The Hidden Cost Of Agentic AI? Misallocated Effort

InnovationThe Hidden Cost Of Agentic AI? Misallocated EffortByShekhar Iyer,Forbes Councils Member.for Forbes Technology CouncilCOUNCIL POSTExpertise from Forbes Councils members, operated under license. Opinions expressed are those of the author. | Membership (fee-based)Jun 01, 2026, 10:15am EDTAs the CEO of Arango, Shekhar Iyer leads the company’s mission to make enterprise AI contextual, scalable and trusted. gettyAs 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?”• “Why are highly skilled teams spending more time stitching together systems than improving outcomes?” • “Why do AI pilots that looked promising suddenly become difficult and expensive to scale?” In my view, AI success ultimately depends on solving data and context architecture challenges. Until that changes, many teams risk being trapped—spending too much time and money on data plumbing rather than building features that generate business value. Why Infrastructure Costs Are Only Part Of The EquationWhen it comes to AI implementation, compute costs are only part of the equation. AI infrastructure costs are visible. AI tokens are akin to a taxi meter. When AI systems generate more...