AI Field Faces New Challenges, Enterprises Need Transformation to "Intelligent Engineering" for Response

As confidence in the field of artificial intelligence (AI) wavers and concerns arise about its ability to generate sufficient practical value to justify its high cost, the industry is on the brink of the fourth AI winter. Despite recent reports from research institutions such as Goldman Sachs, there is still an opportunity to prevent this trend, and the solution has been in front of us for many years.

Reflecting on the High Failure Rate of AI Projects

Currently, breakthroughs in the AI field often remain at the research level and lack a clear path for direct application. Many organizations tend to hire data scientists in the hope that they can develop real-world solutions while making scientific breakthroughs. However, this approach leads to 87% of AI projects ending in failure. The problem lies in the fact that data scientists typically pursue scientific breakthroughs rather than optimizing for practical applications.

"Intelligent Engineering" as a New Direction

In this context, "intelligent engineering" has emerged as a new discipline, focusing on transforming AI research achievements into secure and practical value. Intelligent engineering combines scientific breakthroughs with engineering practices, allowing domain experts, scientists, and engineers to create intelligent solutions without needing to become data scientists. Leading industrial companies are rebuilding the pipeline for translating research into engineering, establishing new partnerships with academia and technology vendors to create a favorable environment for the transfer of AI research achievements to intelligent engineers.

Five Steps to Introduce Intelligent Engineering

To introduce intelligent engineering in an organization, the following five steps, which differ from traditional AI application methods, can be taken:

• Map the expertise heat map in the existing processes: Clearly identify the distribution of expertise in the organization.
• Evaluate and score the value and scarcity of expertise: Determine which expertise is most important and scarce for the organization.
• Select the top five areas of expertise with the highest value: Prioritize the development of intelligent engineering in these directions.
• Analyze return on investment, feasibility, cost, and timeline: Provide data support for the formulation of intelligent solutions.
• Select and execute high-value use cases: Concentrate resources on advancing practical projects.

Opening a New Era of AI Value

As intelligent engineering is deeply applied in organizations, this new capability not only enhances existing expertise but also explores new opportunities for value creation. By building an education and training system for intelligent engineering, companies, individuals, and even the entire society will enjoy the untapped economic and social potential of AI, driving the birth of new professions and a new wave of value creation.