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OPS7766 Master AI in Sales Operations This Year

$199.00
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The Executive Diagnostic and Governance Toolkit

Master AI in Sales Operations This Year

Score your own function red, amber or green, find out which part is weakest, and walk into the next budget round able to defend what you want to fix. Built for leaders reviewing decide which AI tools to adopt this year to improve team productivity and meet revenue targets.

$199 one-time
30-day money-back guarantee Verified against latest insights, updated access provided within 24h

Each order is checked and updated against the latest insights before delivery. That is why access takes up to 24 hours rather than being instant.

What you walk out with
A scored, ranked picture of your own function, and a defensible answer to what to fix first.
1 You stop guessing where you stand.
You finish with a score, not an opinion: every part of your function rated red, amber or green, with the weakest ranked first. Evidence: a Quick Scan for the shape of it, then seven domain assessments of 30 scored questions each, 210 in all, rolled into one scorecard, plus a maturity radar and a current-versus-target gap analysis.
2 You can defend the decision.
You walk into the budget round with the gap named, the owner named and done defined, instead of a case built on instinct. Evidence: project charter, scope statement, RACI, requirements traceability and work breakdown structure, pre-filled in your domain's language.
3 The work actually moves.
The month after the decision is already built, so nothing stalls waiting for someone to design a form. Evidence: more than 60 project templates across all five PMBOK process groups, plus runbooks, SOPs, a KPI framework, audit checklists and a risk matrix. 55 to 65 files in total.
4 You use it the day it lands.
No blank templates to interpret. Every workbook opens with what it is, who uses it, when, how, a 1 to 5 scoring guide, what good looks like, and a worked example you delete and type over.
The Quick Scan is one sitting. You will know your weakest area before the day is out.
Nothing in it is generic project management: the build rejects any file that could belong to another course. Updated after you enrol, so it reflects where the work stands now. The 144-chapter course is included behind it, for the parts you want to go deeper on.
You’re responsible for results, but AI tools are multiplying — and no one agrees on what to adopt.

The situation this is built for

Sales operations leaders are now expected to lead AI adoption without clear frameworks or time to experiment. The tools promise efficiency, but choosing the wrong path risks team confusion, wasted spend, and missed quotas. You need to separate signal from noise, assess real impact on workflows, and make decisions that move the revenue needle — not just impress in a demo.

Who this is for

Sales operations lead in a B2B tech company with 100+ reps, responsible for sales process, tooling, and performance analytics.

Who this is not for

This is not for sales reps, AI product marketers, or technical AI researchers. It is not for those looking for code or model tuning.

What you walk away with

  • Assess your team’s current AI capabilities across core sales functions
  • Prioritize AI use cases by impact on pipeline velocity and forecast accuracy
  • Design integration plans that align with CRM data hygiene and sales manager workflows
  • Build a 90-day execution roadmap with clear milestones and stakeholder checks
  • Define metrics that reflect real operational improvements, not just tool usage

How this maps to your situation

  • Assessing current AI capabilities and gaps
  • Preparing the team and data for AI adoption
  • Selecting high-impact AI use cases
  • Embedding AI into sales execution and management

Before vs. after

Before
Overwhelmed by AI options, unclear on where to start, and pressured to deliver results without disrupting the team.
After
Confident in a prioritized, executable plan that improves pipeline health, forecast accuracy, and rep productivity using AI.

What's included with your purchase

  • 12 modules with 12 chapters each (144 chapters)
  • Downloadable templates and worked examples for every module
  • Hand-built implementation playbook delivered alongside course access
  • 30-day money-back guarantee

Delivery and format

  • Course and learning environment access provisioned within 24 hours of purchase
  • Hand-built implementation playbook delivered alongside course access

Format: Text-based modules and chapters in the Art of Service learning environment, plus downloadable templates and worked examples for every chapter, plus the hand-built implementation playbook delivered alongside course access.

Time investment: Approximately 3 hours per module, designed to be completed over 12 weeks with team discussions and template application.

If nothing changes
Without a clear strategy, AI adoption becomes a series of disjointed experiments that waste time, erode trust in tools, and miss revenue targets due to inconsistent execution and poor data alignment.

How this compares to the alternatives

Unlike vendor-led training or generic AI courses, this program focuses exclusively on the decisions, artifacts, and team rituals of sales operations leaders implementing AI in real-world sales environments.

Also included: the full course, for when you want the reasoning behind a finding (12 modules, 144 chapters)

Depth reference. The diagnostic and the templates stand on their own; this is what to read when you want the reasoning behind a finding.

Module 1. Mapping the Current State of AI in Your Sales Process
Establish a baseline by auditing where AI is already in use — and where it’s missing — across the customer journey.
12 chapters in this module
  1. Identify all AI touchpoints in your current sales workflow
  2. Map AI usage across prospecting, qualification, and closing stages
  3. Assess CRM data quality for AI readiness
  4. Document existing AI tool integrations and limitations
  5. Interview sales managers on AI pain points
  6. Review rep feedback on current AI assistance tools
  7. Benchmark AI use against industry peers
  8. Classify AI activities by automation level and human oversight
  9. Track AI impact on cycle time and win rates
  10. Evaluate compliance and data governance risks
  11. Catalog AI-related support tickets from last quarter
  12. Summarize findings in a current-state assessment report
Module 2. Defining AI Readiness for Sales Operations Teams
Determine whether your team has the data, discipline, and leadership alignment to adopt AI effectively.
12 chapters in this module
  1. Define AI readiness using operational indicators
  2. Assess sales team data entry consistency and timeliness
  3. Measure manager adherence to forecasting rituals
  4. Evaluate rep onboarding content for AI references
  5. Review training completion rates for AI-related modules
  6. Audit CRM field completion rates by role and region
  7. Determine leadership alignment on AI goals
  8. Identify gatekeepers who influence tool adoption
  9. Map decision rights for AI-related changes
  10. Assess bandwidth for change during peak selling periods
  11. Evaluate internal communication channels for AI updates
  12. Score your team’s AI readiness across six dimensions
Module 3. Prioritizing AI Use Cases by Revenue Impact
Focus on high-leverage opportunities that directly affect pipeline health and quota attainment.
12 chapters in this module
  1. List all potential AI applications in sales operations
  2. Categorize use cases by revenue influence and effort
  3. Estimate time saved per rep per week for each use case
  4. Project impact on forecast accuracy by use case
  5. Assess risk of AI error in customer-facing scenarios
  6. Rank use cases using a weighted impact matrix
  7. Validate priorities with sales leadership
  8. Align use cases with annual revenue goals
  9. Identify quick wins with minimal integration lift
  10. Flag high-impact use cases requiring data cleanup
  11. Determine dependencies between AI initiatives
  12. Publish a prioritized AI initiative backlog
Module 4. Designing AI Integration into Daily Sales Routines
Ensure AI tools fit into existing behaviors, not force new ones that won’t stick.
12 chapters in this module
  1. Map the morning routine of an account executive
  2. Identify natural handoff points for AI suggestions
  3. Design AI alerts that reduce rep decision fatigue
  4. Time AI prompts to match deal stage transitions
  5. Integrate AI insights into weekly pipeline reviews
  6. Embed AI-generated talking points in deal dossiers
  7. Adapt coaching templates to include AI feedback
  8. Schedule AI-driven follow-up tasks in CRM workflows
  9. Align AI notifications with manager check-in rhythms
  10. Test integration timing with a pilot team
  11. Adjust based on rep feedback and adoption data
  12. Document integration rules for future rollouts
Module 5. Building AI-Supported Forecasting Workflows
Enhance forecast accuracy by integrating AI signals without replacing human judgment.
12 chapters in this module
  1. Compare AI-generated forecasts to rep inputs
  2. Identify deals with high AI-confidence scores
  3. Flag discrepancies between AI and rep assessments
  4. Design escalation paths for outlier deals
  5. Incorporate AI deal health scores into forecast calls
  6. Train managers to interpret AI risk indicators
  7. Adjust quota attainment projections using AI data
  8. Track forecast bias before and after AI input
  9. Evaluate AI impact on forecast cycle time
  10. Revise forecasting templates to include AI fields
  11. Measure reduction in forecast revisions per rep
  12. Publish updated forecasting playbook with AI guidance
Module 6. Optimizing Sales Playbooks with AI Insights
Update battle cards, objection handlers, and messaging using real-time AI analysis of win/loss data.
12 chapters in this module
  1. Extract themes from AI analysis of lost deals
  2. Identify winning messaging patterns by segment
  3. Update battle cards with AI-discovered differentiators
  4. Revise email templates based on response rates
  5. Incorporate competitor insights from AI monitoring
  6. Adapt playbooks for new product launches using AI
  7. Track adoption of AI-updated content by reps
  8. Measure win rate changes after playbook updates
  9. Schedule quarterly AI-driven playbook refreshes
  10. Link playbook changes to CRM deal stage progression
  11. Train managers to reinforce updated plays
  12. Audit playbook versioning and distribution channels
Module 7. Improving Rep Onboarding with AI Coaching
Accelerate ramp time by delivering personalized, AI-driven feedback during early selling cycles.
12 chapters in this module
  1. Analyze first-quarter deals of new hires
  2. Identify common mistakes using AI pattern detection
  3. Generate personalized coaching plans for each rep
  4. Integrate AI feedback into 1:1 meeting agendas
  5. Track improvement in deal progression over time
  6. Compare AI-coached reps to control group
  7. Adjust onboarding content based on AI insights
  8. Automate early warning alerts for at-risk reps
  9. Measure time to first win with AI support
  10. Incorporate AI suggestions into role-play scenarios
  11. Evaluate manager time saved with AI summaries
  12. Scale coaching using AI-generated development paths
Module 8. Scaling Manager Coaching with AI Signals
Empower sales leaders to focus on high-impact guidance using AI-identified coaching opportunities.
12 chapters in this module
  1. Aggregate AI deal risk scores by sales manager
  2. Highlight deals needing immediate intervention
  3. Generate coaching recommendations for stuck opportunities
  4. Summarize rep performance trends for 1:1s
  5. Prioritize coaching time using AI heat maps
  6. Track manager response rates to AI alerts
  7. Measure win rate improvement after AI prompts
  8. Customize alert thresholds by rep experience
  9. Integrate AI insights into team review meetings
  10. Train managers to question AI suggestions
  11. Balance AI input with qualitative judgment
  12. Evaluate reduction in manager oversight time
Module 9. Strengthening Pipeline Health with AI Diagnostics
Use AI to detect weak signals in deal progression and recommend corrective actions.
12 chapters in this module
  1. Define pipeline health using leading indicators
  2. Train AI to detect stagnation patterns in deals
  3. Identify missing milestones in opportunity records
  4. Alert reps to incomplete discovery checklists
  5. Recommend next steps based on similar won deals
  6. Measure reduction in deals stuck in late stages
  7. Track improvement in stage progression velocity
  8. Audit AI recommendations for bias or gaps
  9. Adjust diagnostic rules based on win/loss data
  10. Integrate pipeline diagnostics into weekly reviews
  11. Measure manager time saved on manual checks
  12. Publish pipeline health score dashboard
Module 10. Aligning AI Initiatives with Revenue Leadership
Secure buy-in and resources by speaking the language of pipeline, forecast, and margin.
12 chapters in this module
  1. Translate AI metrics into revenue terms
  2. Present AI impact on quota attainment projections
  3. Align AI roadmap with sales leadership goals
  4. Communicate progress using pipeline KPIs
  5. Report on forecast accuracy improvements
  6. Highlight reduction in rep ramp time
  7. Showcase wins from AI-driven coaching
  8. Address concerns about automation replacing roles
  9. Share adoption metrics by team and region
  10. Link AI initiatives to customer retention rates
  11. Prepare quarterly business review materials
  12. Document leadership feedback and adjust roadmap
Module 11. Measuring AI Impact on Sales Productivity
Go beyond tool usage to track real changes in rep efficiency and deal outcomes.
12 chapters in this module
  1. Define productivity using time-on-task metrics
  2. Measure reduction in manual data entry per rep
  3. Track increase in customer-facing hours per week
  4. Calculate time saved in weekly reporting
  5. Compare win rates before and after AI rollout
  6. Assess changes in average deal size with AI
  7. Evaluate rep satisfaction with AI tools
  8. Monitor CRM update frequency post-adoption
  9. Measure reduction in forecast revisions
  10. Track coaching session quality with AI input
  11. Calculate ROI using time and revenue data
  12. Publish quarterly AI impact report
Module 12. Sustaining AI Adoption Through Change Management
Ensure long-term success by embedding AI into rituals, training, and performance reviews.
12 chapters in this module
  1. Incorporate AI usage into rep performance reviews
  2. Update onboarding to include AI tool training
  3. Include AI adoption in manager scorecards
  4. Host monthly AI best practice forums
  5. Refresh AI content based on new win data
  6. Address rep concerns about AI oversight
  7. Celebrate teams using AI effectively
  8. Audit AI recommendations for drift over time
  9. Update data hygiene standards annually
  10. Rotate AI champions across teams
  11. Plan for AI tool retirement and replacement
  12. Build a living AI operations playbook

Frequently asked

Who is this course for?
Sales operations leads responsible for sales process, tooling, and performance who need to make AI adoption decisions that impact revenue.
How is the course structured?
12 modules, each containing 12 chapters (144 chapters total).
Does this course cover technical AI model development?
No. This course focuses on operational decisions, integration planning, and team execution — not data science or model building.
Will I get templates I can use immediately?
Yes. Each module includes downloadable templates and real-world examples you can adapt for your team.
Is there a certificate of completion?
Yes. Upon finishing all modules, you’ll receive a certificate of completion in AI for Sales Operations.
What formats do the templates come in?
The implementation playbook downloads as PDF and editable XLSX. The course reads in your learning environment and exports to PDF for offline use. The files are yours to keep.
Can I share this with my team?
The licence is per person. Team pricing opens from three seats: reply to the order confirmation with TEAM and we will set it up.
How quickly can I start?
The diagnostic is one sitting and the templates work straight out of the kit. Account access takes up to 24 hours rather than being instant, because every order is checked and updated against the latest sources before it is delivered.
$199 one-time. Approximately 3 hours per module, designed to be completed over 12 weeks with team discussions and template application..

Within 24 hours your account in the learning environment is provisioned and the tailored implementation playbook is delivered alongside it.

30-day money-back guarantee·Know your weakest area today·210 scored questions·Course included· Account access within 24 hours
30-day money-back guarantee, no questions asked.
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