Complete AI Squared Onboarding Plan for Enterprises

Goals

• Focus AI Squared efforts on areas with the highest potential return and strategic value.
• Ensure chosen use case is well-defined and achievable with AI Squared's AI Activation platform.

Deliverables

• List of potential AI Squared use cases with evaluation scores.
• Prioritized primary AI Squared use case selected for onboarding.
• Detailed description of the selected AI Squared use case.

Goals

• Ensure consistency and compliance with overarching enterprise AI principles and architecture when using AI Squared.
• Leverage existing AI infrastructure or platforms if applicable for connecting models to AI Squared.

Deliverables

• Statement of alignment with enterprise AI strategy and governance for AI Squared.
• List of applicable AI policies and standards for AI Squared usage.
• Engagement plan with AI governance committees regarding AI Squared.

Goals

• Enable objective evaluation of the AI models connected through AI Squared and the implemented solution.
• Provide a basis for ongoing performance monitoring and benefits realization from AI Squared deployments.

Deliverables

• Documented set of AI model performance KPIs and target thresholds (for models connected to AI Squared).
• Documented set of business outcome KPIs linked to the AI Squared solution.
• Baseline measurements for current performance (pre-AI Squared).

Goals

• Ensure dedicated resources with necessary AI Squared-related expertise and clear accountability.
• Promote interdisciplinary collaboration for AI Squared deployment.

Deliverables

• AI Squared project team roster with specialized skills identified.
• Completed RACI matrix for AI Squared project tasks.

Goals

• Build trust and transparency around the AI Squared initiative.
• Manage stakeholder expectations regarding AI Squared capabilities and impact effectively.
• Address potential AI Squared-related anxieties proactively.

Deliverables

• AI Squared stakeholder communication matrix.
• Communication plan including channels for discussing AI ethics and impact related to AI Squared.
• Templates for AI Squared project updates.

Goals

• Gather comprehensive information on AI Squared's capabilities and practices.
• Assess transparency and commitment to responsible AI delivery through AI Squared.

Deliverables

• Due Diligence questionnaire section dedicated to AI Squared's model specifics, data governance, and ethical AI support.
• Questions on AI Squared's MLOps/AIOps practices for connected services.

Goals

• Validate AI model performance and business value when integrated via AI Squared in the enterprise context.
• Understand technical requirements and challenges for AI Squared integration and operation.
• Reduce implementation risk for the AI Squared solution.

Deliverables

• AI Squared PoC plan document with clear objectives and metrics.
• PoC environment setup with enterprise data and models connected to AI Squared.
• AI Squared PoC execution report with quantitative performance results, qualitative findings, and go/no-go recommendation for AI Squared based on the PoC.

Goals

• Ensure AI Squared and its usage adhere to enterprise standards for responsible AI.
• Mitigate ethical, reputational, and regulatory risks associated with using AI Squared.

Deliverables

• Ethical AI due diligence report for AI Squared.
• Assessment of AI Squared's XAI support capabilities for connected models.
• Comparison against enterprise ethical AI checklist/framework for AI Squared usage.

Goals

• Ensure AI Squared's AI model connection and operational practices are secure and well-governed.
• Protect enterprise data used with or generated by services connected via AI Squared.
• Understand how AI Squared manages the lifecycle of connected AI model configurations.

Deliverables

• AI Squared model integration governance and security assessment report.
• Data handling review for AI Squared, confirming compliance with enterprise data security and privacy policies.
• Understanding of AI Squared's model configuration update and maintenance processes.

Goals

• Ensure contractual commitments for AI Squared service quality and performance.
• Provide recourse if AI Squared service fails to meet agreed standards.

Deliverables

• AI Squared-specific SLA addendum in the contract.
• Defined metrics and reporting for SLA monitoring of AI Squared.
• Agreed remedies for AI Squared SLA violations.

Goals

• Protect enterprise intellectual property and data assets when using AI Squared.
• Ensure clarity on ownership and usage rights related to AI Squared configurations, Data Apps, and model outputs.

Deliverables

• Contract clauses clearly defining data usage rights, IP ownership for AI Squared components, and data confidentiality for AI processing via AI Squared.
• Policy on AI Squared's use of enterprise data for platform improvement agreed and documented.

Goals

• Provide high-quality, correctly formatted data to AI models via AI Squared for optimal performance.
• Automate data preparation for AI models using AI Squared where possible.
• Ensure data governance is applied to AI data pipelines connected through AI Squared.

Deliverables

• Data pipeline architecture diagrams (ETL/ELT) for AI Squared integration.
• Data validation and quality check specifications for AI Squared inputs.
• Security design for data pipelines feeding AI Squared.
• Specifications for data transformation logic within AI Squared input schemas.

Goals

• Optimize data for AI model consumption via AI Squared and make AI outputs usable by enterprise systems and users via Data Apps.
• Encapsulate AI-specific data manipulation logic within AI Squared configurations.

Deliverables

• Developed and unit-tested pre-processing configurations in AI Squared.
• Developed and unit-tested post-processing configurations in AI Squared.
• Documentation for pre/post-processing logic applied by AI Squared.

Goals

• Prevent service disruptions due to exceeding AI model API limits when called via AI Squared.
• Optimize AI model service usage via AI Squared to manage costs effectively.
• Ensure resilience against temporary AI model API unavailability when accessed through AI Squared.

Deliverables

• Strategy for managing AI model API rate limits and quotas when using AI Squared.
• Caching design for AI responses within AI Squared (if applicable).
• Design for monitoring AI model API call volume and associated costs via AI Squared.
• Retry mechanisms with backoff for transient AI model API errors handled by AI Squared.

Goals

• Automate the flow of high-quality data to connected AI models via AI Squared.
• Ensure AI Squared data schemas are reliable and maintainable.

Deliverables

• Deployed data input/output schemas in AI Squared.
• Schema execution logs and monitoring dashboards (if available in AI Squared).
• Data quality validation scripts for schema outputs in AI Squared.

Goals

• Create fault-tolerant integrations with AI models via AI Squared.
• Effectively manage the nuances of AI model API responses using AI Squared's schema definitions.

Deliverables

• Source code for AI Squared model connector configurations.
• Comprehensive error handling for various AI model service responses within AI Squared.
• Unit tests covering different AI model API scenarios as handled by AI Squared.

Goals

• Ensure reproducibility and control over which AI model versions are used by applications via AI Squared.
• Facilitate rollback to previous model versions configured in AI Squared if needed.

Deliverables

• Strategy for managing AI model endpoint configurations in AI Squared via CI/CD.
• Pipeline steps for deploying applications with specific AI Squared model configurations.

Goals

• Catch AI Squared integration issues early in the development cycle.
• Ensure changes in the application or AI Squared service don't break the integration.

Deliverables

• Automated AI Squared integration test suite.
• Pipeline stage for running AI Squared integration tests.
• Test reports for AI Squared integration points.

Goals

• Ensure connected AI models meet performance SLAs when accessed via AI Squared.
• Control AI operational costs for models used with AI Squared and avoid budget surprises.
• Detect service outages or degradations of connected models quickly through AI Squared monitoring.

Deliverables

• Dashboards for AI model API performance and cost (related to AI Squared usage).
• Alerts for SLA breaches or budget thresholds being approached for models connected via AI Squared.

Goals

• Detect if the connected AI model's performance is degrading or if its outputs (via AI Squared) are becoming less reliable/accurate over time.
• Provide early warnings for potential issues requiring model retraining/fine-tuning or vendor intervention, based on AI Squared feedback.

Deliverables

• Basic dashboard for tracking key AI output quality indicators from AI Squared.
• Process for collecting and reviewing user feedback on AI outputs via AI Squared Data Apps.
• Alerts for significant deviations in AI output patterns (if feasible) based on AI Squared monitoring.

Goals

• Ensure appropriate security controls are applied based on data sensitivity when using AI Squared.
• Comply with data privacy regulations for AI data processing via AI Squared.

Deliverables

• AI data classification matrix for data used with AI Squared.
• Data protection requirements for each data type used with AI Squared.
• Guidelines for data anonymization/pseudonymization for AI if needed before using with AI Squared.

Goals

• Protect the integrity, availability, and confidentiality of the AI models and their outputs when accessed via AI Squared.
• Reduce vulnerability to AI-specific attacks for systems using AI Squared.

Deliverables

• AI model security risk assessment report for models used with AI Squared.
• AI Squared's statement on adversarial attack mitigation and model security for connected services.
• Internal guidelines for secure interaction with AI models via AI Squared.

Goals

• Prevent unauthorized API access to AI Squared and connected models, and ensure least privilege for API clients.

Deliverables

• AI Squared and connected model API authentication/authorization configured and tested.
• Documentation of API access policies for AI Squared.

Goals

• Protect AI models and backend systems from malicious inputs when data is passed via AI Squared.
• Ensure data integrity for AI processing through AI Squared.

Deliverables

• Input validation rules and routines implemented/configured in AI Squared.
• Security testing for input validation mechanisms in AI Squared.

Goals

• Mitigate risks associated with purely automated decision-making supported by AI Squared.
• Ensure data subject rights are upheld in AI contexts involving AI Squared.

Deliverables

• Assessment of automated decision-making impact in AI-DPIA for AI Squared.
• Defined processes for human oversight or appeal if applicable for decisions based on AI Squared insights.

Goals

• Identify and mitigate unfair biases in AI-driven decisions supported by AI Squared.
• Promote equitable outcomes from AI systems using AI Squared.

Deliverables

• AI fairness and bias assessment report for models connected via AI Squared.
• Results of internal bias testing (if performed) for models used with AI Squared.
• Plan for mitigating identified biases (e.g., data augmentation, model adjustments in consultation with vendor, post-processing of AI Squared outputs).

Goals

• Enhance trust and understanding of AI systems connected via AI Squared.
• Facilitate debugging, auditing, and compliance with regulations requiring explanations for AI decisions, for insights delivered by AI Squared.

Deliverables

• Assessment of vendor's XAI capabilities and model transparency as surfaced by AI Squared.
• Internal guidelines on using and communicating AI explanations from AI Squared Data Apps.
• Plan for leveraging XAI features in relevant workflows using AI Squared.

Goals

• Ensure accurate forecasting of all AI Squared-related expenditures.
• Understand the cost structure of the AI Squared service and connected models in detail.

Deliverables

• Detailed AI Squared cost breakdown worksheet.
• Model for projecting AI operational expenses based on AI Squared usage drivers.
• Comparison of different AI Squared pricing models if applicable.

Goals

• Create effective and intuitive human-AI partnerships using AI Squared Data Apps.
• Ensure human oversight and control in AI-assisted processes involving AI Squared.
• Maximize the combined intelligence of humans and AI with AI Squared tools.

Deliverables

• Documented human-AI collaboration workflows for AI Squared Data Apps.
• Guidelines for interpreting and acting on AI recommendations from AI Squared.
• Processes for escalating AI errors or problematic outputs from AI Squared Data Apps.

Goals

• Equip employees with the foundational knowledge and practical skills to work effectively with AI Squared.
• Promote responsible and ethical use of AI Squared tools.

Deliverables

• AI literacy training materials.
• Role-based AI Squared tool training modules and job aids.
• LMS content for AI Squared training.

Goals

• Build employee trust and reduce anxiety associated with AI Squared adoption.
• Foster a positive and realistic outlook on AI in the workplace with AI Squared.

Deliverables

• Communication materials addressing common AI Squared concerns.
• FAQ documents about AI Squared impact.
• Plan for employee engagement and feedback sessions regarding AI Squared.

Goals

• Ensure AI outputs via AI Squared Data Apps are valuable and usable by end-users in their daily workflows.
• Test human-AI interaction design within AI Squared Data Apps.

Deliverables

• UAT test scripts focused on AI output quality and usability from AI Squared Data Apps.
• Criteria for evaluating AI-assisted task completion using AI Squared.

Goals

• Identify when the connected AI model may no longer be performing optimally due to changes in underlying data patterns, using AI Squared feedback as an indicator.
• Trigger investigation or requests for model updates from the vendor based on AI Squared monitoring.

Deliverables

• Drift detection monitoring implemented (e.g., tracking statistical properties of inputs/outputs, analyzing AI Squared feedback trends).
• Alerts for significant model drift configured based on AI Squared data.

Goals

• Maintain a high standard of ethical AI practice throughout the lifecycle of the AI Squared solution.
• Proactively identify and address any ethical issues that may arise over time with AI Squared usage.

Deliverables

• Ethical AI audit schedule and methodology for AI Squared solutions.
• Periodic fairness and bias assessment reports for AI Squared, incorporating user feedback.
• Action plans for remediating any identified ethical concerns related to AI Squared.