Introducing Engineered Intelligence: Engineering the Future of AI
As the hype around artificial intelligence (AI) continues to grow, so does the skepticism regarding its actual value and return on investment. Many organizations are investing in AI projects, only to see them fail, resulting in wasted resources and disappointment. However, there is a solution that has been overlooked for years, and it lies in the concept of engineered intelligence.
Engineered intelligence, also known as intelligence engineering, is a discipline focused on the real-world application of AI research through engineering principles. While breakthroughs in other scientific disciplines are seamlessly transitioned into practical solutions by engineers, AI has been lacking this clear handoff. Instead, organizations rely on data scientists, who were trained to make scientific breakthroughs in AI research, to engineer real-world solutions. This mismatch leads to the failure of 87% of AI projects.
To address this issue, leading industrial organizations are beginning to establish research-to-engineering pipelines, partnering with academia and technology vendors, and creating the necessary ecosystem for AI research to be handed off to intelligence engineers. This shift allows for breakthrough applications with tangible use cases that create value and would not have been discovered by data scientists or technology vendors alone.
Implementing intelligence engineering in your organization can be achieved through five practical steps:
1. Assess Existing Expertise: Evaluate the expertise available within your organization and create a heatmap to identify areas of strength and scarcity.
2. Identify Valuable Expertise: Determine which expertise areas are most valuable to your organization and score their abundance or scarcity.
3. Select Top Expertise Areas: Choose the top five most valuable and scarce expertise areas within your organization to focus on.
4. Analyze for ROI: Analyze the potential return on investment, feasibility, cost, and timeline for engineering intelligent solutions in these selected areas.
5. Invest in Execution: Choose a subset of the identified value cases and invest in executing the intelligent solutions.
By following these steps, organizations can leverage intelligence engineering to create new waves of value with AI. Once the initial intuitive applications are developed and put into production, this capability can be extended to explore new opportunities for engineering practical value across the organization and the wider ecosystem.
The introduction of engineered intelligence not only benefits organizations but also has larger societal implications. As programs for engineered intelligence are built in organizations, industries, and educational institutions, the economic and societal potential of AI can be realized. This leads to the creation of new jobs and a new wave of value creation for individuals and society as a whole.
In conclusion, engineered intelligence is the key to avoiding another AI winter and unlocking the true value of AI. By establishing the discipline of intelligence engineering and leveraging existing expertise, organizations can engineer practical solutions that generate tangible value. This shift requires a change in the traditional approach to introducing AI and a focus on the real-world application of AI research. As organizations embrace intelligence engineering, they pave the way for a more human future in the era of artificial intelligence.
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