AI in sales: Laying the foundations for future research

Figures & data

Table 1. Practitioner perspective: AI applications and implications.

Steps in the sales process	Types of AI in use	Impact on salesperson	Impact on sales manager	Impact on organization	Impact on customer	Examples of AI tools
Prospecting	Automated email messaging Automated calling Lead scoring Predictive analytics White space identification	Reduced workload Increased focus Increase in analytical skills Uncertainty about accuracy of algorithms	Time management of salespeople Resource allocation Coaching of salespeople to use AI	Lead generation capabilities Upskilling sales managers Content creation Market intelligence capabilities	Adequate information to identify right needs Focused attention from suppliers	Gong helps salespeople to “score” and “rate” their leads and prospects. A benefit of this is that it can remove much of the guesswork and subjectivity that was involved in the process. In addition, Gong also supervises deals and interactions with salespeople to help them understand the customer and where issues may arise. SalesIntel offers AI-generated insights into the customer research process, improving salespeople’s intelligence gathering.
Pre-Approach	Market intelligence Intent data Social listening	Better targeting Reduced workload Increased efficiency	Increased coaching KPI setting Performance management	Coaching of sales managers Sales manager KPI setting Performance management	Adequate information to identify right needs Focused attention from suppliers	LinkedIn Sales Navigator, powered by generative AI can help salespeople to search and curate prospects. It can provide salespeople with ideas on how to open a dialogue.
Approach	Conversation analytics Call transcriptions Sentiment analysis Sales enablement	Improved call effectiveness Self-coaching Peer coaching Analytical skills Accuracy of reports Pushback from customers Fear of incompetence being exposed	Reduced manager workload Individual coaching Team coaching Salesperson training Performance management	Ethical governance concerning use of customer and salesperson data	Effective needs assessment Increased efficiency	ActiveCampaign uses generative AI to automate contact with customers. Eloqa (an Oracle company) helps salespeople to engage with and develop leads at various stages of the buying process. Eloqa uses generative AI to help salespeople target and approach customers in the most efficacious way possible. Generative AI allows Eloqa to understand novel insights (i.e. email fatigue) levels about potential and current customers to time email messages.
Presentation	Sales enablement Content analytics Automated proposal generators Automated simulations Dynamic pricing	Quality sales interactions Increased sales efficiency Shortened sales cycle times	Activity based KPI setting Coaching based on AI and tool usage	Enabling content creation and analytics across various silos Activity based KPI setting for salespeople Joint KPI setting for marketing and sales	Personalized information per stage in the buying process	Leexi summarizes sales meetings, identifies salespeople’s questions, and the customer’s objections. SetSail records, tracks, and rates salespeople’s activities.
Overcoming Objections	Conversation analytics Call transcriptions Sentiment analysis Sales enablement	Increased hit rates Shortened sales cycle times	Salesperson coaching Sales team coaching	Clearer and better understanding of the customer	Risk mitigation	Closem.AI helps to send follow-up emails with responses to objections. Dasha.AI helps provide real time generative responses to customer objections that are adaptive and based upon the characteristics of the interaction.
Close	Conversation analytics Call transcriptions Sentiment analysis Sales enablement Automated scanning of legal documents	Reduced errors Win analytics	Salesperson coaching Sales team coaching	Investment in new capabilities	Customer education Value promise	Salesforce.com offers an AI integrated CRM to analyze customer interactions/conversations/presentations. Showpad with PitchAI offers real-time feedback on salespeople’s verbal and nonverbal communication.
Follow-Up	Customer onboarding Customer engagement	Increased customer satisfaction Increased repurchase intent	Team viability Team performance	Investment in new capabilities	Customer education Value in Use Increased effectiveness Increased efficiency	Sentiment analysis from Amazon Web Services (AWS) allows for organizations to pick up on various aspects of customer sentiment in different channels (i.e. social media, conversations, emails, texts etc.) to understand and predict what customers are “saying”. Docebo, an AI-powered LMS, is used by companies worldwide for education and training. Sciolytix has a virtual role-play system that finds, screens, and utilizes AI-driven role-plays for pre- and post-sales hires.

Figure 1. AI in sales.

Table 2. Salespeople and AI: implications and applications.

	Types of AI applications for salespeople			Implications
	Machine Learning	Natural Language Processing	Generative AI	Opportunities	Challenges
Prospecting	Identify potential prospects utilizing demographic information Large-scale knowledge gathering and lead scoring Demand prediction	Respond to prospect query information Predict potential customers Assessing risks and opportunities Segmentation; Competitive analysis; Search engine optimization (SEO); Content Marketing; generate awareness	Blogs, social media posts Create unique marketing and sales content Develop specific messaging based on prospect information Develop targeted sales scripts	Reduced workload for salespeople More qualified leads are identified Reduced stress for salespeople Improved productivity	Could reduce time spent with current customers Data security and privacy issues Increased stress for counterparties or other organizational members
Pre-Approach	Examine large datasets for novel customer/marketplace insights Text analytics for qualification	Monitor social media or text mine other types of communication Predict purchase likelihood	Synthesize important customer information for salesperson/sales teams Brainstorming ideas and creative approaches	More sophisticated understanding of customer	Analytical skills become more important Potential loss of tacit knowledge Individual customer variance
Approach	Personalized links and information Task automation	Analyze email responses to set up meetings Chatbot Customize the pre-trained data to the specific customer	“Cold Calling” Email outreach Send the right message at the “right time”	Task delegation	May lose out on rapport development Decreased responsibility/ ownership for salespeople
Presentation	Opportunity scoring Real time optimization Understand the specific aspect of the customer journey	Record and analyze the conversation in real-time Deliver value propositions Rapport development	Create relevant product/service descriptions Dynamic messaging capabilities Personalized content (e.g., custom images)	Streamlined and efficient process of developing sales presentations	Customer may have questions that only a human could answer
Overcoming Objections	Voice analysis Facial recognition Analyzing previous objections from interactions to help tailor a response	Examine customer grammar and granular sales interaction details Provide natural sounding responses	Real-time responses Customized responses to objections/issues Understand the customer persona	Help salespeople better understand what the customer is saying	AI may not “hear” the actual objection
Closing	Recommendations on how to best close a customer Purchase prediction Dynamic pricing	Monitor call and transcript to provide closing suggestions Sentiment analysis Accurate responses to customer issues	Observe the closing process and guide both parties Provide recommendations Streamline the negotiations process	Help salespeople to optimize the “close” Start relationship on the right foot	Customers may feel uneasy that a computer is analyzing their words
Follow Up	Customer Lifetime Value prediction Analyze CRM/Purchase data for targeted outreach Churn prediction	Help with CRM input Chatbots can help to personalize post-sales service and route the appropriate help for issues	Personalized training content for customers/general “care” provisions Coach salespeople and other service personnel with immediate feedback/guidance Content generation and follow up with digital voice assistants	A more holistic view of post-sales service	A mechanical process will preclude anything that is not captured in the data

Note: ◆ bullet points indicate applications that are well established; ○ bullet points indicate applications that are not well established.

Table 3. Sales managers and AI: implications and applications.

	Types of AI applications for sales managers			Implications
	Machine learning	Natural language processing	Generative AI	Opportunities	Challenges
Training & Coaching	Analyze sales metrics to identify salespeople that may need training Identify top sales performers to act as mentors	Analyze communications and negotiations of sales team members to identify areas of opportunity Identify the success rate of negotiation approaches	Develop personalized coachings and trainings for individual sales team members Recommend additional coachings to sales teams Provide role play scenarios or trainings for sales team	Increased transparency in sales processes/methods for each sales member Reduced training workload Implementation of personalized training to match the unique needs of the sales team members	Additional data may be hard to manage Adoption of AI technology will require ongoing training Some of the skills needed may be difficult to train
Performance Management	Analyze product portfolio sales margins and unit sales Analyze performance trends of sales teams	Summarize sales goals and areas of opportunity for sales team members	Provide feedback throughout sales process on areas to improve	Up to date performance on sales teams Improved productivity Improved metrics from sales teams	Identify optimal points of the customer journey to utilize AI vs salespeople Identify KPIs to match new sales roles
Managing Accounts	Identify opportunities to improve customer satisfaction and retention Analyze current customers purchase behavior for growth opportunities Identify key drivers of customer satisfaction Determine market potential of sales territories Develop forecasting trajectories	Identify customer satisfaction through sentiment analysis Respond to customer inquiries or questions	Recommend products or services to upsell or cross sell to customer Develop customized communication	Improved customer satisfaction Increased efficiency and productivity More accurate understanding of customer needs and buying habits	Inaccuracies in data may lead to incorrect recommendations of products or services Loss of direct interaction with customer
Recruitment & Hiring	Identify potential employees utilizing the specified criterion Predict potential job performance or other measures Score resumes and applications	Respond to questions from applicants Identify qualifications and skills on resumes Conduct job analyses to determine requirements Send job opportunities to qualified individuals through social media or email	Develop recruitment communication based on segmentation information Develop interview questions based on job requirements and necessary skills Draft job descriptions Draft customized messages to candidates	Increased productivity for recruiters and sales managers Improved efficiency in hiring process More qualified applicants are identified	Human bias in systems may exist and lead to inaccurate scoring of resumes and applications Removal of the human element may deter candidates Accurate task text data is necessary for job analysis

Note: ◆ bullet points indicate applications that are well established; ○ bullet points indicate applications that are not well established.

Table 4. Organizations and AI: implications and applications.

	Types of AI applications for organizations			Implications
	Machine Learning	Natural Language Processing	Generative AI	Opportunities	Challenges
Strategic management capabilities	Assess current performance against strategic goals Evaluate the allocation of capital expenditures	Evaluate communications against strategic objectives and goals Summarize information and feedback regarding strategic actions Analyze feedback given by top management across departments	Recommend future strategic objectives and actions Develop messaging for employees and consumers that matches strategic goals	Cohesive messaging received at all levels of the organization Increased efficiency in capital expenditures	Overall strategic goals may not match departmental goals
External stakeholder capabilities	Assess and score performance of channel partners Identify potential channel partners utilizing specified criterion	Summarize strategic goals of relationships Evaluate channel partner offerings	Recommend new channel partners Develop customized messages to channel partners Recommend solutions to channel partners	Increased transparency in channel partner performance Increased value created in relationships	Governance of data Accurate and timely information is necessary from all parties
Operational capabilities	Identify inefficiencies in processes Analyze labor cost expenditures	Develop standard operating procedures from task information Analyze communication across departments	Recommend opportunities for new products or services to fit customer segments Recommend innovations or reconfigurations of business aspects	Increased employee productivity Increased cooperation across departments	Integration of systems and data across departments may be difficult

Note: ◆ bullet points indicate applications that are well established; ○ bullet points indicate applications that are not well established.

Table 5. Customers and AI: implications and applications.

	Types of AI applications for customers			Implications
	Machine learning	Natural language processing	Generative AI	Opportunities	Challenges
Need recognition, consideration, & search	Analyze expenditure Optimize stages of the purchasing process Contract management and KPI monitoring Search for optimized price/value by comparing suppliers’ real-time prices online	Matching systems that analyze all the features of a supplier Quality control of various internal data Identify the supplier’s online WOM	Chatbots to contact, select, and share information with suppliers Summarizing general conditions and areas for improvement Summarizing online reviews/information to have a clear overview of the market Offer relevant product comparisons, customer reviews, and testimonials to help buyers evaluate their options.	Reduced workload Optimized processes New and more competitive suppliers	Risk of too much relying on AI and lose sight of what is important Selecting suppliers on analytical elements and not considering it as a trusted partner
Choice, ordering & payment	Analyze prior purchase decisions Improve decision making and selecting the right supplier	Analyze supplier’s message and emotions Scan through all supplier’s quotations to optimize choice	Provide in-real time advice for improved negotiation Support the production of complex purchasing documents (e.g., calls for tenders, contracts)	Buyer focuses on more complex tasks Optimized purchase decisions Improved efficiency	Buyer’s opinion might be challenged by AI Mechanical process could lead to a “battle of the machines”
Consumption & usage	Send direct updates and information about product/service usage to supplier	Analyze any post-usage social media content to gauge sentiment	Usage assistance and recommendations	Better usage reduces negative customer experience	A firm getting information about in-real time usage can be seen as intrusive for customers
Engagement & service request	Electronic supplier relationship management enhanced by real-time data to stay the supplier’s best client	Evaluate reviews, social media chatter, or communication with salespeople to determine courses of action	Direct feedback to send to suppliers	Sharing direct feedback to firms allow them to better match customer’s preferences and improve their product and services to match customer needs	AI may not be able to build trust with the customer Engagements rely on competitiveness

Note: ◆ bullet points indicate applications that are well established; ○ bullet points indicate applications that are not well established.

Figure 2. The implementation of AI in the sales process: evolution and tradeoffs.

Table 6. Future Research Questions.

Stakeholder	Area of impact	Research questions
Salesperson	Sales Process: Augmentation & Automation	What sales tasks will become AI tasks, AI + human tasks, or remain human tasks (see first insights in Chang 2022; Pappas et al. 2023). what specific sales role will AI support or replace (e.g. purely prospecting roles, like SDR versus purely relational roles like KAM)? As repetitive tasks are being done by the AI, what new skills and competencies should salespeople learn to be ‘irreplaceable’, or in other words, what human characteristics can or cannot be taught to AI? To what extent do salespeople need to understand the technical complexities vs a basic understanding of AI to utilize the technology? To what extent will salespeople be responsible for training and supervising the AI? What are the tradeoffs between efficiency and quality with AI in the sales process? How can/when do salespeople utilize sentiment analysis during negotiations to adapt their approach or closing tactics?
	Relationship Development	How do service expectations from customers differ between AI-customer and salesperson-customer interactions? How will the development of customer-salesperson relationships change due to the addition of AI? What are the implications of strong customer-AI relationships and how will they differ from customer-salesperson relationships? To what extent can salespeople develop a relationship with AI technology? To what degree should the AI share information with salespeople to enable enough transparency and explanation so that the salesperson trusts the AI, but not too much information to avoid information overload? Does AI impact the salesperson’s feeling of relational ownership or responsibility?
	Mental Health & Well-Being	What impact will AI have on a salesperson’s well-being as the traditional role of the salesperson changes? While AI can unburden salespeople from monotonous tasks, it forces them to deal with more complex issues on a regular basis. How will the removal of the simple tasks impact salesperson well-being? What impact does AI adoption have on salesperson role stress? Will AI have a positive or negative impact on salesperson burnout/attrition?
Sales manager	Training & coaching	What new skills and competencies will be necessary for salespeople to be successful? Following salespeople’s need for new skills and competencies in the AI era, to what extent is it possible to train salespeople on such new skills (e.g. soft skills)? Should sales managers have new recruitment criteria? To what extent can skills such as business acumen, emotional intelligence, and interpretation of data be taught? Will the AI generate better feedback than the manager, and to what extent will salespeople accept and follow recommendations of AI feedback versus the manager’s feedback? How can managers encourage the adoption or “buy-in” of AI for their sales teams?
	Performance management	How will performance measures and KPIs adjust as AI is integrated into sales? To what extent should sales managers redefine the sales compensation plan considering AI’s integration into the sales process? How can managers utilize real-time feedback from AI to encourage their team and increase engagement? How can sales managers navigate the potential divide between salespeople and sales engineers? How can sales managers organize and facilitate the optimum level of involvement amongst different departments?
	Account management	To what extent will the sales manager’s role be transformed when AI takes over the role of forecasting, sales monitoring, and providing instant feedback to salespeople? To what extent will AI be able to integrate DEI aspects into the system knowing this information remains confidential and personal (e.g. allowing more flexibility for someone disabled, granting higher work-life balance after pregnancy), and how will managers balance AI’s recommendations considering this perspective?
	Recruitment & hiring	As AI automates much of the hiring and recruitment process, how does the role of the manager change? Are there tradeoffs in terms of applicant quality with the usage of AI? What traits and/or competencies can or cannot be captured by AI during the hiring process?
Organization	Strategic management	To what extent can organizations implement their culture, values, or strategic objectives into AI-generated content? What are the short- and long-term implications of AI on firm resources? As AI requires a colossal amount of computing power and data, increasing the energy footprint and computing costs, how do organizations integrate this in cost/benefits analysis and ensure their sustainable goals? How can organizations that use private LLMs to overcome and resolve ethical concerns (e.g. transparency, data privacy) among their internal stakeholders (i.e. salespeople, sales managers) and external stakeholders (i.e. customers, third parties) when AI is used as a customer-interaction tool?
	External stakeholders	How can organizations prevent AI from leaking proprietary information to external stakeholders? What impact will transparency between channel partners have on the risks associated with resource exchange and outsourcing (i.e. opportunism)? How can organizations use AI to source external stakeholders, synthesize KPIs and evaluate external stakeholder performance?
	Operations	To what extent should organizations involve business functions in early stages of AI adoption to avoid disconnects between the technology and departmental tasks? What department (e.g. IT, sales, marketing) or function (e.g. the user, the manager) within the organization is responsible for training employees on AI technology? What elements need to be considered to measure the cost/benefits of implementing AI in the sales process? While organizations use mass lead generation allowed by AI, how do they ensure they don’t lose more of what they get? Should organizations adjust their pricing strategies considering AI, a cost-effective tool, might partially replace some salespeople?
Customer	Purchasing process	At what stage of the purchasing process will the customer need a human, and what is the core reason why customer’s need humans (e.g. trust, more creative solution, someone who “understands”)? How will interactions and negotiations change as AI is incorporated in the purchasing process?
	Customer journey	At what stage(s) of the customer journey is AI most impactful or harmful to negotiations or customer-seller relationships? How do customers perceive the sales message generated by an AI versus a salesperson especially if it replicates human characteristics (e.g. empathy)? In what ways can customers identify if the content/message is generated from an AI or a salesperson and should there be a mention of the fact that it is AI generated? How will customer’s pricing perceptions and negotiations change when dealing with an AI salesperson?

Chang, Woojung. 2022. “The Effectiveness of AI Salesperson vs. Human Salesperson across the Buyer-Seller Relationship Stages.” Journal of Business Research 148: 241–51. https://doi.org/10.1016/j.jbusres.2022.04.065.

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Pappas, Alec, Elena Fumagalli, Maria Rouziou, and Willy Bolander. 2023. “More than Machines: The Role of the Future Retail Salesperson in Enhancing the Customer Experience.” Journal of Retailing 99 (4): 518–31. https://doi.org/10.1016/j.jretai.2023.10.004.

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