State of AI Engineering 2026: From Experimentation to Production

What 800+ worldwide developers and engineering leaders reveal about the state of AI engineering

AI is moving quickly from individual experimentation into production applications, workflows and agents. But adoption alone doesn’t tell us whether organizations are ready to operate AI reliably, securely and at scale.

Join Progress and SlashData experts as we unpack the findings from our new State of AI Engineering 2026 research, based on a survey of 800+ experienced developers, architects and engineering leaders.

We’ll explore what separates organizations that are still experimenting with AI from those building repeatable, production-ready AI engineering practices, and what that means for architecture, evaluation, governance, economics and the future role of developers.

What We’ll Discuss

• From pilots to production: Progress in scaling AI across internal applications, customer-facing software and business workflows.
• What mature AI engineering looks like: Practices spanning architecture, data, evaluation, monitoring, governance and cost management.
• Agents, autonomy and control: Whether governance and safeguards match agents’ growing responsibilities.
• The economics of AI engineering: Tracking costs, assessing ROI and managing operational trade-offs at scale.
• How engineering roles are changing: Where developers expect to spend more time as AI embeds across the software development lifecycle.

Benchmark Your Organization

We’ll also introduce the AI Engineering Maturity Index and show how organizations can benchmark their own practices against the research.

Join the Conversation

Hear what the research tells us about the current state of AI engineering, where the biggest gaps remain and what engineering leaders should be thinking about as AI moves deeper into production.