More than half of UK organizations have reported "surprise" bills for monitoring IT systems, according to new research from Cisco, and AI is a key factor behind surging costs.
In a new survey from the firm, 65% of respondents said their observability costs had exceeded infrastructure or operational costs this year, with half reporting surprise costs with telemetry data, and another third saying that led to reduced data coverage or retention.
The complaint isn't just high costs, but those that are higher than expected, with 55% of those asked pointing to surprise expenses that are straining operations more than typically baseline costs.
General AI adoption costs are also exacerbating the issue, Cisco found. Another 55% said they are finding it impossible to control AI costs, a problem as AI observability has become a priority, according to nine-in-ten respondents.
That echoes a previous study by KPMG, that found many companies struggle with costs and lack visibility into AI spending.
“For today’s enterprises, the excitement of AI is clashing with the reality of the bill,” added Kamal Hathi, SVP and GM, Splunk at Cisco.
It's not just Cisco spotting this trend, either. A poll by Asana earlier this year found that eight-in-ten UK IT leaders had seen unexpected costs increases from AI over the last year, with Gartner predicting that AI costs could exceed developer salaries by 2028.
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Given the rising costs, companies began cracking down on "tokenmaxxing" and other AI overuse this summer, with Uber introducing a monthly AI cap and Accenture telling staff to cut back on AI use for basic tasks.
While monitoring AI is a clear priority, more than half of those polled said they don't have a clear business outcome tied to their AI usage, and 56% said they're unable to measure AI workload metrics such as LLM response quality, which Cisco said was a core metric.
Indeed, 85% said they need to have visibility into AI hallucinations, but fewer than six-in-ten said they're effective at spotting such concerns.
“We believe observability is the essential bridge to restoring predictability and confidence to the agentic AI era," Hathi added.
"By providing the necessary guardrails and incorporating observability into an AI tokenomics framework, leaders can transform AI from an unpredictable expense into a measurable and strategic long-term business investment."
Collaboration and teamwork
One silver lining in the confluence of challenges faced by enterprises lies in heightened teamwork and collaboration, according to Cisco. More than half (55%) of those polled are worried about the unintended consequences of AI agents, prompting closer synergy between departments.
Eight-in-ten IT professionals have observed a marked increase in collaboration between observability and security teams as a result.
Nearly three-quarters (73%) also revealed they can accurately trace performance issues to security root causes, an increase from 65% in the survey last year. Similarly, there’s been a marked increase in data sharing between these two teams from 74% to 84% over the last year, with a similar leap seen in working together on troubleshooting.
"Most companies will soon have far more agents at work than people," said Jeetu Patel, president and chief product officer (CPO) at Cisco.
"Managing agents at scale requires reimagining both security and observability. Have our agents been compromised? Are they doing their job well? In the world of agentic AI, these questions are inseparable, and they're moving at machine speed."