AI adoption in business has reached an inflection point — enterprises’ AI success is increasingly measured by real-world production results rather than pilot projects. Yet many companies find that moving from pilot to production is fraught with challenges that can result in delays, cost overruns, and outright dead-ends.

Technical integration proves complex, costs often exceed projections, and security vulnerabilities create legitimate concerns. Without a realistic deployment strategy, AI initiatives that showed early promise can become resource drains instead of competitive drivers.

One recent global survey found that only 1 in 4 companies successfully implemented AI projects in production in the previous 12 months, with 20% reporting significant delays during deployment.

Deploying and scaling transformational AI requires substantial groundwork that many companies underestimate. This article examines five of the most significant deployment obstacles and how companies can navigate them.

Costs: AI can get expensive fast — API-based service fees mount up, while private deployments require significant upfront investment. Smart budgeting is a must.
Security and compliance: Regulations are tightening, and AI systems handling sensitive data need bulletproof safeguards to stay compliant.
Performance and scale: Capacity issues and computing bottlenecks can cripple performance without the correct optimizations.
Integration: AI’s efficacy is limited if it can’t talk to your systems. Smart use of data connectors and retrieval-augmented generation (RAG) help avoid AI silos.
Customization: Off-the-shelf AI may not cut it for complex needs. Companies must fine-tune models with high-quality data and expert tweaks.

Now, let’s examine these challenges in more detail.

Consider a hybrid AI approach to manage costs

The financial burden of AI deployment can rise fast and take companies by surprise. One recent survey of business leaders found that 15% of AI projects have been put on hold, and 21% failed to scale due to cost concerns.

While many organizations begin with API-based AI services, attracted by their low initial costs, the pay-per-query model can lead to budget-breaking monthly bills as usage expands. The alternative — a private AI deployment through on-premises or virtual private cloud (VPC) solutions — demands sizable upfront investments in hardware and the scarce talent needed to manage it.

In the long run, private deployments can help the bottom line because companies pay a fixed cost instead of a variable per-token cost. That also means more predictability for IT budgets. Plus, private deployments have the added benefit of safeguarding sensitive data, giving companies more control over their data.

Many firms are embracing a hybrid approach — hosting private AI models in on-premises data centers, VPCs or using a combination of them for certain workloads while leveraging cloud managed or public APIs for others. Some 72% of executives agree that hybrid cloud solutions will be essential for scaling AI and managing the cost of compute.

For example, a large bank might use its on-prem resources to handle routine tasks like fraud detection and customer-facing AI assistance, and deploy its cloud resources during periods of high demand or market volatility.

Organizations can also reduce costs by deploying automation to scale up or down computing power depending on demand or usage patterns. Monitoring AI expenditures through regular financial reviews can also help companies spot inefficiencies and refine their approach for cost-effectiveness.

Boosting security with private deployments

Ensuring that AI deployment doesn’t result in leaks of sensitive data is a critical concern for executives — especially in industries like healthcare and banking where highly sensitive personal data is subject to strict regulation. Rules governing how companies can store, process, and use personal data have proliferated, from Europe’s GDPR data protection law to America’s HIPAA Patient Privacy Act and the Basel III rules affecting financial firms.

Private AI deployments alleviate many of these security concerns by ring-fencing data from the outside world and giving organizations complete control over their AI infrastructure. Customizing and serving models in a private environment avoids the risk of sensitive or proprietary data leaking as it avoids passing data through the public internet. Another security advantage is the ability to implement strong access controls because the environment isn’t shared by third parties.

To understand how private deployments can insulate organizations from data risks, consider this finding in a recent cybersecurity report: 38% of survey respondents said they have shared sensitive work information with AI tools without their employer’s knowledge.

Private deployment does come with higher up-front costs and a greater need for technical expertise, either internally or with strong AI partners. Leaders must carefully assess their security needs against other factors such as cost, scalability, and convenience before deciding on a private deployment.

Cloud-based services, either exclusively or as part of a hybrid solution, could still be an acceptable strategy for many organizations — especially for smaller companies or those that use AI primarily for tasks that rely on less sensitive data.

Optimizing performance and scaling

As enterprises move into production with complex workflows and more data-intensive operations, they can quickly run into bottlenecks that degrade performance and limit their ability to scale.

With API-based solutions, performance problems are apt to intensify because companies quickly run into limits with the processing power needed for their AI systems. Capacity constraints can also arise when these services become overloaded with requests, slowing response times and increasing error rates. While private deployments allow for more direct control over AI compute resources, enterprises still need to ensure their infrastructure can handle rising workloads consistently and cost-effectively.

For example, Cohere’s model containers are readily deployable in Kubernetes clusters, enabling organizations to integrate AI workloads into highly scalable architectures.

In addition to deployment types, companies should also consider model-level optimization techniques such as:

Distillation: Transfers knowledge from a large, complex model (the “teacher”) to a smaller, simpler model (the “student”), allowing the smaller model to approximate the performance of the larger one while being more efficient.
Quantization: Reduces numerical precision in calculations to lower memory usage and computational costs. Many weights and activations within models can be represented with lower precision without much impact on accuracy.
Model pruning: Removes redundant or less significant parameters to improve latency and lower hardware loads without compromising performance.
Load balancing: Distributes AI workloads efficiently across multiple model instances to prevent congestion.

Integrating systems and processes

The power of AI gets diluted when it can’t access the data it needs — a common challenge in organizations with large amounts of data stored across different systems and formats.

Developing robust connector frameworks allows AI models to draw data from applications such as CRM systems, ERP platforms, and analytics tools.

Retrieval-augmented generation (RAG) is a powerful way to integrate and leverage existing systems and data. RAG systems can retrieve information from structured and unstructured data sources grounded in company knowledge bases, ensuring more accurate, relevant, and up-to-date responses. Think of it as giving your AI direct access to your company’s brain.

Agentic AI advances this concept by enabling retrieval systems to operate as agents — autonomously selecting, sequencing, and executing tools to accomplish more complex goals. AI systems are empowered not just to retrieve information, but to plan, collaborate, and execute tasks across systems. Platforms like North enable enterprises to securely deploy and manage interconnected AI agents, accelerating integration and orchestrating sophisticated workflows across internal systems.

These systems enhance retrieval accuracy, foster autonomous behavior, and unlock the ability to use external tools and APIs dynamically, making AI integration not just smarter but more capable.

Faster paths to custom AI

Off-the-shelf AI models rarely meet the demands of real-world business users. Model customization is essential, but it’s also a resource-intensive process that can add friction, delay deployment, and increase costs.

Depending on the complexity of the use case, data availability, and compute resources, model customization can take days or months — with costs ranging from tens of thousands to millions of dollars.

To move quickly and cost-effectively, it’s critical to start with a clear evaluation strategy: What level of accuracy is needed? Where can you compromise? What’s the return on deeper investment?

Four common approaches to model customization are:

Parameter-efficient fine-tuning: Adjusts a small subset of model parameters for fast, low-cost optimization.
Full fine-tuning: Updates the entire model to meet highly specialized needs.
Custom post-training: Adds proprietary data to the post-training datasets after initial pre-training to boost relevance and performance.
Custom pre-training: Builds a model from the ground up by including expanded language or domain-specific unstructured datasets.

Keep in mind that customization can involve more than tuning a model — it can also include application, infrastructure, or connector customization. And it should be treated as an ongoing — rather than a “one-and-done” — process.

Looking ahead

Rather than reacting to cost overruns, security concerns, or integration challenges as they arise, successful organizations build AI deployment plans with flexibility, scalability, and compliance baked in from the start.

A few key approaches can help:

Prevent cost surprises: Implementing dynamic scaling strategies, using a hybrid deployment model, and conducting regular financial audits can keep budgets under control as AI adoption expands.
Bake in security and compliance: Prioritizing private deployment, encryption, and continuous monitoring ensures AI systems remain secure and adaptable to evolving regulations.
Build with scalability in mind: Leveraging containerized models, load balancing, and AI optimization techniques can prevent performance bottlenecks.
Target seamless integration: Strong integration strategies together with RAG and agentic AI solutions enable AI to assimilate into existing enterprise systems rather than becoming a siloed tool.
Invest in custom AI: Tailoring models with proprietary data and leveraging synthetic data for continuous improvement preserves AI’s accuracy and value over time. It’s vital to choose a true AI partner for this, not just a vendor, and to go beyond off-the-shelf AI models.

By embedding these strategies into their AI roadmap, businesses can accelerate deployment, minimize setbacks, and drive real-world impact.

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Michael Hao