As the CEO of Arango, Shekhar Iyer leads the company’s mission to make enterprise AI contextual, scalable and trusted.
As organizations shift from AI pilots to production, I’ve observed that many leaders are focusing on metrics that are relatively easy to measure, such as GPU utilization, token consumption and infrastructure spend. But in my experience working with enterprise AI and data leaders, there are hidden costs that need to be measured and monitored closely.
The hidden cost of agentic AI is the engineering effort required to continuously rebuild real-time business context across fragmented systems. The urgency is growing quickly. According to Gartner, “only 17% of organizations have deployed AI agents to date, yet more than 60% expect to do so within the next two years.”
In light of that, it’s no surprise that in my conversations with enterprise AI and data leaders, many are grappling with these questions at their organizations:
• “Why are our AI costs rising faster than business value?”
