Real Barriers to Corporate AI Adoption
One of the biggest challenges companies face when adopting AI today is security concerns. Many office workers are experiencing incredible productivity gains by personally using AI tools like ChatGPT and Claude, but they find themselves unable to use these same tools at work due to corporate security policies.
What's particularly interesting is that AI usage policies vary not just between companies, but even between departments within the same organization. Companies designated as national security facilities are collaborating with Meta to deploy language models like Llama in local environments. Large corporations are downloading local LLMs to build their own chatbots or implement AI features. Meanwhile, in areas not directly related to security—like HR departments using AI chatbots for leadership coaching—some companies are connecting external APIs.
Employee A's Dilemma - A Real-World Case Study
I recently met an office worker whose experience perfectly illustrates this reality. This person had been personally using various AI tools, starting with ChatGPT's paid version and expanding to Claude, Gemini, and others. One day, when their team leader urgently requested a report based on research findings, they went home and used AI to quickly produce a high-quality report. The team leader's reaction was overwhelmingly positive, and similar tasks kept coming their way.
However, two problems emerged over time. First, the security burden of having to take company work home. Second, the unfairness of spending personal money on company tasks. Eventually, they stopped using AI, but then the team leader expressed dissatisfaction, asking "Why can't you perform like before?"
This dilemma is likely to intensify. Current college students and graduate students are naturally using AI in their studies. When they join companies as new hires next year and ask, "How do we work without AI?" how should we respond?
AI Doesn't Replace Applications
This brings us to an important insight: AI never replaces single-function applications. No matter how good AI tools become, they won't completely replace MS PowerPoint or Word. Instead,AI fills the gaps between applications.
For example, there are connection gaps between MS Word and Teams, between Teams and company announcement systems, and between ERP systems and other business systems. AI fills precisely these gaps, providing connectivity between systems.
From this perspective, individuals need to break down their work to leverage AI, while companies need to identify gaps between systems from an application architecture standpoint and deploy AI accordingly.
The Emergence of Intelligent Workflows
What changes when all applications are connected through AI?Intelligent workflowsbecome possible. Until now, it was impossible to see how company operations flow from a single system. Information that was previously managed separately by each department can now be connected through AI, allowing real-time visibility into the entire company's flow, like blood vessels in the human body.
This isn't just about getting interesting answers—it's about enabling executives to understand the overall company flow and make informed decisions. While inventory management, logistics, production, and HR were previously viewed separately, AI now enables integrated management by connecting all these elements.
Real-World Agent AI Use Cases
A complex task processing example using Claude demonstrates the potential of agent AI. When given the complex requirement to "find Southeast Asian steel production partners with ISO quality certification, revenue over $150 million, experience trading with Korean companies, and no negative management issues," the AI performed the following steps:
1. Search for Southeast Asian steel production partners
2. Additional manufacturing company search and verification
3. Confirm revenue over $150 million requirement
4. Cross-check trading history with Korean companies
5. Re-verify performance records
6. Search news sites for negative issues
7. Provide comprehensive company list after analysis
8. Visualize in dashboard format
9. Add strengths and weaknesses analysis
What would take humans days to complete, AI accomplished in minutes. More importantly, this functionality is available as open source. Companies can download such sources and integrate them with their legacy systems.
Changes in Corporate Application Architecture
These changes are also transforming how companies manage applications. Previously, applications were independent entities, but now how to connect and scale them has become more important. The number of APIs an application provides has become a crucial decision-making factor for companies.
Consider a company operating 100% on cloud services. If Office 365, Workday, Salesforce, and various SaaS services all provide APIs, connecting them to generate insights only requires ideas. However, existing on-premises systems don't provide APIs, requiring development teams to create new ones.
Mutual Growth of Individuals and Companies
To survive in the AI era at an individual level, you must understand the context around your work. People who can grasp work context and identify areas for innovation will definitely survive. Similarly, companies must create environments where AI can serve as the linkage between applications.
We've developed for nearly 100 years focusing only on our assigned tasks within hierarchical structures. We've worked with the fixed mindset that you need to be promoted to see the bigger picture, making it difficult to adapt to the AI era. But now we need three-dimensional work understanding rather than fragmented tasks.
Paradigm Shift from Industrialization to the AI Era
Interestingly, this change aligns with historical patterns. In the past, one person handled the entire process of making traditional Korean paste. Industrialization brought division of labor, fragmenting work so that some people only made brushes while others only made paste. Now, with AI, one person can again handle the entire process.
AI steps back to view the whole picture. It approaches work with the perspective of "I can handle this entire task," so employees' working methods must also shift toward being more comprehensive and understanding the context of surrounding tasks.
Conclusion
The corporate security dilemma in the AI era isn't just a technical problem—it demands a fundamental change in how we work. Individuals must develop the ability to understand work context and leverage AI, while companies must enhance system connectivity to build intelligent workflows.
The future is always right in front of us. We never fail to prepare because we don't know what the future holds. The changes we're experiencing right now are exactly what the future looks like, and we've already entered an era where both individuals and companies must adapt to survive..