Mastering AI-Powered CPQ Strategies for Enterprise Sales Leaders
You’re under pressure. Revenue targets are rising, deal complexity is increasing, and your sales teams are struggling to keep pace with customer expectations for speed, accuracy, and personalisation. Manual quoting processes create bottlenecks. Pricing errors cost millions. Missed upsell opportunities erode margins. You need a solution that scales-not another theoretical framework that gathers dust. The truth is, legacy CPQ systems are no longer enough. What’s powering the world’s most efficient sales organisations is not just automation-it’s AI-driven intelligence built into every stage of the Configure, Price, Quote process. Leaders who master this shift don’t just improve accuracy. They unlock faster deal velocity, higher win rates, and predictable revenue growth. Mastering AI-Powered CPQ Strategies for Enterprise Sales Leaders is not another generic course on sales tools. It’s a precision-engineered roadmap for transforming your entire quoting and pricing function into a competitive weapon-one that delivers board-level ROI within 90 days. You’ll go from uncertainty to execution, building a fully scoped, AI-enhanced CPQ strategy with implementation checkpoints, governance models, and measurable KPIs-all culminating in a board-ready business case you can present with confidence. One recent participant, a VP of Sales Operations at a $1.2B enterprise software firm, used the framework to reduce quote turnaround time by 72% and increase average deal size by 19% in under four months. No more guesswork. No more fragmented pilots. You’ll gain the strategic clarity and tactical playbooks used by top-tier sales transformation leaders at Fortune 500 firms and high-growth SaaS enterprises. Here’s how this course is structured to help you get there.Course Format & Delivery Details Self-Paced, On-Demand Access with Immediate Online Enrollment
This course is designed for senior executives and sales leaders who need maximum flexibility and minimum disruption. You gain full access to all materials the moment you enrol, with no fixed start dates, no required attendance times, and no rigid schedules. Complete the modules at your own pace, on your own terms, from any device. Complete in as Little as 15 Hours - See Strategic Shifts in Days
Most learners complete the course within 2 to 3 weeks of part-time engagement, dedicating 4 to 5 hours per week. However, you can begin applying core frameworks-the AI opportunity assessment, pricing governance model, and CPQ maturity audit-as early as Module 2. Real strategic momentum starts fast, with measurable clarity often emerging within the first 72 hours of engagement. Lifetime Access, Including All Future Updates at No Extra Cost
Once enrolled, you own lifetime access to this course. This includes every update, refinement, and new case study as AI-powered CPQ strategies evolve. The content is continuously reviewed and enhanced by our enterprise strategy team to reflect emerging best practices from Fortune 500 deployments, regulatory shifts, and AI model advancements. 24/7 Global, Mobile-Friendly Access
Access your course materials anytime, anywhere. Whether you're in a boardroom, airport lounge, or hotel room, the platform is fully responsive and optimised for smartphones, tablets, and desktops. Study during travel downtime, review frameworks before executive meetings, or pull up templates during implementation planning. Dedicated Instructor Support for Strategic Guidance
Throughout your journey, you have direct access to our enterprise AI strategy advisory team. Submit questions, request feedback on your CPQ maturity assessment, or schedule a one-time consultation with our CPQ transformation architects. This is not automated chat support-it’s real, human guidance from practitioners who have led AI-CPQ rollouts across 17 industries. Certificate of Completion Issued by The Art of Service
Upon fulfilling all completion criteria, you will receive a verifiable Certificate of Completion issued by The Art of Service, a globally recognised leader in enterprise transformation education. This certificate is leveraged by alumni in 112 countries for promotions, internal stakeholder buy-in, and organisational credibility. It signals mastery of AI-CPQ strategy at the enterprise level and is frequently cited in leadership development portfolios and succession planning dossiers. Transparent Pricing - No Hidden Fees
The course cost is straightforward and all-inclusive. There are no additional charges, no subscription traps, no surprise fees. What you see is exactly what you get-lifetime access, full materials, expert support, and certification-all bundled into one clear investment. Accepted Payment Methods
- Visa
- Mastercard
- PayPal
Full 30-Day Satisfaction Guarantee - Refunded if You’re Not Convinced
We reverse the risk. If, at any point within 30 days of enrolment, you determine this course does not deliver the clarity, strategic frameworks, and implementation value promised, simply request a full refund. No forms, no excuses, no loss. This is our promise to ensure your confidence in moving forward. Secure Access Confirmation Process
After enrolment, you will receive a confirmation email. Your access credentials and course onboarding details will be sent in a follow-up message once your course materials are fully prepared and ready for engagement. This ensures a seamless, high-fidelity learning experience from your very first session. This Course Works Even If…
- You’ve already invested in a CPQ platform but aren’t getting the ROI you expected
- Your organisation resists change or lacks AI maturity
- You’re not technically inclined but need to speak confidently with IT and finance stakeholders
- You’re competing with internal initiatives for budget and executive attention
- You’ve tried digital transformation projects before that stalled or failed
Social Proof: Role-Specific Results from Senior Leaders
A Sales Transformation Director at a global industrial equipment provider applied the CPQ governance framework from Module 5 to unify pricing rules across 8 regions. Within one quarter, pricing compliance rose from 61% to 98%, eliminating $4.2M in margin leakage annually. A Chief Revenue Officer at a cloud infrastructure company used the deal acceleration playbook to reduce quote cycles from 11 days to 3, directly contributing to a 28% improvement in quarterly pipeline conversion. You’re Not Just Learning-You’re Building Real Organisational Value
This course converts knowledge into action. Every module includes editable templates, readiness assessments, and implementation checklists you can customise and deploy immediately. You’re not just consuming content-you're building a live, board-ready CPQ transformation plan as you progress.
Extensive and Detailed Course Curriculum
Module 1: Foundations of AI-Driven CPQ Strategy - Understanding the evolution of CPQ from manual to AI-powered systems
- Defining AI in the context of enterprise sales operations
- Core components of a modern CPQ architecture
- Why traditional CPQ platforms fail to scale in complex enterprises
- The business case for AI integration in Configure, Price, Quote
- Identifying revenue leakage points in current quoting processes
- Mapping CPQ impact across sales, finance, and customer success
- Establishing the link between CPQ efficiency and customer experience
- Common pitfalls in CPQ implementation and how to avoid them
- Global trends in AI adoption within enterprise sales tech stacks
Module 2: Assessing Enterprise CPQ Maturity - CPQ Maturity Model: Stages 1 to 5 (Reactive to Predictive)
- Conducting a diagnostic audit of your organisation's current state
- Scoring your quote accuracy, speed, and compliance performance
- Evaluating integration depth across CRM, ERP, and billing systems
- Identifying data quality gaps that inhibit AI performance
- Assessing sales team adoption and usage patterns of existing tools
- Measuring pricing variance across regions, reps, and product lines
- Benchmarking against industry-specific CPQ performance metrics
- Recognising signs of underutilised CPQ capabilities
- Creating a baseline scorecard for progress tracking
Module 3: Strategic Alignment of AI-CPQ with Business Goals - Aligning CPQ initiatives with corporate growth and margin targets
- Defining KPIs that matter: Win rate, TTV, ACV, upsell rate
- Building a value map linking CPQ outcomes to financial metrics
- Engaging CFOs and Finance leaders in CPQ transformation
- Gaining executive sponsorship through data-driven storytelling
- Positioning AI-CPQ as a revenue enablement function, not IT
- Developing cross-functional governance for pricing decisions
- Creating a business case that speaks to revenue, risk, and scalability
- Integrating CPQ strategy into annual sales planning cycles
- Establishing a CPQ Centre of Excellence charter and mandate
Module 4: AI-Powered CPQ Functional Architecture - Breakdown of AI engines in CPQ: Pricing, validation, recommendation
- AI-driven product configuration logic and constraint mapping
- Dynamic pricing models: Cost-plus, value-based, competitive
- Real-time discounting authorisation with AI risk scoring
- Automated upsell and cross-sell suggestion algorithms
- AI for handling complex bundling and SKU rationalisation
- Service and subscription configuration with usage variables
- Handling multi-currency, multi-language, and regional compliance
- Quote versioning and audit trails powered by AI tagging
- Natural language processing for quote commentary and approvals
Module 5: Data Strategy for AI-CPQ Success - Data requirements for training and operating AI models in CPQ
- Structuring product, pricing, and customer hierarchy data
- Ensuring data lineage and master data governance
- Designing a CPQ data lake for model training and simulation
- Historical win-loss data analysis to improve AI recommendations
- Using past quote revisions to train AI accuracy over time
- Implementing data cleansing protocols before AI onboarding
- Real-time data pipelines from CRM and ERP into CPQ
- Secure data handling and access controls for sensitive pricing
- Monitoring data drift and model decay in production AI
Module 6: Integration and Interoperability - Mapping CPQ integration points with Salesforce, HubSpot, Dynamics
- ERP integration with SAP, Oracle, NetSuite for billing and accounting
- Syncing CPQ with CPQ analytics and revenue intelligence platforms
- Embedding CPQ within customer portals and partner ecosystems
- Developing API-first CPQ architecture principles
- Handling asynchronous data flows during high-volume periods
- Ensuring bidirectional sync integrity across systems
- Testing integration resilience under edge-case conditions
- Using middleware for complex enterprise landscapes
- Creating failover and data recovery protocols
Module 7: AI Governance and Risk Management - Establishing an AI ethics and compliance framework for CPQ
- Defining who owns AI model decisions in pricing and configuration
- Implementing human-in-the-loop approval gates for high-risk deals
- Transparency requirements for AI recommendations (Explainable AI)
- Monitoring for unintended bias in discounting or upsell logic
- Regulatory considerations: SOX, GDPR, CCPA in pricing automation
- Creating audit trails for AI-driven pricing changes
- Implementing AI model validation and testing procedures
- Setting thresholds for manual override and escalation
- Training sales leaders to supervise AI recommendations effectively
Module 8: Change Management and User Adoption - Understanding sales team resistance to AI automation
- Developing personas for CPQ user segments (reps, ops, managers)
- Designing role-based training and onboarding programs
- Creating quick-win use cases to build early momentum
- Establishing CPQ champions within regional sales teams
- Running pilot programmes to validate and socialise success
- Communicating benefits in terms of rep productivity and deal support
- Addressing fears of job displacement due to automation
- Implementing feedback loops for continuous improvement
- Measuring and reporting on user adoption KPIs
Module 9: AI-Driven Pricing Strategy - Differentiating between list, deal, and dynamic pricing
- Training AI on customer lifetime value signals for pricing
- Competitive pricing intelligence using AI scrapers and feeds
- AI-based deal desk decision support for complex negotiations
- Real-time margin protection during quoting
- Segment-specific pricing models powered by AI clustering
- Automated approval workflows for out-of-policy discounts
- AI simulation of pricing scenarios and elasticity testing
- Dynamic pricing for time-sensitive opportunities
- Aligning pricing strategy with contract lifecycle management
Module 10: Deal Acceleration and Sales Enablement - Reducing quote turnaround time with AI pre-configuration
- Automating approval chains based on risk and size
- AI-powered quote templates for recurring customer types
- Integrating e-signature and contracting workflows
- Using AI to prioritise high-potential renewal quotes
- Embedding CPQ insights into sales playbooks
- AI suggestions for timing, channel, and negotiation tactics
- Real-time CPQ dashboards for sales managers
- Mobile access for field reps generating quotes on-site
- Integrating CPQ data into forecast accuracy models
Module 11: ROI Measurement and Business Value Tracking - Defining baseline metrics before AI-CPQ implementation
- Calculating cost savings from reduced manual effort
- Quantifying revenue uplift from improved upsell capture
- Measuring reduction in pricing errors and reconciliations
- Tracking improvement in sales rep productivity (quotes per day)
- Assessing win rate impact across deal segments
- Monitoring average deal size and discounting trends
- Calculating time saved in quote-to-cash cycle
- Reporting to executives using CPQ business scorecards
- Linking CPQ KPIs to broader revenue operations dashboards
Module 12: Advanced AI Techniques in CPQ - Predictive quoting: Forecasting deal closure probability
- AI for reselling and recommissioning legacy product lines
- NLP-based parsing of RFx documents into CPQ inputs
- AI optimisation for multi-year, multi-product deals
- Simulating customer trade-off preferences in real-time
- Using reinforcement learning to improve AI recommendations
- AI-driven scenario modelling for alternative configurations
- Handling incomplete inputs with intelligent defaults
- Automated quote comparison across versions and competitors
- AI for generating executive summary narratives in quotes
Module 13: CPQ Implementation Project Management - Developing a phased rollout strategy: Pilot to enterprise
- Building a cross-functional implementation team
- Timeline design with milestones and decision gates
- Stakeholder communication planning
- Resource allocation and budgeting for AI-CPQ deployment
- Risk assessment and mitigation planning
- Vendor selection criteria for AI-CPQ platforms
- Scope definition: What’s in, what’s out, and why
- Change control processes during implementation
- Post-go-live support and hypercare planning
Module 14: Scalability and Global Deployment - Designing CPQ for multi-division, multi-geography rollouts
- Managing localisation of pricing, language, and compliance
- Centralised vs decentralised CPQ governance models
- Handling mergers, acquisitions, and system consolidation
- Standardising product catalogues across divisions
- Customisability vs consistency trade-offs in global CPQ
- Building regional CPQ support teams
- Training plans for large-scale, global user bases
- Monitoring performance across time zones and regions
- Scaling infrastructure for peak quote volume periods
Module 15: Future-Proofing AI-CPQ Strategy - Anticipating next-generation AI advancements in CPQ
- Preparing for autonomous negotiation systems
- Integrating generative AI for quote documentation
- Exploring CPQ’s role in ecosystems and platform economies
- AI for proactive opportunity identification (quote before ask)
- Adapting to economic volatility with AI pricing agility
- Building organisational learning loops from CPQ data
- Creating a roadmap for AI-CPQ innovation sprints
- Measuring CPQ’s contribution to enterprise digital maturity
- Positioning yourself as a thought leader in AI-driven sales
Module 16: Building Your Board-Ready AI-CPQ Proposal - Structuring a compelling executive presentation
- Translating technical capabilities into business value
- Designing financial models with clear ROI and payback period
- Visualising the future state with before-after scenarios
- Incorporating risk assessment and mitigation plans
- Drafting governance and success measurement frameworks
- Aligning the proposal with strategic initiatives
- Anticipating and answering executive objections
- Using data stories to demonstrate urgency and opportunity
- Finalising your end-to-end AI-CPQ transformation plan
Module 17: Certification and Next Steps - Completing the final review of your CPQ strategy deliverables
- Validating alignment with enterprise goals and technical realities
- Submitting your capstone project for assessment
- Receiving structured feedback from AI-CPQ advisors
- Finalising documentation for internal stakeholder review
- Preparing for post-course action: next 30, 60, 90 days
- Leveraging your Certificate of Completion strategically
- Joining The Art of Service alumni network for ongoing support
- Accessing template library and toolkits post-certification
- Identifying your next leadership-level transformation initiative
Module 1: Foundations of AI-Driven CPQ Strategy - Understanding the evolution of CPQ from manual to AI-powered systems
- Defining AI in the context of enterprise sales operations
- Core components of a modern CPQ architecture
- Why traditional CPQ platforms fail to scale in complex enterprises
- The business case for AI integration in Configure, Price, Quote
- Identifying revenue leakage points in current quoting processes
- Mapping CPQ impact across sales, finance, and customer success
- Establishing the link between CPQ efficiency and customer experience
- Common pitfalls in CPQ implementation and how to avoid them
- Global trends in AI adoption within enterprise sales tech stacks
Module 2: Assessing Enterprise CPQ Maturity - CPQ Maturity Model: Stages 1 to 5 (Reactive to Predictive)
- Conducting a diagnostic audit of your organisation's current state
- Scoring your quote accuracy, speed, and compliance performance
- Evaluating integration depth across CRM, ERP, and billing systems
- Identifying data quality gaps that inhibit AI performance
- Assessing sales team adoption and usage patterns of existing tools
- Measuring pricing variance across regions, reps, and product lines
- Benchmarking against industry-specific CPQ performance metrics
- Recognising signs of underutilised CPQ capabilities
- Creating a baseline scorecard for progress tracking
Module 3: Strategic Alignment of AI-CPQ with Business Goals - Aligning CPQ initiatives with corporate growth and margin targets
- Defining KPIs that matter: Win rate, TTV, ACV, upsell rate
- Building a value map linking CPQ outcomes to financial metrics
- Engaging CFOs and Finance leaders in CPQ transformation
- Gaining executive sponsorship through data-driven storytelling
- Positioning AI-CPQ as a revenue enablement function, not IT
- Developing cross-functional governance for pricing decisions
- Creating a business case that speaks to revenue, risk, and scalability
- Integrating CPQ strategy into annual sales planning cycles
- Establishing a CPQ Centre of Excellence charter and mandate
Module 4: AI-Powered CPQ Functional Architecture - Breakdown of AI engines in CPQ: Pricing, validation, recommendation
- AI-driven product configuration logic and constraint mapping
- Dynamic pricing models: Cost-plus, value-based, competitive
- Real-time discounting authorisation with AI risk scoring
- Automated upsell and cross-sell suggestion algorithms
- AI for handling complex bundling and SKU rationalisation
- Service and subscription configuration with usage variables
- Handling multi-currency, multi-language, and regional compliance
- Quote versioning and audit trails powered by AI tagging
- Natural language processing for quote commentary and approvals
Module 5: Data Strategy for AI-CPQ Success - Data requirements for training and operating AI models in CPQ
- Structuring product, pricing, and customer hierarchy data
- Ensuring data lineage and master data governance
- Designing a CPQ data lake for model training and simulation
- Historical win-loss data analysis to improve AI recommendations
- Using past quote revisions to train AI accuracy over time
- Implementing data cleansing protocols before AI onboarding
- Real-time data pipelines from CRM and ERP into CPQ
- Secure data handling and access controls for sensitive pricing
- Monitoring data drift and model decay in production AI
Module 6: Integration and Interoperability - Mapping CPQ integration points with Salesforce, HubSpot, Dynamics
- ERP integration with SAP, Oracle, NetSuite for billing and accounting
- Syncing CPQ with CPQ analytics and revenue intelligence platforms
- Embedding CPQ within customer portals and partner ecosystems
- Developing API-first CPQ architecture principles
- Handling asynchronous data flows during high-volume periods
- Ensuring bidirectional sync integrity across systems
- Testing integration resilience under edge-case conditions
- Using middleware for complex enterprise landscapes
- Creating failover and data recovery protocols
Module 7: AI Governance and Risk Management - Establishing an AI ethics and compliance framework for CPQ
- Defining who owns AI model decisions in pricing and configuration
- Implementing human-in-the-loop approval gates for high-risk deals
- Transparency requirements for AI recommendations (Explainable AI)
- Monitoring for unintended bias in discounting or upsell logic
- Regulatory considerations: SOX, GDPR, CCPA in pricing automation
- Creating audit trails for AI-driven pricing changes
- Implementing AI model validation and testing procedures
- Setting thresholds for manual override and escalation
- Training sales leaders to supervise AI recommendations effectively
Module 8: Change Management and User Adoption - Understanding sales team resistance to AI automation
- Developing personas for CPQ user segments (reps, ops, managers)
- Designing role-based training and onboarding programs
- Creating quick-win use cases to build early momentum
- Establishing CPQ champions within regional sales teams
- Running pilot programmes to validate and socialise success
- Communicating benefits in terms of rep productivity and deal support
- Addressing fears of job displacement due to automation
- Implementing feedback loops for continuous improvement
- Measuring and reporting on user adoption KPIs
Module 9: AI-Driven Pricing Strategy - Differentiating between list, deal, and dynamic pricing
- Training AI on customer lifetime value signals for pricing
- Competitive pricing intelligence using AI scrapers and feeds
- AI-based deal desk decision support for complex negotiations
- Real-time margin protection during quoting
- Segment-specific pricing models powered by AI clustering
- Automated approval workflows for out-of-policy discounts
- AI simulation of pricing scenarios and elasticity testing
- Dynamic pricing for time-sensitive opportunities
- Aligning pricing strategy with contract lifecycle management
Module 10: Deal Acceleration and Sales Enablement - Reducing quote turnaround time with AI pre-configuration
- Automating approval chains based on risk and size
- AI-powered quote templates for recurring customer types
- Integrating e-signature and contracting workflows
- Using AI to prioritise high-potential renewal quotes
- Embedding CPQ insights into sales playbooks
- AI suggestions for timing, channel, and negotiation tactics
- Real-time CPQ dashboards for sales managers
- Mobile access for field reps generating quotes on-site
- Integrating CPQ data into forecast accuracy models
Module 11: ROI Measurement and Business Value Tracking - Defining baseline metrics before AI-CPQ implementation
- Calculating cost savings from reduced manual effort
- Quantifying revenue uplift from improved upsell capture
- Measuring reduction in pricing errors and reconciliations
- Tracking improvement in sales rep productivity (quotes per day)
- Assessing win rate impact across deal segments
- Monitoring average deal size and discounting trends
- Calculating time saved in quote-to-cash cycle
- Reporting to executives using CPQ business scorecards
- Linking CPQ KPIs to broader revenue operations dashboards
Module 12: Advanced AI Techniques in CPQ - Predictive quoting: Forecasting deal closure probability
- AI for reselling and recommissioning legacy product lines
- NLP-based parsing of RFx documents into CPQ inputs
- AI optimisation for multi-year, multi-product deals
- Simulating customer trade-off preferences in real-time
- Using reinforcement learning to improve AI recommendations
- AI-driven scenario modelling for alternative configurations
- Handling incomplete inputs with intelligent defaults
- Automated quote comparison across versions and competitors
- AI for generating executive summary narratives in quotes
Module 13: CPQ Implementation Project Management - Developing a phased rollout strategy: Pilot to enterprise
- Building a cross-functional implementation team
- Timeline design with milestones and decision gates
- Stakeholder communication planning
- Resource allocation and budgeting for AI-CPQ deployment
- Risk assessment and mitigation planning
- Vendor selection criteria for AI-CPQ platforms
- Scope definition: What’s in, what’s out, and why
- Change control processes during implementation
- Post-go-live support and hypercare planning
Module 14: Scalability and Global Deployment - Designing CPQ for multi-division, multi-geography rollouts
- Managing localisation of pricing, language, and compliance
- Centralised vs decentralised CPQ governance models
- Handling mergers, acquisitions, and system consolidation
- Standardising product catalogues across divisions
- Customisability vs consistency trade-offs in global CPQ
- Building regional CPQ support teams
- Training plans for large-scale, global user bases
- Monitoring performance across time zones and regions
- Scaling infrastructure for peak quote volume periods
Module 15: Future-Proofing AI-CPQ Strategy - Anticipating next-generation AI advancements in CPQ
- Preparing for autonomous negotiation systems
- Integrating generative AI for quote documentation
- Exploring CPQ’s role in ecosystems and platform economies
- AI for proactive opportunity identification (quote before ask)
- Adapting to economic volatility with AI pricing agility
- Building organisational learning loops from CPQ data
- Creating a roadmap for AI-CPQ innovation sprints
- Measuring CPQ’s contribution to enterprise digital maturity
- Positioning yourself as a thought leader in AI-driven sales
Module 16: Building Your Board-Ready AI-CPQ Proposal - Structuring a compelling executive presentation
- Translating technical capabilities into business value
- Designing financial models with clear ROI and payback period
- Visualising the future state with before-after scenarios
- Incorporating risk assessment and mitigation plans
- Drafting governance and success measurement frameworks
- Aligning the proposal with strategic initiatives
- Anticipating and answering executive objections
- Using data stories to demonstrate urgency and opportunity
- Finalising your end-to-end AI-CPQ transformation plan
Module 17: Certification and Next Steps - Completing the final review of your CPQ strategy deliverables
- Validating alignment with enterprise goals and technical realities
- Submitting your capstone project for assessment
- Receiving structured feedback from AI-CPQ advisors
- Finalising documentation for internal stakeholder review
- Preparing for post-course action: next 30, 60, 90 days
- Leveraging your Certificate of Completion strategically
- Joining The Art of Service alumni network for ongoing support
- Accessing template library and toolkits post-certification
- Identifying your next leadership-level transformation initiative
- CPQ Maturity Model: Stages 1 to 5 (Reactive to Predictive)
- Conducting a diagnostic audit of your organisation's current state
- Scoring your quote accuracy, speed, and compliance performance
- Evaluating integration depth across CRM, ERP, and billing systems
- Identifying data quality gaps that inhibit AI performance
- Assessing sales team adoption and usage patterns of existing tools
- Measuring pricing variance across regions, reps, and product lines
- Benchmarking against industry-specific CPQ performance metrics
- Recognising signs of underutilised CPQ capabilities
- Creating a baseline scorecard for progress tracking
Module 3: Strategic Alignment of AI-CPQ with Business Goals - Aligning CPQ initiatives with corporate growth and margin targets
- Defining KPIs that matter: Win rate, TTV, ACV, upsell rate
- Building a value map linking CPQ outcomes to financial metrics
- Engaging CFOs and Finance leaders in CPQ transformation
- Gaining executive sponsorship through data-driven storytelling
- Positioning AI-CPQ as a revenue enablement function, not IT
- Developing cross-functional governance for pricing decisions
- Creating a business case that speaks to revenue, risk, and scalability
- Integrating CPQ strategy into annual sales planning cycles
- Establishing a CPQ Centre of Excellence charter and mandate
Module 4: AI-Powered CPQ Functional Architecture - Breakdown of AI engines in CPQ: Pricing, validation, recommendation
- AI-driven product configuration logic and constraint mapping
- Dynamic pricing models: Cost-plus, value-based, competitive
- Real-time discounting authorisation with AI risk scoring
- Automated upsell and cross-sell suggestion algorithms
- AI for handling complex bundling and SKU rationalisation
- Service and subscription configuration with usage variables
- Handling multi-currency, multi-language, and regional compliance
- Quote versioning and audit trails powered by AI tagging
- Natural language processing for quote commentary and approvals
Module 5: Data Strategy for AI-CPQ Success - Data requirements for training and operating AI models in CPQ
- Structuring product, pricing, and customer hierarchy data
- Ensuring data lineage and master data governance
- Designing a CPQ data lake for model training and simulation
- Historical win-loss data analysis to improve AI recommendations
- Using past quote revisions to train AI accuracy over time
- Implementing data cleansing protocols before AI onboarding
- Real-time data pipelines from CRM and ERP into CPQ
- Secure data handling and access controls for sensitive pricing
- Monitoring data drift and model decay in production AI
Module 6: Integration and Interoperability - Mapping CPQ integration points with Salesforce, HubSpot, Dynamics
- ERP integration with SAP, Oracle, NetSuite for billing and accounting
- Syncing CPQ with CPQ analytics and revenue intelligence platforms
- Embedding CPQ within customer portals and partner ecosystems
- Developing API-first CPQ architecture principles
- Handling asynchronous data flows during high-volume periods
- Ensuring bidirectional sync integrity across systems
- Testing integration resilience under edge-case conditions
- Using middleware for complex enterprise landscapes
- Creating failover and data recovery protocols
Module 7: AI Governance and Risk Management - Establishing an AI ethics and compliance framework for CPQ
- Defining who owns AI model decisions in pricing and configuration
- Implementing human-in-the-loop approval gates for high-risk deals
- Transparency requirements for AI recommendations (Explainable AI)
- Monitoring for unintended bias in discounting or upsell logic
- Regulatory considerations: SOX, GDPR, CCPA in pricing automation
- Creating audit trails for AI-driven pricing changes
- Implementing AI model validation and testing procedures
- Setting thresholds for manual override and escalation
- Training sales leaders to supervise AI recommendations effectively
Module 8: Change Management and User Adoption - Understanding sales team resistance to AI automation
- Developing personas for CPQ user segments (reps, ops, managers)
- Designing role-based training and onboarding programs
- Creating quick-win use cases to build early momentum
- Establishing CPQ champions within regional sales teams
- Running pilot programmes to validate and socialise success
- Communicating benefits in terms of rep productivity and deal support
- Addressing fears of job displacement due to automation
- Implementing feedback loops for continuous improvement
- Measuring and reporting on user adoption KPIs
Module 9: AI-Driven Pricing Strategy - Differentiating between list, deal, and dynamic pricing
- Training AI on customer lifetime value signals for pricing
- Competitive pricing intelligence using AI scrapers and feeds
- AI-based deal desk decision support for complex negotiations
- Real-time margin protection during quoting
- Segment-specific pricing models powered by AI clustering
- Automated approval workflows for out-of-policy discounts
- AI simulation of pricing scenarios and elasticity testing
- Dynamic pricing for time-sensitive opportunities
- Aligning pricing strategy with contract lifecycle management
Module 10: Deal Acceleration and Sales Enablement - Reducing quote turnaround time with AI pre-configuration
- Automating approval chains based on risk and size
- AI-powered quote templates for recurring customer types
- Integrating e-signature and contracting workflows
- Using AI to prioritise high-potential renewal quotes
- Embedding CPQ insights into sales playbooks
- AI suggestions for timing, channel, and negotiation tactics
- Real-time CPQ dashboards for sales managers
- Mobile access for field reps generating quotes on-site
- Integrating CPQ data into forecast accuracy models
Module 11: ROI Measurement and Business Value Tracking - Defining baseline metrics before AI-CPQ implementation
- Calculating cost savings from reduced manual effort
- Quantifying revenue uplift from improved upsell capture
- Measuring reduction in pricing errors and reconciliations
- Tracking improvement in sales rep productivity (quotes per day)
- Assessing win rate impact across deal segments
- Monitoring average deal size and discounting trends
- Calculating time saved in quote-to-cash cycle
- Reporting to executives using CPQ business scorecards
- Linking CPQ KPIs to broader revenue operations dashboards
Module 12: Advanced AI Techniques in CPQ - Predictive quoting: Forecasting deal closure probability
- AI for reselling and recommissioning legacy product lines
- NLP-based parsing of RFx documents into CPQ inputs
- AI optimisation for multi-year, multi-product deals
- Simulating customer trade-off preferences in real-time
- Using reinforcement learning to improve AI recommendations
- AI-driven scenario modelling for alternative configurations
- Handling incomplete inputs with intelligent defaults
- Automated quote comparison across versions and competitors
- AI for generating executive summary narratives in quotes
Module 13: CPQ Implementation Project Management - Developing a phased rollout strategy: Pilot to enterprise
- Building a cross-functional implementation team
- Timeline design with milestones and decision gates
- Stakeholder communication planning
- Resource allocation and budgeting for AI-CPQ deployment
- Risk assessment and mitigation planning
- Vendor selection criteria for AI-CPQ platforms
- Scope definition: What’s in, what’s out, and why
- Change control processes during implementation
- Post-go-live support and hypercare planning
Module 14: Scalability and Global Deployment - Designing CPQ for multi-division, multi-geography rollouts
- Managing localisation of pricing, language, and compliance
- Centralised vs decentralised CPQ governance models
- Handling mergers, acquisitions, and system consolidation
- Standardising product catalogues across divisions
- Customisability vs consistency trade-offs in global CPQ
- Building regional CPQ support teams
- Training plans for large-scale, global user bases
- Monitoring performance across time zones and regions
- Scaling infrastructure for peak quote volume periods
Module 15: Future-Proofing AI-CPQ Strategy - Anticipating next-generation AI advancements in CPQ
- Preparing for autonomous negotiation systems
- Integrating generative AI for quote documentation
- Exploring CPQ’s role in ecosystems and platform economies
- AI for proactive opportunity identification (quote before ask)
- Adapting to economic volatility with AI pricing agility
- Building organisational learning loops from CPQ data
- Creating a roadmap for AI-CPQ innovation sprints
- Measuring CPQ’s contribution to enterprise digital maturity
- Positioning yourself as a thought leader in AI-driven sales
Module 16: Building Your Board-Ready AI-CPQ Proposal - Structuring a compelling executive presentation
- Translating technical capabilities into business value
- Designing financial models with clear ROI and payback period
- Visualising the future state with before-after scenarios
- Incorporating risk assessment and mitigation plans
- Drafting governance and success measurement frameworks
- Aligning the proposal with strategic initiatives
- Anticipating and answering executive objections
- Using data stories to demonstrate urgency and opportunity
- Finalising your end-to-end AI-CPQ transformation plan
Module 17: Certification and Next Steps - Completing the final review of your CPQ strategy deliverables
- Validating alignment with enterprise goals and technical realities
- Submitting your capstone project for assessment
- Receiving structured feedback from AI-CPQ advisors
- Finalising documentation for internal stakeholder review
- Preparing for post-course action: next 30, 60, 90 days
- Leveraging your Certificate of Completion strategically
- Joining The Art of Service alumni network for ongoing support
- Accessing template library and toolkits post-certification
- Identifying your next leadership-level transformation initiative
- Breakdown of AI engines in CPQ: Pricing, validation, recommendation
- AI-driven product configuration logic and constraint mapping
- Dynamic pricing models: Cost-plus, value-based, competitive
- Real-time discounting authorisation with AI risk scoring
- Automated upsell and cross-sell suggestion algorithms
- AI for handling complex bundling and SKU rationalisation
- Service and subscription configuration with usage variables
- Handling multi-currency, multi-language, and regional compliance
- Quote versioning and audit trails powered by AI tagging
- Natural language processing for quote commentary and approvals
Module 5: Data Strategy for AI-CPQ Success - Data requirements for training and operating AI models in CPQ
- Structuring product, pricing, and customer hierarchy data
- Ensuring data lineage and master data governance
- Designing a CPQ data lake for model training and simulation
- Historical win-loss data analysis to improve AI recommendations
- Using past quote revisions to train AI accuracy over time
- Implementing data cleansing protocols before AI onboarding
- Real-time data pipelines from CRM and ERP into CPQ
- Secure data handling and access controls for sensitive pricing
- Monitoring data drift and model decay in production AI
Module 6: Integration and Interoperability - Mapping CPQ integration points with Salesforce, HubSpot, Dynamics
- ERP integration with SAP, Oracle, NetSuite for billing and accounting
- Syncing CPQ with CPQ analytics and revenue intelligence platforms
- Embedding CPQ within customer portals and partner ecosystems
- Developing API-first CPQ architecture principles
- Handling asynchronous data flows during high-volume periods
- Ensuring bidirectional sync integrity across systems
- Testing integration resilience under edge-case conditions
- Using middleware for complex enterprise landscapes
- Creating failover and data recovery protocols
Module 7: AI Governance and Risk Management - Establishing an AI ethics and compliance framework for CPQ
- Defining who owns AI model decisions in pricing and configuration
- Implementing human-in-the-loop approval gates for high-risk deals
- Transparency requirements for AI recommendations (Explainable AI)
- Monitoring for unintended bias in discounting or upsell logic
- Regulatory considerations: SOX, GDPR, CCPA in pricing automation
- Creating audit trails for AI-driven pricing changes
- Implementing AI model validation and testing procedures
- Setting thresholds for manual override and escalation
- Training sales leaders to supervise AI recommendations effectively
Module 8: Change Management and User Adoption - Understanding sales team resistance to AI automation
- Developing personas for CPQ user segments (reps, ops, managers)
- Designing role-based training and onboarding programs
- Creating quick-win use cases to build early momentum
- Establishing CPQ champions within regional sales teams
- Running pilot programmes to validate and socialise success
- Communicating benefits in terms of rep productivity and deal support
- Addressing fears of job displacement due to automation
- Implementing feedback loops for continuous improvement
- Measuring and reporting on user adoption KPIs
Module 9: AI-Driven Pricing Strategy - Differentiating between list, deal, and dynamic pricing
- Training AI on customer lifetime value signals for pricing
- Competitive pricing intelligence using AI scrapers and feeds
- AI-based deal desk decision support for complex negotiations
- Real-time margin protection during quoting
- Segment-specific pricing models powered by AI clustering
- Automated approval workflows for out-of-policy discounts
- AI simulation of pricing scenarios and elasticity testing
- Dynamic pricing for time-sensitive opportunities
- Aligning pricing strategy with contract lifecycle management
Module 10: Deal Acceleration and Sales Enablement - Reducing quote turnaround time with AI pre-configuration
- Automating approval chains based on risk and size
- AI-powered quote templates for recurring customer types
- Integrating e-signature and contracting workflows
- Using AI to prioritise high-potential renewal quotes
- Embedding CPQ insights into sales playbooks
- AI suggestions for timing, channel, and negotiation tactics
- Real-time CPQ dashboards for sales managers
- Mobile access for field reps generating quotes on-site
- Integrating CPQ data into forecast accuracy models
Module 11: ROI Measurement and Business Value Tracking - Defining baseline metrics before AI-CPQ implementation
- Calculating cost savings from reduced manual effort
- Quantifying revenue uplift from improved upsell capture
- Measuring reduction in pricing errors and reconciliations
- Tracking improvement in sales rep productivity (quotes per day)
- Assessing win rate impact across deal segments
- Monitoring average deal size and discounting trends
- Calculating time saved in quote-to-cash cycle
- Reporting to executives using CPQ business scorecards
- Linking CPQ KPIs to broader revenue operations dashboards
Module 12: Advanced AI Techniques in CPQ - Predictive quoting: Forecasting deal closure probability
- AI for reselling and recommissioning legacy product lines
- NLP-based parsing of RFx documents into CPQ inputs
- AI optimisation for multi-year, multi-product deals
- Simulating customer trade-off preferences in real-time
- Using reinforcement learning to improve AI recommendations
- AI-driven scenario modelling for alternative configurations
- Handling incomplete inputs with intelligent defaults
- Automated quote comparison across versions and competitors
- AI for generating executive summary narratives in quotes
Module 13: CPQ Implementation Project Management - Developing a phased rollout strategy: Pilot to enterprise
- Building a cross-functional implementation team
- Timeline design with milestones and decision gates
- Stakeholder communication planning
- Resource allocation and budgeting for AI-CPQ deployment
- Risk assessment and mitigation planning
- Vendor selection criteria for AI-CPQ platforms
- Scope definition: What’s in, what’s out, and why
- Change control processes during implementation
- Post-go-live support and hypercare planning
Module 14: Scalability and Global Deployment - Designing CPQ for multi-division, multi-geography rollouts
- Managing localisation of pricing, language, and compliance
- Centralised vs decentralised CPQ governance models
- Handling mergers, acquisitions, and system consolidation
- Standardising product catalogues across divisions
- Customisability vs consistency trade-offs in global CPQ
- Building regional CPQ support teams
- Training plans for large-scale, global user bases
- Monitoring performance across time zones and regions
- Scaling infrastructure for peak quote volume periods
Module 15: Future-Proofing AI-CPQ Strategy - Anticipating next-generation AI advancements in CPQ
- Preparing for autonomous negotiation systems
- Integrating generative AI for quote documentation
- Exploring CPQ’s role in ecosystems and platform economies
- AI for proactive opportunity identification (quote before ask)
- Adapting to economic volatility with AI pricing agility
- Building organisational learning loops from CPQ data
- Creating a roadmap for AI-CPQ innovation sprints
- Measuring CPQ’s contribution to enterprise digital maturity
- Positioning yourself as a thought leader in AI-driven sales
Module 16: Building Your Board-Ready AI-CPQ Proposal - Structuring a compelling executive presentation
- Translating technical capabilities into business value
- Designing financial models with clear ROI and payback period
- Visualising the future state with before-after scenarios
- Incorporating risk assessment and mitigation plans
- Drafting governance and success measurement frameworks
- Aligning the proposal with strategic initiatives
- Anticipating and answering executive objections
- Using data stories to demonstrate urgency and opportunity
- Finalising your end-to-end AI-CPQ transformation plan
Module 17: Certification and Next Steps - Completing the final review of your CPQ strategy deliverables
- Validating alignment with enterprise goals and technical realities
- Submitting your capstone project for assessment
- Receiving structured feedback from AI-CPQ advisors
- Finalising documentation for internal stakeholder review
- Preparing for post-course action: next 30, 60, 90 days
- Leveraging your Certificate of Completion strategically
- Joining The Art of Service alumni network for ongoing support
- Accessing template library and toolkits post-certification
- Identifying your next leadership-level transformation initiative
- Mapping CPQ integration points with Salesforce, HubSpot, Dynamics
- ERP integration with SAP, Oracle, NetSuite for billing and accounting
- Syncing CPQ with CPQ analytics and revenue intelligence platforms
- Embedding CPQ within customer portals and partner ecosystems
- Developing API-first CPQ architecture principles
- Handling asynchronous data flows during high-volume periods
- Ensuring bidirectional sync integrity across systems
- Testing integration resilience under edge-case conditions
- Using middleware for complex enterprise landscapes
- Creating failover and data recovery protocols
Module 7: AI Governance and Risk Management - Establishing an AI ethics and compliance framework for CPQ
- Defining who owns AI model decisions in pricing and configuration
- Implementing human-in-the-loop approval gates for high-risk deals
- Transparency requirements for AI recommendations (Explainable AI)
- Monitoring for unintended bias in discounting or upsell logic
- Regulatory considerations: SOX, GDPR, CCPA in pricing automation
- Creating audit trails for AI-driven pricing changes
- Implementing AI model validation and testing procedures
- Setting thresholds for manual override and escalation
- Training sales leaders to supervise AI recommendations effectively
Module 8: Change Management and User Adoption - Understanding sales team resistance to AI automation
- Developing personas for CPQ user segments (reps, ops, managers)
- Designing role-based training and onboarding programs
- Creating quick-win use cases to build early momentum
- Establishing CPQ champions within regional sales teams
- Running pilot programmes to validate and socialise success
- Communicating benefits in terms of rep productivity and deal support
- Addressing fears of job displacement due to automation
- Implementing feedback loops for continuous improvement
- Measuring and reporting on user adoption KPIs
Module 9: AI-Driven Pricing Strategy - Differentiating between list, deal, and dynamic pricing
- Training AI on customer lifetime value signals for pricing
- Competitive pricing intelligence using AI scrapers and feeds
- AI-based deal desk decision support for complex negotiations
- Real-time margin protection during quoting
- Segment-specific pricing models powered by AI clustering
- Automated approval workflows for out-of-policy discounts
- AI simulation of pricing scenarios and elasticity testing
- Dynamic pricing for time-sensitive opportunities
- Aligning pricing strategy with contract lifecycle management
Module 10: Deal Acceleration and Sales Enablement - Reducing quote turnaround time with AI pre-configuration
- Automating approval chains based on risk and size
- AI-powered quote templates for recurring customer types
- Integrating e-signature and contracting workflows
- Using AI to prioritise high-potential renewal quotes
- Embedding CPQ insights into sales playbooks
- AI suggestions for timing, channel, and negotiation tactics
- Real-time CPQ dashboards for sales managers
- Mobile access for field reps generating quotes on-site
- Integrating CPQ data into forecast accuracy models
Module 11: ROI Measurement and Business Value Tracking - Defining baseline metrics before AI-CPQ implementation
- Calculating cost savings from reduced manual effort
- Quantifying revenue uplift from improved upsell capture
- Measuring reduction in pricing errors and reconciliations
- Tracking improvement in sales rep productivity (quotes per day)
- Assessing win rate impact across deal segments
- Monitoring average deal size and discounting trends
- Calculating time saved in quote-to-cash cycle
- Reporting to executives using CPQ business scorecards
- Linking CPQ KPIs to broader revenue operations dashboards
Module 12: Advanced AI Techniques in CPQ - Predictive quoting: Forecasting deal closure probability
- AI for reselling and recommissioning legacy product lines
- NLP-based parsing of RFx documents into CPQ inputs
- AI optimisation for multi-year, multi-product deals
- Simulating customer trade-off preferences in real-time
- Using reinforcement learning to improve AI recommendations
- AI-driven scenario modelling for alternative configurations
- Handling incomplete inputs with intelligent defaults
- Automated quote comparison across versions and competitors
- AI for generating executive summary narratives in quotes
Module 13: CPQ Implementation Project Management - Developing a phased rollout strategy: Pilot to enterprise
- Building a cross-functional implementation team
- Timeline design with milestones and decision gates
- Stakeholder communication planning
- Resource allocation and budgeting for AI-CPQ deployment
- Risk assessment and mitigation planning
- Vendor selection criteria for AI-CPQ platforms
- Scope definition: What’s in, what’s out, and why
- Change control processes during implementation
- Post-go-live support and hypercare planning
Module 14: Scalability and Global Deployment - Designing CPQ for multi-division, multi-geography rollouts
- Managing localisation of pricing, language, and compliance
- Centralised vs decentralised CPQ governance models
- Handling mergers, acquisitions, and system consolidation
- Standardising product catalogues across divisions
- Customisability vs consistency trade-offs in global CPQ
- Building regional CPQ support teams
- Training plans for large-scale, global user bases
- Monitoring performance across time zones and regions
- Scaling infrastructure for peak quote volume periods
Module 15: Future-Proofing AI-CPQ Strategy - Anticipating next-generation AI advancements in CPQ
- Preparing for autonomous negotiation systems
- Integrating generative AI for quote documentation
- Exploring CPQ’s role in ecosystems and platform economies
- AI for proactive opportunity identification (quote before ask)
- Adapting to economic volatility with AI pricing agility
- Building organisational learning loops from CPQ data
- Creating a roadmap for AI-CPQ innovation sprints
- Measuring CPQ’s contribution to enterprise digital maturity
- Positioning yourself as a thought leader in AI-driven sales
Module 16: Building Your Board-Ready AI-CPQ Proposal - Structuring a compelling executive presentation
- Translating technical capabilities into business value
- Designing financial models with clear ROI and payback period
- Visualising the future state with before-after scenarios
- Incorporating risk assessment and mitigation plans
- Drafting governance and success measurement frameworks
- Aligning the proposal with strategic initiatives
- Anticipating and answering executive objections
- Using data stories to demonstrate urgency and opportunity
- Finalising your end-to-end AI-CPQ transformation plan
Module 17: Certification and Next Steps - Completing the final review of your CPQ strategy deliverables
- Validating alignment with enterprise goals and technical realities
- Submitting your capstone project for assessment
- Receiving structured feedback from AI-CPQ advisors
- Finalising documentation for internal stakeholder review
- Preparing for post-course action: next 30, 60, 90 days
- Leveraging your Certificate of Completion strategically
- Joining The Art of Service alumni network for ongoing support
- Accessing template library and toolkits post-certification
- Identifying your next leadership-level transformation initiative
- Understanding sales team resistance to AI automation
- Developing personas for CPQ user segments (reps, ops, managers)
- Designing role-based training and onboarding programs
- Creating quick-win use cases to build early momentum
- Establishing CPQ champions within regional sales teams
- Running pilot programmes to validate and socialise success
- Communicating benefits in terms of rep productivity and deal support
- Addressing fears of job displacement due to automation
- Implementing feedback loops for continuous improvement
- Measuring and reporting on user adoption KPIs
Module 9: AI-Driven Pricing Strategy - Differentiating between list, deal, and dynamic pricing
- Training AI on customer lifetime value signals for pricing
- Competitive pricing intelligence using AI scrapers and feeds
- AI-based deal desk decision support for complex negotiations
- Real-time margin protection during quoting
- Segment-specific pricing models powered by AI clustering
- Automated approval workflows for out-of-policy discounts
- AI simulation of pricing scenarios and elasticity testing
- Dynamic pricing for time-sensitive opportunities
- Aligning pricing strategy with contract lifecycle management
Module 10: Deal Acceleration and Sales Enablement - Reducing quote turnaround time with AI pre-configuration
- Automating approval chains based on risk and size
- AI-powered quote templates for recurring customer types
- Integrating e-signature and contracting workflows
- Using AI to prioritise high-potential renewal quotes
- Embedding CPQ insights into sales playbooks
- AI suggestions for timing, channel, and negotiation tactics
- Real-time CPQ dashboards for sales managers
- Mobile access for field reps generating quotes on-site
- Integrating CPQ data into forecast accuracy models
Module 11: ROI Measurement and Business Value Tracking - Defining baseline metrics before AI-CPQ implementation
- Calculating cost savings from reduced manual effort
- Quantifying revenue uplift from improved upsell capture
- Measuring reduction in pricing errors and reconciliations
- Tracking improvement in sales rep productivity (quotes per day)
- Assessing win rate impact across deal segments
- Monitoring average deal size and discounting trends
- Calculating time saved in quote-to-cash cycle
- Reporting to executives using CPQ business scorecards
- Linking CPQ KPIs to broader revenue operations dashboards
Module 12: Advanced AI Techniques in CPQ - Predictive quoting: Forecasting deal closure probability
- AI for reselling and recommissioning legacy product lines
- NLP-based parsing of RFx documents into CPQ inputs
- AI optimisation for multi-year, multi-product deals
- Simulating customer trade-off preferences in real-time
- Using reinforcement learning to improve AI recommendations
- AI-driven scenario modelling for alternative configurations
- Handling incomplete inputs with intelligent defaults
- Automated quote comparison across versions and competitors
- AI for generating executive summary narratives in quotes
Module 13: CPQ Implementation Project Management - Developing a phased rollout strategy: Pilot to enterprise
- Building a cross-functional implementation team
- Timeline design with milestones and decision gates
- Stakeholder communication planning
- Resource allocation and budgeting for AI-CPQ deployment
- Risk assessment and mitigation planning
- Vendor selection criteria for AI-CPQ platforms
- Scope definition: What’s in, what’s out, and why
- Change control processes during implementation
- Post-go-live support and hypercare planning
Module 14: Scalability and Global Deployment - Designing CPQ for multi-division, multi-geography rollouts
- Managing localisation of pricing, language, and compliance
- Centralised vs decentralised CPQ governance models
- Handling mergers, acquisitions, and system consolidation
- Standardising product catalogues across divisions
- Customisability vs consistency trade-offs in global CPQ
- Building regional CPQ support teams
- Training plans for large-scale, global user bases
- Monitoring performance across time zones and regions
- Scaling infrastructure for peak quote volume periods
Module 15: Future-Proofing AI-CPQ Strategy - Anticipating next-generation AI advancements in CPQ
- Preparing for autonomous negotiation systems
- Integrating generative AI for quote documentation
- Exploring CPQ’s role in ecosystems and platform economies
- AI for proactive opportunity identification (quote before ask)
- Adapting to economic volatility with AI pricing agility
- Building organisational learning loops from CPQ data
- Creating a roadmap for AI-CPQ innovation sprints
- Measuring CPQ’s contribution to enterprise digital maturity
- Positioning yourself as a thought leader in AI-driven sales
Module 16: Building Your Board-Ready AI-CPQ Proposal - Structuring a compelling executive presentation
- Translating technical capabilities into business value
- Designing financial models with clear ROI and payback period
- Visualising the future state with before-after scenarios
- Incorporating risk assessment and mitigation plans
- Drafting governance and success measurement frameworks
- Aligning the proposal with strategic initiatives
- Anticipating and answering executive objections
- Using data stories to demonstrate urgency and opportunity
- Finalising your end-to-end AI-CPQ transformation plan
Module 17: Certification and Next Steps - Completing the final review of your CPQ strategy deliverables
- Validating alignment with enterprise goals and technical realities
- Submitting your capstone project for assessment
- Receiving structured feedback from AI-CPQ advisors
- Finalising documentation for internal stakeholder review
- Preparing for post-course action: next 30, 60, 90 days
- Leveraging your Certificate of Completion strategically
- Joining The Art of Service alumni network for ongoing support
- Accessing template library and toolkits post-certification
- Identifying your next leadership-level transformation initiative
- Reducing quote turnaround time with AI pre-configuration
- Automating approval chains based on risk and size
- AI-powered quote templates for recurring customer types
- Integrating e-signature and contracting workflows
- Using AI to prioritise high-potential renewal quotes
- Embedding CPQ insights into sales playbooks
- AI suggestions for timing, channel, and negotiation tactics
- Real-time CPQ dashboards for sales managers
- Mobile access for field reps generating quotes on-site
- Integrating CPQ data into forecast accuracy models
Module 11: ROI Measurement and Business Value Tracking - Defining baseline metrics before AI-CPQ implementation
- Calculating cost savings from reduced manual effort
- Quantifying revenue uplift from improved upsell capture
- Measuring reduction in pricing errors and reconciliations
- Tracking improvement in sales rep productivity (quotes per day)
- Assessing win rate impact across deal segments
- Monitoring average deal size and discounting trends
- Calculating time saved in quote-to-cash cycle
- Reporting to executives using CPQ business scorecards
- Linking CPQ KPIs to broader revenue operations dashboards
Module 12: Advanced AI Techniques in CPQ - Predictive quoting: Forecasting deal closure probability
- AI for reselling and recommissioning legacy product lines
- NLP-based parsing of RFx documents into CPQ inputs
- AI optimisation for multi-year, multi-product deals
- Simulating customer trade-off preferences in real-time
- Using reinforcement learning to improve AI recommendations
- AI-driven scenario modelling for alternative configurations
- Handling incomplete inputs with intelligent defaults
- Automated quote comparison across versions and competitors
- AI for generating executive summary narratives in quotes
Module 13: CPQ Implementation Project Management - Developing a phased rollout strategy: Pilot to enterprise
- Building a cross-functional implementation team
- Timeline design with milestones and decision gates
- Stakeholder communication planning
- Resource allocation and budgeting for AI-CPQ deployment
- Risk assessment and mitigation planning
- Vendor selection criteria for AI-CPQ platforms
- Scope definition: What’s in, what’s out, and why
- Change control processes during implementation
- Post-go-live support and hypercare planning
Module 14: Scalability and Global Deployment - Designing CPQ for multi-division, multi-geography rollouts
- Managing localisation of pricing, language, and compliance
- Centralised vs decentralised CPQ governance models
- Handling mergers, acquisitions, and system consolidation
- Standardising product catalogues across divisions
- Customisability vs consistency trade-offs in global CPQ
- Building regional CPQ support teams
- Training plans for large-scale, global user bases
- Monitoring performance across time zones and regions
- Scaling infrastructure for peak quote volume periods
Module 15: Future-Proofing AI-CPQ Strategy - Anticipating next-generation AI advancements in CPQ
- Preparing for autonomous negotiation systems
- Integrating generative AI for quote documentation
- Exploring CPQ’s role in ecosystems and platform economies
- AI for proactive opportunity identification (quote before ask)
- Adapting to economic volatility with AI pricing agility
- Building organisational learning loops from CPQ data
- Creating a roadmap for AI-CPQ innovation sprints
- Measuring CPQ’s contribution to enterprise digital maturity
- Positioning yourself as a thought leader in AI-driven sales
Module 16: Building Your Board-Ready AI-CPQ Proposal - Structuring a compelling executive presentation
- Translating technical capabilities into business value
- Designing financial models with clear ROI and payback period
- Visualising the future state with before-after scenarios
- Incorporating risk assessment and mitigation plans
- Drafting governance and success measurement frameworks
- Aligning the proposal with strategic initiatives
- Anticipating and answering executive objections
- Using data stories to demonstrate urgency and opportunity
- Finalising your end-to-end AI-CPQ transformation plan
Module 17: Certification and Next Steps - Completing the final review of your CPQ strategy deliverables
- Validating alignment with enterprise goals and technical realities
- Submitting your capstone project for assessment
- Receiving structured feedback from AI-CPQ advisors
- Finalising documentation for internal stakeholder review
- Preparing for post-course action: next 30, 60, 90 days
- Leveraging your Certificate of Completion strategically
- Joining The Art of Service alumni network for ongoing support
- Accessing template library and toolkits post-certification
- Identifying your next leadership-level transformation initiative
- Predictive quoting: Forecasting deal closure probability
- AI for reselling and recommissioning legacy product lines
- NLP-based parsing of RFx documents into CPQ inputs
- AI optimisation for multi-year, multi-product deals
- Simulating customer trade-off preferences in real-time
- Using reinforcement learning to improve AI recommendations
- AI-driven scenario modelling for alternative configurations
- Handling incomplete inputs with intelligent defaults
- Automated quote comparison across versions and competitors
- AI for generating executive summary narratives in quotes
Module 13: CPQ Implementation Project Management - Developing a phased rollout strategy: Pilot to enterprise
- Building a cross-functional implementation team
- Timeline design with milestones and decision gates
- Stakeholder communication planning
- Resource allocation and budgeting for AI-CPQ deployment
- Risk assessment and mitigation planning
- Vendor selection criteria for AI-CPQ platforms
- Scope definition: What’s in, what’s out, and why
- Change control processes during implementation
- Post-go-live support and hypercare planning
Module 14: Scalability and Global Deployment - Designing CPQ for multi-division, multi-geography rollouts
- Managing localisation of pricing, language, and compliance
- Centralised vs decentralised CPQ governance models
- Handling mergers, acquisitions, and system consolidation
- Standardising product catalogues across divisions
- Customisability vs consistency trade-offs in global CPQ
- Building regional CPQ support teams
- Training plans for large-scale, global user bases
- Monitoring performance across time zones and regions
- Scaling infrastructure for peak quote volume periods
Module 15: Future-Proofing AI-CPQ Strategy - Anticipating next-generation AI advancements in CPQ
- Preparing for autonomous negotiation systems
- Integrating generative AI for quote documentation
- Exploring CPQ’s role in ecosystems and platform economies
- AI for proactive opportunity identification (quote before ask)
- Adapting to economic volatility with AI pricing agility
- Building organisational learning loops from CPQ data
- Creating a roadmap for AI-CPQ innovation sprints
- Measuring CPQ’s contribution to enterprise digital maturity
- Positioning yourself as a thought leader in AI-driven sales
Module 16: Building Your Board-Ready AI-CPQ Proposal - Structuring a compelling executive presentation
- Translating technical capabilities into business value
- Designing financial models with clear ROI and payback period
- Visualising the future state with before-after scenarios
- Incorporating risk assessment and mitigation plans
- Drafting governance and success measurement frameworks
- Aligning the proposal with strategic initiatives
- Anticipating and answering executive objections
- Using data stories to demonstrate urgency and opportunity
- Finalising your end-to-end AI-CPQ transformation plan
Module 17: Certification and Next Steps - Completing the final review of your CPQ strategy deliverables
- Validating alignment with enterprise goals and technical realities
- Submitting your capstone project for assessment
- Receiving structured feedback from AI-CPQ advisors
- Finalising documentation for internal stakeholder review
- Preparing for post-course action: next 30, 60, 90 days
- Leveraging your Certificate of Completion strategically
- Joining The Art of Service alumni network for ongoing support
- Accessing template library and toolkits post-certification
- Identifying your next leadership-level transformation initiative
- Designing CPQ for multi-division, multi-geography rollouts
- Managing localisation of pricing, language, and compliance
- Centralised vs decentralised CPQ governance models
- Handling mergers, acquisitions, and system consolidation
- Standardising product catalogues across divisions
- Customisability vs consistency trade-offs in global CPQ
- Building regional CPQ support teams
- Training plans for large-scale, global user bases
- Monitoring performance across time zones and regions
- Scaling infrastructure for peak quote volume periods
Module 15: Future-Proofing AI-CPQ Strategy - Anticipating next-generation AI advancements in CPQ
- Preparing for autonomous negotiation systems
- Integrating generative AI for quote documentation
- Exploring CPQ’s role in ecosystems and platform economies
- AI for proactive opportunity identification (quote before ask)
- Adapting to economic volatility with AI pricing agility
- Building organisational learning loops from CPQ data
- Creating a roadmap for AI-CPQ innovation sprints
- Measuring CPQ’s contribution to enterprise digital maturity
- Positioning yourself as a thought leader in AI-driven sales
Module 16: Building Your Board-Ready AI-CPQ Proposal - Structuring a compelling executive presentation
- Translating technical capabilities into business value
- Designing financial models with clear ROI and payback period
- Visualising the future state with before-after scenarios
- Incorporating risk assessment and mitigation plans
- Drafting governance and success measurement frameworks
- Aligning the proposal with strategic initiatives
- Anticipating and answering executive objections
- Using data stories to demonstrate urgency and opportunity
- Finalising your end-to-end AI-CPQ transformation plan
Module 17: Certification and Next Steps - Completing the final review of your CPQ strategy deliverables
- Validating alignment with enterprise goals and technical realities
- Submitting your capstone project for assessment
- Receiving structured feedback from AI-CPQ advisors
- Finalising documentation for internal stakeholder review
- Preparing for post-course action: next 30, 60, 90 days
- Leveraging your Certificate of Completion strategically
- Joining The Art of Service alumni network for ongoing support
- Accessing template library and toolkits post-certification
- Identifying your next leadership-level transformation initiative
- Structuring a compelling executive presentation
- Translating technical capabilities into business value
- Designing financial models with clear ROI and payback period
- Visualising the future state with before-after scenarios
- Incorporating risk assessment and mitigation plans
- Drafting governance and success measurement frameworks
- Aligning the proposal with strategic initiatives
- Anticipating and answering executive objections
- Using data stories to demonstrate urgency and opportunity
- Finalising your end-to-end AI-CPQ transformation plan