Large language models offer a wide range of use cases and can be tailored to address virtually any task. AI is increasingly integrated into web applications and SaaS products, and businesses are adopting it at a rapid pace. However, this rapid adoption has raised growing concerns about how AI is implemented and used.
The challenge is that market trends are moving so fast that it’s easy to get caught up in the hype—deploying AI without fully understanding its capabilities, limitations, or risks. In this article, we break down AI technology, discuss key considerations for adoption, and explore what it takes to run AI in a controlled environment.
AI has made significant strides, with 2025 shaping up to be a transformative year, especially in the realm of coding. Tools like Microsoft 365 Copilot are becoming essential productivity assistants, helping users brainstorm, write, and automate documentation. Businesses are already leveraging AI to reduce the time spent drafting standard operating procedures (SOPs), summarizing content, and generating code.
AI’s coding capabilities have advanced rapidly. For example, informal benchmarks have circulated claims that, eight months ago, an AI language model ranked around the millionth-best coder globally, then jumped to approximately the 10,000th-best just seven months later, and now—with versions like ChatGPT-o3-high—it’s said to rank around the 175th best. (Note: These specific rankings are based on informal benchmarks and should be viewed as anecdotal.) By the end of 2025, some experts predict that AI coding capabilities could reach or exceed human-level performance in generating near-flawless code. However, while AI can write efficient code, it won’t necessarily replace human-led design and creativity.
Beyond coding, AI agents are evolving to handle more complex tasks, such as interacting with websites, opening browser windows, performing in-depth searches, and comparing prices across platforms.
Despite rapid integration and expansion, AI still has limitations. For instance, it cannot perform physical tasks at this time. As adoption grows and capabilities improve, businesses must remain aware of both its strengths and its constraints.
There is no one-size-fits-all ROI model for AI implementation; the return on investment depends entirely on how AI is used. A company leveraging AI to automate coding tasks will measure ROI differently from one using it to draft SOPs. The value AI delivers is directly tied to the specific use case.
Many businesses risk either underutilizing AI and missing out on its potential or overinvesting and failing to see a substantial return. Given that AI evolves so rapidly, excessive customization can quickly become wasteful.
To realize real value, organizations must evaluate AI investments based on practical applications rather than hype. The goal should be to streamline workflows, reduce inefficiencies, and align spending with actual needs. Avoiding unnecessary customization is crucial, and AI adoption should be measured not only in immediate cost savings but also in terms of long-term scalability.
No AI system is completely secure. Regardless of design, risks will always exist—whether through integration points, data handling, or inherent model vulnerabilities. While private infrastructure and locally hosted language models can offer better security, they do not eliminate risks entirely.
For organizations prioritizing security, open-source models are often preferred because they allow for code inspection, security verification, and customized protections. However, security is only as strong as the weakest link—a single compromised integration can expose an entire system. Thus, AI security must be viewed as an overall strategy rather than merely a matter of tool selection.
Public AI platforms introduce additional concerns, particularly around data leaks. Businesses using third-party AI services must carefully assess how their data is processed, stored, and accessed. Although private infrastructure can mitigate some risks, it requires continuous oversight to remain effective.
The key is to maintain a balance between accessibility and security. AI should enhance efficiency without compromising data protection, and organizations must ensure that convenience does not come at the cost of security.
Running large language models requires significant computational resources. These models demand substantial memory for loading and GPU processing power that far exceeds what traditional CPUs can handle. Unlike CPUs, which rely on a limited number of high-performance cores, GPUs leverage thousands of CUDA cores to process AI workloads efficiently. Without the proper hardware infrastructure, performance bottlenecks become inevitable.
The cost of training models at scale is another major factor. Training a model like ChatGPT can cost tens of millions of dollars, making it impractical for most organizations to develop their own models from scratch. To optimize AI performance while reducing computational overhead, some companies have turned to model distillation techniques.
Choosing between open-source and closed-source models adds another layer of complexity. Open-source models offer greater transparency and flexibility but require in-house expertise to maintain and secure. In contrast, closed-source options are often more polished and easier to integrate but may have limitations on customization and raise potential data privacy concerns.
Integration complexity also varies depending on the existing technology stack. Before deploying AI, it’s important to assess:
Different use cases demand different approaches, and efficiency is critical when determining the best AI model. For example, some companies use model distillation to significantly reduce resource consumption while maintaining performance.
When choosing between open-source and closed-source models, businesses must weigh the benefits of community-driven development against the control and support offered by proprietary solutions. Open-source models provide transparency and adaptability, while closed-source options often come with more polished integrations and vendor support.
The AI landscape is shifting rapidly, with several key trends shaping its evolution:
Deploying AI into an IT infrastructure is a complex undertaking that requires a solid strategy and flawless execution. Given the ever-expanding possibilities of AI, ensuring its secure implementation cannot be overemphasized. To learn more about using AI securely or to discuss how you can integrate it into your infrastructure, please contact us.