Enterprise AI is often described as a transformative force, but too little attention is paid to what it takes to realize its promise. The opportunity is immense – trillions of dollars in potential value, new industries reshaped, and unprecedented efficiency gains. Yet with that opportunity comes tension: costs are rising, trust is fragile, and technical complexity remains daunting. The companies that succeed won’t be those chasing hype, but those that can operationalize AI at scale – rapidly, responsibly, and reliably.

At SingleStore, we’ve seen this firsthand. And as the industry enters a once-in-a-generation platform shift, the ability to accelerate – and to make AI practical at scale – matters more than ever.

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The Big Picture: A Rare Technology Inflection Point

History offers few moments like this. The rise of the internet in the 1990s and mobile in the 2000s created structural tailwinds that reshaped entire economies. AI is now at that same threshold.

But unlike consumer AI, enterprise AI faces tougher requirements. Trust and security are non-negotiable. Performance and cost must hold up under real-world scale. And integration into existing database ecosystems remains a significant hurdle. Above all, enterprises are struggling with the context problem: AI models fail to deliver value when they don’t have fast, fresh, and relevant context to ground their outputs.

Without addressing these fundamentals, AI’s potential will remain stalled at the prototype stage. We’re beginning to see that, as recent studies from Boston Consulting Group and MIT show most companies failing to scale value from their AI investments. In fact, the MIT study notes that only 5% of enterprise AI pilots reach production and have meaningful P&L impact.  

Opportunity and Challenge in Enterprise AI

This gap between potential and reality defines the enterprise AI opportunity. The promise is enormous with entirely new categories of intelligent applications. Yet, enterprises confront:

  • Fragmented ecosystems, with dozens of tools that don’t seamlessly connect.
  • Rising costs as models become larger and infrastructure more complex.
  • Slow deployment cycles that prevent AI from moving beyond pilot projects.

The challenge is clear: turn experimentation into operational reality. The organizations that succeed will be those that solve the context problem and make AI a reliable, repeatable part of how businesses run.

Building the Foundation for Enterprise AI

Closing the gap between promise and reality has been SingleStore’s focus from the start. By consolidating transactional and analytical workloads in a single, high-performance database, we’ve helped enterprises move beyond pilots and put AI into production. SingleStore has a track record of helping enterprises deliver mission-critical AI performance at scale – and that’s occurring in parallel with massive potential for nearly every industry.

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The market is taking notice, with McKinsey estimating AI could add $13 trillion to the global economy by 2030. Increasing investment in companies solving these foundational challenges signals growing confidence in the future of enterprise AI.

Our recent partnership with Vector Capital, a firm with nearly three decades of experience helping technology companies grow, is one example. Their backing support signals industry belief in SingleStore’s main mission: to enable our customers to obtain value from AI apps by delivering the performance needed for enterprise AI. But more importantly, Vector’s support gives SingleStore the resources and firepower to invest further in product innovation, expand customer success programs, strengthen partnerships and integrations, and scale globally at a faster pace.

Looking Ahead

The enterprise AI era is still young, but its trajectory is undeniable. Over the next three to five years, the industry will determine which platforms become foundational to how businesses operationalize AI.

If done right, this new era could unlock not just productivity, but entirely new categories of intelligent applications. The organizations that thrive will be those able to harness AI with trust, scale, and reliability. They’ll be able to move from mere automation to true innovation, using AI to make the impossible possible.

The Takeaway

We are at the start of a rare technology shift, one that will shape business for decades. The way forward is to establish a mature data foundation and clear governance. Without clean, trusted, easily accessible data and defined decision rights, efforts to scale AI will stall.

Instead of chasing broad “AI everywhere” ambitions, organizations should focus on specific, high-impact use cases that deliver early wins. Such wins build momentum, demonstrate value, and create the trust needed to scale.

The key is acceleration: moving from promise to practice, from pilots to production. With strong foundations and the right partnerships, enterprises can make AI real and make it last.

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