Survey Surfaces Benefits of Applying AI to FinOps

A survey of 200 enterprise IT decision-makers published this week found organizations that have infused artificial intelligence (AI) into financial operations (FinOps) workflows to reduce IT costs are 53% more likely to report cost savings of more than 20%.

Conducted by the market research firm Foundry on behalf of Tangoe, a provider of tools for managing IT and telecommunications expenses, the survey found organizations that embraced FinOps without any AI capabilities averaged less than 10% in cost savings.

The top three drivers for adopting FinOps/cloud cost management programs are the need to increase cloud resource production and performance (70%), reduce budgets (60%) and rising costs (58%), and simpler overall program management (50%), the survey found. Major benefits included productivity savings (46%), cost savings (43%) and reduced security risks (43%). Nearly two-thirds of respondents cited service utilization and right-sizing of services as another reason to embrace FinOps.

FinOps describes a methodology for embedding programmatic controls within DevOps workflows to reduce costs. In the face of increased economic headwinds, IT leaders are looking to reduce cloud computing costs, but it’s turning out to be more challenging than many of them anticipated. Cloud infrastructure is typically provisioned by developers using infrastructure-as-code (IaC) tools with little to no supervision. The reason for this is developers have long argued that waiting for an IT team to provision cloud infrastructure took too long. Developers would be more productive if they just provisioned cloud infrastructure themselves.

However, after ten years of cloud computing, it’s become apparent there are a lot of wasted cloud infrastructure resources. Developers who don’t pay the monthly bills for cloud services tend to view available infrastructure resources as essentially infinite. It’s usually not until someone from the finance department starts raising cost concerns that developers even become aware there might be an issue.

The challenge is that adopting FinOps best practices is not quite as easy as it might seem. In fact, more than half (54%) of survey respondents cited challenges in building the right process and human support systems for FinOps into workflows that have been in place for years.

Chris Ortbals, chief product officer for Tangoe, said the simplest path to FinOps is to rely on a software-as-a-service (SaaS) platform designed from the ground up to leverage AI to help IT teams manage cloud computing and telecommunications expenses both before and after applications are deployed. Each DevOps team will ultimately need to determine how much they will implement metrics to foster more efficient consumption of cloud computing resources. The more aware of those costs DevOps teams are, the more likely that better decisions about what types of workloads should be run where and, just as importantly in the age of the cloud, at what time, given all the pricing options provided.

Developers, of course, tend to jealously guard their prerogatives. Convincing them to give up their ability to provision cloud infrastructure on demand is going to be a challenge, at least until someone makes it plain how much all those cloud instances wind up costing the organization each and every month.