MarTech: How to stop overpaying for AI complexity

This article was written by Greg Kihlström for MarTech. Read the full article here.

You wouldn’t purchase a forklift to carry a coffee cup. Yet that’s effectively what happens when enterprises apply complex AI systems to tasks that don’t need it. As AI adoption increases, it is essential to understand the costs of running these systems and their impact on existing people and processes.

The initial promises of what an AI system can do often don’t account for long-term costs, the human oversight required, and the total cost of ownership. Those, along with potential failure points when a highly automated system breaks down or misses a step, are often glossed over in the excitement of adding a system to the stack. In most marketing organizations, that decision is made by a different person or team than the one managing procurement.

Definitions matter when there are four potential approaches

Nearly every AI feature on your list of priorities uses one of four mechanisms, which, ordered from simplest to most elaborate, are: rule-based, predictive, generative, and agentic. Briefly exploring each will quickly identify where they diverge.

Rule-based is if-this-then-that logic someone wrote by hand. It costs almost nothing to run and produces the same output every time. Hand it a case the author never anticipated, and it fails in the open, so you find out on the spot.

This article was written by Greg Kihlström for MarTech. Read the full article here.