To prepare for introducing AI in your organization, consider the following steps:

1. Define Goals: Identify problems to solve or opportunities to leverage with AI.
2. Develop a Strategy: Create a roadmap for AI initiatives, assess ROI and resource allocation.
3. Build a Team: Gather experts in AI, including data scientists and engineers.
4. Address Ethics: Consider data privacy, bias, and transparency in AI systems.
5. Assess Data: Ensure data quality and accessibility for AI applications.
6. Start Small and Scale Out: Test AI solutions with pilot projects and scale out.
7. Train Employees: Provide necessary knowledge and skills for AI technologies.
8. Monitor Performance: Continuously evaluate AI solutions and adjust as needed.
9. Encourage Innovation: Foster a culture of experimentation and collaboration.
10. Stay Updated: Keep up with AI advancements and best practices.

Read Pega's AI Manifesto for guidance on how to scale AI value responsibly.

Effective AI governance requires a comprehensive framework that balances innovation with responsibility, ensuring AI systems operate ethically while delivering business value. Pega's approach embeds governance throughout the AI lifecycle with built-in guardrails and transparency mechanisms that maintain compliance while maximizing the benefits of AI technologies. Key features include:

• Explanations API for adaptive models: Provides both local and global explanations of AI-driven decisions, allowing users to understand the reasoning behind specific predictions and overall model behavior, building trust with customers and regulators.
• Bias check for decisions and models: Employs ethical bias simulations to identify and mitigate unwanted biases in AI systems, measuring bias in protected fields like age or gender and setting compliance limits to prevent discriminatory outcomes.
• Transparency “T switch” settings: Defines transparency thresholds for different business issues in Prediction Studio, ensuring models comply with company explainability policies and allowing organizations to balance the power of complex models with the need for interpretability.
• Decision transparency: Provides stakeholders with clear visibility into the rationale behind AI-driven decisions, particularly crucial in regulated industries where explainability is a compliance requirement.
• Knowledge Buddy source citations: Ensures AI-generated responses are traceable to their sources, providing references that validate the information and enable verification of AI outputs.
• Next Best Action (NBA) empathy - prioritization of NBAs: Incorporates customer context and needs into Next-Best-Action recommendations, ensuring AI-driven suggestions are relevant, timely, and considerate of the customer's situation.
• PII filtering in GenAI architecture: Implements strong data masking capabilities that protect sensitive personally identifiable information in prompts and completions, enhancing compliance and security.
• Privacy-related features: Supports the right to be forgotten through capabilities that enable the removal of personal data, ensuring compliance with privacy regulations while maintaining system functionality.
• Continuous monitoring and reporting: Provides tools for ongoing oversight of AI models, enabling early detection of drift or performance issues to maintain accuracy and fairness over time.
• Model lifecycle management: Implements structured governance processes for model development, testing, deployment, and retirement, ensuring appropriate controls at each stage.

Pega provides a comprehensive data integration framework that connects seamlessly with diverse data sources while maintaining robust privacy controls and security measures. Key features include:

• Live Data Integration: Delivers the right data to the right process steps at the right time, managing data requests behind the scenes and adapting easily to new data sources across thousands of users without requiring custom code.
• Data Flow: Enables complex bulk, real-time and event streaming data processing by sourcing, transforming and storing data
• Data Sets: Supports multiple types including Database Table (relational databases), File (stored data), Streaming (continuous data), and Summary (aggregated stream data).
• Streaming Support - Integrates with Apache Kafka (high-throughput processing), Amazon Kinesis (large volume processing), and WebSockets (persistent connections).
• Vendor-Specific Integrations: Provides native connectors for Google BigQuery, Google Pub/Sub, and other cloud data services.
• Privacy & Security Features:
  • PII Protection: Implements data masking to protect sensitive information before calling external GenAI services
  • Role-Based Access Control: Restricts access based on user roles
  • Data Encryption: Ensures only authorized users can decrypt sensitive data
  • Regulatory Compliance: Supports GDPR, HIPAA through client-based access control