A recent Reuters story about Target caught my eye. They are deepening their AI adoption while also carefully reviewing costs as vendors shift toward usage-based and token-based pricing. I believe this story perfectly illustrates the next phase of enterprise AI.
The first wave was all about adoption: tools, pilots, productivity experiments, and early workflow automation. But the next wave is different. It’s about moving from merely “using AI” to truly “running on AI.”
The reality is that most organizations are not there yet. A Capgemini study found that only 2% of organizations have deployed AI agents at scale, with another 12% deploying them at partial scale. Despite the market buzz around agentic AI, most companies are still in the initial adoption phase.
When AI becomes a core part of daily workflows, cost is no longer just a procurement detail—it becomes a central architectural and management question. This forces leaders to ask tough questions:
- Which processes justify the use of expensive frontier models?
- Where are smaller, more efficient models sufficient?
- How do we control usage and costs without stifling innovation?
- How do we measure the value generated, not just the activity?
This is the transition point where enterprise AI gets real, moving from hype to strategic execution.