New research links generative AI usage to improved sales performance and administrative efficiency

Sales professionals frequently face a conflict between their primary goal and their daily reality. Their main objective is to interact with clients and close deals, yet they often spend a significant portion of their day on administrative duties. Tasks such as data entry, email composition, and internal reporting can consume time that would otherwise be spent on revenue-generating activities.

Organizations have long sought technological solutions to this imbalance. In recent years, Generative Artificial Intelligence (GenAI) has emerged as a potential tool to automate these routine tasks. Unlike traditional software that simply organizes data, GenAI can create new content, such as drafting emails or summarizing meeting notes.

Despite the potential benefits, adoption rates for these tools in sales departments have remained relatively low. Business leaders often hesitate to implement GenAI due to concerns about accuracy or the loss of the human element in selling. A recent study published in the Journal of Business & Industrial Marketing investigates this specific tension.

Psychology of Selling

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Investigating the Impact of New Technology

The study was designed to provide empirical evidence regarding the actual value of GenAI in a business-to-business (B2B) setting. The researchers sought to move beyond anecdotal claims and measure whether these tools truly enhance performance. They also aimed to identify the organizational factors that encourage employees to use the technology.

Michael Rodriguez from the Department of Marketing at East Carolina University led the investigation. He collaborated with Dawn R. Deeter-Schmelz and Michael T. Krush from the Department of Marketing at Kansas State University. The team structured their inquiry around a framework known as the Technology Acceptance Model (TAM).

Understanding the Theoretical Framework

The Technology Acceptance Model is a theory used to explain why users accept or reject information technology systems. It posits that external variables influence an individual’s attitude toward a tool, which in turn dictates whether they will use it. The researchers adapted this model to focus on two specific external factors: upper management support and technology self-efficacy.

Upper management support refers to the extent to which leadership promotes and champions the use of a new tool. This includes providing necessary resources and modeling the behavior themselves. The researchers hypothesized that strong backing from leadership would lead to higher usage rates among the sales force.

Technology self-efficacy represents a person’s belief in their own capability to use digital systems effectively. The team proposed that salespeople with higher confidence in their technical skills would be more likely to adopt GenAI. They also looked at how these factors interacted with one another.

The Distinction Between AI and GenAI

The researchers provided necessary context regarding the evolution of sales technology. They distinguished GenAI from traditional artificial intelligence methods used in the past. Previous AI tools in sales focused primarily on predictive analytics, such as forecasting revenue or scoring leads based on historical data.

GenAI represents a shift from prediction to creation. This technology uses learning models to generate original outputs that mimic human creation. In a sales context, this might involve creating personalized outreach materials or simulating customer service dialogues.

Designing the Methodology

The research team collaborated with a single company to ensure a controlled environment. The participating firm operates in the health-care industry and specializes in the sale and distribution of medical goods. This organization had already integrated GenAI tools into its sales process, providing a suitable population for the study.

The investigation proceeded in distinct phases to ensure the validity of the data. The first phase involved qualitative data collection through in-depth interviews. The researchers spoke with key leaders, including the Head of Sales and the Head of Sales Enablement.

These interviews helped the team understand how the company used GenAI in practice. The insights gained from these discussions allowed the researchers to refine their survey questions. They needed to ensure the language used in the questionnaire matched the terminology used by the sales representatives.

Gathering the Data

Following the interviews, the team conducted a pretest with a smaller group of 64 sales representatives. This step allowed them to verify the reliability of their measurement scales. Once the measures were confirmed, the main study began.

The main survey was distributed online to the company’s larger sales force. To encourage participation, sales managers sent internal emails announcing the project. The final dataset consisted of 163 usable responses from B2B sales professionals.

The demographics of the sample reflected a largely experienced group. The average respondent had over nine years of sales experience. Approximately 82 percent of the participants had been working in sales for six years or more.

Measuring the Variables

The researchers used established scales to measure the key concepts in their model. To assess GenAI usage, they asked respondents to rate how often they used the tool for specific tasks. These tasks included planning selling activities, preparing for sales calls, and creatively serving customers.

The study also measured three specific outcome variables. The first was sales process effectiveness, which refers to the ability to achieve immediate results like securing meetings. The second was administrative efficiency, defined as the ability to complete non-selling tasks like reporting.

The final outcome variable was overall sales performance. This measured broader results such as generating sales volume and increasing market share. The researchers then analyzed the relationships between these variables using a statistical method called partial least squares.

Findings on Sales Outcomes

The analysis revealed a consistent positive link between the use of GenAI and all three measured outcomes. The data indicated that as sales professionals increased their use of GenAI, their administrative efficiency improved. This suggests the tool successfully automated time-consuming tasks.

The study also showed a positive association between GenAI use and sales process effectiveness. Respondents who utilized the technology reported better capabilities in analyzing wins and losses compared to their peers. This implies the technology aided in the strategic aspects of the sales cycle.

Finally, the research confirmed a positive relationship between GenAI use and overall sales performance. The analysis showed that the adoption of these tools was linked to better results in generating sales volume and developing new accounts. These findings provided empirical support for the value of the technology.

The Role of Management Support

The researchers then examined the factors that drove salespeople to use the technology in the first place. The results showed a strong, positive relationship between upper management support and GenAI use. When supervisors actively encouraged the technology, employees were significantly more likely to use it.

This finding aligns with previous research on technology adoption. It suggests that leadership plays a necessary role in validating new tools. Passive availability of software is often insufficient to drive adoption without active managerial endorsement.

Surprising Results on Self-Confidence

The study produced an unexpected finding regarding technology self-efficacy. Contrary to the researchers’ initial hypothesis, there was no significant direct link between a salesperson’s technical self-confidence and their use of GenAI. Believing one was good with technology did not automatically lead to higher usage of this specific tool.

To understand this better, the researchers conducted a post hoc analysis. They looked at how management support and self-efficacy influenced each other. This secondary analysis revealed a significant interaction between the two factors.

The Interaction of Support and Confidence

The analysis suggested that upper management support acted as a leveling factor. In scenarios where management support was high, salespeople used the tool regardless of their personal technology self-efficacy. The strong external encouragement appeared to override individual variations in confidence.

Conversely, technology self-efficacy played a clearer role when management support was low. In the absence of strong leadership, those with high confidence were more likely to figure out the tool on their own. This indicates that management support can compensate for a lack of individual technical confidence among the staff.

Implications for Business Leaders

These findings offer specific direction for business executives and sales managers. The positive links to performance suggest that the investment in GenAI can yield tangible returns. However, the data implies that successful implementation requires more than just purchasing licenses.

The strong influence of management support indicates that leaders must actively champion the technology. Managers should consider modeling the use of GenAI in their own workflows. Demonstrating the tool’s value in team meetings could signal its importance to the wider team.

The study also suggests that training programs should focus on reducing the perceived complexity of the tool. Since GenAI generates content, it requires different skills than standard data entry. Organizational support systems can help bridge the gap for employees who may lack technical confidence.

Reducing Administrative Burdens

The findings regarding administrative efficiency are particularly relevant for resource allocation. By automating routine tasks, companies may be able to increase the percentage of time salespeople spend with customers. This shift could lead to improved revenue without necessarily increasing the headcount of the sales force.

The researchers note that this efficiency does not replace the salesperson. Instead, it alters the nature of their daily work. The technology handles the repetitive aspects of the job, allowing the human worker to focus on relationship building.

Limitations of the Study

The authors identified several limitations to their work. The study focused on a single company within the health-care industry. This focus allowed for high internal control but may limit how well the findings apply to other sectors.

Additionally, the study relied on self-reported data from the sales professionals. While this is common in social science research, it captures the user’s perception rather than objective system logs. The measures for performance were also subjective comparisons to peers rather than raw financial data.

Directions for Future Research

The results of this study open several new avenues for investigation. Future researchers could examine the impact of GenAI on the buyer’s experience. It remains to be seen if customers can detect AI-generated content and how that influences their trust in the seller.

Researchers might also explore these relationships in different industries. The sales process in technology or manufacturing may present different challenges than those in health care. Comparing these sectors could reveal if the value of GenAI is universal or industry-specific.

Finally, long-term studies could track how usage evolves over time. As GenAI becomes more common, the factors driving its adoption may change. Future studies could determine if the reliance on management support decreases as the workforce becomes more familiar with the technology.