Closing the AI Gap:
Turning Technical Delivery into a Revenue Engine

The pace of AI development has made technical teams faster and more adaptable than ever. What was cutting-edge a year ago is table stakes today.

The harder question is whether the business strategy is keeping pace. Technical teams are delivering while the bar for what’s achievable keeps rising. But somewhere between what’s being built and what the business expects when approving the investment, there’s a gap that’s proving harder to close.

Most organizations are quietly navigating the same tension: the gap between AI that works technically and AI that shows up on the P&L. How wide that gap is, and how fast it’s closing, looks different from one company to the next. But the decisions being made right now, on infrastructure, on data architecture, on where to consolidate and where to stay flexible, are the ones that will determine who’s ahead in two years.

SingleStore is bringing together a small group of CTOs, CIOs, and senior technical leaders to compare notes on how they’re approaching it.

What we’ll get into:

• Where the gap between AI infrastructure and business outcomes is showing up, and what’s actually closing it
• The architectural decisions that separate AI programs that scale from those that stall
• How technical leaders are building the case for transformation investment, and what’s making it land
• How organizations are rationalizing fragmented data infrastructure to move faster on AI without starting from scratch
• What “done” looks like when the business starts measuring AI on revenue terms

Come join a focused conversation with peers who are working through the same problems, at different stages, with different constraints, and with a lot of insights to share.