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Artificial intelligence (AI) is reshaping Sales. From chatbots to predictive analytics, Machine Learning and Generative AI tools are enabling sales professionals to automate tedious tasks, optimize their workflows, and enhance their customer relationships. But how will AI affect the different roles and functions within the sales organization? And what are the implications for sales leaders who need to manage and develop their teams in the age of AI?
What is Artificial Intelligence?
Tools or techniques that simulate human intelligence in machines to perform tasks.
Machine Learning
Algorithms that look for patterns within datasets to predict expected outcomes.
Generative AI
Artificial neural networks that process, transform, and generate content using Natural Language Processing.
Natural Language Processing Tools
E.g., Autocorrect, autocomplete
Recommendation Engines
E.g., Browse recommended products, prediction-based decision-making
Image Recognition
E.g., Classify objects and patterns with computer vision
Large Language Models
E.g., Write questions and prompts to ChatGPT
This interactive whitepaper will address these questions by examining how AI will transform five key sales jobs: lead generation reps, field sellers, inside sellers, customer success managers and first-line sales managers. These roles span the buyer journey, leveraging their unique talents to convert new leads, drive incremental sales, develop customer loyalty and guide the sales process. There are countless AI tools available for each of these roles, existing as niche tools that plug seamlessly into existing tech stacks, as add-ons to existing sales systems, or even as Large Language Models developed in-house by companies well ahead of the AI curve.
Currently, none of these tools are “silver bullets” that will replace sellers and sales managers altogether. AI tools and algorithms are limited by data quality and availability, ethical and legal implications, adoption readiness from both sellers and customers, as well as tool acquisition and operating costs. As such, there is little-to-no risk of AI replacing entire sales jobs in today’s environment. Instead, AI will act as an accelerant to many sales workflows. It will help sellers enhance their efficiency and effectiveness through tools that automate routine tasks, generate insights and recommendations for sellers to follow, enhance communication and engagement with hyper-relevant drafts, and provide on-demand coaching.
For each key sales role, this whitepaper will outline:
Key impacts and challenges of AI implementation, including examples from conversations Alexander Group has had with commercial AI experts at leading B2B firms
The day-to-day productivity impacts sellers and sales leaders will see in the short term, including CRO predictions on the impact of AI across industries
Insights on how the sales role will evolve in the long term vis-à-vis AI in the sales organization
Expected changes to seller competencies that will enable continued success in the AI era
Lead Generation Reps
Lead generation reps are responsible for generating, qualifying and routing leads to appropriate salespeople. They typically use outbound methods such as cold calls, email and social media outreach to identify and contact potential customers. They also employ inbound methods such as leads from webinars, events and content marketing to capture interested prospects.
The most impactful AI tools for lead generation reps help them find qualified prospects, including scraping online data sources to gain access to contacts and identifying the signals and keywords that may indicate the prospects’ needs, goals and interests. A growing challenge in the age of AI is generating attention-grabbing and relevant messaging, especially when prospects’ inboxes are overburdened by hyper-personalized outreach from other vendors.
AI in Action:
Transforming Lead Generation
An AI expert at a major data storage company provided an example of how their lead generation workflows were being improved by a Machine Learning algorithm that produced a real-time propensity to buy score for each lead.
The algorithm clusters lead based on a comprehensive set of identifiers, such as website navigation patterns, first-party data and consumed content. Using the purchase history of customers within those clusters, the algorithm ranks each lead by likelihood to perform a certain action, such as buy a certain product, renew a subscription or buy additional products. As new leads are generated, they immediately get clustered and ranked. As this process re-runs, the algorithm needs fewer pieces of identifying information to accurately place a lead into a cluster and determine the propensity to buy.
Each day, lead gen reps work through a prioritized list of leads, grouped by their predicted intent. Reps can better tailor their outreach message to the prospect and act with appropriate urgency. Since trialing this new workflow, the company has seen a material increase in both email click-through and response rates.
Short-Term Productivity Impacts
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Nearly 80% of surveyed revenue leaders expect lead generation reps to have a moderate-to-high productivity lift from AI investments, particularly Generative AI. Lead gen workflow improvements include:
While there are many specialized AI tools that are fine-tuned for lead gen activities, most will integrate directly into the CRM system, allowing reps to continue spending time in technologies familiar to them.
Long-Term Focus Impacts
AI will significantly shift the scope and value proposition of the lead generation role. Traditional outbound outreach will become largely automated; however, as prospect inboxes become saturated with highly customized messages, these outbound motions will lose effectiveness. Rather than casting a large net and hoping for the best, the lead generation rep will take a much more focused approach, appropriately capturing inbound leads, as well as landing specific large and strategic accounts. They will become market developers, focused on creating and nurturing relationships with key decision-makers and influencers to provide them with relevant and valuable insights and solutions. Lead generation rep skillsets will evolve to prioritize expertise in market research, account planning and business development, moving away from conventional cold calling and emailing tactics.
Field Sellers
Field sellers are responsible for selling products and services to customers in-person. They typically handle more complex and high-value sales that require customization, consultation, negotiation and coordination with internal and external stakeholders, such as technical specialists, delivery teams and channel partners. Depending on the company’s sales organization structure, field sellers may also manage long-term and strategic customer relationships.
As field sellers spend most of their engaged selling time in person, they will get the most benefit from AI tools that make their customer interactions feel more bespoke, delivering a more tailored offering and talk track. AI tools designed to minimize administrative tasks, such as CRM updates, will free up more time for active selling, enhancing seller productivity.
Strategic Selling:
Optimizing Account Planning with GenAI
Alexander Group interviewed a sales leader of a major software solutions company to understand how AI is impacting field seller workflows. This company leveraged AI for their account planning process, which took up a significant portion of their field sellers’ time and often relied on incomplete data, leading to missed opportunities and inefficient resource allocation.
Sellers now use private instances of Generative AI tools like Bard and ChatGPT to quickly query the web for customer information, using tools that are engineered to reference factual sources (e.g., the company’s website, specific social media and news sites, annual reports and earning call presentations) rather than creating realistic yet false insights. The GPT then returns key information relevant to account planning: organizational structure, potential leads/champions, customer objectives tied to potential opportunities, risks, customers’ competitors, and even brand voice. Sellers then use this summarized information to quickly build their account planning decks, including drafting materials with LLMs based on their findings about the customer. These GPT-generated insights have greatly improved process speed, accuracy of insights, and reps’ creativity in structuring solution offerings.
To ensure responsible use of these tools, the sales organization conducts global training sessions examining prompt engineering techniques and risks/benefits of various Generative AI tools.
Short-Term Productivity Impacts
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Revenue leaders are divided on the perceived impact of AI tools on field seller productivity, with 55% expecting a moderate-to-high productivity lift and 45% expecting minimal-to-no impact, especially within industries that depend on face-to-face interactions such as Life Sciences and Distribution. The tools that will drive the highest impacts to field sales workflows will be those that help sellers wield actionable and data-backed insights during in-person pitches:
We recently initiated a global pitch contest around our security offers. We wanted to make sure that every salesperson at this company could adequately and effectively communicate our security pitch. We leveraged AI translations in the initial scoring of this pitch contest, so that every seller could do the pitch in their own language. We didn't need to ask them to do the pitch in English so that our English-speaking judges could evaluate their pitch. Because guess what? Our AI speaks all languages, so it's able to evaluate their pitch using their native language.
Long-Term Focus Impacts
The focus of Field Sellers will undergo a significant transformation as AI enables inside and hybrid sellers to handle more complex sales scenarios. Field sellers will increasingly need to specialize in managing key and strategic accounts, where face-to-face interactions are essential. They will act as trusted advisors who can understand and solve the customers’ challenges and goals, providing them with long-term and strategic outcomes. Field sellers will offer value-added and consultative solutions, rather than standalone products and services, and build strong and lasting customer relationships.
Given the in-person nature of the role, the skills necessary for field sales will not change dramatically. However, the ability to effectively incorporate recommendation algorithms and other productivity tools into their day-to-day workflow will separate the leaders from the pack. As the role transitions into a more consultative position, field sellers will need to hone their ability to speak to and sell a wide range of products, effectively learn customers’ strategic objectives, and eloquently pitch offerings that drive tangible value for the customer.
We automated our account planning process a few years back because we wanted to primarily focus on the things that were the unique value adds of a seller in that account planning process. I didn't want them to have to go and find a bunch of data and bring it into their account plan. I wanted to push that data to them, and I wanted them to be able to add what they uniquely bring to the table, their perspective on what the customer's priorities are. Now, that's not even really something that they have to do, because we can summarize all customer communication, their 10-K data, other points of information and tell them what their customer's priorities are. Now, their value-added component and account planning is: ‘What are we going to do about it? How are we going to act on it?’ We're continually raising the bar for what their unique contribution is in the account planning process.
Inside Sellers
Inside sellers are responsible for selling products and services to customers remotely, usually over the phone or online. They typically handle transactional and long-tail sales that require less specialization and consultation. As companies moved their salesforce online during the 2020 pandemic, virtual selling and associated sales management techniques rose to center stage. Even as most industries return to in-person selling, virtual and hybrid sellers continue to grow in prevalence due to the role’s cost-efficiency and to continued employee and customer demand.
The most impactful AI tools for inside sellers help reps prioritize their long account lists, align products and services to customer needs, and communicate that offering’s value proposition. A growing challenge in the AI age is differentiating and personalizing the virtual sales experience, especially as customers are effectively targeted by a growing number of vendors.
From Guesswork to Guidance:
Transforming Digital Selling With AI-Enabled Lead Routing
In an interview with the Alexander Group, a director of product development at a major financial services organization described how AI helped their digital sellers double their conversion rates.
As the Covid-19 pandemic turned most B2B sales interactions virtual, this company realized that the sales process needed a major readjustment. This company also lacked a system to identify incremental sales opportunities within existing accounts, as marketing and sales contacts were tracked in disconnected systems. As a result, the company sought a connective solution that would help sellers unlock additional revenue potential within existing accounts while making prospective customer conversions more efficient.
The company developed an in-house machine learning algorithm to dynamically route prospects from Marketing to Sales pipeline at the most opportune points, meaning that any lead below a certain opportunity score would continue to get marketing outreach until a key combination of events classified them as a warm lead. When their digital sellers received an AI-qualified lead, they knew that they needed to move with urgency. They also had a wealth of customer intent data to leverage in their outreach.
This algorithm helped their digital sellers move away from selling “in the dark” to focusing their time and talents on customers that were ready to engage. In the 18-month pilot, these sellers achieved 1.5-2x higher conversion and higher incremental spend by leads funneled through the AI-enabled pipeline.
Short-Term Productivity Impacts
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Due to their virtual nature, inside sellers are uniquely positioned to benefit from the use of AI tools, which can provide real-time notifications and recommendations in their everyday applications. Over 70% of revenue leaders expect inside sellers to have a moderate-to-high productivity lift from AI investments, particularly within industries that have a critical mass of customer and transaction data such as Business Services, Financial Services and Digital Health. Inside seller workflow improvements include:
Long-Term Focus Impacts
The long-term impact of AI investments on the inside sales role will depend on whether the organization is equipped with a self-service sales motion.
AI tools like smart search technologies and GPT chatbots can transform the self-service customer experience, especially for transactional and low-value purchases, minimizing the need for inside seller support and therefore driving a more foundational change in the role. With productivity, coaching and insight-generation AI tools at their disposal, inside sellers will be primed to pursue more complex and high-value opportunities. In this case, the role will transition away from being a step-stone to field selling, instead performing similar sales activities virtually. AI coaching tools will transform inside seller onboarding, offering personalized training and the ability to perfect sales pitches with real-time feedback. Using chatbots equipped with customer intelligence, inside sellers will prepare for meetings by brainstorming challenges and rebuttals, isolating impactful success stories to demonstrate ROI, and leveraging product recommenders to understand what additional products may provide value to the customer. Inside seller skillsets will evolve substantially, transcending cursory knowledge of products and customers to using AI tools that empower eloquent and value-laden sales pitches for complex sales.
Within companies not offering a self-service purchasing channel for their transactional customers, AI-enabled inside sellers will become capable of handling higher sales volumes. Prioritization AI tools will help reps with large account loads focus their daily efforts on opportunities with high lead scores, off-loading initial efforts to increase lead scores to marketing and lead generation teams. AI-generated call summaries that convert directly into timed CRM tasks will ensure that next steps across hundreds of accounts are executed on time. Other GenAI productivity enablers, such as tools that quickly draft contextualized follow-up emails, will speed up inside seller outreach workflows. To successfully manage higher sales volumes, these Inside Sellers will need to be intimately familiar with all productivity AI tools available to them. Efficiency and an eye for detail will be key success drivers in this role.
Customer Success Managers
Customer success managers (CSMs) are responsible for driving adoption and, at times, expansion of products and services, as well as ensuring the satisfaction and retention of existing customers. They typically handle post-sales and recurring activities, such as implementation, training, support and renewal.
In short, CSMs are responsible for a customer’s satisfaction with the company’s products or services, and AI insights unlock new levers with which to manage customer sentiment. AI add-ins within a CRM system help CSMs learn about the customer and anticipate issues or additional needs ahead of time, smoothing the customer experience. CSMs can also work alongside AI tools to train and onboard customers, providing advanced support when AI chatbots reach their limits. Knowledge Management AI tools also help CSMs access the right supporting materials quickly, elevating customer satisfaction.
The Omniscient CSM:
Centralizing AI Insights
Alexander Group spoke to a leader of global marketing & sales transformation at a major energy & utilities firm to learn about how their sales organization uses AI tools to help their customer success managers improve adoption and reduce churn.
One major transformation enabling the CSMs was centralizing and enriching their previously disjointed CRM systems with AI capabilities. Prior to the transformation, CSMs would need to switch across several CRM systems, spending >50% of their time on administrative and repetitive work. The organization integrated several turnkey AI solutions into a new standardized CRM system, ensuring that CSMs did all of their account planning, opportunity management, and task management work in one portal. One AI integration also automatically logged the next steps by customer in the CRM, giving CSMs a view into all outstanding tasks and associated deadlines. Using ML-derived account health scores, CSMs can discern what opportunities are high-priority and escalate their follow-ups accordingly. CSMs are also given advanced AI nudges suggesting follow-up timelines and techniques for contract renewals, helping ensure reps successfully deliver value to the customer ahead of renewal discussions, and that all relevant terms & conditions are discussed during contract negotiations.
This centralized and AI-enabled CRM system offers a single view of the customer, with full visibility into customer interactions across Marketing, Sales and Service, to ensure a seamless customer experience across pre- and post-sales. This reduced seller reliance on additional sales support and management involvement, causing a significant reduction in operating expenses. Having a single point of contact also increased engagement with the portal, significantly increasing the amount of time reps were spending in the CRM and giving sales managers confidence that CSMs were following up on the right opportunities in a timely manner.
Short-Term Productivity Impacts
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75% of revenue leaders expect a moderate-to-high AI-driven productivity lift for customer success managers, increasing to 90 – 100% within industries with more consultative sales.
The most impactful AI tools for customer success manager workflows include:
We've developed customer health scoring, leveraging data across sales, marketing and service to really understand whether a particular customer is at-risk, and if so, what next best action should be taken to guard against that. Within the whole account team, we have members wearing all different hats, whether it’s a sales engineer or the customer success manager, or the account manager; across each of those roles, based on their role and responsibility, the AI helps us determine what action should be taken.
Long-Term Focus Impacts
AI will shift the scope and value proposition of the customer success manager role, by off-loading some of the adoption-based activities (particularly within smaller customer segments) to AI-enabled tools and platforms, such as chatbots and self-service portals equipped with personalized client onboarding modules, all of which can provide faster and easier solutions for customers. Customer success managers will then have more time to focus more on expansion-based activities via customized and consultative sales conversations. They will develop expertise in sales strategy, negotiation and problem-solving to supplement their deep customer and product knowledge. They will focus on delivering and demonstrating value to their customers and finding creative ways to embed their products and services into the customer’s operations.
First-Line Sales Managers
First-line sales managers are responsible for leading and managing sales teams and individuals, ensuring their performance and productivity. Their day-to-day activities include hiring, training, coaching, motivating, rewarding and evaluating salespeople. They also oversee and monitor sales activities and results and manage pipeline.
AI tools can transform some first-line sales manager workflows, helping managers understand the strengths and weaknesses of their team and quickly draft personalized development plans for reps. AI tools like Deal Scoring algorithms and CRM enrichment and automation tools also enable first-line sales managers to more effectively manage their pipeline and forecasting.
Predictive Power:
How AI Summaries are Shaping Sales Success
A commercial leader of a mid-sized software vendor explained how AI call summarization was transforming their internal revenue forecasting process.
The company’s revenue operations team scrapes AI-generated call summaries from weekly sales forecasting calls led by first-line sales managers, using the summaries to understand whether key questions were discussed in each manager’s sales forecasting process. The RevOps team was then able to determine what elements make a sales forecasting call successful, analyzing summaries from managers whose teams consistently met their sales quotas in contrast to those whose teams did not perform as well.
Using these learnings, the RevOps team was able to direct sales managers toward a process that made deal-level forecasting more scientific, including providing a list of questions to ask during forecast calls and additional AI tools that help qualify sales manager confidence with evidence from emails, call transcripts, and other engagement measures. RevOps teams continue to monitor weekly forecast calls with AI call summarization to ensure that sales managers adhere to best-practice guidelines.
Short-Term Productivity Impacts
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While AI is likely to make a large impact on the day-to-day workflows of individual sellers, there are relatively fewer AI tools that revolutionize the day-to-day of a first-line sales manager. About a third of surveyed revenue leaders are predicting little to no impact on the role’s productivity from AI investment. Monitoring and reporting tools that help managers understand the productivity of their direct reports have been around for years, so the incremental impact of Generative AI may seem small in comparison, however, the true lasting impact is still to be determined. Currently, the most impactful tools can help first-line sales managers better execute strategic functions and determine the best course of action to improve their team’s operations. These tools include:
Long-Term Focus Impacts
As coaching AI tools are implemented to provide micro-training opportunities for individual sellers, first-line sales managers will take on a less “hands-on” approach in favor of a more strategic one. As their day-to-day coaching responsibilities transition to the purview of real-time AI support, the number of sellers under their supervision will likely markedly increase. Managers will focus more on creating and executing a sales strategy that aligns with the organizational goals and objectives, translating these objectives into direction for their sellers.
First-line sales manager skillsets will transition away from providing individualized day-to-day coaching support and toward expertise in strategy development. Managers will need to learn how to translate detailed AI-generated information on individual sellers and customers into programs that drive performance and productivity. As first-line sales managers make this transition, spans of control may widen to encourage managers to manage costs and allow sellers to act independently.
Conclusion
Alexander Group research shows that ~75% of companies will implement at least one AI tool in their commercial organization in the next two years. Currently, only ~20% have a working AI use case, and ~55% are investigating potential solutions. As companies evaluate AI tools and use cases, they need to consider how their investments will impact the day-to-day activities of sales reps and managers, as well as how AI tools will shift their time allocation, focus and skillsets in the long-term.
One of the overarching themes emphasized in Alexander Group research is that AI will help drive sales role focus, allowing reps and managers to spend their time on workflows that are meaningful, that leverage their unique skillsets, and that more clearly tie effort to performance, all of which drive greater job satisfaction. This translates to better seller retention, better sales conversations with customers, and—the ultimate goal of the commercial organization—greater customer satisfaction and loyalty.
As AI becomes increasingly prevalent and sophisticated, sales professionals must embrace the changes and opportunities it presents, adapting their roles and respective skills to stay ahead. Those who do so will not only be well-positioned to thrive but will also maintain a competitive edge in the future of sales.
More Resources
Five AI Use Cases to Support Your 2024 Revenue Growth Plan
Keys to Leveraging Artificial Intelligence for a Competitive Edge
Artificial Intelligence for Marketing
About Alexander Group
Alexander Group understands your revenue growth challenges. Since 1985, we’ve served more than 3,000 companies across the globe. This experience gives us not only a highly sophisticated set of best practices to grow revenue—we also have a rich repository of unique industry data that informs all our recommendations. Aligning product, marketing, operations and finance efforts behind a successful sales organization takes insight and hard work. We help the world’s leading organizations build the right revenue vision, transform their organizations and deliver results.