Straits Institute for Applied AI
Catalogue/Tier 2 · Job Roles/Sales & Commercial
AI+ Sales Leaders cover
T2-84 · Tier 2 · Job Roles

AI+ Sales Leaders

Drive Revenue and Build AI-Powered Sales Organisations

Forecasts you can defend. Pipeline you can trust.

Your team is experimenting with AI individually — no standards, no strategy, no competitive edge. The gap isn't awareness; it's leadership. This book gives revenue leaders the CLOSE Protocol for AI data governance, the SCALE scorecard for sales technology investment, and a 90-day plan spanning personal mastery, team adoption, and process redesign. Twelve case studies. Twenty prompts. Written by engineers who build production AI systems — for revenue leaders who know scattered AI adoption is worse than none.

Tier
Tier 2 · Job Roles
Category
Sales & Commercial
Format
Guide
Updated
Q2 2026
You'll build
  • The CLOSE Protocol — five-check framework for Customer and pipeline data classification, Lead scoring bias review, Outreach integrity, Sign-off accountability, Exposure and competitive intelligence
  • The SCALE scorecard for sales tech investment — distinguishing genuine AI capability from AI-washed CRM features
  • A 90-day leadership plan spanning personal mastery, team adoption, forecast governance, and board reporting
  • A team AI adoption strategy that converts individual experiments into a coordinated competitive advantage
  • A revenue forecast governance discipline that combines AI inputs with the leader's professional judgement
Who this is for

Chief Revenue Officers (CROs), VP Sales, Head of Sales, Sales Directors, and senior commercial leaders who own the organisation's revenue target and report to the CEO or executive committee. They manage sales teams ranging from 10 to 200+ people — account executives, SDRs, sales engineers, sales operations, and enablement staff. They set quotas, design territories, determine hiring profiles, choose CRM and sales technology, and are accountable for the number. They think in terms of pipeline coverage, win rates, average deal size, sales cycle length, revenue per rep, and market share — not individual deals. Typically 10+ years in sales, with at least 3–5 in leadership. They are comfortable with sales technology (CRM, engagement platforms, revenue intelligence tools) but may not have explored AI beyond what their existing platforms now advertise as "AI-powered."

Also for:Revenue Operations leaders shaping the sales technology stack and data infrastructure; Sales Enablement leaders designing rep training and AI adoption programmes; newly promoted Sales Directors inheriting a team mid-transformation; CMOs and COOs closely partnered with sales leadership on go-to-market strategy.

You’ll be able to
  • Assess the organisation's sales AI readiness across rep capability, process design, technology infrastructure, and data governance dimensions
  • Design a sales team AI adoption strategy covering capability building, process redesign, and change management — with a defensible plan they can present to the CEO and board
  • Apply the PRISM Prompting Framework to at least five strategic sales leadership tasks (revenue board presentations, pipeline analysis narratives, win/loss strategy reviews, sales QBR preparation, investor updates)
  • Apply the CLOSE Protocol before any AI interaction involving pipeline data, customer financials, lead scoring systems, or board-bound revenue forecasts
  • Evaluate sales technology investments using the AI Investment Scorecard (SCALE) and build proportionate AI governance for the revenue function
What’s inside
Diagnostic
How AI-Ready Is Your Revenue Organisation?
Chapter 1
AI in Sales Leadership Right Now
Chapter 2
The Revenue Leader's AI Landscape
Chapter 3
The Sales Leader's Own AI Mastery
Chapter 4
Building the AI-Ready Sales Team
Chapter 5
Designing AI-Enabled Sales Processes
Chapter 6
Revenue Intelligence and AI-Assisted Forecasting
Chapter 7
Investing in Sales Technology
Chapter 8
Sales AI Governance and the CLOSE Protocol
Chapter 9
Your 90-Day Sales Leadership AI Plan
Back matter
Skill Summary · Recommended Next Reads · Glossary · Tool Reference

Written by engineers who build production AI systems, not consultants who present them — for revenue leaders whose forecast accuracy is the credibility, not the slide deck.

Appears in 2 bundles
Reads well with

Often packaged with this title.

How this was made

Every AI+ title is written by AI engineers who build production AI systems, then verified by practising professionals in the field it serves. Titles are reviewed quarterly and updated whenever the technology or regulation shifts. Localised editions are reviewed by in-region experts before release.

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