McKinsey’s latest State of AI report dropped some uncomfortable numbers. Nearly nine in ten organizations now use AI regularly. Adoption is up. Investment is flowing. Everyone’s talking about their AI initiatives.
While high adoption is evident, McKinsey’s findings also underscore that the path to value depends on six drivers: strategy, talent, operating model, technology, data, and scaling/adoption. Recognizing these dimensions sets the stage for understanding why so many still struggle to move from pilots to production.
But here’s what the data actually shows: while 88% of companies have AI running somewhere, only about a third have moved beyond pilots. When you look at who’s capturing meaningful value (defined as more than 5% EBIT impact), you’re down to just 6% of respondents.
That’s not an adoption problem. That’s an infrastructure problem. Infrastructure is the bedrock, but the journey also demands reimagining workflows, fostering leadership commitment, and tightening governance. SmythOS was built with this holistic approach in mind, enabling reliability, governance, and operational best practices at scale. And it’s exactly why the SmythOS Runtime Environment exists: to bridge the gap between impressive demos and production systems that actually work.
The Demo-to-Production Graveyard
According to Gartner, 30% of generative AI projects won’t survive 2025. Companies build proof-of-concepts that work beautifully in demos, then everything falls apart in production. The agent can’t handle real-world data. It can’t scale. Security won’t approve it. It doesn’t integrate with existing systems.
This cycle of failed productionization often occurs when organizations treat AI as an isolated tool, rather than embedding it deeply into core business processes. Without integrating AI into the operational fabric, benefits remain isolated or fade after a demo.
The McKinsey data confirms this pattern. Larger organizations with revenues above $5 billion are nearly twice as likely to reach the scaling phase compared to companies under $100 million. That gap isn’t about having better AI models. It’s about having infrastructure that works. Development frameworks like LangChain or Zapier are excellent for prototypes, but they lack the durability, compliance, and observability required for production environments. Organizations encounter scaling issues, fragmented governance, and integration headaches when they push pilot tools beyond their intended scope.
What the 6% Do Differently
Organizations that are actually winning with AI share three key characteristics. First, they think bigger. High performers are 3.6 times more likely to say they’re using AI for transformative change rather than incremental improvements. They’re not automating a few tasks. They’re reimagining entire workflows.
Second, they redesign those workflows. 55% of high performers fundamentally redesign their workflows to incorporate AI, compared to just 20% of other organizations. They rebuild processes around what AI can actually do.
Third, they invest in real infrastructure. More than one-third of high performers allocate over 20% of their digital budgets to AI technologies, nearly five times the rate of other organizations. They understand that production AI requires production-grade systems.
These organizations stand out not just for their infrastructure investment, but also for their ability to tightly link technology with strategy, agile processes, and leadership buy-in. This is the interplay that McKinsey highlights as essential for real value.
The Agent Opportunity
AI agents represent the next phase. McKinsey found that 62% of organizations are experimenting with agentic AI, systems that autonomously plan and execute multi-step workflows. The most common deployments are in IT and knowledge management, functions like service desk automation and deep research.
But even among organizations scaling agents, most are only doing so in one or two functions. Anthropic’s research indicates that building reliable agents necessitates meticulous attention to workflow design, tool selection, and oversight. That’s where infrastructure becomes the bottleneck.
Why Infrastructure Is Everything
Frameworks like LangChain are excellent for prototypes. Workflow tools like Zapier make automation accessible. But none of these are true production runtimes. None provides the kernel-level guarantees enterprises need: security, memory persistence, orchestration reliability, and cross-platform deployment.
The companies capturing real value have figured this out. They’ve built or adopted infrastructure that provides true multi-environment deployment, enterprise-grade security, durable state management, and real interoperability.
The SmythOS Runtime Environment was built specifically to solve this. It provides production-grade orchestration to remove fragility, security to enforce governance, memory to guarantee durability, and interoperability to avoid lock-in.
What This Means for Your Organization
The McKinsey data paints a clear picture. Most organizations are stuck in pilot purgatory because they’re trying to run production workloads on development infrastructure. Gartner projects that by 2028, over 75% of organizations will be running AI workloads in production. The question isn’t whether your organization will use AI agents. The question is whether you’ll have the infrastructure to deploy them reliably.
The 6% who are winning understand this. They’re not just buying AI tools. They’re building AI-ready infrastructure. They’re redesigning workflows. They’re investing seriously in foundational capabilities that enable AI to deliver on its full potential. That’s how you escape the 95% graveyard.
SmythOS bridges this gap. Developers gain powerful infrastructure through our comprehensive SDK. Non-developers access the same capabilities through our Visual Studio platform. Teams requiring complete control can deploy on their own infrastructure.
Don’t let your agents stall in proof-of-concept limbo. Give them the durability, governance, and portability they need.Star the SmythOS repo to follow updates and contribute to the open agent ecosystem. Join our Discord, where over 20,000 developers are building production agents daily.