Mastering AI-Driven IT Due Diligence for Strategic Business Outcomes
You’re under pressure. Mergers are accelerating. Tech stacks are merging. And your due diligence process still runs on outdated checklists, fragmented data, and gut instinct. One missed integration risk. One overlooked AI dependency. One blind spot in cybersecurity readiness. That's all it takes to derail a billion-dollar deal. You're not alone. A senior IT Director at a global logistics firm recently inherited a post-merger outage caused by undetected AI model drift in the target’s analytics engine. The fix? $2.8M in rework, six weeks of delayed value extraction, and a damaged executive reputation. This is the cost of flying blind - and it’s why traditional due diligence no longer cuts it. Now imagine a different path. A world where you can map, audit, and stress-test AI-powered IT systems with precision, using structured frameworks that flag risks before they become headlines. Where you don’t just assess technology - you validate its strategic alignment, compliance posture, and scalability with confidence. That’s exactly what Mastering AI-Driven IT Due Diligence for Strategic Business Outcomes delivers. This course guides you from fragmented assessment to board-ready validation - turning IT due diligence into a value accelerator, not a compliance chore. You’ll go from idea to fully scoped, AI-audited due diligence report in under 30 days, complete with executive summary, risk heatmaps, and integration roadmap. One recent participant, a Technology Risk Manager at a Fortune 500 financial services company, used the methodology within two weeks to uncover a critical AI bias flaw in a proposed SaaS acquisition. Her report stopped the deal until remediation, saving an estimated $4.3M in potential regulatory exposure and reputational fallout. She was personally commended by the CISO. The tools, frameworks, and workflows in this program are battle-tested by enterprise architects, compliance leads, and M&A tech integrators across regulated industries. They work not because they’re theoretical - but because they’re built from real-world war rooms, where precision determines outcomes. Here’s how this course is structured to help you get there.Course Format & Delivery Details Designed for Demanding Professionals - Zero Friction, Maximum Clarity
This course is completely self-paced with immediate online access upon enrollment. No scheduled sessions, no deadlines, no time conflicts. You control when and where you learn - whether during a quiet morning or between flights across time zones. Most learners complete the core due diligence framework and produce their first validated assessment in under 18 hours of total engagement. Many apply the templates to live projects within the first week and see measurable improvements in deal readiness clarity by day 10. Forever Access, Continuous Relevance
You receive lifetime access to all course materials, including every update released in the future. As AI governance standards evolve, model risk frameworks shift, and new compliance requirements emerge, your access automatically includes all revisions - at no extra cost, ever. All resources are mobile-friendly, optimised for seamless reading, editing, and reference on any device. Whether you’re in the office, at a site visit, or offline on a client call, your toolkit travels with you. Actionable Expertise with Direct Support
Throughout your journey, you’ll have access to structured guidance from certified instructors with deep experience in IT governance, AI risk, and M&A due diligence. This support comes through dedicated Q&A channels, curated feedback loops, and real-time clarification on implementation questions - ensuring you never get stuck. Your progress is private, trackable, and focused on outcomes. Each module includes practical exercises you can directly adapt to your current work - no hypotheticals, only real-world relevance. Global Trust, Industry-Recognised Credential
Upon completion, you earn a Certificate of Completion issued by The Art of Service - a globally recognised institution trusted by over 120,000 professionals across 94 countries. This credential signals advanced competency in AI-driven technology assessment and is increasingly referenced in IT governance, audit, and transformation roles. Employers in financial services, healthcare, energy, and government agencies actively seek professionals trained in these methodologies, particularly those with documented, standardised due diligence capability. Purpose-Built to Remove Risk and Build Confidence
Pricing is straightforward with no hidden fees, subscriptions, or upsells. What you see is exactly what you get - full and permanent access to the most comprehensive AI-driven due diligence curriculum available. We accept all major payment methods including Visa, Mastercard, and PayPal - secure, encrypted, and globally accessible. If at any point you find the course does not meet your expectations, you are covered by our 30-day satisfied or refunded guarantee. Your investment is fully protected - we remove the risk so you can focus on the reward. After enrollment, you’ll receive a confirmation email. Once your access is fully configured, your course materials and entry portal details will be sent in a separate email. This ensures seamless onboarding, regardless of your location or access requirements. This Works Even If...
- You've never led a due diligence project involving AI or machine learning systems
- Your organisation uses outdated or inconsistent assessment templates
- You’re not technically trained in data science but need to evaluate AI risks with authority
- You work in a regulated environment where audit trails and traceability are non-negotiable
- You’re expected to deliver board-level insights but lack structured frameworks to back your findings
Our participants include IT auditors, compliance officers, enterprise architects, M&A advisors, technology risk managers, and CISOs - many starting with limited AI exposure. What they all gain is a repeatable, defensible process that turns uncertainty into action. This isn’t theory. It’s operational resilience, packaged into a step-by-step system backed by real cases, tested workflows, and trusted standards.
Extensive and Detailed Course Curriculum
Module 1: Foundations of AI-Driven IT Due Diligence - Understanding the evolution of IT due diligence in the AI era
- Why traditional checklists fail in complex technology acquisitions
- Key differences between human-reviewed and AI-enhanced diligence
- Defining strategic business outcomes in technology assessment
- Mapping stakeholder expectations across legal, finance, and IT
- Role of due diligence in post-merger integration success
- Identifying high-impact risk categories in AI-powered systems
- Core principles of model transparency, explainability, and auditability
- Introducing the AI Due Diligence Maturity Scale
- Setting your personal success criteria for the course
Module 2: Strategic Frameworks for Technology Evaluation - The Five-Pillar AI Due Diligence Framework
- Aligning technical findings with business KPIs and growth objectives
- Weighting risk domains based on acquisition strategy
- Integrating SWOT analysis into technical validation
- Using the Decision Readiness Index to prioritise findings
- Developing an evidence-based scoring system for technical debt
- Creating a due diligence roadmap by phase and function
- Linking technical risks to enterprise risk management frameworks
- Incorporating environmental, social, and governance (ESG) factors
- Adapting frameworks for public sector vs private equity use cases
- Differential application across cloud-native, hybrid, and on-premise environments
- Mapping technology capabilities to market positioning and competitive advantage
Module 3: AI System Inventory and Architecture Mapping - Conducting a complete AI system census
- Distinguishing between AI, ML, automation, and analytics tools
- Documenting model versioning, training data sources, and output usage
- Reverse-engineering architecture from limited documentation
- Identifying dependencies between AI components and core systems
- Mapping data flows and inference pipelines
- Classifying AI models by risk level and business impact
- Creating interactive architecture diagrams using standard notation
- Validating model ownership and maintenance responsibilities
- Detecting shadow AI and unauthorised model deployments
- Assessing integration depth with ERP, CRM, and financial systems
- Evaluating resilience of inference infrastructure under load
- Measuring system modularity and adaptability to change
- Using metadata collection for rapid system profiling
- Applying software composition analysis to AI toolchains
Module 4: Model Risk and Governance Compliance - Understanding model risk management (MRM) in regulated industries
- Applying SR 11-7 principles to non-financial sectors
- Validating model development lifecycle documentation
- Auditing training data lineage and provenance
- Detecting data leakage and contamination risks
- Assessing feature engineering practices and stability
- Evaluating model retraining frequency and trigger mechanisms
- Reviewing model validation reports and backtesting results
- Analyzing performance decay and drift detection protocols
- Verifying model monitoring dashboards and alerting systems
- Checking for algorithmic fairness and bias mitigation controls
- Assessing adherence to internal model governance policies
- Mapping controls to NIST AI Risk Management Framework
- Reviewing ethical AI guidelines and enforcement mechanisms
- Testing explainability outputs for business interpretability
- Validating human-in-the-loop decision oversight protocols
Module 5: Data Health, Lineage, and Quality Assurance - Conducting a data trustworthiness assessment
- Mapping complete data lineage from source to AI output
- Identifying stale, synthetic, or corrupted training data
- Assessing data retention and deletion policies
- Reviewing data labelling accuracy and annotator qualifications
- Validating data preprocessing and transformation pipelines
- Checking for data skews and class imbalance issues
- Assessing data freshness and temporal relevance
- Measuring data drift using statistical techniques
- Evaluating data security and access controls
- Reviewing synthetic data generation methods and limitations
- Auditing data sharing agreements and third-party dependencies
- Identifying undocumented data sources and black-box ingestion
- Calculating data completeness and missing value rates
- Mapping data governance roles and stewardship models
- Assessing compliance with GDPR, CCPA, and similar regulations
Module 6: Security, Privacy, and Resilience of AI Systems - Conducting adversarial testing readiness reviews
- Identifying susceptibility to model inversion attacks
- Assessing defences against model poisoning and prompt injection
- Reviewing secure deployment practices for inference endpoints
- Validating API security and rate limiting configurations
- Testing input validation and sanitisation for AI interfaces
- Auditing model parameter encryption and storage practices
- Assessing workforce segregation of duties for model access
- Reviewing incident response plans for AI system failures
- Evaluating resilience to denial-of-service attacks on AI APIs
- Mapping third-party AI vendor risk exposure
- Conducting privacy impact assessments for personal data use
- Verifying data anonymisation and de-identification techniques
- Checking for compliance with ISO 27001 and SOC 2 controls
- Assessing business continuity planning for AI dependencies
- Developing fallback strategies for AI model outage scenarios
Module 7: Operational Viability and Maintainability - Assessing operational support maturity for AI models
- Reviewing documented runbooks and troubleshooting guides
- Identifying single points of failure in deployment architecture
- Measuring mean time to detect and resolve model issues
- Validating CI/CD pipelines for model updates
- Assessing rollback and version recovery capabilities
- Reviewing logging, tracing, and observability coverage
- Checking integration with existing IT service management tools
- Measuring model uptime and inference latency SLAs
- Evaluating scalability and load distribution methods
- Assessing resource consumption and cost efficiency
- Reviewing documentation completeness and accessibility
- Testing automated alerting severity and accuracy
- Identifying undocumented or tribal knowledge dependencies
- Assessing team bandwidth and skills alignment with AI stack
- Designing post-acquisition operational transition plans
Module 8: Financial and Commercial Exposure Analysis - Estimating total cost of ownership for AI systems
- Identifying hidden licensing and usage fees
- Reviewing cloud infrastructure cost optimisation
- Assessing vendor lock-in and subscription risks
- Mapping AI dependencies to revenue-generating processes
- Calculating financial exposure to model underperformance
- Valuing AI assets using intangible technology accounting principles
- Assessing third-party AI marketplace dependencies
- Reviewing commercial terms for open-source AI components
- Evaluating insurance coverage for AI failure scenarios
- Identifying budget misalignments and funding gaps
- Projecting ongoing operational and training costs
- Validating ROI claims made by model developers
- Assessing vendor financial health and sustainability
- Analyzing contract termination clauses and exit costs
- Developing value retention scenarios under different ownership models
Module 9: Compliance, Regulatory, and Legal Implications - Mapping AI systems to industry-specific regulations
- Assessing compliance with AI Act, NIST, and upcoming standards
- Identifying regulatory reporting obligations for model usage
- Reviewing audit trail completeness and immutability
- Detecting regulatory grey zones and untested applications
- Assessing liability frameworks for AI decision outcomes
- Reviewing intellectual property ownership of trained models
- Validating training data rights and licensing permissions
- Assessing export control and cross-border data flow risks
- Checking for regulatory certifications and attestations
- Mapping oversight requirements for autonomous decisions
- Preparing for regulatory inspection readiness
- Developing compliance exception registers and remediation plans
- Assessing whistleblower protection and reporting mechanisms
- Reviewing consent mechanisms for AI-based customer interactions
- Estimating potential fines and penalties for non-compliance
Module 10: Integration Readiness and Change Impact - Assessing compatibility with target organisation’s IT landscape
- Identifying integration points and data exchange protocols
- Mapping change management requirements for technical teams
- Estimating transition timelines and critical path dependencies
- Developing data migration validation checklists
- Conducting risk-based integration testing planning
- Identifying required skill transfers or training programs
- Assessing cultural readiness for AI-driven decision making
- Planning for phased decommissioning of legacy systems
- Designing integration KPIs and success metrics
- Reviewing governance model alignment across organisations
- Creating integrated incident response coordination plans
- Developing communication strategies for stakeholders
- Assessing impact on existing service level agreements
- Planning for integration testing environments and sandboxes
- Drafting pre-integration risk acceptance documentation
Module 11: Reporting, Visualisation, and Executive Communication - Structuring the executive summary for board consumption
- Developing risk heatmaps by business criticality
- Creating technical debt dashboards for leadership
- Translating AI risks into financial exposure estimates
- Designing executive-level visualisations for clarity
- Writing clear, actionable recommendations
- Using RAG status coding (Red-Amber-Green) effectively
- Aligning findings with strategic acquisition goals
- Preparing for quesmodelling sessions with technical and non-technical audiences
- Structuring appendices for auditor and regulator access
- Ensuring reproducibility and traceability of all findings
- Incorporating confidence levels in assessment statements
- Formatting reports for version control and auditability
- Developing presentation decks from assessment outputs
- Anticipating and addressing common stakeholder objections
- Using storytelling techniques to convey technical urgency
Module 12: Real-World Due Diligence Simulation - Case study: AI-powered customer service platform acquisition
- Step 1: Initial scoping and stakeholder alignment
- Step 2: System inventory and architecture analysis
- Step 3: Risk domain assessment and evidence collection
- Step 4: Model performance and data quality review
- Step 5: Security and compliance gap analysis
- Step 6: Financial exposure estimation
- Step 7: Integration feasibility determination
- Step 8: Drafting risk register with mitigation pathways
- Step 9: Creating executive summary with strategic implications
- Step 10: Presenting findings to simulated executive committee
- Reviewing assessor feedback and improvement suggestions
- Finalising due diligence package for archival
- Documenting lessons learned and process improvements
- Comparing results against industry benchmark assessments
- Receiving personalised completion insights and next steps
Module 13: Certification and Continuous Improvement - Preparing your final assessment for certification submission
- Reviewing assessment against The Art of Service AI due diligence standard
- Submitting your work for credentialing review
- Receiving expert feedback on strengths and development areas
- Understanding the Certificate of Completion requirements
- Accessing post-course resources and reference libraries
- Updating your profile on The Art of Service credential registry
- Joining the alumni network for ongoing peer learning
- Receiving alerts for major updates to frameworks and standards
- Accessing industry benchmark reports and trend analyses
- Participating in advanced practice forums
- Developing your personal due diligence playbook
- Setting goals for next-level implementation projects
- Using gamified progress tracking to maintain momentum
- Building a portfolio of assessments for career advancement
- Leveraging your certification in performance reviews and promotions
Module 1: Foundations of AI-Driven IT Due Diligence - Understanding the evolution of IT due diligence in the AI era
- Why traditional checklists fail in complex technology acquisitions
- Key differences between human-reviewed and AI-enhanced diligence
- Defining strategic business outcomes in technology assessment
- Mapping stakeholder expectations across legal, finance, and IT
- Role of due diligence in post-merger integration success
- Identifying high-impact risk categories in AI-powered systems
- Core principles of model transparency, explainability, and auditability
- Introducing the AI Due Diligence Maturity Scale
- Setting your personal success criteria for the course
Module 2: Strategic Frameworks for Technology Evaluation - The Five-Pillar AI Due Diligence Framework
- Aligning technical findings with business KPIs and growth objectives
- Weighting risk domains based on acquisition strategy
- Integrating SWOT analysis into technical validation
- Using the Decision Readiness Index to prioritise findings
- Developing an evidence-based scoring system for technical debt
- Creating a due diligence roadmap by phase and function
- Linking technical risks to enterprise risk management frameworks
- Incorporating environmental, social, and governance (ESG) factors
- Adapting frameworks for public sector vs private equity use cases
- Differential application across cloud-native, hybrid, and on-premise environments
- Mapping technology capabilities to market positioning and competitive advantage
Module 3: AI System Inventory and Architecture Mapping - Conducting a complete AI system census
- Distinguishing between AI, ML, automation, and analytics tools
- Documenting model versioning, training data sources, and output usage
- Reverse-engineering architecture from limited documentation
- Identifying dependencies between AI components and core systems
- Mapping data flows and inference pipelines
- Classifying AI models by risk level and business impact
- Creating interactive architecture diagrams using standard notation
- Validating model ownership and maintenance responsibilities
- Detecting shadow AI and unauthorised model deployments
- Assessing integration depth with ERP, CRM, and financial systems
- Evaluating resilience of inference infrastructure under load
- Measuring system modularity and adaptability to change
- Using metadata collection for rapid system profiling
- Applying software composition analysis to AI toolchains
Module 4: Model Risk and Governance Compliance - Understanding model risk management (MRM) in regulated industries
- Applying SR 11-7 principles to non-financial sectors
- Validating model development lifecycle documentation
- Auditing training data lineage and provenance
- Detecting data leakage and contamination risks
- Assessing feature engineering practices and stability
- Evaluating model retraining frequency and trigger mechanisms
- Reviewing model validation reports and backtesting results
- Analyzing performance decay and drift detection protocols
- Verifying model monitoring dashboards and alerting systems
- Checking for algorithmic fairness and bias mitigation controls
- Assessing adherence to internal model governance policies
- Mapping controls to NIST AI Risk Management Framework
- Reviewing ethical AI guidelines and enforcement mechanisms
- Testing explainability outputs for business interpretability
- Validating human-in-the-loop decision oversight protocols
Module 5: Data Health, Lineage, and Quality Assurance - Conducting a data trustworthiness assessment
- Mapping complete data lineage from source to AI output
- Identifying stale, synthetic, or corrupted training data
- Assessing data retention and deletion policies
- Reviewing data labelling accuracy and annotator qualifications
- Validating data preprocessing and transformation pipelines
- Checking for data skews and class imbalance issues
- Assessing data freshness and temporal relevance
- Measuring data drift using statistical techniques
- Evaluating data security and access controls
- Reviewing synthetic data generation methods and limitations
- Auditing data sharing agreements and third-party dependencies
- Identifying undocumented data sources and black-box ingestion
- Calculating data completeness and missing value rates
- Mapping data governance roles and stewardship models
- Assessing compliance with GDPR, CCPA, and similar regulations
Module 6: Security, Privacy, and Resilience of AI Systems - Conducting adversarial testing readiness reviews
- Identifying susceptibility to model inversion attacks
- Assessing defences against model poisoning and prompt injection
- Reviewing secure deployment practices for inference endpoints
- Validating API security and rate limiting configurations
- Testing input validation and sanitisation for AI interfaces
- Auditing model parameter encryption and storage practices
- Assessing workforce segregation of duties for model access
- Reviewing incident response plans for AI system failures
- Evaluating resilience to denial-of-service attacks on AI APIs
- Mapping third-party AI vendor risk exposure
- Conducting privacy impact assessments for personal data use
- Verifying data anonymisation and de-identification techniques
- Checking for compliance with ISO 27001 and SOC 2 controls
- Assessing business continuity planning for AI dependencies
- Developing fallback strategies for AI model outage scenarios
Module 7: Operational Viability and Maintainability - Assessing operational support maturity for AI models
- Reviewing documented runbooks and troubleshooting guides
- Identifying single points of failure in deployment architecture
- Measuring mean time to detect and resolve model issues
- Validating CI/CD pipelines for model updates
- Assessing rollback and version recovery capabilities
- Reviewing logging, tracing, and observability coverage
- Checking integration with existing IT service management tools
- Measuring model uptime and inference latency SLAs
- Evaluating scalability and load distribution methods
- Assessing resource consumption and cost efficiency
- Reviewing documentation completeness and accessibility
- Testing automated alerting severity and accuracy
- Identifying undocumented or tribal knowledge dependencies
- Assessing team bandwidth and skills alignment with AI stack
- Designing post-acquisition operational transition plans
Module 8: Financial and Commercial Exposure Analysis - Estimating total cost of ownership for AI systems
- Identifying hidden licensing and usage fees
- Reviewing cloud infrastructure cost optimisation
- Assessing vendor lock-in and subscription risks
- Mapping AI dependencies to revenue-generating processes
- Calculating financial exposure to model underperformance
- Valuing AI assets using intangible technology accounting principles
- Assessing third-party AI marketplace dependencies
- Reviewing commercial terms for open-source AI components
- Evaluating insurance coverage for AI failure scenarios
- Identifying budget misalignments and funding gaps
- Projecting ongoing operational and training costs
- Validating ROI claims made by model developers
- Assessing vendor financial health and sustainability
- Analyzing contract termination clauses and exit costs
- Developing value retention scenarios under different ownership models
Module 9: Compliance, Regulatory, and Legal Implications - Mapping AI systems to industry-specific regulations
- Assessing compliance with AI Act, NIST, and upcoming standards
- Identifying regulatory reporting obligations for model usage
- Reviewing audit trail completeness and immutability
- Detecting regulatory grey zones and untested applications
- Assessing liability frameworks for AI decision outcomes
- Reviewing intellectual property ownership of trained models
- Validating training data rights and licensing permissions
- Assessing export control and cross-border data flow risks
- Checking for regulatory certifications and attestations
- Mapping oversight requirements for autonomous decisions
- Preparing for regulatory inspection readiness
- Developing compliance exception registers and remediation plans
- Assessing whistleblower protection and reporting mechanisms
- Reviewing consent mechanisms for AI-based customer interactions
- Estimating potential fines and penalties for non-compliance
Module 10: Integration Readiness and Change Impact - Assessing compatibility with target organisation’s IT landscape
- Identifying integration points and data exchange protocols
- Mapping change management requirements for technical teams
- Estimating transition timelines and critical path dependencies
- Developing data migration validation checklists
- Conducting risk-based integration testing planning
- Identifying required skill transfers or training programs
- Assessing cultural readiness for AI-driven decision making
- Planning for phased decommissioning of legacy systems
- Designing integration KPIs and success metrics
- Reviewing governance model alignment across organisations
- Creating integrated incident response coordination plans
- Developing communication strategies for stakeholders
- Assessing impact on existing service level agreements
- Planning for integration testing environments and sandboxes
- Drafting pre-integration risk acceptance documentation
Module 11: Reporting, Visualisation, and Executive Communication - Structuring the executive summary for board consumption
- Developing risk heatmaps by business criticality
- Creating technical debt dashboards for leadership
- Translating AI risks into financial exposure estimates
- Designing executive-level visualisations for clarity
- Writing clear, actionable recommendations
- Using RAG status coding (Red-Amber-Green) effectively
- Aligning findings with strategic acquisition goals
- Preparing for quesmodelling sessions with technical and non-technical audiences
- Structuring appendices for auditor and regulator access
- Ensuring reproducibility and traceability of all findings
- Incorporating confidence levels in assessment statements
- Formatting reports for version control and auditability
- Developing presentation decks from assessment outputs
- Anticipating and addressing common stakeholder objections
- Using storytelling techniques to convey technical urgency
Module 12: Real-World Due Diligence Simulation - Case study: AI-powered customer service platform acquisition
- Step 1: Initial scoping and stakeholder alignment
- Step 2: System inventory and architecture analysis
- Step 3: Risk domain assessment and evidence collection
- Step 4: Model performance and data quality review
- Step 5: Security and compliance gap analysis
- Step 6: Financial exposure estimation
- Step 7: Integration feasibility determination
- Step 8: Drafting risk register with mitigation pathways
- Step 9: Creating executive summary with strategic implications
- Step 10: Presenting findings to simulated executive committee
- Reviewing assessor feedback and improvement suggestions
- Finalising due diligence package for archival
- Documenting lessons learned and process improvements
- Comparing results against industry benchmark assessments
- Receiving personalised completion insights and next steps
Module 13: Certification and Continuous Improvement - Preparing your final assessment for certification submission
- Reviewing assessment against The Art of Service AI due diligence standard
- Submitting your work for credentialing review
- Receiving expert feedback on strengths and development areas
- Understanding the Certificate of Completion requirements
- Accessing post-course resources and reference libraries
- Updating your profile on The Art of Service credential registry
- Joining the alumni network for ongoing peer learning
- Receiving alerts for major updates to frameworks and standards
- Accessing industry benchmark reports and trend analyses
- Participating in advanced practice forums
- Developing your personal due diligence playbook
- Setting goals for next-level implementation projects
- Using gamified progress tracking to maintain momentum
- Building a portfolio of assessments for career advancement
- Leveraging your certification in performance reviews and promotions
- The Five-Pillar AI Due Diligence Framework
- Aligning technical findings with business KPIs and growth objectives
- Weighting risk domains based on acquisition strategy
- Integrating SWOT analysis into technical validation
- Using the Decision Readiness Index to prioritise findings
- Developing an evidence-based scoring system for technical debt
- Creating a due diligence roadmap by phase and function
- Linking technical risks to enterprise risk management frameworks
- Incorporating environmental, social, and governance (ESG) factors
- Adapting frameworks for public sector vs private equity use cases
- Differential application across cloud-native, hybrid, and on-premise environments
- Mapping technology capabilities to market positioning and competitive advantage
Module 3: AI System Inventory and Architecture Mapping - Conducting a complete AI system census
- Distinguishing between AI, ML, automation, and analytics tools
- Documenting model versioning, training data sources, and output usage
- Reverse-engineering architecture from limited documentation
- Identifying dependencies between AI components and core systems
- Mapping data flows and inference pipelines
- Classifying AI models by risk level and business impact
- Creating interactive architecture diagrams using standard notation
- Validating model ownership and maintenance responsibilities
- Detecting shadow AI and unauthorised model deployments
- Assessing integration depth with ERP, CRM, and financial systems
- Evaluating resilience of inference infrastructure under load
- Measuring system modularity and adaptability to change
- Using metadata collection for rapid system profiling
- Applying software composition analysis to AI toolchains
Module 4: Model Risk and Governance Compliance - Understanding model risk management (MRM) in regulated industries
- Applying SR 11-7 principles to non-financial sectors
- Validating model development lifecycle documentation
- Auditing training data lineage and provenance
- Detecting data leakage and contamination risks
- Assessing feature engineering practices and stability
- Evaluating model retraining frequency and trigger mechanisms
- Reviewing model validation reports and backtesting results
- Analyzing performance decay and drift detection protocols
- Verifying model monitoring dashboards and alerting systems
- Checking for algorithmic fairness and bias mitigation controls
- Assessing adherence to internal model governance policies
- Mapping controls to NIST AI Risk Management Framework
- Reviewing ethical AI guidelines and enforcement mechanisms
- Testing explainability outputs for business interpretability
- Validating human-in-the-loop decision oversight protocols
Module 5: Data Health, Lineage, and Quality Assurance - Conducting a data trustworthiness assessment
- Mapping complete data lineage from source to AI output
- Identifying stale, synthetic, or corrupted training data
- Assessing data retention and deletion policies
- Reviewing data labelling accuracy and annotator qualifications
- Validating data preprocessing and transformation pipelines
- Checking for data skews and class imbalance issues
- Assessing data freshness and temporal relevance
- Measuring data drift using statistical techniques
- Evaluating data security and access controls
- Reviewing synthetic data generation methods and limitations
- Auditing data sharing agreements and third-party dependencies
- Identifying undocumented data sources and black-box ingestion
- Calculating data completeness and missing value rates
- Mapping data governance roles and stewardship models
- Assessing compliance with GDPR, CCPA, and similar regulations
Module 6: Security, Privacy, and Resilience of AI Systems - Conducting adversarial testing readiness reviews
- Identifying susceptibility to model inversion attacks
- Assessing defences against model poisoning and prompt injection
- Reviewing secure deployment practices for inference endpoints
- Validating API security and rate limiting configurations
- Testing input validation and sanitisation for AI interfaces
- Auditing model parameter encryption and storage practices
- Assessing workforce segregation of duties for model access
- Reviewing incident response plans for AI system failures
- Evaluating resilience to denial-of-service attacks on AI APIs
- Mapping third-party AI vendor risk exposure
- Conducting privacy impact assessments for personal data use
- Verifying data anonymisation and de-identification techniques
- Checking for compliance with ISO 27001 and SOC 2 controls
- Assessing business continuity planning for AI dependencies
- Developing fallback strategies for AI model outage scenarios
Module 7: Operational Viability and Maintainability - Assessing operational support maturity for AI models
- Reviewing documented runbooks and troubleshooting guides
- Identifying single points of failure in deployment architecture
- Measuring mean time to detect and resolve model issues
- Validating CI/CD pipelines for model updates
- Assessing rollback and version recovery capabilities
- Reviewing logging, tracing, and observability coverage
- Checking integration with existing IT service management tools
- Measuring model uptime and inference latency SLAs
- Evaluating scalability and load distribution methods
- Assessing resource consumption and cost efficiency
- Reviewing documentation completeness and accessibility
- Testing automated alerting severity and accuracy
- Identifying undocumented or tribal knowledge dependencies
- Assessing team bandwidth and skills alignment with AI stack
- Designing post-acquisition operational transition plans
Module 8: Financial and Commercial Exposure Analysis - Estimating total cost of ownership for AI systems
- Identifying hidden licensing and usage fees
- Reviewing cloud infrastructure cost optimisation
- Assessing vendor lock-in and subscription risks
- Mapping AI dependencies to revenue-generating processes
- Calculating financial exposure to model underperformance
- Valuing AI assets using intangible technology accounting principles
- Assessing third-party AI marketplace dependencies
- Reviewing commercial terms for open-source AI components
- Evaluating insurance coverage for AI failure scenarios
- Identifying budget misalignments and funding gaps
- Projecting ongoing operational and training costs
- Validating ROI claims made by model developers
- Assessing vendor financial health and sustainability
- Analyzing contract termination clauses and exit costs
- Developing value retention scenarios under different ownership models
Module 9: Compliance, Regulatory, and Legal Implications - Mapping AI systems to industry-specific regulations
- Assessing compliance with AI Act, NIST, and upcoming standards
- Identifying regulatory reporting obligations for model usage
- Reviewing audit trail completeness and immutability
- Detecting regulatory grey zones and untested applications
- Assessing liability frameworks for AI decision outcomes
- Reviewing intellectual property ownership of trained models
- Validating training data rights and licensing permissions
- Assessing export control and cross-border data flow risks
- Checking for regulatory certifications and attestations
- Mapping oversight requirements for autonomous decisions
- Preparing for regulatory inspection readiness
- Developing compliance exception registers and remediation plans
- Assessing whistleblower protection and reporting mechanisms
- Reviewing consent mechanisms for AI-based customer interactions
- Estimating potential fines and penalties for non-compliance
Module 10: Integration Readiness and Change Impact - Assessing compatibility with target organisation’s IT landscape
- Identifying integration points and data exchange protocols
- Mapping change management requirements for technical teams
- Estimating transition timelines and critical path dependencies
- Developing data migration validation checklists
- Conducting risk-based integration testing planning
- Identifying required skill transfers or training programs
- Assessing cultural readiness for AI-driven decision making
- Planning for phased decommissioning of legacy systems
- Designing integration KPIs and success metrics
- Reviewing governance model alignment across organisations
- Creating integrated incident response coordination plans
- Developing communication strategies for stakeholders
- Assessing impact on existing service level agreements
- Planning for integration testing environments and sandboxes
- Drafting pre-integration risk acceptance documentation
Module 11: Reporting, Visualisation, and Executive Communication - Structuring the executive summary for board consumption
- Developing risk heatmaps by business criticality
- Creating technical debt dashboards for leadership
- Translating AI risks into financial exposure estimates
- Designing executive-level visualisations for clarity
- Writing clear, actionable recommendations
- Using RAG status coding (Red-Amber-Green) effectively
- Aligning findings with strategic acquisition goals
- Preparing for quesmodelling sessions with technical and non-technical audiences
- Structuring appendices for auditor and regulator access
- Ensuring reproducibility and traceability of all findings
- Incorporating confidence levels in assessment statements
- Formatting reports for version control and auditability
- Developing presentation decks from assessment outputs
- Anticipating and addressing common stakeholder objections
- Using storytelling techniques to convey technical urgency
Module 12: Real-World Due Diligence Simulation - Case study: AI-powered customer service platform acquisition
- Step 1: Initial scoping and stakeholder alignment
- Step 2: System inventory and architecture analysis
- Step 3: Risk domain assessment and evidence collection
- Step 4: Model performance and data quality review
- Step 5: Security and compliance gap analysis
- Step 6: Financial exposure estimation
- Step 7: Integration feasibility determination
- Step 8: Drafting risk register with mitigation pathways
- Step 9: Creating executive summary with strategic implications
- Step 10: Presenting findings to simulated executive committee
- Reviewing assessor feedback and improvement suggestions
- Finalising due diligence package for archival
- Documenting lessons learned and process improvements
- Comparing results against industry benchmark assessments
- Receiving personalised completion insights and next steps
Module 13: Certification and Continuous Improvement - Preparing your final assessment for certification submission
- Reviewing assessment against The Art of Service AI due diligence standard
- Submitting your work for credentialing review
- Receiving expert feedback on strengths and development areas
- Understanding the Certificate of Completion requirements
- Accessing post-course resources and reference libraries
- Updating your profile on The Art of Service credential registry
- Joining the alumni network for ongoing peer learning
- Receiving alerts for major updates to frameworks and standards
- Accessing industry benchmark reports and trend analyses
- Participating in advanced practice forums
- Developing your personal due diligence playbook
- Setting goals for next-level implementation projects
- Using gamified progress tracking to maintain momentum
- Building a portfolio of assessments for career advancement
- Leveraging your certification in performance reviews and promotions
- Understanding model risk management (MRM) in regulated industries
- Applying SR 11-7 principles to non-financial sectors
- Validating model development lifecycle documentation
- Auditing training data lineage and provenance
- Detecting data leakage and contamination risks
- Assessing feature engineering practices and stability
- Evaluating model retraining frequency and trigger mechanisms
- Reviewing model validation reports and backtesting results
- Analyzing performance decay and drift detection protocols
- Verifying model monitoring dashboards and alerting systems
- Checking for algorithmic fairness and bias mitigation controls
- Assessing adherence to internal model governance policies
- Mapping controls to NIST AI Risk Management Framework
- Reviewing ethical AI guidelines and enforcement mechanisms
- Testing explainability outputs for business interpretability
- Validating human-in-the-loop decision oversight protocols
Module 5: Data Health, Lineage, and Quality Assurance - Conducting a data trustworthiness assessment
- Mapping complete data lineage from source to AI output
- Identifying stale, synthetic, or corrupted training data
- Assessing data retention and deletion policies
- Reviewing data labelling accuracy and annotator qualifications
- Validating data preprocessing and transformation pipelines
- Checking for data skews and class imbalance issues
- Assessing data freshness and temporal relevance
- Measuring data drift using statistical techniques
- Evaluating data security and access controls
- Reviewing synthetic data generation methods and limitations
- Auditing data sharing agreements and third-party dependencies
- Identifying undocumented data sources and black-box ingestion
- Calculating data completeness and missing value rates
- Mapping data governance roles and stewardship models
- Assessing compliance with GDPR, CCPA, and similar regulations
Module 6: Security, Privacy, and Resilience of AI Systems - Conducting adversarial testing readiness reviews
- Identifying susceptibility to model inversion attacks
- Assessing defences against model poisoning and prompt injection
- Reviewing secure deployment practices for inference endpoints
- Validating API security and rate limiting configurations
- Testing input validation and sanitisation for AI interfaces
- Auditing model parameter encryption and storage practices
- Assessing workforce segregation of duties for model access
- Reviewing incident response plans for AI system failures
- Evaluating resilience to denial-of-service attacks on AI APIs
- Mapping third-party AI vendor risk exposure
- Conducting privacy impact assessments for personal data use
- Verifying data anonymisation and de-identification techniques
- Checking for compliance with ISO 27001 and SOC 2 controls
- Assessing business continuity planning for AI dependencies
- Developing fallback strategies for AI model outage scenarios
Module 7: Operational Viability and Maintainability - Assessing operational support maturity for AI models
- Reviewing documented runbooks and troubleshooting guides
- Identifying single points of failure in deployment architecture
- Measuring mean time to detect and resolve model issues
- Validating CI/CD pipelines for model updates
- Assessing rollback and version recovery capabilities
- Reviewing logging, tracing, and observability coverage
- Checking integration with existing IT service management tools
- Measuring model uptime and inference latency SLAs
- Evaluating scalability and load distribution methods
- Assessing resource consumption and cost efficiency
- Reviewing documentation completeness and accessibility
- Testing automated alerting severity and accuracy
- Identifying undocumented or tribal knowledge dependencies
- Assessing team bandwidth and skills alignment with AI stack
- Designing post-acquisition operational transition plans
Module 8: Financial and Commercial Exposure Analysis - Estimating total cost of ownership for AI systems
- Identifying hidden licensing and usage fees
- Reviewing cloud infrastructure cost optimisation
- Assessing vendor lock-in and subscription risks
- Mapping AI dependencies to revenue-generating processes
- Calculating financial exposure to model underperformance
- Valuing AI assets using intangible technology accounting principles
- Assessing third-party AI marketplace dependencies
- Reviewing commercial terms for open-source AI components
- Evaluating insurance coverage for AI failure scenarios
- Identifying budget misalignments and funding gaps
- Projecting ongoing operational and training costs
- Validating ROI claims made by model developers
- Assessing vendor financial health and sustainability
- Analyzing contract termination clauses and exit costs
- Developing value retention scenarios under different ownership models
Module 9: Compliance, Regulatory, and Legal Implications - Mapping AI systems to industry-specific regulations
- Assessing compliance with AI Act, NIST, and upcoming standards
- Identifying regulatory reporting obligations for model usage
- Reviewing audit trail completeness and immutability
- Detecting regulatory grey zones and untested applications
- Assessing liability frameworks for AI decision outcomes
- Reviewing intellectual property ownership of trained models
- Validating training data rights and licensing permissions
- Assessing export control and cross-border data flow risks
- Checking for regulatory certifications and attestations
- Mapping oversight requirements for autonomous decisions
- Preparing for regulatory inspection readiness
- Developing compliance exception registers and remediation plans
- Assessing whistleblower protection and reporting mechanisms
- Reviewing consent mechanisms for AI-based customer interactions
- Estimating potential fines and penalties for non-compliance
Module 10: Integration Readiness and Change Impact - Assessing compatibility with target organisation’s IT landscape
- Identifying integration points and data exchange protocols
- Mapping change management requirements for technical teams
- Estimating transition timelines and critical path dependencies
- Developing data migration validation checklists
- Conducting risk-based integration testing planning
- Identifying required skill transfers or training programs
- Assessing cultural readiness for AI-driven decision making
- Planning for phased decommissioning of legacy systems
- Designing integration KPIs and success metrics
- Reviewing governance model alignment across organisations
- Creating integrated incident response coordination plans
- Developing communication strategies for stakeholders
- Assessing impact on existing service level agreements
- Planning for integration testing environments and sandboxes
- Drafting pre-integration risk acceptance documentation
Module 11: Reporting, Visualisation, and Executive Communication - Structuring the executive summary for board consumption
- Developing risk heatmaps by business criticality
- Creating technical debt dashboards for leadership
- Translating AI risks into financial exposure estimates
- Designing executive-level visualisations for clarity
- Writing clear, actionable recommendations
- Using RAG status coding (Red-Amber-Green) effectively
- Aligning findings with strategic acquisition goals
- Preparing for quesmodelling sessions with technical and non-technical audiences
- Structuring appendices for auditor and regulator access
- Ensuring reproducibility and traceability of all findings
- Incorporating confidence levels in assessment statements
- Formatting reports for version control and auditability
- Developing presentation decks from assessment outputs
- Anticipating and addressing common stakeholder objections
- Using storytelling techniques to convey technical urgency
Module 12: Real-World Due Diligence Simulation - Case study: AI-powered customer service platform acquisition
- Step 1: Initial scoping and stakeholder alignment
- Step 2: System inventory and architecture analysis
- Step 3: Risk domain assessment and evidence collection
- Step 4: Model performance and data quality review
- Step 5: Security and compliance gap analysis
- Step 6: Financial exposure estimation
- Step 7: Integration feasibility determination
- Step 8: Drafting risk register with mitigation pathways
- Step 9: Creating executive summary with strategic implications
- Step 10: Presenting findings to simulated executive committee
- Reviewing assessor feedback and improvement suggestions
- Finalising due diligence package for archival
- Documenting lessons learned and process improvements
- Comparing results against industry benchmark assessments
- Receiving personalised completion insights and next steps
Module 13: Certification and Continuous Improvement - Preparing your final assessment for certification submission
- Reviewing assessment against The Art of Service AI due diligence standard
- Submitting your work for credentialing review
- Receiving expert feedback on strengths and development areas
- Understanding the Certificate of Completion requirements
- Accessing post-course resources and reference libraries
- Updating your profile on The Art of Service credential registry
- Joining the alumni network for ongoing peer learning
- Receiving alerts for major updates to frameworks and standards
- Accessing industry benchmark reports and trend analyses
- Participating in advanced practice forums
- Developing your personal due diligence playbook
- Setting goals for next-level implementation projects
- Using gamified progress tracking to maintain momentum
- Building a portfolio of assessments for career advancement
- Leveraging your certification in performance reviews and promotions
- Conducting adversarial testing readiness reviews
- Identifying susceptibility to model inversion attacks
- Assessing defences against model poisoning and prompt injection
- Reviewing secure deployment practices for inference endpoints
- Validating API security and rate limiting configurations
- Testing input validation and sanitisation for AI interfaces
- Auditing model parameter encryption and storage practices
- Assessing workforce segregation of duties for model access
- Reviewing incident response plans for AI system failures
- Evaluating resilience to denial-of-service attacks on AI APIs
- Mapping third-party AI vendor risk exposure
- Conducting privacy impact assessments for personal data use
- Verifying data anonymisation and de-identification techniques
- Checking for compliance with ISO 27001 and SOC 2 controls
- Assessing business continuity planning for AI dependencies
- Developing fallback strategies for AI model outage scenarios
Module 7: Operational Viability and Maintainability - Assessing operational support maturity for AI models
- Reviewing documented runbooks and troubleshooting guides
- Identifying single points of failure in deployment architecture
- Measuring mean time to detect and resolve model issues
- Validating CI/CD pipelines for model updates
- Assessing rollback and version recovery capabilities
- Reviewing logging, tracing, and observability coverage
- Checking integration with existing IT service management tools
- Measuring model uptime and inference latency SLAs
- Evaluating scalability and load distribution methods
- Assessing resource consumption and cost efficiency
- Reviewing documentation completeness and accessibility
- Testing automated alerting severity and accuracy
- Identifying undocumented or tribal knowledge dependencies
- Assessing team bandwidth and skills alignment with AI stack
- Designing post-acquisition operational transition plans
Module 8: Financial and Commercial Exposure Analysis - Estimating total cost of ownership for AI systems
- Identifying hidden licensing and usage fees
- Reviewing cloud infrastructure cost optimisation
- Assessing vendor lock-in and subscription risks
- Mapping AI dependencies to revenue-generating processes
- Calculating financial exposure to model underperformance
- Valuing AI assets using intangible technology accounting principles
- Assessing third-party AI marketplace dependencies
- Reviewing commercial terms for open-source AI components
- Evaluating insurance coverage for AI failure scenarios
- Identifying budget misalignments and funding gaps
- Projecting ongoing operational and training costs
- Validating ROI claims made by model developers
- Assessing vendor financial health and sustainability
- Analyzing contract termination clauses and exit costs
- Developing value retention scenarios under different ownership models
Module 9: Compliance, Regulatory, and Legal Implications - Mapping AI systems to industry-specific regulations
- Assessing compliance with AI Act, NIST, and upcoming standards
- Identifying regulatory reporting obligations for model usage
- Reviewing audit trail completeness and immutability
- Detecting regulatory grey zones and untested applications
- Assessing liability frameworks for AI decision outcomes
- Reviewing intellectual property ownership of trained models
- Validating training data rights and licensing permissions
- Assessing export control and cross-border data flow risks
- Checking for regulatory certifications and attestations
- Mapping oversight requirements for autonomous decisions
- Preparing for regulatory inspection readiness
- Developing compliance exception registers and remediation plans
- Assessing whistleblower protection and reporting mechanisms
- Reviewing consent mechanisms for AI-based customer interactions
- Estimating potential fines and penalties for non-compliance
Module 10: Integration Readiness and Change Impact - Assessing compatibility with target organisation’s IT landscape
- Identifying integration points and data exchange protocols
- Mapping change management requirements for technical teams
- Estimating transition timelines and critical path dependencies
- Developing data migration validation checklists
- Conducting risk-based integration testing planning
- Identifying required skill transfers or training programs
- Assessing cultural readiness for AI-driven decision making
- Planning for phased decommissioning of legacy systems
- Designing integration KPIs and success metrics
- Reviewing governance model alignment across organisations
- Creating integrated incident response coordination plans
- Developing communication strategies for stakeholders
- Assessing impact on existing service level agreements
- Planning for integration testing environments and sandboxes
- Drafting pre-integration risk acceptance documentation
Module 11: Reporting, Visualisation, and Executive Communication - Structuring the executive summary for board consumption
- Developing risk heatmaps by business criticality
- Creating technical debt dashboards for leadership
- Translating AI risks into financial exposure estimates
- Designing executive-level visualisations for clarity
- Writing clear, actionable recommendations
- Using RAG status coding (Red-Amber-Green) effectively
- Aligning findings with strategic acquisition goals
- Preparing for quesmodelling sessions with technical and non-technical audiences
- Structuring appendices for auditor and regulator access
- Ensuring reproducibility and traceability of all findings
- Incorporating confidence levels in assessment statements
- Formatting reports for version control and auditability
- Developing presentation decks from assessment outputs
- Anticipating and addressing common stakeholder objections
- Using storytelling techniques to convey technical urgency
Module 12: Real-World Due Diligence Simulation - Case study: AI-powered customer service platform acquisition
- Step 1: Initial scoping and stakeholder alignment
- Step 2: System inventory and architecture analysis
- Step 3: Risk domain assessment and evidence collection
- Step 4: Model performance and data quality review
- Step 5: Security and compliance gap analysis
- Step 6: Financial exposure estimation
- Step 7: Integration feasibility determination
- Step 8: Drafting risk register with mitigation pathways
- Step 9: Creating executive summary with strategic implications
- Step 10: Presenting findings to simulated executive committee
- Reviewing assessor feedback and improvement suggestions
- Finalising due diligence package for archival
- Documenting lessons learned and process improvements
- Comparing results against industry benchmark assessments
- Receiving personalised completion insights and next steps
Module 13: Certification and Continuous Improvement - Preparing your final assessment for certification submission
- Reviewing assessment against The Art of Service AI due diligence standard
- Submitting your work for credentialing review
- Receiving expert feedback on strengths and development areas
- Understanding the Certificate of Completion requirements
- Accessing post-course resources and reference libraries
- Updating your profile on The Art of Service credential registry
- Joining the alumni network for ongoing peer learning
- Receiving alerts for major updates to frameworks and standards
- Accessing industry benchmark reports and trend analyses
- Participating in advanced practice forums
- Developing your personal due diligence playbook
- Setting goals for next-level implementation projects
- Using gamified progress tracking to maintain momentum
- Building a portfolio of assessments for career advancement
- Leveraging your certification in performance reviews and promotions
- Estimating total cost of ownership for AI systems
- Identifying hidden licensing and usage fees
- Reviewing cloud infrastructure cost optimisation
- Assessing vendor lock-in and subscription risks
- Mapping AI dependencies to revenue-generating processes
- Calculating financial exposure to model underperformance
- Valuing AI assets using intangible technology accounting principles
- Assessing third-party AI marketplace dependencies
- Reviewing commercial terms for open-source AI components
- Evaluating insurance coverage for AI failure scenarios
- Identifying budget misalignments and funding gaps
- Projecting ongoing operational and training costs
- Validating ROI claims made by model developers
- Assessing vendor financial health and sustainability
- Analyzing contract termination clauses and exit costs
- Developing value retention scenarios under different ownership models
Module 9: Compliance, Regulatory, and Legal Implications - Mapping AI systems to industry-specific regulations
- Assessing compliance with AI Act, NIST, and upcoming standards
- Identifying regulatory reporting obligations for model usage
- Reviewing audit trail completeness and immutability
- Detecting regulatory grey zones and untested applications
- Assessing liability frameworks for AI decision outcomes
- Reviewing intellectual property ownership of trained models
- Validating training data rights and licensing permissions
- Assessing export control and cross-border data flow risks
- Checking for regulatory certifications and attestations
- Mapping oversight requirements for autonomous decisions
- Preparing for regulatory inspection readiness
- Developing compliance exception registers and remediation plans
- Assessing whistleblower protection and reporting mechanisms
- Reviewing consent mechanisms for AI-based customer interactions
- Estimating potential fines and penalties for non-compliance
Module 10: Integration Readiness and Change Impact - Assessing compatibility with target organisation’s IT landscape
- Identifying integration points and data exchange protocols
- Mapping change management requirements for technical teams
- Estimating transition timelines and critical path dependencies
- Developing data migration validation checklists
- Conducting risk-based integration testing planning
- Identifying required skill transfers or training programs
- Assessing cultural readiness for AI-driven decision making
- Planning for phased decommissioning of legacy systems
- Designing integration KPIs and success metrics
- Reviewing governance model alignment across organisations
- Creating integrated incident response coordination plans
- Developing communication strategies for stakeholders
- Assessing impact on existing service level agreements
- Planning for integration testing environments and sandboxes
- Drafting pre-integration risk acceptance documentation
Module 11: Reporting, Visualisation, and Executive Communication - Structuring the executive summary for board consumption
- Developing risk heatmaps by business criticality
- Creating technical debt dashboards for leadership
- Translating AI risks into financial exposure estimates
- Designing executive-level visualisations for clarity
- Writing clear, actionable recommendations
- Using RAG status coding (Red-Amber-Green) effectively
- Aligning findings with strategic acquisition goals
- Preparing for quesmodelling sessions with technical and non-technical audiences
- Structuring appendices for auditor and regulator access
- Ensuring reproducibility and traceability of all findings
- Incorporating confidence levels in assessment statements
- Formatting reports for version control and auditability
- Developing presentation decks from assessment outputs
- Anticipating and addressing common stakeholder objections
- Using storytelling techniques to convey technical urgency
Module 12: Real-World Due Diligence Simulation - Case study: AI-powered customer service platform acquisition
- Step 1: Initial scoping and stakeholder alignment
- Step 2: System inventory and architecture analysis
- Step 3: Risk domain assessment and evidence collection
- Step 4: Model performance and data quality review
- Step 5: Security and compliance gap analysis
- Step 6: Financial exposure estimation
- Step 7: Integration feasibility determination
- Step 8: Drafting risk register with mitigation pathways
- Step 9: Creating executive summary with strategic implications
- Step 10: Presenting findings to simulated executive committee
- Reviewing assessor feedback and improvement suggestions
- Finalising due diligence package for archival
- Documenting lessons learned and process improvements
- Comparing results against industry benchmark assessments
- Receiving personalised completion insights and next steps
Module 13: Certification and Continuous Improvement - Preparing your final assessment for certification submission
- Reviewing assessment against The Art of Service AI due diligence standard
- Submitting your work for credentialing review
- Receiving expert feedback on strengths and development areas
- Understanding the Certificate of Completion requirements
- Accessing post-course resources and reference libraries
- Updating your profile on The Art of Service credential registry
- Joining the alumni network for ongoing peer learning
- Receiving alerts for major updates to frameworks and standards
- Accessing industry benchmark reports and trend analyses
- Participating in advanced practice forums
- Developing your personal due diligence playbook
- Setting goals for next-level implementation projects
- Using gamified progress tracking to maintain momentum
- Building a portfolio of assessments for career advancement
- Leveraging your certification in performance reviews and promotions
- Assessing compatibility with target organisation’s IT landscape
- Identifying integration points and data exchange protocols
- Mapping change management requirements for technical teams
- Estimating transition timelines and critical path dependencies
- Developing data migration validation checklists
- Conducting risk-based integration testing planning
- Identifying required skill transfers or training programs
- Assessing cultural readiness for AI-driven decision making
- Planning for phased decommissioning of legacy systems
- Designing integration KPIs and success metrics
- Reviewing governance model alignment across organisations
- Creating integrated incident response coordination plans
- Developing communication strategies for stakeholders
- Assessing impact on existing service level agreements
- Planning for integration testing environments and sandboxes
- Drafting pre-integration risk acceptance documentation
Module 11: Reporting, Visualisation, and Executive Communication - Structuring the executive summary for board consumption
- Developing risk heatmaps by business criticality
- Creating technical debt dashboards for leadership
- Translating AI risks into financial exposure estimates
- Designing executive-level visualisations for clarity
- Writing clear, actionable recommendations
- Using RAG status coding (Red-Amber-Green) effectively
- Aligning findings with strategic acquisition goals
- Preparing for quesmodelling sessions with technical and non-technical audiences
- Structuring appendices for auditor and regulator access
- Ensuring reproducibility and traceability of all findings
- Incorporating confidence levels in assessment statements
- Formatting reports for version control and auditability
- Developing presentation decks from assessment outputs
- Anticipating and addressing common stakeholder objections
- Using storytelling techniques to convey technical urgency
Module 12: Real-World Due Diligence Simulation - Case study: AI-powered customer service platform acquisition
- Step 1: Initial scoping and stakeholder alignment
- Step 2: System inventory and architecture analysis
- Step 3: Risk domain assessment and evidence collection
- Step 4: Model performance and data quality review
- Step 5: Security and compliance gap analysis
- Step 6: Financial exposure estimation
- Step 7: Integration feasibility determination
- Step 8: Drafting risk register with mitigation pathways
- Step 9: Creating executive summary with strategic implications
- Step 10: Presenting findings to simulated executive committee
- Reviewing assessor feedback and improvement suggestions
- Finalising due diligence package for archival
- Documenting lessons learned and process improvements
- Comparing results against industry benchmark assessments
- Receiving personalised completion insights and next steps
Module 13: Certification and Continuous Improvement - Preparing your final assessment for certification submission
- Reviewing assessment against The Art of Service AI due diligence standard
- Submitting your work for credentialing review
- Receiving expert feedback on strengths and development areas
- Understanding the Certificate of Completion requirements
- Accessing post-course resources and reference libraries
- Updating your profile on The Art of Service credential registry
- Joining the alumni network for ongoing peer learning
- Receiving alerts for major updates to frameworks and standards
- Accessing industry benchmark reports and trend analyses
- Participating in advanced practice forums
- Developing your personal due diligence playbook
- Setting goals for next-level implementation projects
- Using gamified progress tracking to maintain momentum
- Building a portfolio of assessments for career advancement
- Leveraging your certification in performance reviews and promotions
- Case study: AI-powered customer service platform acquisition
- Step 1: Initial scoping and stakeholder alignment
- Step 2: System inventory and architecture analysis
- Step 3: Risk domain assessment and evidence collection
- Step 4: Model performance and data quality review
- Step 5: Security and compliance gap analysis
- Step 6: Financial exposure estimation
- Step 7: Integration feasibility determination
- Step 8: Drafting risk register with mitigation pathways
- Step 9: Creating executive summary with strategic implications
- Step 10: Presenting findings to simulated executive committee
- Reviewing assessor feedback and improvement suggestions
- Finalising due diligence package for archival
- Documenting lessons learned and process improvements
- Comparing results against industry benchmark assessments
- Receiving personalised completion insights and next steps