Generative (Gen) AI for business use is here and continues to evolve rapidly. According to PWC, “The openness to tap into the power of generative AI will likely only continue to grow with an estimated 15.7 trillion dollars of potential contribution to the global economy by 2030.”
Leaders in all industries have many questions about generative AI and how to implement it in their organizations. They want to understand actual cost and efficiency gains in various business functions, including operations, marketing and sales, IT and engineering, risk and legal, tax, and R&D. They want to understand how to turn generative AI experience and knowledge into business outcomes. They want to identify use cases to design and build applications to serve their customers and employees better. They want to understand governance, privacy, security, ethics, and responsible design.
We have seen from early generative AI adapters that it significantly boosts productivity and employee satisfaction. Expanding the same generative AI models to other uses like procurement, accounts payable, compliance, HR, and supply chain management is possible. This can significantly boost short-term profits while enabling new business models — such as ones based on hyper-personalization for every customer, large and small — that had previously been too costly to be viable.
All this sounds great, but with great power comes greater responsibility. Leaders must demonstrate ongoing governance over data and performance and show that their organizations are responsive to emerging issues around human-machine interaction, job displacement, and unintended consequences of algorithms.
Deploying private AI is one of the best ways to implement generative AI in an organization. Private AI implementation in an organization involves implementing artificial intelligence (AI) solutions while prioritizing data privacy, security, and ethical considerations. Personal AI focuses on preserving individual privacy and sensitive data while leveraging AI’s capabilities. Here’s a step-by-step guide on how to deploy private AI in your organization:
- Assess Data Privacy Requirements
- Identify the data types your organization deals with, including personally identifiable information (PII) and sensitive data
- Understand relevant data protection regulations and compliance requirements (e.g., GDPR, HIPAA) that apply to your industry and region
- Define Use Cases
- Determine specific use cases where AI can provide value without compromising data privacy. Examples could include customer insights, predictive maintenance, or anomaly detection
- Prioritize use cases that can benefit from private AI techniques like federated learning, homomorphic encryption, or differential privacy.
- Data Collection and Management
- Implement data collection practices that prioritize data minimization and anonymization. Collect only the necessary data for your AI objectives
- Establish robust data management practices, including encryption, access controls, and secure storage.
- Select Private AI Techniques
- Research and choose appropriate private AI techniques based on your use cases. Common techniques include
- Federated Learning: Training models on decentralized data without sharing raw data
- Homomorphic Encryption: Performing computations on encrypted data without decrypting it
- Differential Privacy: Adding noise to data to protect individual privacy while still extracting valuable insights
- Develop AI Models
- Train AI models using the selected private techniques. This might involve specialized tools, libraries, or frameworks that support private AI.
- Ensure that model training processes follow best practices for privacy, including secure aggregation of decentralized data in federated learning or proper encryption in homomorphic encryption.
- Model Deployment
- Deploy AI models in a secure and controlled environment, such as on-premises servers or private cloud infrastructure
- Implement appropriate access controls and authentication mechanisms to restrict model access to authorized users
- Testing and Validation
- Thoroughly test and validate the AI models to ensure they provide accurate and reliable insights while maintaining privacy
- Monitor model performance and retrain periodically as needed
- Privacy Impact Assessment
- Conduct a privacy impact assessment to evaluate potential privacy risks associated with the deployed private AI solution
- Mitigate identified risks and ensure compliance with privacy regulations
- Staff Training and Education
- Provide training to staff managing and using private AI solutions. Ensure they understand the importance of data privacy and how to handle sensitive information
- Continuous Improvement
- Stay updated with the latest advancements in private AI techniques and practices
- Continuously monitor and adapt your private AI deployment to address new challenges and ensure ongoing compliance with privacy regulations
- Transparency and Communication
- Communicate with stakeholders, including employees and customers, about your organization’s commitment to data privacy and the use of private AI technologies.
- Legal and Compliance Review
- Work closely with legal and compliance teams to ensure your private AI deployment aligns with relevant regulations and internal policies
Deploying private AI requires a comprehensive approach that involves technical expertise, legal understanding, and a solid commitment to data privacy. It’s essential to prioritize protecting individuals’ privacy while leveraging AI’s potential for valuable insights.
