Will artificial intelligence become a product of commercial innovation?

Last year was dubbed the “AI Testing Ground,” with numerous AI-based pilot projects being tested across companies. However, there is growing disappointment with the results of these projects. For AI to achieve true innovation, it must go beyond mere technological discovery and become a practical business model. Ultimately, this means AI systems need to evolve to a level where they can reliably make critical decisions.

From the pragmatic perspective of former Intel CEO Andy Grove, we can more effectively evaluate AI’s value. Grove advocates that when measuring business outcomes, the focus should be on “output,” implying that the introduction of AI should not only stay at the activity or goal level but must lead to tangible business results.

The case of Trax Technologies offers valuable insights in this regard. This global freight management service provider successfully used AI to improve operational efficiency. Trax’s efforts to increase the proportion of business anomalies processed by AI ultimately resulted in resolving over 2 million additional anomalies. This demonstrates the practical value of AI applications and clearly shows the results that continuous innovation in the right direction can achieve.

To determine whether AI is effective, clear expectations for business outcomes must be set and used as the core measure. AI tools should deliver substantial performance as expected, and necessary adjustments should be made without hesitation. This approach is key to enabling AI to create meaningful value within enterprises.