COMMENTARY: The real story about Agentic AI is not about what it can do; it is about what it costs to make it useful. Enterprises are struggling with too many Agentic AI projects that look brilliant on paper but are just collapsing under the weight of costs and poor adoption. Blair Sammons is right when he says that the hidden trap isn’t the tech itself, it’s the lack of change management. You can’t automate chaos. If employees don’t adapt and leaders don’t link outcomes to business value, you’re just paying more to move the same problems around faster.
When generative AI first entered the enterprise space, many CIOs felt comfortable. Workloads were compute-heavy but predictable. A model was prompted, tokens were consumed, and a charge appeared on the bill. The equation wasn’t simple, but it was understandable.
Agentic AI is different. Instead of one-off prompts, agents plan, reason, and call other agents. They generate long chains of activity that are more difficult to monitor and price, and that unpredictability is proving costly. Analysts project that more than 40% of agentic AI projects will be abandoned by 2027 because expenses rise faster than business value.
The bottom line: Agentic AI is multiplying costs, and even well-designed projects will struggle to deliver returns without disciplined change management.
Where hidden costs appear
Agentic AI introduces technical pressures that earlier generations of generative models never faced. When multiple agents collaborate, even minor missteps can cascade into thousands of wasted actions. Each back-and-forth exchange inflates API calls, drives up cloud costs, and slows performance. Speed itself has become a budget line item, with organizations now paying premiums to trim mere seconds off response times.
Each of these issues carries a real price tag, and together they explain why analysts expect a large share of agentic projects to be abandoned before reaching maturity. As serious as these technical traps are, the biggest cost driver isn’t in the code — it’s in change management.
Many organizations deploy agent-driven systems without preparing employees, linking projects to revenue goals, or defining clear adoption targets. The result: efficiency stalls while costs continue to climb.
For example, a company may launch agent-based workflows for IT service requests, but without training or clear incentives, employees continue submitting tickets manually. Meanwhile, agents keep running in the background, performing low-value tasks that quietly inflate compute costs.
It’s not a technical failure so much as an operational one — and it drains budgets faster than any software problem.
How enterprises can ensure AI innovation pays off
The cost traps tied to agentic AI are real, but they’re not inevitable. Enterprises can stay ahead by choosing partners who bring structure, accountability, and financial discipline to adoption, helping ensure innovation translates into measurable business value.
1. Leverage expert partnerships
Most organizations that achieve meaningful results with AI adoption don’t go it alone. According to MIT’s
State of AI in Business 2025 report, external partnerships yield significantly higher success rates for AI builds (67% vs. 33% internal). That reliance reflects how complex agentic systems have become — and how quickly costs can spiral without oversight.
Working with an experienced partner gives enterprises access to governance frameworks that keep projects aligned with business goals. These partners bring proven experience in cost attribution and performance monitoring, areas still developing inside many enterprises. Just as important, they can help determine when an agent is providing meaningful value and when it should be scaled back.
By partnering with experts, enterprises avoid much of the trial and error that slows adoption. Projects move forward with greater financial discipline, and AI investments have a stronger chance of translating into measurable outcomes.
2. Link projects to revenue and ROI
Traditional FinOps disciplines were built to track infrastructure costs like storage, compute hours, and bandwidth. With agentic AI, that lens is too narrow. The real measure is attribution: Was the task worth what the provider charged?
By working with partners who specialize in AI economics, enterprises can build this connection — treating every efficiency gain as a financial outcome, not just a technical win.
Consider a support agent whose workload is reduced by three hours each week. On paper, that looks like time saved. In practice, enterprises should push to quantify the dollar value of those hours and show how the freed capacity contributes to revenue growth, higher customer retention, or improved margins. That requires benchmarking people’s costs and translating them into terms executives recognize.
When projects are tied to these business metrics, they hold up under scrutiny. When they’re not, they risk being dismissed as side experiments. Enterprises that work with partners to expand FinOps from cost accounting to value attribution will deliver more defensible business cases that keep agent-driven initiatives funded.
3. Prioritize change management
Enterprises can prevent wasted investment by putting change management at the center of every deployment. Instead of doing a quick rollout and a training video, staff need structured programs that take them from awareness to proficiency. Leaders must also define adoption goals that can be checked at regular intervals.
Strong governance forces executives to explain how agentic AI connects to core business priorities, not just technical novelty. Each deployment should be tied to a specific problem and a measurable improvement; otherwise, agents simply run in the background and consume resources without moving the business forward.
The companies that succeed are the ones that measure adoption, keep managers accountable for usage, and pace new projects so that people and processes evolve alongside the technology.
Build a sustainable future for agentic AI
Agentic AI is reshaping how enterprises interact with cloud infrastructure, but it comes at a cost. Constant agent exchanges, repeated task cycles, and the rising price of speed are straining budgets in unpredictable ways. While these technical issues demand attention, the bigger threat is poor transformation planning.
Enterprises that treat adoption as a cultural transformation — not just a tech upgrade — will capture lasting value. Without disciplined change management, organizations risk burning resources without making real progress.
Going forward, the organizations and partners that prioritize both cost containment and operational readiness will set the standard for sustainable, scalable agentic AI.
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