5 blindspots CIOs are missing when it comes to AI

Avoid these costly AI pitfalls: Key infrastructure, security, and performance risks CIOs must address.

Generative AI is rapidly reshaping the enterprise landscape. However, as CIOs rush to integrate AI into their organizations, several critical blind spots remain overlooked. In this article, we will explore several key areas—from understanding the difference between training and inference to managing GPU inefficiencies—that CIOs must address to build a secure, efficient, and future-proof AI infrastructure.

Five blindspots in AI to avoid

Inference versus training

One of the first challenges many CIOs face is understanding the operational differences between training and inference.

• Training involves optimizing Large Language Models (LLMs) iteraively, using methods like gradient descent and backpropagation. It requires significant computational resources, high-bandwidth memory, and often a distributed computing setup environment.
• Inference, on the other hand, refers to using these pre-trained models to generate outputs based on new input data. Although it is less compute-intensive on a per-task basis, inference in an enterprise setting—especially for applications like chatbots or virtual assistants—requires low latency, high throughput, and efficient resource usage allocation.

Misunderstanding these distinctions can lead to misallocated infrastructure investments. Due to cost constraints and resource requirements, enterprises typically focus on inference rather than training, making it imperative for CIOs to assess their needs accurately.

For a deeper dive into these differences, see “Inference vs. Training: A CIO’s Guide.”

What Is open source AI?

Open source AI represents a shift towards transparency, collaboration, and innovation in developing AI models.

• It emphasizes the use of open-licensed model weights combined with community-driven improvements.
• This approach fosters rapid innovation and ensures that organizations are not locked into proprietary systems, thereby increasing agility and vendor neutrality.

For CIOs, leveraging open source AI means accessing a broader ecosystem of tools, resources, and collaborative projects that can significantly lower the barrier to entry. Embracing open source AI can lead to more customizable, secure, and cost-effective AI solutions.

Learn more from the insights in “Open source AI: Red Hat’s point-of-view.“

Cyber threats – Everything old is new again

Cybersecurity remains a perennial concern, and the advent of AI has only amplified the stakes.

• Traditional vulnerabilities—such as adversarial attacks, alignment faking, and misconfigurations—are re-emerging in new forms as AI systems evolve.
• The unmonitored deployment of AI tools, sometimes called “shadow AI,” can expose organizations to significant risks. These include data leakage and exploiting vulnerabilities inherent in AI models.

As enterprises rapidly adopt AI-driven processes, familiar cyber threats find new ways to compromise security. CIOs must revisit legacy security challenges with an AI-aware lens and ensure robust protocols safeguard sensitive data.

For more detailed examples, refer to “Why CIOs Should Be Cautious About Storing Sensitive Data in RAG Systems and AI Models” and “Navigating Shadow AI and Technical Debt.“

Understanding benchmarks and why they will be important tools for CIOs

In the race to deploy AI at scale, benchmarks have become essential tools for guiding infrastructure decisions.

• Benchmarking suites, such as MLPerf, offer standardized metrics for measuring the performance of AI systems across training, inference, and storage.
• These benchmarks help CIOs identify inefficiencies, balance cost and performance, and ultimately make data-driven decisions about AI investments.

Without benchmarking, organizations risk overinvesting in expensive hardware or underutilizing existing resources. For CIOs, incorporating these metrics into their strategic planning can lead to more efficient and scalable AI solutions.

Explore more about the role of benchmarks in “Maximize AI Investments with Benchmarking.“

Read more: GenAI’s promise and peril: Tools, risks, and opportunities

GPU management, GPU starving, and alternatives for accelerators

GPUs (Graphics processing units) are the backbone behind many AI applications, yet their efficient management remains a significant challenge.

• Many enterprises experience “GPU starving,” where critical AI workloads are delayed or throttled due to inefficient scheduling or underutilization.
• Inadequate GPU management can lead to bottlenecks, increased latency, and a waste of substantial investments.

CIOs must explore alternative strategies, such as CPU offloading and next-generation accelerator solutions, to ensure balanced workloads and optimal performance. Efficient GPU management isn’t just about maximizing current resources—it’s also about planning for future scalability and innovation. Several providers, such as IBM’s Turbonomic, offer better tools for utilizing GPU resources.

Insights into these challenges and potential solutions are discussed in “Inefficient GPU Utilization for LLM Inference in Enterprises” and benchmark reports.

Conclusion

It becomes even more critical for the CIO to embrace AI as enterprises adopt it. The full potential of AI can be realized by understanding the distinctions between inference and training, leveraging open source AI, addressing evolving cyber threats, incorporating robust benchmarking tools, and managing GPU resources effectively.

AI’s future in enterprise environments depends on proactively addressing these blindspots. CIOs need to reassess their AI strategies, strengthen their security protocols, and optimize their infrastructure investments to remain competitive.

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