WHY IS AI Strategy STUCK In FIRST Gear?

MULTIPLICITY OF COMPUTE OPTIONS, FAILED POCS, BUDGET Woes and Governance NIGHTMarès make For a TouGH Drive!

- Jatinder Singh

I remember discussing Amazon's secret 2018 trial of an AI-based recruitment tool with a group of tech journalist friends. It ultimately served as a warning sign about the risks of relying entirely on technology. Amazon developed the tool to streamline hiring but systematically discriminated against female candidates. This bias arose because the AI was trained on historical hiring data from Amazon's existing tech workforce, which is predominantly male.

Other companies faced similar problems with their integrated AI models back then. IBM Watson for Oncology gave incorrect treatment advice. Google Photos had issues with racial bias in its image recognition. Tesla's self-driving technology raised safety concerns. Of course, those were the early days of AI.

At the time, these AI projects excited us, but their rapid failures made us question whether AI could deliver results in the short term and what it would take to ensure its success. Many organizations focus on automating their processes for exceptional user experience and productivity. Still, these experiences served as a reminder that AI requires continuous bias checks, diverse training data, and strong human oversight to be genuinely effective.

While AI has made considerable progress since then, with generative AI and agentic AI opening up significant opportunities for productivity and innovation, not all those challenges have been addressed.

For instance, McDonald's recently ended its much-publicized AI voice ordering system at more than 100 drive-thru locations in the U.S. However, this service, introduced to improve customer experience and reduce wait times, could not meet the objectives. Instead, it frustrated McDonald's customers and pushed them to mock the initiative as AI could not understand their orders.