities of AI. These companies may sense the winds of change at earlier and less obvious stages (such as increases in the time various buy-cycle steps take) and so can proactively minimize threats and take advantage of opportunities.

Customized process.

This is a level where companies build on their agile AI experiences and begin to anticipate change rather than just react nimbly to it. Predictive analytics are a key capability needed to do that but cannot truly be implemented until AI and machine learning are continually scanning sales, marketing, and other data streams and identifying possible threats and opportunities. The increased insights and flexibility AI generates allow sellers to tailor messaging and proposals and implement account-based marketing and account-based selling.

In the two lowest levels of processes—ad hoc and informal—AI again can be used to increase efficiency. At the third level, formal, it increases the effectiveness of activities such as coaching and reporting. At the top two levels, agile and customized, AI mines data to produce deep insights about customers’ realized and unrealized needs.

As a rule, if your organization wants to have more-sophisticated relationships with customers, it should also have higher-level processes. For instance, to be strategic collaborators or trusted co-creators, companies almost always need agile or customized processes. (They also need the right data and analytics.) Again, effective applications of AI can get them to the process level where they want to be.

In the following section, we’ll explore three AI-for-sales implementations. The first example, which offers lessons to companies that would like to become strategic collaborators, is about how Accenture built its own proprietary AI sales solution from scratch. The second example, which provides a road map to organizations that want to make their processes more agile, shows how Honeywell worked with Aviso, a provider of AI-powered CRM tools, to completely redesign its sales forecasting and pipeline management processes and systems. The final example, which outlines an approach that could help preferred suppliers become solution consultants, describes SAP’s partnership with Grapevine6 (now Seismic LiveSocial) to apply AI to client social media data.

AI-Powered Sales Intelligence

Accenture’s Value Insights Platform (VIP) is a digital research assistant powered by AI and machine learning. It assesses the business imperatives of the firm’s current and potential clients by deeply analyzing transcripts of their earnings calls. Before VIP’s development, skilled analysts would spend a minimum of six hours a call reviewing and extracting insights into companies’ key priorities. VIP, in contrast, can process about 7,500 earnings call transcripts in 480 minutes, or 3.8 seconds a call.

VIP’s benefits go far beyond time savings. After identifying specific client priorities, such as a goal of realizing $22 billion in savings over five years, the platform matches them with specific solutions. Whether the client wants to optimize revenues, drive operational efficiencies, or reduce its carbon footprint, VIP reviews its performance relative to its peers, identifies solutions, and calculates the financial impact of successfully achieving those goals.

The AI’s predictive capabilities allow Accenture’s sales teams and technical solution architects to take an unequaled value-based selling approach, and the speed at which VIP provides supporting insights gives Accenture a key advantage in the marketplace. VIP now has roughly 20,000 users worldwide and has helped Accenture generate more than $1 billion in sales over the past two years.

AI for Forecast Management

In 2018, the technology company Honeywell began looking for a tool that would help its business units improve the accuracy of their sales forecasts, make more-informed decisions, and predict short- and long-term performance. Honeywell, which builds solutions for the aerospace, performance materials, and safety and productivity industries, has multiple global teams and uses a mix of sales tools and spreadsheets. It wanted an automated system that would bring all its sales processes onto a single platform, offer real-time insights, and boost efficiency.

After evaluating several partners and vendors, Honeywell chose Aviso. In addition to building a unified global forecasting process, Aviso customized the solution by giving Honeywell the ability to analyze its deal portfolio and forecasts by geography, team, product line, and business model (new or recurring). Honeywell later added deal intelligence capabilities to the system to gain insights on its sales pipeline, scoring its health and identifying the top deals that could help reps beat their quotas.

Aviso’s tools allowed Honeywell’s sales teams to provide their individual perspectives and predictions for success and get both a combined forecast and forecasts for the discrete components they were responsible for. Aviso also created a customized dashboard for each salesperson providing a view of his or her own forecast, accounts, communications, and more. Aviso’s tools helped sales managers spot trends and deal opportunities and identify obstacles to closing sales and take action to mitigate them.

Later Aviso introduced conversational intelligence, which captures information from calls, web meetings, and email, as a solution on its platform, giving Honeywell’s sales management deeper insights into the status of all forecast deals. The results have been overwhelmingly positive. Average yearly improvements for Honeywell include $150 million in total estimated revenue won and more than $1 million in CRM cost savings at some divisions. Additionally, pipeline activity and online interactions between reps and customers have grown by more than 80% each, and the number of new deals has increased by more than 70%.

AI-Powered Social Selling

Working with its sales and marketing teams, the German software company SAP began using Grapevine6, a social engagement platform. Acquired by Seismic in 2020, Grapevine6 has since been renamed Seismic LiveSocial. Sales professionals connect their social media accounts to it, and it produces two profiles for each. The first covers the salesperson’s professional interests (goals, markets, challenges), and the second covers the salesperson’s personal interests (sports teams, hobbies, and the like). LiveSocial’s AI engine then uses this information to search through millions of media articles from more than 10,000 sources a day and identify content that might be relevant for the salesperson. Salespeople review the flagged content and, when appropriate, share it with their clients. LiveSocial tracks the posts that the customers interact with, providing insights into their interests. The AI helps SAP position its salespeople as subject matter experts who are on top of the latest changes in the market—a tactic that other companies could use to raise their relationships to the solution consultant level.

More than 10,000 sales professionals within SAP and its partners are on LiveSocial. Many of them use it in conjunction with LinkedIn Sales Navigator, a tool for finding and engaging leads. Account executives who’ve harnessed both report significantly higher closing rates and higher average deal sizes. They also see a 20% increase in their LinkedIn Social Selling Index (SSI) scores, a measure of sales effectiveness. SAP account executives with the highest SSI scores, in turn, have seen a 55% increase in their sales performance, close 3.6 times more deals than their peers do, and make deals that are 516% larger than those of their peers. In addition, they’re 3.4 times more likely to achieve or exceed their sales quotas. Overall, SAP has attributed €2 billion of its pipeline and €1 billion of its closed deals to this social selling program.

Getting Started with AI for Sales

To get the most out of AI solutions, organizations must have the necessary hardware, software, and processes in place. They also need high-quality data to feed into AI tools and the right people to leverage them.

What steps should organizations take to successfully implement AI sales solutions? First, they should clearly articulate an AI strategy: What are they trying to achieve? Second, they should examine whether their structures support that strategy: Are teams set up to achieve the AI goals? The AI tools will be part of an integrated framework that includes people, processes, traditional technologies, and knowledge: Are those components aligned? Next, organizations must ensure that they have the right systems—for data collection, performance management, training, and communications.

The right culture is also key to success. People throughout the organization need the skills to understand and apply AI tools—starting at the top. AI initiatives must begin with senior executives, but buy-in by involved employees is critical for full adoption. The culture needs to support experimentation and learning. The rollout process should be managed carefully, employing the change management strategies that are necessary for the success of any new initiative. It must involve goal setting, benchmarking, and accountability. (See “AI-for-Sales Dos and Don’ts” for additional tips for success.)

. . .

As more firms begin to implement AI-for-sales solutions, those that don’t will find themselves losing ground. Because these tools’ efficacy increases over time, first movers will gain a meaningful and sustainable lead. Eighty-one percent of our 2022 survey respondents said that organizations without AI sales tools would be at a “significant competitive disadvantage” or missing “an important/key addition to their CRM.” However, that percentage increased to 94% among respondents who had already implemented or were currently implementing AI solutions. In other words, people who have firsthand experience with AI-for-sales solutions feel even more strongly about their importance.

Some companies have successfully applied AI and automation to sales processes for almost a decade. Others have done it for even longer. Those sales organizations that haven’t gotten started, or have tried and failed, may get left behind for good.

The AI-for-sales landscape is packed with promise and challenge. The technology is evolving rapidly, reshaping how sellers sell and buyers buy. Trellix, Accenture, Honeywell, and SAP prove that combining AI with well-defined sales processes and strong customer relationships leads to high sales. No matter how deep your bonds with your customers are or how complex your sales processes, AI can help your company maximize its profitability.

Disclosure: SAP and Accenture have both been clients of Sales Mastery.