PLAYBOOK ATLASPlaybook Atlas

Sales

AI in sales: explore 33 applications.

Give sellers reviewable account evidence, useful drafts and calibrated planning inputs.

Explore account research, outreach preparation, qualification, coaching and revenue forecasting for sales teams.

Plan your first AI move See the 33 applications

Where the 33 applications sit

Explore the domains
  1. 13Lead Generation
  2. 9Sales Enablement
  3. 8Opportunity Management
  4. 7Customer Intelligence
  5. 3Account Management

01Why AI now

Why sales is moving on AI, in three figures.

Each figure links to its source.

  1. CRM sales software market: $20.6B through 2027

    Gartner says AI-related spending will exceed 10% of CRM sales software spending by 2027.

    Source: Gartner

  2. AI sales teams: 1.3x likelier revenue growth

    Salesforce found that 83% of sales teams using AI reported revenue growth, compared with 66% of teams without AI.

    Source: Salesforce

  3. Gen AI could lift sales productivity by 3%–5%

    McKinsey estimates that generative AI could increase sales productivity by approximately 3% to 5% of current global sales expenditures.

    Source: McKinsey & Company

How to evaluate a first project

Choose one seller decision and verify the customer, source information and product claims behind it. Review drafts before outreach and compare prioritization with the existing process. Track qualified opportunities and forecast calibration alongside activity metrics, with explicit definitions of success and missed opportunities.

02Capability map

Where sales puts AI to work, domain by domain.

Pick a domain to see its applications. Each one opens a full report.

Inside Lead Generation

Applications in Lead Generation

What the use cases ask AI to do

Share of the sales use cases, by the kind of work the AI does.

  1. Reasoning

    Analyzes, predicts and decides: scoring, forecasting, classifying.

    100%

Under 1% each: perception, generation, optimization and agentic.

03Workflow ideas

Ways AI could change the work, in sales.

Atlas groups these applications by the changes they propose to a workflow. These preliminary classifications help you explore ideas; each application still needs a review of its sources and operating requirements.

Workflow categories in this catalog

Share of the 21 applications assigned a category.

How to evaluate an application

  1. Start with a bounded task

    Identify the inputs, the person responsible, and the decision or output the workflow must produce.

  2. Check the support for each method

    Look for source-backed details about the specific implementation, including its limits and human review.

  3. Measure the pilot

    Record the current baseline, agree on acceptance criteria, and test on representative cases before expanding.

Explore applications

04What gets built

The approaches sales uses most, and when to skip them.

Knowing when not to use one matters as much as knowing when to.

  1. 01 · 9 applications

    Generative AI

    Models that learn data distributions to generate new text, images, code, and media.

    Use it when

    • When you need to generate natural language, code, or media that would be expensive or slow for humans to produce manually.
    • When tasks involve ambiguity, open-ended questions, or creative exploration rather than strict right-or-wrong answers.
    • When you can ground the model on reliable internal or external data sources to improve factuality.

    Skip it when

    • When the task requires strict determinism, formal guarantees, or regulatory-grade traceability (e.g., certain financial calculations, safety-critical control systems) without a robust verification layer.
    • When you lack access to high-quality, authoritative data or subject-matter expertise to validate outputs in a high-risk domain.
    • When a simple rules engine, search, or template-based system can solve the problem more reliably and cheaply.
    Generative AI, in depth

  2. 02 · 7 applications

    Workflow Automation

    Use AI inside orchestrated workflows to automate multi-step business processes.

    Use it when

    • When you have repetitive, rule-heavy workflows that still require human judgment or document understanding.
    • When the process spans multiple systems (ERP, CRM, ticketing, email) and manual coordination is slow or error-prone.
    • When large volumes of semi-structured or unstructured data (emails, PDFs, forms) must be processed consistently.

    Skip it when

    • When the process is highly creative, exploratory, or non-repeatable, with no stable sequence of steps.
    • When decisions are extremely high-stakes (e.g., life-critical medical decisions, major legal judgments) and regulations require full human control.
    • When you lack reliable access to the necessary systems or data sources to complete the workflow end-to-end.
    Workflow Automation, in depth

  3. 03 · 3 applications

    AutoML-Platform

    Managed platform for training and operating predictive models quickly.

    AutoML-Platform, in depth

05The applications

33 sales applications, each with a full report.

The widest first, by the number of use cases each covers. Every report shows what changed, how it works, who has done it and what can go wrong.

  1. Predictive Lead ScoringThis AI solution uses machine learning and CRM data to score and prioritize leads based on their likelihood to convert and expected deal value. It continuously analyzes behavioral, firmographic, and engagement signals to surface the best next accounts and contacts for sales reps. By focusing effort on the highest-propensity leads, sales teams increase win rates, shorten sales cycles, and align sales and marketing on revenue outcomes.AutoML Platform23 use cases
  2. Sales Email Copy PersonalizationThis AI solution focuses on automating the research, drafting, and optimization of outbound sales emails so they are personalized to each prospect at scale. Instead of reps manually combing through LinkedIn, websites, and CRM notes to craft one‑off messages, these tools generate tailored outreach and follow‑up emails that reference prospect context, pain points, and prior interactions. The goal is to increase reply and conversion rates while maintaining or improving message quality. AI is used to ingest prospect and account data, infer relevant hooks or value propositions, and produce ready‑to‑send or lightly editable email content within existing sales engagement workflows. More advanced systems also analyze large volumes of historical outreach to learn what works, then continuously optimize subject lines, copy, and personalization snippets. This matters because outbound email remains a core growth channel, yet manual personalization doesn’t scale; automating it unlocks higher outbound volume, better targeting, and improved pipeline generation without equivalent headcount growth.Human Creative → Augmented22 use cases
  3. AI Lead Qualification AgentAI Lead Qualification Agents automatically engage, triage, and score inbound and outbound leads across channels like email, chat, and phone. They act as always-on SDRs that ask qualifying questions, enrich records in CRM tools like HubSpot and Dynamics, and route only high-intent prospects to sales reps. This boosts pipeline quality, shortens response times, and lets sales teams focus on closing rather than filtering leads.Expert → AI14 use cases
  4. Lead Routing OrchestrationThis AI solution uses AI agents to find, score, and qualify sales leads across channels, then orchestrates personalized outreach and nurturing at scale. It integrates with CRM and sales tools to prioritize high-intent prospects, automate SDR-like workflows, and maintain clean, actionable lead data. The result is higher pipeline quality, faster response times, and more revenue from the same (or lower) prospecting effort.Expert → AI11 use cases
  5. Lead Qualification WorkflowLead Scoring and Qualification is the systematic ranking and evaluation of prospects based on their likelihood to become paying customers. It combines firmographic, demographic, and behavioral data (such as website visits, email engagement, and product usage) to assign scores and determine which leads are sales-ready, which need further nurturing, and which should be deprioritized. The goal is to focus sales effort on the highest‑value, highest‑intent opportunities. This application matters because most sales teams are flooded with inbound and outbound leads but have limited capacity to engage them all effectively. Without a data‑driven scoring and qualification process, reps rely on intuition and inconsistent rules, leading to wasted outreach, delayed responses to high‑intent prospects, and friction between marketing and sales. By automating and optimizing lead scoring and qualification, organizations improve conversion rates, shorten sales cycles, align marketing and sales, and generate more predictable, higher‑quality pipeline from the same or lower level of activity.Workflow Automation10 use cases
  6. AI Voice-of-Customer Sales EnablementThis AI solution captures and analyzes voice-of-customer data across calls, emails, and meetings to generate actionable insights for sales and go-to-market teams. It automatically turns conversations into tailored playbooks, coaching, and talk tracks, enabling high-velocity and B2B teams to improve win rates, pipeline quality, and revenue predictability.Expert → AI6 use cases
All 33 applications, A to Z

Work with us

The catalog shows what is possible. A sprint decides what you should fund.

Reading every report still leaves the hardest question open: which move is worth funding first, here, given your constraints. That is the sprint: two weeks, fixed scope, and a candid answer either way.

Book a 30-minute callHow the sprint works

Two weeks Fixed scope A candid fit-or-no-fit answer

  1. 01

    Three ranked opportunities

    A decision-ready shortlist scored against value, feasibility, evidence, risk, and time to proof.

  2. 02

    One pilot-ready workflow

    A bounded operating design with actors, handoffs, data needs, controls, and success measures.

  3. 03

    A defensible 90-day plan

    Build-versus-buy guidance, ROI assumptions, risk treatment, sequencing, and executive decisions.

06Rules & standards

Rules and standards to check, with their scope and sources.

Selected US and EU references for these workflows. Applicability depends on jurisdiction, organization, data and intended use. The planning notes are Atlas editorial guidance; they are not official implementation-time estimates or an exhaustive list of obligations.

  1. TCPA · artificial or prerecorded voice calls

    Law or regulation

    United States · calls within TCPA scope

    The FCC treats AI-generated human voices as artificial or prerecorded voices under the TCPA. Consent requirements and exemptions depend on the call, recipient and purpose.

    FCC: Declaratory Ruling FCC 24-17

    Source checked

    Project planning note

    Check the rules for each calling campaign before enabling generated voice outreach.

  2. CAN-SPAM · commercial email

    Law or regulation

    United States · covered commercial email

    Commercial email must meet requirements including accurate sender information, non-deceptive subjects and a working opt-out. US CAN-SPAM and European consent rules should not be treated as interchangeable.

    FTC: CAN-SPAM Act compliance guide

    Source checked

    Project planning note

    Verify sender identity, commercial-message treatment, opt-out processing and suppression before sending.

Your next step

Find your first move in sales.

Ask the workspace which move fits your company. Add your size, systems and constraints, and it works from these 33 applications to a plan your team can act on.

Plan your first AI move

Rather talk it through? Book a 30-minute call