Concerns about an impending fourth AI winter are rising as doubts emerge over whether artificial intelligence will deliver enough practical benefits to justify its high costs. Recent reports from Goldman Sachs and other research bodies highlight this skepticism. However, a solution has been present for some time—engineered intelligence, a concept emphasizing the practical application of AI through engineering principles.

The Missing Link in AI Development

Traditionally, scientific breakthroughs in fields like chemistry and physics are first made in laboratories and then transferred to engineers to develop real-world applications. This process ensures that discoveries are turned into practical solutions. However, AI lacks a similar transition mechanism. Instead of a dedicated discipline for applied AI, organizations often hire data scientists—primarily researchers—to work on developing practical AI solutions. This mismatch has contributed to a significant failure rate, with 87% of AI projects not reaching successful outcomes.

What is Engineered Intelligence?

Engineered intelligence, or intelligence engineering, is emerging as a new field focusing on applying AI research in practical settings, much like how chemical or mechanical engineers utilize scientific discoveries. This discipline allows experts, scientists, and engineers to develop intelligent solutions without needing to become data scientists. By reestablishing research-to-engineering pipelines and forming partnerships with academic institutions and technology vendors, industrial organizations are setting the stage for engineered intelligence. This approach mirrors how breakthroughs in other scientific areas are handed off to specialized engineers.

The Benefits of Intelligence Engineering

With intelligence engineering, AI research can be transformed into breakthrough applications, yielding tangible value and producing outcomes that might not have been identified by data scientists alone. The process facilitates creating value-driven AI solutions that are feasible and safe for production use, contributing to meaningful advancements across various industries.

Implementing Intelligence Engineering: A Five-Step Approach

To introduce intelligence engineering into an organization, practical experience is crucial. Here’s a five-step guide that differs from traditional AI implementation methods:

1. Map Existing Expertise: Identify areas of expertise within current organizational processes.
2. Evaluate Expertise Value: Assess which expertise is most valuable and determine its scarcity or abundance.
3. Prioritize Expertise: Focus on the top five areas of valuable and scarce expertise.
4. Analyze for Feasibility and ROI: Examine these areas for potential return on investment, feasibility, cost, and timeline.
5. Invest in Execution: Select a subset of these valuable use cases and proceed with development.

A New Wave of AI-Driven Value Creation

By adopting intelligence engineering, organizations can expand their capabilities, moving beyond existing expertise to identify new opportunities. This approach enables safe and practical value creation, both within organizations and across broader ecosystems. As more industries and educational institutions develop programs focused on engineered intelligence, the resulting innovations will unlock unrealized economic and societal benefits, paving the way for new job categories and a surge in value creation.

Brian Evergreen, author of Autonomous Transformation: Creating a More Human Future in the Era of Artificial Intelligence, and Kence Anderson, author of Designing Autonomous AI, both emphasize the potential of engineered intelligence to redefine AI’s impact in practical, everyday applications.