We've spent the last few years obsessed with the AI models themselves, but the reality of 2026 is that the model is the easiest part of the deployment process. It's essentially a commodity.
Of course, model improvements and efficiencies will continue, but when we look at why 62% of organizations are still hitting a wall trying to get into production, it isn't because the model lacks intelligence. If anything, that's the easy part. They're failing because the surrounding infrastructure and operational requirements are far more complex and dynamic.
The real friction in moving to production
The most significant challenges companies face when moving from development to production are rooted in the complexities of the enterprise environment. These hurdles often involve deep-seated issues with legacy systems that weren't designed to handle the high-velocity data requirements for modern inference. It's the unpredictable nature of live environments where data quality varies and security requirements are constantly shifting that's causing the difficulty. Many projects stall because teams underestimate the volume of custom integration work needed to make a model useful for end users.
The following are the top five challenges organizations run into outside of the model itself:
Security and compliance. This is the leading roadblock. Enterprise environments have established security protocols and data sovereignty rules that models must adhere to before they can access production data. I would argue that poor data foundations and governance gaps are the primary causes of project failure as we head into 2026.
Scalability. Systems that function during initial testing often collapse when they must support thousands of simultaneous users. The underlying infrastructure must be able to support uneven and unpredictable demand while maintaining low latency across the entire enterprise network.
Data integration. Connecting siloed data to a model is a massive technical hurdle. It's a big reason we're seeing so many announcements from data and analytics providers related to the Model Context Protocol. It's an open standard that provides a universal way to build the plumbing between LLMs and enterprise data without requiring custom code for every integration.
Change management. The human friction of using AI creates a massive barrier that requires a balance of psychological safety and job reinvention. I'm not going to give the cliché of freeing people up for strategic work. What I'm seeing is that it's more about giving people their time back to finish their to-do lists within a normal workday. When employees see that the new AI tool helps them close their laptops at the end of the workday, they start to find real ways to reinvent their own workflows.
Deployment automation. Moving a model into production requires a shift in traditional DevOps and container management. IT teams are forced to manage heavy AI containers with granular resource allocation for GPUs and specialized memory limits. It also involves orchestrating a complex web of API connections between models and vector databases, each requiring its own security handshake and latency monitoring.
The new operational front line
There's a misconception that AI is a playground for AI engineers and data scientists. However, the data tells a different story. While data scientists help develop a model, their involvement drops to 40% when it comes to managing it in production. The heavy lifting falls to IT operations and data engineers, who are involved in more than 60% of deployment and ongoing management. This shift proves that inference is an operational and infrastructure problem, not a research project.
Once you move to inference, compute components and data management systems become the priorities. This is where hardware fractures. While the media focuses on GPUs, a quarter of organizations use CPUs for their primary inference because they're matching the hardware to the cost and latency requirements of the business task.
The nondeterministic nature of LLMs means that traditional monitoring is insufficient. A server can be healthy while the model provides noncompliant or inaccurate answers. This gap is forcing IT operations and AI observability to merge into a single control plane focusing on the following functions:
Retrieval quality and grounding. This involves monitoring the accuracy and relevance of the data being fed into the model from your knowledge bases to prevent hallucinations.
Prompt and response auditing. These tasks use real-time filters that ensure personally identifiable information doesn't leak out and malicious injections don't get in.
Identity and access governance. This manages exactly what data the model is authorized to see based on the identity of the user asking the question.
Resource and cost management. This centers on monitoring the compute effectiveness of your silicon to prevent surprise overages and optimize token spend.
Now, if you read those bullets and think there are a bunch of personas involved here, you're not wrong, because this could be IT ops, SecOps, DevOps or DataOps. The lines between these departments are blurring as the enterprise moves toward a centralized AI control plane. Successful organizations are moving away from siloed responsibilities and toward integrated operations teams that can manage the intersection of infrastructure, security and data flow simultaneously.
The path forward for enterprise AI
The real win for most organizations comes from recognizing that the ideal model for a task will likely change over time.
Building a model is a high-stakes engineering feat that requires specialized talent and millions upon millions of dollars in compute to manage the iterative rounds of training and hyperparameter tuning needed for convergence. The leading companies building these foundation models are doing excellent work. The real win for most organizations comes from recognizing that the ideal model for a task will likely change over time.
Success belongs to the companies that prioritize agility and focus on the plumbing required to implement these models effectively. By working with the right partners to build robust security layers, data pipelines and AI observability, organizations can ensure that whatever model they select works at scale without crashing. This operational focus is what ultimately keeps the attention of employees, as the technology moves past the hype and starts helping them save time and money.
Omdia is a division of Informa TechTarget. Its analysts have business relationships with technology vendors.
