Enterprises flocked to public cloud infrastructure with the promise of speed, flexibility and efficiency.
But many are now grappling with a different reality: Cloud environments are more complex to manage than anticipated, and the associated costs have spiraled far beyond early projections.
Enterprise IT leaders haven’t been shy about sharing this fact. Last year, 63% of enterprise IT leaders responding to an IDC survey said they actively evaluate hybrid or multi-cloud strategies to regain control over cost and performance. In 2023, a report from Andreessen Horowitz cited examples of companies repatriating workloads to on-premises data centers amid questions about whether the cloud was still worth the cost.
Looking ahead, however, the proliferation of agentic AI could represent a turning point. That is, if enterprises approach it correctly.
The Problem Isn’t the Cloud — It’s the Complexity
The infrastructure challenges dragging down cloud ROI aren’t just about cost per compute hour or storage. They stem from the effort required to translate infrastructure goals into action.
Writing infrastructure code, orchestrating dev and test environments, enforcing governance standards, troubleshooting errors and decommissioning unused resources are all labor-intensive processes. Even teams that have invested in infrastructure-as-code (IaC) and automation often struggle to move fast enough to justify the cost of cloud infrastructure.
When we talk with our customers, we typically see these challenges materialize in two ways: lost productivity and inflated cloud costs. Delays to deliver cloud environments extend timelines, resulting in higher labor costs and slower time to market, while wasted cloud costs leave less budget for other technologies.
Agentic AI has the potential to tackle some of these challenges by performing the day-to-day tasks that hold infrastructure teams back.
Consider a state where infrastructure engineers incorporate AI agents to write infrastructure code, review that code for security and compliance standards before it’s deployed, and monitor the state of that infrastructure to identify risks, perform root-cause analysis on errors, or detect oversized or idle resources.
Some of these tasks require hours of work from skilled infrastructure engineers. Others aren’t even being done today.
What Could Hold Back This Transformation
The economic stakes for this transformation are high.
Enterprises that successfully harness agentic AI to automate and optimize their cloud operations can accelerate time to market, price their products more competitively and show profitability gains.
And that’s in addition to the day-to-day benefits: less monotonous work for DevOps engineers and SREs, happier developers who get what they need on demand and lower turnover as a result.
But despite the promise of agentic AI, adoption is far from guaranteed. These tools often require a significant upfront investment in new platforms, training and change management. For IT leaders already facing budget scrutiny over ballooning cloud bills, the idea of spending even more on a new class of tools may seem like throwing good money after bad — especially if past cloud initiatives failed to live up to expectations. Without clear, measurable short-term wins, they may hesitate to adopt solutions that promise long-term transformation.
And then there’s the psychological hurdle for the teams responsible for integrating agentic AI into legacy processes and platforms. Developers and engineers may be wary of embracing AI tools that appear to threaten job security.
At a time when IT decision makers have started looking for the exits to their cloud strategy, they’ll need to consider whether it makes more sense to modernize how they manage their infrastructure. How this plays out will be interesting to watch.
The cloud, for all its promise, demands a new level of operational maturity to deliver ROI at scale. Agentic AI may be the missing ingredient that finally makes cloud living up to its potential.