Salesforce's 2024 State of Sales report shows reps spend 70% of their time on non-selling tasks. Learn how AI targets this for major productivity gains.

According to the Salesforce State of Sales 6th Edition report from 2024, sales representatives spend approximately 70% of their workweek on non-selling activities, leaving only 30% for active selling. [10] The report details that these non-selling tasks include manual data entry, administrative duties, and preparation, each consuming about 9% of a rep's week. [4] While Salesforce quantifies the problem, other industry analyses suggest generative AI can save reps around two hours per day by automating these specific functions. [14] Ultimately, the 2024 Salesforce data indicates a massive opportunity for efficiency gains through AI adoption.

TL;DR

Sales reps spend 70% of their week on non-selling tasks like data entry and admin work, according to Salesforce's 2024 data. [2, 4]
81% of sales teams are already using or experimenting with AI to reclaim that lost time and boost productivity. [2, 10]
Sales teams using AI reported 83% revenue growth, significantly higher than the 66% growth for teams without AI. [2, 10]
Data quality is a major obstacle; only 35% of sales professionals completely trust their organization's data for use with AI. [4, 11]
Top non-selling time sinks that AI can automate include manual data entry (9% of the week) and administrative tasks (9% of the week). [4]

Sales Reps Spend 70% of Their Week on Non-Selling Tasks

The core challenge facing modern sales organizations is a profound misalignment of their most valuable asset: the sales representative's time. According to the Salesforce State of Sales, 6th Edition (2024), which surveyed 5,500 sales professionals, reps spend only 30% of their workweek on active selling. This means a staggering 70% of their time is consumed by a host of non-revenue-generating activities. The 2024 report details a week fragmented by tasks such as generating quotes and proposals (10%), manual data entry (9%), general administrative duties (9%), and internal meetings (9%). This lopsided time allocation has remained stubbornly consistent, showing only a marginal 2% improvement in selling time from the 28% reported in the 5th edition of the survey in 2022. This persistent inefficiency highlights that despite the introduction of new technologies, the fundamental structure of a seller's day remains buried in operational drag rather than focused on customer engagement and closing deals.

This chronic time deficit directly correlates with widespread performance issues and declining quota attainment. The same 2024 Salesforce research that identified the 70% non-selling time sink also revealed a crisis in confidence and results: 67% of sales representatives did not expect to meet their quota in 2024, a sentiment grounded in the fact that 84% had missed their quota in the preceding year, 2023. The connection is straightforward arithmetic; every hour dedicated to updating a CRM or attending an internal sync is an hour not spent prospecting, conducting demos, or negotiating contracts. This administrative burden creates a system where, as one analysis of the data points out, sales professionals function less as sellers and more as part-time administrators. The problem is compounded by what Gartner research from September 2024 found: 50% of B2B sellers (n=1,026) feel overwhelmed by their tech stack, and this group is 45% less likely to achieve their quota.

The financial and operational impact of this time drain is significant, directly limiting a company's revenue potential and increasing operational friction. When 70% of a sales team's paid hours are diverted from direct selling, the opportunity cost is immense. This time is not just lost; it is actively spent on activities that, while necessary for business function, do not directly contribute to closing deals or fostering customer relationships that lead to new revenue streams. The issue is systemic, creating a drag on the entire revenue engine. As detailed in the Salesforce State of Sales, 6th Edition (2024), this administrative overhead includes everything from manual data entry to seeking internal approvals, tasks that bottleneck deal momentum and delay revenue recognition. Ultimately, this misallocation of time means that a company's growth is throttled not by a lack of market opportunity, but by an internal, structural inefficiency that prevents its sales team from capitalizing on it, a problem highlighted by multiple industry analyses.

Where Do 28 Hours Go? A Breakdown of Non-Selling Activities

Manual data entry and general administrative work represent a significant and costly drain on a sales representative's week, consuming a combined 18% of their available time. The Salesforce "State of Sales, 6th Edition" report, based on a 2024 survey of 5,500 sales professionals, specifies that manually entering customer and sales information into a CRM accounts for 9% of the workweek. This figure is mirrored by another 9% spent on miscellaneous administrative tasks like reporting and process compliance. When calculated against a standard 40-hour workweek, these activities amount to more than seven hours spent on non-revenue-generating functions that are prime candidates for automation. Other industry analyses reinforce this finding, with some placing the time lost to manual data entry even higher, between 8 and 12 hours per week. The cumulative effect of this administrative burden is substantial; a Forrester Activity Study that tracked over 3,000 sales reps found they burn nearly two full days weekly on such tasks, directly reducing the time available for engaging with customers and closing deals. This highlights a critical inefficiency that generative AI is well-positioned to solve, with some analyses showing automation can reduce CRM data entry time by as much as 70%. The consensus from reports like the Salesforce State of Sales 6th Edition is that this administrative overhead is a primary driver of lost productivity.

Prospect and account research, a critical preparatory step, consumes another significant portion of a sales professional's time, accounting for approximately 9% of their week according to the 2024 Salesforce data. This research phase, which includes identifying key decision-makers, understanding a prospect's business challenges, and analyzing their competitive landscape, is often performed manually across a disconnected set of tools. Some analyses break this down further, showing that manual account research can consume one to three hours per account, which quickly scales to over 20 hours per week for a rep managing a large territory. This time-intensive process is compounded by the challenge of inaccurate data; research from ZoomInfo and Everstage indicates reps can spend over 27% of their time, or 546 hours annually, working with faulty contact information. This is precisely the type of structured, data-intensive work where AI excels. Account intelligence platforms can automate this research, reducing the time spent per account by 50% to 85% and reclaiming valuable hours for direct selling activities. By leveraging AI for tasks like dynamic audience targeting and segmentation, as detailed in reports from firms like McKinsey, sales teams can transform this time sink into a strategic advantage, ensuring reps approach every conversation with relevant, up-to-date insights.

Internal meetings and trainings constitute another major category of non-selling work, consuming 9% of a representative's weekly hours, an amount equal to that spent on data entry and administrative tasks. While essential for team coordination, skill development, and strategy alignment, these internal obligations directly subtract from the time available for customer interaction. The Salesforce "State of Sales, 6th Edition" identifies this as a distinct and significant time block, separate from deal-specific preparation or customer-facing activities. The productivity cost is substantial, as a 2025 Flowtrace report on meetings found that just two weekly meetings without clear agendas could waste over 100 hours per rep annually. This time, which produces no direct pipeline or revenue, adds to the 70% of the week spent on non-selling functions. Gartner research from a 2024 survey of over 1,000 B2B sellers echoes this sentiment, finding that 72% of sellers feel overwhelmed by the number of skills required for their roles, underscoring the need for effective, not just time-consuming, training. By automating other administrative and research tasks, organizations can preserve the necessary time for valuable, collaborative meetings and impactful training while increasing the overall percentage of the week dedicated to active selling, as noted by sources like Gartner.

Non-Selling Task Category	Percentage of Week (Salesforce 2024)	Equivalent Hours (40-Hour Week)	Potential for AI Automation	Example AI Use Case
Manual Data Entry	9%	3.6 hours	High	Automated CRM activity logging from email and calendar
General Administrative Tasks	9%	3.6 hours	Medium	AI-generated summaries and internal reporting
Prospect & Account Research	9%	3.6 hours	High	Automated account intelligence and contact data verification
Internal Meetings & Training	9%	3.6 hours	Low to Medium	AI-powered meeting summaries and personalized coaching bots
Generating Quotes & Proposals	10%	4.0 hours	High	Automated quote and proposal document generation
Lead Prioritization & Prep	25% (includes downtime)	10.0 hours	Medium	Predictive lead scoring and AI-generated call prep sheets

81% of Sales Teams Have Deployed AI to Reclaim Lost Time

Widespread adoption of artificial intelligence is a clear trend, with the Salesforce "State of Sales, 6th Edition" report from 2024 indicating that 81% of sales teams have already deployed AI. This figure, derived from a double-anonymous survey of 5,500 sales professionals across 27 countries, breaks down into two distinct groups: 40% of organizations are actively experimenting with the technology, while a slightly larger 41% report having fully implemented AI into their operations. This high rate of adoption underscores a significant strategic shift within the sales industry as teams move to automate tasks and uncover new efficiencies. The investment is not merely tactical; it represents a fundamental change in how sales organizations approach their workflows and customer interactions. The data suggests that AI is no longer a peripheral or experimental tool for a select few but has become a mainstream component of the modern sales toolkit, with a vast majority of teams recognizing its potential to reclaim time lost to non-selling activities and drive better performance outcomes.

The significant investment in AI is yielding measurable financial returns, as teams that have adopted the technology are 1.3 times more likely to experience revenue growth compared to their non-AI counterparts. According to the Salesforce "State of Sales, 6th Edition" report, a compelling 83% of sales teams with AI integrated into their workflows saw revenue growth in the past year, a stark contrast to the 66% of teams without AI who reported similar growth. This growth multiplier is not an anomaly but the direct result of leveraging AI for high-impact activities. As detailed in analyses of the 2024 sales landscape, AI-powered teams benefit from enhanced productivity and superior data utilization, which translates directly to top-line gains. For example, AI-driven predictive lead scoring can improve conversion rates, while automated data analysis provides deeper insights into customer behavior, allowing for more effective and personalized engagement strategies that ultimately close more deals and drive revenue. This clear correlation between AI adoption and financial success provides a powerful incentive for organizations to move beyond experimentation and toward full, strategic implementation.

Despite the high adoption rates and proven revenue benefits, a significant sense of apprehension remains within sales organizations. Nearly half, or 49%, of sales professionals are concerned that their company is failing to capitalize on the full potential of generative AI. This concern highlights a gap between mere implementation and strategic mastery of the technology. While many teams use AI for basic functions like content creation, as noted in a 2025 Forbes analysis, fewer deploy it for more complex, high-value tasks such as strategic lead qualification or predictive forecasting. This hesitation may stem from several factors, including a lack of training, which 70% of marketers report their employers do not yet provide, and persistent worries about data accuracy and privacy. A 2023 survey by Salesforce and YouGov found that 71% of marketers believe AI's lack of human creativity and contextual knowledge is a potential barrier, suggesting that even with tools in hand, many teams feel unprepared to leverage them for more than surface-level productivity gains.

The push to integrate artificial intelligence is also a powerful catalyst for broader technological transformation, compelling sales teams to modernize and consolidate their tools. Over half of the teams that have fully implemented AI have also updated their technology stack, indicating that successful AI adoption is not about adding another tool but about creating a more unified and efficient ecosystem. The average sales team uses around ten different tools to close deals, a complexity that overwhelms 66% of sales representatives, according to Salesforce research. By consolidating these disparate systems into a single, AI-powered CRM platform like Sales Cloud, organizations can create a single source of truth for their customer data. This integration is critical because, as experts note, AI is only as powerful as the data that feeds it. A connected and comprehensive data foundation allows AI to automate emails, identify cross-sell opportunities, and provide real-time insights that genuinely boost productivity, turning a cluttered tech stack into a streamlined, revenue-generating engine.

The Data Quality Crisis: Why Only 35% of Reps Trust Their AI's Foundation

A profound crisis of confidence in data integrity is the single greatest barrier to realizing returns on generative AI investments in sales. According to the Salesforce State of Sales 6th Edition report, which surveyed 5,500 sales professionals globally between March and April 2024, only 35% of sales professionals completely trust the accuracy of their organization's data. [10] This widespread distrust forms a precarious foundation for advanced AI tools, which depend entirely on the quality of underlying data to generate reliable outputs. Compounding this issue, the same research reveals a significant apprehension towards the technology itself, with 71% of IT decision-makers believing generative AI will introduce new security threats to their data. [9] A separate 2024 Cisco study reinforces this concern, reporting that 48% of employees have already input non-public company information into generative AI tools, creating immediate data exposure risks. [37] This confluence of low data trust and high security anxiety creates a paralyzing dilemma for sales leaders, where the very tool meant to unlock productivity is perceived as a potential vector for catastrophic error and data leakage.

The principle of 'Garbage In, Garbage Out' (GIGO) moves from a theoretical concept to an expensive reality when applied to sales AI, directly undermining its potential return on investment. [5, 17] An AI model is only as effective as the data it learns from; when trained on incomplete, outdated, or inaccurate CRM records, it can only amplify those flaws at scale, producing flawed forecasts, misleading customer insights, and nonsensical outreach recommendations. [19, 23] This problem is not trivial. Research from Gartner has quantified the cost of poor data quality at an average of $12.9 million per organization annually, a figure that predates the widespread adoption of generative AI, which is known to amplify rather than fix data problems. [13, 17] According to a 2026 report from SAS and IDC, organizations with stronger trustworthy-AI practices, which include data quality and governance, are significantly more likely to report strong ROI. [13] Conversely, when AI is built on a weak data foundation, it leads to what one expert calls 'confidently automated errors,' where the system produces incorrect outputs with such conviction that they are accepted without scrutiny, leading to wasted resources and eroded trust in the technology itself. [24]

Mass-market data providers, while offering immense scale, often fail to deliver the verified, accurate data required for effective sales outreach, particularly within the small-to-medium business (SMB) sector. Platforms like ZoomInfo and Apollo.io, which are central to many sales teams' prospecting efforts, show significant variance in data quality. For instance, a 2026 benchmark test of 1,000 leads found that while ZoomInfo had stronger phone data for US enterprise accounts, its email accuracy was 84%, compared to Apollo's 78%. [25] However, other user reviews and comparisons note that ZoomInfo's data on smaller companies and startups can be lacking, while Apollo's data accuracy is sometimes criticized for weaker email verification. [7] This creates a structural gap, as B2B contact data decays at a rate of 22-30% annually due to job changes and company restructurings, making point-in-time accuracy claims from vendors potentially misleading. [29, 34] The core issue is that these platforms often sell 'informed guesses' or 'junk inferences' rather than verified facts, a problem that generic AI cannot solve and may even worsen by creating narratives based on this thin, unreliable data. [42]

In response to the unreliability of AI-generated narratives built on flawed data, a 'plain-facts' lead model offers a more dependable foundation for sales outreach by prioritizing the verification of core data points. This approach emphasizes confirming the absolute essentials for communication, such as a deliverable email address and a working direct-dial phone number, before any other data enrichment or AI analysis occurs. [1, 3] The logic is straightforward: a sophisticated, AI-generated profile of a prospect is useless if the email bounces and the phone number is disconnected. The industry average accuracy for B2B data providers is a mere 50%, while high-quality providers can achieve 97% or more, demonstrating a vast gap between unverified and verified data. [26] Focusing on a plain-facts model means investing in processes like real-time API verification and human-in-the-loop validation, which, while more rigorous, build a trustworthy data asset. [4, 11] This verified foundation not only improves sales efficiency by ensuring reps connect with real prospects but also enhances brand reputation by minimizing spam-like interactions and building trust from the first point of contact. [1]

Data Verification Method	Typical Accuracy Claim	Real-World Benchmark	Best Use Case	Key Weakness
Automated Email Pinging (SMTP)	95-99%	87-97% deliverability, depending on provider and list age. [31]	Pre-campaign list cleaning to reduce hard bounces.	Cannot definitively verify catch-all domains; can stress mail servers if done at high volume.
Manual Human Verification	98-99%+	~95% accuracy, limited by human error and time lag. [11]	Verifying high-value leads for strategic accounts or C-level outreach.	Extremely slow and expensive; not scalable for large datasets.
Social Profile Cross-Referencing	Not typically sold as a standalone service.	Highly variable; depends on profile privacy settings and user updates.	Confirming job titles, company changes, and recent activity for personalization.	Data is often unstructured, private, or self-reported and may not be current.
Waterfall Enrichment (Multi-Provider)	90%+ Match Rate	Can achieve 90%+ contact match rates vs. 50-62% for single sources. [29]	Maximizing coverage and accuracy across large, diverse datasets.	Can be more complex and costly to implement than a single-provider solution.
Phone Number Verification (Automated)	N/A	Varies widely; top providers hit 55-70% direct-dial accuracy. [12]	Scrubbing call lists to remove disconnected or invalid numbers.	Does not confirm the number belongs to the target contact, only that it is active.
Real-time Point-of-Use Verification	95%+ Accuracy Guarantee	Can achieve <1% bounce rate by verifying data at the moment of export/use. [29, 34]	Ensuring maximum deliverability for immediate outbound campaigns.	Dependent on API integration; may be slower than pulling from a static, pre-verified list.

Connecting AI to Revenue: The Link Between Automation and Growth

A direct correlation between AI adoption and revenue growth is now firmly established, with the technology acting as a clear differentiator for high-performing sales organizations. According to the Salesforce "State of Sales, 6th Edition" report from 2024, which surveyed 5,500 sales professionals across 27 countries, 83% of sales teams using AI experienced revenue growth, compared to only 66% of teams that have not integrated AI into their workflows. This 17-point gap highlights a significant competitive advantage, demonstrating that AI is not merely a tool for efficiency but a core driver of financial performance. The report further specifies that 81% of all sales teams are at least experimenting with AI, indicating a widespread recognition of its potential. The revenue impact stems from AI's ability to enhance critical sales functions, from predictive lead scoring and more accurate forecasting to automating the research and qualification process, allowing teams to focus their efforts on the most promising opportunities and close deals more effectively.

The primary return on investment from generative AI is realized when saved time is converted into revenue-generating activities, a transition that directly impacts top-line growth. Research from McKinsey highlights this connection, showing that when sales organizations successfully redirect representative time toward high-value, customer-facing activities, they can increase revenue per full-time employee by an average of 3 to 15 percent. This uplift is not theoretical; it is the direct result of reallocating the hours previously lost to administrative burdens, such as manual data entry and report generation, toward strategic work. Instead of managing their CRM, reps can invest that reclaimed time in preparing for negotiations, building deeper relationships through more frequent and meaningful interactions, and identifying new opportunities within their accounts. As detailed in a May 2020 analysis, early adopters of sales automation report not only a sales uplift potential of up to 10 percent but also efficiency improvements of 10 to 15 percent, confirming that operational gains are a direct pathway to commercial success.

Automating administrative work with AI provides the crucial bandwidth for sales representatives to focus on personalization, a key factor that separates market leaders from the competition. According to a November 2021 McKinsey report titled "Next in Personalization 2021," companies that grow faster drive 40 percent more of their revenue from personalization than their slower-growing counterparts. This performance gap underscores the immense value of tailoring outreach and solutions to specific customer needs, a task that requires significant time and cognitive effort. When generative AI handles repetitive tasks, it frees sales professionals to conduct deeper discovery, understand nuanced customer challenges, and craft bespoke proposals that resonate more strongly. This shift from administrative upkeep to strategic engagement is critical, as 71% of consumers now expect personalized interactions and 76% become frustrated when they do not receive them. By enabling reps to meet and exceed these expectations, AI automation directly facilitates the high-touch, value-driven conversations that build loyalty and accelerate revenue growth.

Frequently Asked Questions

How much time can AI save a sales team?

AI can save a sales team between four and seven hours per representative each week by automating administrative and content creation tasks. [2] According to a 2026 report, this time is reclaimed from activities like drafting emails, updating CRM systems, and preparing for meetings. [2] By handling these repetitive functions, AI allows sales professionals to reallocate their efforts toward high-value activities like engaging prospects and advancing deals. [2]

What percentage of their time do sales reps spend selling?

Sales representatives spend only 30% of their time on active selling, a figure that has remained largely unchanged since 2022. [13, 20] This means approximately 70% of a sales rep's workweek is consumed by non-selling responsibilities. [21] These ancillary tasks include prioritizing leads, manual data entry, and generating quotes, all of which detract from direct customer interaction and relationship building. [13]

What are the main benefits of using AI in sales?

The main benefits of using AI in sales are increased efficiency and sharper prospecting, leading to more time spent on revenue-generating activities. [4] AI automates repetitive work like data entry and provides real-time guidance, freeing reps from low-value tasks. [4, 6] For example, AI-powered lead scoring highlights prospects with the strongest buying signals, which helps sales teams focus their energy on opportunities that are more likely to close. [4]

What are the biggest challenges of implementing AI in sales?

The biggest challenge of implementing AI in sales is poor data quality, which can lead to unreliable predictions and a lack of trust in the system. [22, 23] Many organizations suffer from fragmented and inconsistent data, with one survey finding that 52% of professionals see data quality as the top barrier to AI adoption. [16, 23] Other significant challenges include the high cost of implementation, the complexity of integrating AI with legacy systems, and a shortage of employees with specialized data science skills. [12, 18]

Does AI help increase sales revenue?

Yes, AI helps increase sales revenue by improving seller efficiency and enabling better customer experiences. [11] Research from Salesforce shows that sales teams using AI are 1.3 times more likely to experience revenue growth, with 83% of AI-adopting teams reporting increases compared to 66% of those without it. [21] According to a 2024 McKinsey analysis, 66% of marketing and sales departments reported revenue increases directly attributable to their use of generative AI. [14]

Last updated: October 2026

How GenAI Recaptures the 70% of a Sales Rep's Week