The second generation of AI has arrived. If your organization has not defined its AI strategy by now, it risks falling behind in a pivotal industry shift. Emerging multimodal models, large language models (LLMs), and retrieval-augmented generation (RAG) systems offer significant opportunities—and raise important questions about whether these technologies should be deployed privately.
In the past, organizations adopted public, fully managed databases (e.g., AWS RDS, Oracle Cloud) because of their convenience and rapid setup. Over time, many realized that private, self-managed databases provided stronger data control and advanced customization. A similar pattern is unfolding with AI.
As AI becomes central to staying ahead in the market, public AI often fails to capture enterprise-specific nuances. By leveraging private AI, organizations can train models on proprietary data while maintaining comprehensive oversight of performance, costs, and security. This control fosters long-term differentiation that is difficult for competitors to replicate.
Because data is a core asset, using public AI for mission-critical processes may expose proprietary information or intellectual property. Private AI retains data in-house, enabling stronger governance for regulatory compliance (GDPR, CCPA, HIPAA) and ensuring exclusive ownership of insights.
Public AI services can still be highly effective for low-risk tasks—such as producing marketing copy or summarizing public information—or for prototyping new ideas. However, reliance on multi-tenant environments introduces the possibility of performance variability, lack of deep customization, and escalating costs.
For highly regulated sectors or use cases involving unique data, private AI becomes essential. Equally important is adopting a hybrid model, using public AI for rapid experimentation and private AI for core, proprietary workflows that demand stringent security, scalability, and compliance. As regulations like the EU AI Act gain momentum, private AI can position organizations to meet forthcoming legal requirements by offering better control over data governance, model accountability, and auditability.
To begin, assess your data and identify which assets require the highest level of protection or customization. Next, build or partner with AI/LLMOps expertise, then pilot a specific use case—such as a private chatbot or supply chain optimization model—to prove value before scaling. Choose an infrastructure (on-premises or private cloud) that aligns with security and integration needs, and establish robust governance to ensure explainability and compliance.
Ultimately, AI is now a critical factor in enterprise success. Public AI solutions can rapidly jump-start exploration, but private AI typically delivers the secure, tailored implementations necessary for lasting competitive advantage. Much like the database era, companies that own and protect their data infrastructure are poised to reap the greatest rewards. Balancing public and private AI is thus a strategic choice that provides flexibility, control, and the potential for sustained innovation in an increasingly AI-driven world.
