The Next Phase of AI Is Built on Strong Foundations
The next phase of AI adoption is here, and it’s being powered by a new class of tools called AI enablers. These are essential software infrastructure components that help developers create scalable, efficient, and dependable AI applications.

Recent advances from companies like Google and DeepMind show how quickly AI capabilities are evolving. Training models is now faster, more cost-effective, and far more powerful. But to move from impressive demos to real-world enterprise adoption, the focus must shift to tools that support long-term development and reliability.

Learning from the Internet Revolution
Much like the early days of the internet, when companies had to invest in basic infrastructure, today’s AI growth depends on building strong technical foundations. Back then, companies like Cisco and Oracle became essential by providing the backbone for internet services.

Now, we’re seeing the same pattern emerge with AI. While many “point solutions” are available today, the next phase of AI requires deeper investment in systems that make AI easier to build, deploy, and manage.

Three Key Areas Driving the Next Phase of AI

1. Reliable Cloud Workflows for Enterprise-Grade AI

As AI tools move into large businesses and public institutions, consistency and reliability are non-negotiable. AI applications must produce repeatable results, manage errors smoothly, and scale without breaking.

To meet these needs, developers are turning to advanced workflow patterns that provide strong guarantees. These help ensure systems stay stable, letting engineers focus on innovation instead of fixing breakdowns.

2. Efficient Resource Management Tools

Even as AI becomes more affordable, companies must still watch how they use infrastructure. Poorly integrated tools waste time, money, and computing power.

AI enablers like smart resource management systems make it easier to handle databases, storage, streaming, and caching. They allow engineers to build more efficient workflows that are better suited to the demands of AI-native applications.

3. Better DevOps for Faster AI Development

DevOps has always been key to fast, high-quality software delivery. Now, it’s just as important in AI.

New tools for automated testing, remote builds, and AI-specific development environments are speeding up the creation of production-ready systems. Platforms like CoPilot and Cursor are just the beginning. With the right DevOps setup, developers can move from ideas to working AI systems much faster.

Enabling the Future of AI
The next phase of AI isn’t just about smarter models — it’s about building the right systems to support them. As demand for AI grows, so will the need for foundational tools that enable reliable performance at scale.

Enterprises that invest in these enablers now will be better positioned to lead in the next wave of AI innovation.