Generative AI (GenAI) is poised to revolutionize various industries, enhancing productivity and driving revenue growth. A recent report by Deloitte indicates that 80% of enterprises with advanced AI capabilities are planning to increase their investments in AI. However, the journey to harnessing GenAI is fraught with complexities that organizations must navigate.
Challenges of the Dominant AI Development Models
To embark on the GenAI journey, companies must modernize their legacy systems and address data-related challenges. Unfortunately, many organizations, including those on the Fortune 500 list, continue to rely on outdated technology that is ill-equipped to handle the demands of AI workloads. Transitioning from these legacy systems can be costly and disruptive, leading to significant downtime.
Moreover, businesses are eager to see a return on their investments, with nearly half of US-based generative AI decision-makers expecting their AI investments to yield results within three years. This aggressive timeline often overlooks the inherent complexities involved in AI implementation.
While cloud computing offers scalability and rapid deployment, the reality of cost efficiency can be elusive. Spending in cloud environments is often decentralized and challenging to manage. Many enterprises have adopted financial operations frameworks, known as FinOps, to better control their expenditures.
Additionally, concerns surrounding third-party cloud services—such as data sovereignty, regulatory compliance, and the risk of sensitive information leaks during AI model training—are prompting businesses to consider on-premises and hybrid solutions. Deloitte predicts that by 2025, approximately half of enterprises will incorporate on-premises models for AI development, highlighting a shift towards safeguarding intellectual property and adhering to compliance regulations.
How To Choose the Right Database for GenAI
Despite the challenges, achieving AI innovation is attainable, particularly when selecting the right platform. For many organizations, adopting a robust and versatile database solution like PostgreSQL can be pivotal. Here are four compelling reasons why PostgreSQL is an excellent choice for GenAI development:
- Extensibility and vector search support: PostgreSQL’s support for various native data types, including vectors, is essential for AI applications. This capability allows businesses to prototype and iterate models without overhauling their database infrastructure, reducing costs and accelerating development timelines.
- Seamless integration: Leveraging PostgreSQL enables gradual modernization without the need to migrate to an entirely new database. Organizations can continue utilizing familiar SQL tools while exploring new AI capabilities, minimizing disruption.
- Open source advantages: The community-driven nature of PostgreSQL ensures continuous improvement and free accessibility. The absence of licensing fees positively impacts ROI, and organizations can opt for enhanced offerings like Percona for PostgreSQL, which provides tailored support for enterprise needs.
- Freedom of deployment: With PostgreSQL and tools like Percona Everest, businesses can deploy AI projects in the most suitable environment—whether on-premises, in a private cloud, or through a hybrid approach—thus mitigating risks associated with data exfiltration.
AI Success Factors
Insights from customers reveal three critical factors for a successful GenAI strategy: develop quickly, fail inexpensively, and scale effectively. Percona can assist organizations in achieving these objectives, paving the way for successful AI initiatives.
