What is happening to sales managers
- Impact
AI tools are automating forecasting, lead scoring, performance monitoring, and assisting in personalized communication. This shifts Sales Managers' focus towards high-level strategic planning, advanced coaching, validating AI outputs, ensuring ethical AI use, and fostering human-centric sales relationships.
- Risk
Significant augmentation; emphasis on strategic leadership, advanced coaching, and AI tool mastery.
The Sales Manager role will be heavily augmented by AI. AI will handle much of the data analysis, performance monitoring, and routine sales operations, demanding that managers pivot to leveraging these tools for deeper insights, overseeing AI-generated content, focusing on strategic vision, team development, and the human elements of sales leadership. Ethical considerations and bias mitigation in AI performance evaluation will also be paramount.
- Sector readiness
Rapid & Deep Integration
The sales industry is aggressively integrating AI into CRM, sales enablement, and sales intelligence platforms. Companies are adopting AI tools to improve sales efficiency, forecasting accuracy, and rep productivity. The sector is characterized by rapid experimentation and implementation of new AI-powered solutions.
Where you stand
The Sales Manager role is at a significant inflection point, with AI profoundly transforming how sales operations are managed and teams are led.
AI will automate routine data analysis, performance tracking, and lead prioritization, freeing up Sales Managers for strategic leadership, advanced coaching, and fostering high-value human connections.
Success will increasingly depend on a Sales Manager's ability to master AI tools, interpret AI-driven insights, champion ethical sales practices, and develop their team's human-centric selling skills in an AI-augmented sales landscape.
What this means for you
Concrete changes to how the work gets done, in the order you are likely to meet them.
- 01
AI-Driven Sales Forecasting & Pipeline Management. Sales Managers are increasingly leveraging AI tools that analyze historical sales data, pipeline velocity, market trends, and external factors to generate highly accurate sales forecasts. This allows for more precise resource allocation and strategic planning, moving beyond traditional spreadsheet-based forecasting.
- 02
AI-Powered Performance Monitoring & Coaching Insights. Sales Managers will benefit from AI systems that analyze sales rep activities (ee.g., call recordings, email interactions, CRM data) to identify patterns, highlight coaching opportunities, and predict performance trends. This enables more personalized and data-driven coaching interventions.
- 03
Automated Lead Scoring & Opportunity Prioritization. Sales Managers are overseeing AI tools that automatically score leads based on their likelihood to convert and prioritize sales opportunities within the pipeline. This ensures sales teams focus their efforts on the most promising prospects, increasing conversion rates and efficiency.
- 04
Generative AI for Sales Enablement Content. Sales Managers will find generative AI to be a powerful asset for creating sales enablement materials. This includes drafting initial versions of personalized sales emails, pitch decks, proposals, and training content, allowing managers to accelerate content creation and ensure messaging consistency.
- 05
AI for Sales Process Optimization & Anomaly Detection. AI tools are analyzing sales processes to identify bottlenecks, inefficiencies, or deviations from optimal workflows. Sales Managers will use these insights to refine sales methodologies, pinpoint areas where reps might struggle, and proactively address systemic issues.
- 06
Market & Competitive Intelligence with AI. Sales Managers are utilizing AI-powered platforms to gather and synthesize real-time market data, competitive strategies, and customer sentiment. This provides deeper intelligence to inform sales strategy, identify market gaps, and train sales teams on competitive positioning.
- 07
AI-Augmented Territory & Account Planning. AI can analyze customer data, market potential, and sales rep performance to suggest optimized territory assignments and account prioritization. Sales Managers will leverage these insights for more equitable distribution of workload and maximizing revenue potential across their teams.
- 08
Voice & Conversation Intelligence. Sales Managers are employing AI tools that analyze sales calls for key phrases, sentiment, talk-to-listen ratios, and adherence to sales scripts. This provides objective insights into rep performance, customer objections, and successful sales tactics for more effective coaching.
- 09
Ethical AI Use & Bias Mitigation in Sales Operations. Sales Managers will be responsible for ensuring that AI tools used in sales operations (e.g., lead scoring, territory assignment) are applied ethically, are free from unintended biases, and comply with data privacy regulations. This requires active oversight and validation.
- 10
Human-AI Teaming for Sales Reps. Sales Managers will coach their teams on how to effectively collaborate with AI tools. This includes training reps to use AI for personalized outreach, real-time call assistance, and data analysis, ensuring they leverage AI as a force multiplier without losing the human touch.
- 11
AI for Onboarding & Training Personalization. AI can analyze new rep performance and learning styles to personalize training modules, suggest relevant resources, and identify areas needing improvement. Sales Managers will use AI to accelerate ramp-up time and improve the effectiveness of their sales training programs.
- 12
AI-Driven Customer Churn Prediction & Retention. Sales Managers are leveraging AI models that predict which existing clients are at risk of churning based on their usage patterns, support interactions, and sentiment. This enables proactive outreach and tailored retention strategies, reducing customer attrition.
- 13
Strategic Relationship Building & Complex Negotiation. As AI automates data and routine interactions, the Sales Manager's focus will intensify on coaching their team in complex deal structuring, high-stakes negotiations, and building profound, trust-based relationships with key clients and decision-makers.
- 14
Continuous Learning of SalesTech & AI Tools. The sales technology landscape is evolving rapidly with AI integration. Sales Managers must continuously update their proficiency with new AI-powered CRM features, sales intelligence platforms, and sales enablement tools to effectively lead their teams.
- 15
Data Storytelling & Influence. Sales Managers will need to effectively translate complex AI-driven sales performance data into clear, compelling narratives for senior leadership, demonstrating ROI, justifying resource needs, and influencing strategic business decisions.
What is pushing this change
- 01
Demand for Higher Sales Productivity & Efficiency. Businesses constantly seek to maximize revenue with fewer resources, driving AI adoption to boost rep productivity.
- 02
Advancements in AI/ML (Predictive Analytics, NLP, Generative AI). Breakthroughs in these AI fields enable sophisticated analysis of sales data, personalized communication, and content generation.
- 03
Growth of CRM & Sales Enablement Platforms. These platforms provide the data infrastructure and integration points for AI tools to enhance sales workflows.
- 04
Need for Accurate Sales Forecasting. Accurate sales forecasts are critical for business planning, resource allocation, and financial health; AI significantly improves this.
- 05
Competitive Pressures in Sales. Companies must leverage technology to gain an edge in lead generation, conversion rates, and customer retention.
- 06
Volume of Sales Data (CRM, interactions, market). The vast amount of data from CRM systems, sales calls, emails, and market activity provides rich input for AI analysis.
- 07
Demand for Personalized Customer Engagement. Customers expect tailored experiences, and AI enables sales teams to personalize interactions at scale.
- 08
Shortage of Highly Skilled Sales Professionals. AI augmentation is seen as a way to increase the capacity and effectiveness of existing sales teams.
- 09
Pressure for Cost Optimization. AI automation of routine tasks and optimization of sales processes can lead to reduced operational costs.
- 10
Digital Transformation in Sales. Sales organizations are undergoing digital transformation, embedding AI into every stage of the sales lifecycle.
Impact by sector
The headline figure is an average. Where you work changes the picture.
- Enterprise Sales Managers
AI for forecasting complex deal cycles, identifying key accounts for strategic focus, and analyzing executive-level interactions. Focus on coaching strategic sales.
- Inside Sales Managers
AI for lead prioritization, optimizing sales cadences, and analyzing high-volume call/email interactions. Focus on coaching efficiency and conversion rates.
- Channel Sales Managers
AI for partner performance analysis, identifying co-selling opportunities, and optimizing partner programs. Focus on channel growth and relationship management.
- Sales Operations Managers
Heavy use of AI for sales forecasting model development, territory optimization, compensation plan analysis, and sales tech stack management. Focus on sales strategy and infrastructure.
- Sales Enablement Managers
AI for personalizing training content, analyzing rep skill gaps, and optimizing content delivery for sales reps. Focus on content effectiveness and rep readiness.
Skills to build
The skills that keep the human part of this work valuable as the routine part is automated.
- 01
Leadership & Team Development. Ability to motivate, develop, and lead a sales team, fostering a high-performance culture and ensuring alignment with sales strategy.
- 02
Data Analysis & Interpretation (Sales Metrics). Skill in analyzing complex sales data, interpreting AI-generated insights (e.g., performance trends, pipeline health), and making data-driven decisions.
- 03
Advanced Coaching & Mentorship. Providing personalized feedback, skill development, and strategic guidance to sales reps based on performance data and individual needs.
- 04
AI Tool Proficiency & SalesTech Mastery. Proficiency in using and managing AI-powered CRM features, sales intelligence platforms, forecasting tools, and sales enablement software.
- 05
Strategic Planning & Execution. Ability to define sales targets, develop comprehensive sales strategies, and oversee their execution to achieve revenue goals.
- 06
Communication & Influence. Articulating complex sales strategies, communicating performance insights, and influencing both sales teams and senior leadership.
- 07
Ethical Sales Practice & AI Bias Awareness. Understanding potential biases in AI tools (e.g., in lead scoring or performance evaluation) and ensuring sales practices comply with ethical guidelines and privacy regulations.
- 08
Adaptability & Change Management. Willingness to learn new sales technologies, adapt sales methodologies, and lead a team through technological and market changes.
Tools in use
Kinds of tool worth knowing
- 01
CRM Systems with AI (e.g., Salesforce Einstein). Leading CRM platforms with integrated AI for lead scoring, opportunity insights, forecasting, and personalized recommendations.
- 02
Sales Intelligence Platforms with AI. Platforms that use AI to gather and analyze firmographic, technographic, and intent data to identify high-potential leads and accounts.
- 03
Sales Engagement Platforms (AI-powered). Software that automates and personalizes sales outreach across multiple channels (email, social, calls) using AI for optimization.
- 04
Conversation Intelligence Platforms. AI tools that analyze sales call recordings for insights into rep performance, customer sentiment, and effective sales tactics.
- 05
Sales Forecasting Software (AI/ML-based). Software solutions that leverage AI and machine learning algorithms to generate highly accurate sales forecasts and predictive analytics.
- 06
Generative AI for Sales Content. Large Language Models (LLMs) used to assist in drafting personalized emails, proposals, pitch decks, and sales collateral.
Named tools already in use
Salesforce Sales Cloud (Einstein AI) / HubSpot Sales Hub (AI features)
VisitLeading CRM platforms that embed AI for automated insights, lead scoring, forecasting, and sales process optimization.
ZoomInfo / Apollo.io / Lusha
VisitPlatforms that leverage AI to provide comprehensive B2B sales intelligence, including contact data, company insights, and buying signals.
Outreach / Salesloft / Groove
VisitSales engagement platforms that use AI for automated outreach, content personalization, and sequence optimization across various channels.
Gong.io / Chorus.ai
VisitAI-powered platforms that record, transcribe, and analyze sales conversations to provide insights into rep performance, customer objections, and deal health.
Clari / Anaplan (for Sales Planning & Forecasting)
VisitAI-driven sales forecasting and revenue operations platforms that use machine learning to provide highly accurate predictions and insights.
ChatGPT / Jasper / Copy.ai (for sales content)
Generative AI models that can assist in drafting personalized sales emails, proposals, and other sales-related content.
In practice
Ways people in this role are already using AI, and what they get from it.
- Optimize Sales Forecasting AccuracyExample 1
- How
Implement an AI-powered sales forecasting tool that integrates data from the CRM, sales activities, and market trends to provide highly accurate revenue predictions. The Sales Manager uses this for strategic planning.
GainSignificantly improves the accuracy of sales predictions, leading to better resource allocation and business planning.
- Get AI-Powered Coaching InsightsExample 2
- How
Utilize a conversation intelligence platform that analyzes recorded sales calls. The AI identifies key moments, talk-to-listen ratios, objections handled, and successful sales tactics, providing the Sales Manager with data-backed coaching points for reps.
GainProvides objective, data-driven coaching insights, leading to more effective sales teams and improved individual rep performance.
- Automate Lead QualificationExample 3
- How
Implement an AI lead scoring system that automatically ranks incoming leads based on their likelihood to convert, utilizing demographic, firmographic, and behavioral data. The Sales Manager ensures the sales team prioritizes high-value leads.
GainEnsures sales reps focus on the most promising leads, increasing conversion rates and overall sales efficiency.
- Generate Personalized Outreach EmailsExample 4
- How
Use a generative AI tool to draft initial personalized outreach emails for sales reps, incorporating prospect-specific details and pain points. The Sales Manager reviews and coaches on tailoring for maximum impact.
GainAccelerates content creation for sales reps, ensures consistent messaging, and enables highly personalized outreach at scale.
- Analyze Sales Call PerformanceExample 5
- How
Access a dashboard from a conversation intelligence platform that summarizes sales call performance across the team. The AI highlights trends in successful calls, common objections, and coaching opportunities, allowing the Sales Manager to focus training efforts.
GainOffers clear, data-backed insights into team performance, identifies areas for improvement, and enables targeted coaching strategies for the Sales Manager.
How this role compares
Three neighbouring roles chosen to show the direction of travel, then the roles either side of yours on the exposure scale.
- Sales Development Representatives (SDRs/BDRs - cold outreach)More exposed
- AI impact
High (AI can automate lead qualification, personalized cold outreach, and initial follow-ups, reducing the need for manual BDR tasks.)
Work moves toRole contraction or redefinition towards managing AI outreach campaigns, handling warm hand-offs, or specializing in complex initial engagements.
- Sales Operations Analysts / AI Sales Tool AdministratorsDifferent skills, growing
- AI impact
Foundational (They implement, manage, and optimize the AI sales tech stack, ensuring data flow and model effectiveness.)
Work moves toDeep expertise in CRM administration, data analysis, sales process optimization, and technical proficiency with sales AI tools.
- Chief Sales Officer (CSO) / VP of Sales (Strategic Vision)Complementary, less exposed
- AI impact
High Augmentation (Leverage AI-driven insights for overall sales strategy, market expansion, and GTM planning), but core leadership, executive decision-making, and organizational culture remain human.
Work moves toOverall sales strategy, market expansion, revenue growth, talent development across the organization, and executive stakeholder management.
- 455–10 yrs
- 452–6 yrs
- 453–7 yrs
Sales Managers · this report
452–6 yrs- 506–11 yrs
Business Development Executives
502–6 yrs- 502–6 yrs
Closing judgement
For Sales Managers, AI is a powerful force that will transform their leadership and strategic impact. By automating many operational and analytical tasks, AI frees managers to focus on what truly drives results: developing high-performing teams, crafting winning strategies, and fostering the human connections that are indispensable in sales. Sales Managers who embrace AI as an enabler will lead the next generation of sales success.
Evidence and revisions
What the exposure figure rests on, what changed when it was last revised, and the published work cited for this role.
50 → 45
Window2-6 years (unchanged)
The 4 October 2026 review moved the score down by 5 points.
Microsoft's AI applicability score for the matching occupation is 0.16, in the upper half of 785 US occupations; Anthropic's observed exposure (the share of the occupation's tasks already being done with Claude) is 0.04, which is minimal by the standards of the 756 occupations measured; the US Bureau of Labor Statistics places it in the 'high' AI-exposure tier; BLS projects employment to grow 4.5% over 2025–35. Blending our 2025 editorial figure (60%) with the 2026 evidence composite (40%) moves the score from 50 to 45.
US Bureau of Labor Statistics · Employment Projections 2025–35 and AI Exposure Categories
Official statistics · 27 August 2026AI-exposure tier: High. Projected employment change 2025–35: +4.5%. Matched to Sales managers.
Microsoft Research · Working with AI: Measuring the Applicability of Generative AI to Occupations
Working paper · 10 July 2025AI applicability score 0.16 (percentile 57 of 785 occupations) for SOC 11-2022.
Anthropic · Anthropic Economic Index report: Cadences
Report · 26 June 2026Observed exposure 0.04 for SOC 11-2022 (percentile 65 of 756 occupations).
UK Department for Science, Innovation and Technology · Assessment of AI capabilities and the impact on the UK labour market
Report · 28 January 2026UK context: around 70% of UK workers are in occupations with tasks AI could perform or enhance, above the US average; a one-standard-deviation rise in exposure was associated with a 3.9% fall in UK job postings.
Microsoft · 2026 Work Trend Index Annual Report: Agents, human agency and the opportunity for every organization
Report · 5 May 2026Microsoft's 2026 Work Trend Index documents the shift toward managing agents alongside people; leadership roles change in content more than in headcount.
Archived copies are served only where the licence permits; otherwise the link goes to the publisher. Full research library →
Readers' view
What people who do this work make of our reading: their own scores, their reasons, and the notes they left on each section.
Our report is one reading of the evidence. This section is the other dataset: what people who do or know this work make of it. Nobody has scored this role yet. Sign in to add yours.
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45
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Method and sources
Each report was written from a large body of published research and then, in October 2026, re-scored against occupation-level evidence: the US Bureau of Labor Statistics AI-exposure classification and 2025–35 projections, Microsoft Research’s AI applicability scores and Anthropic’s observed-exposure data, cross-checked against the reports listed in the Evidence section above. The organisations and publications below are the standing literature behind the narrative sections. Every source, with dates, licences and archived copies where we are permitted to hold them, is catalogued in the research library.
Scores are revised by blending the previous editorial figure (60%) with a composite of the three occupation-level measures (40%), capped at fifteen points per revision and rounded to the nearest five. The window shifts one notch when a score moves ten points or more. Hand adjustments are recorded with their reason in the revision log.
Research library: every source, with dates, licences and archived copies →
- World Economic Forum
- The Future of Jobs Report series — Employer survey of expected job growth and decline, skill shifts and technology adoption (2020, 2023 and 2025 editions); Artificial Intelligence and the Future of Entry-Level Work (2026).
- AI governance and transformation reports — Frameworks on ethical AI, talent strategy and industry transformation.
- McKinsey Global Institute
- AI, Automation and the Future of Work series — Research quantifying automation potential by task, sector and demographic, from "Jobs Lost, Jobs Gained" to "Agents, robots, and us" (2025).
- Industry-specific reports — Financial services, healthcare, manufacturing and others.
- PwC
- Global AI Jobs Barometer — Annual analysis of job postings and productivity by AI exposure (2024–2026 editions).
- Upskilling Hopes and Fears survey — Employee perceptions and readiness.
- Microsoft Research and Anthropic
- Working with AI (2025); Anthropic Economic Index (2025–2026) — Occupation-level usage data from Copilot and Claude conversations, the two observed-usage measures behind the 2026 revision.
- Stanford Digital Economy Lab and Stanford HAI
- Canaries in the Coal Mine? (2025–2026); AI Index Report (annual) — Payroll evidence on early-career employment in exposed occupations; annual measurement of AI capability, investment and adoption.
- Deloitte
- Human Capital Trends series — Workforce, talent and HR technology trends.
- Tech Trends series — Emerging technologies and their business implications.
- Accenture
- Technology Vision series — Forward-looking analysis of emerging technology, with emphasis on AI.
- Fjord Trends — Design, innovation and human experience in a digital world.
- Boston Consulting Group
- AI/ML insights and industry solutions — "The AI Revolution in the Workplace" and related research.
- EY
- AI and workforce reports — Adoption, talent strategy and ethics.
- IBM Institute for Business Value
- AI and automation studies — Business models, workforce evolution and leadership.
- OECD
- AI Policy Observatory — International data and policy on AI, labour markets and skills.
- Employment Outlook — Labour-market trends including technological impact (2023–2026 editions).
- International Labour Organization
- Generative AI and Jobs: A Refined Global Index of Occupational Exposure (2025) — Task-level exposure gradients for every ISCO occupation; successor to the 2023 global index.
- International Monetary Fund
- Staff Discussion Notes on AI and work (2024, 2026) — Complementarity framing: where AI augments and where it substitutes.
- UK Department for Science, Innovation and Technology
- Assessment of AI capabilities and the impact on the UK labour market (2026) — UK occupational exposure and job-posting evidence.
- Brookings Institution
- AI and automation research — Economic and social implications, displacement and skills.
- Yale Budget Lab and Goldman Sachs Research
- Tracking the Impact of AI on the Labor Market; AI and the US labour market (2026) — Aggregate labour-market monitoring; macro displacement estimates.
- Oxford University (Oxford Martin School)
- The Future of Employment — Frey & Osborne and subsequent research on susceptibility to automation.
- MIT Technology Review
- AI & Work — Reporting on AI research and its implications for industries and jobs.
- Gartner
- Hype Cycle for Artificial Intelligence — Maturity and adoption of AI technologies.
- Future of Work reports — Workplace models and talent strategy.
- U.S. Bureau of Labor Statistics
- Employment Projections 2025–35; AI Exposure Categories; Occupational Outlook Handbook — Ten-year employment projections and, from the 2025 cycle, an AI-exposure tier for every detailed occupation.
- Indeed Hiring Lab
- AI at Work Report (2025) and posting-market updates — Skill-level transformation estimates and job-posting trends by occupation.
- OpenAI
- Research papers, blog and API documentation — Large language models, generative AI, safety and societal impact.
- Google DeepMind
- Research papers and blog — Reinforcement learning, AI for science, AGI and ethics.
- Meta AI
- Research papers and blog — Large language models, computer vision, AI for social good.
- Hugging Face
- Transformers library and model hub — Open-source state-of-the-art NLP models.
- TensorFlow and PyTorch
- Documentation and community forums — Core frameworks illustrating practical capability.
- arXiv
- cs.AI, cs.LG, cs.CV, cs.CL — Pre-print research.
- NeurIPS and ICML
- Conference proceedings — Top-tier academic research.
- ACM and IEEE
- Journals and proceedings — ACM Computing Surveys; IEEE Transactions on AI.
- Kaggle
- Datasets and competition solutions — Applied machine learning on real-world problems.
- The Alan Turing Institute
- Research and reports — Responsible and applied AI.
- NIST
- AI Risk Management Framework — Voluntary framework for managing AI risk.
- European Commission
- AI Act — Risk-tiered legal framework for AI.
- Ethics Guidelines for Trustworthy AI — Principles for responsible development.
- Partnership on AI
- Research and best practice — Responsible AI development.
- AI Now Institute
- Annual reports — Social implications: power, inequality, rights.
- ACM FAccT
- Proceedings — Fairness, accountability and transparency.
- Data & Society
- Publications — Social implications of data-centric technology.
- WIPO
- Conversation on IP and AI — Intellectual-property implications of AI.
- IEEE Global Initiative on Ethics of A/IS
- Ethically Aligned Design — Recommendations for ethical AI design.
- Center for AI and Digital Policy
- Policy briefs — Accountable AI policy.