In Q1 2026, we fielded a survey of over two hundred IT and business leaders who operate enterprise applications.
One of the key findings is that cloud application change velocity now exceeds what most IT teams can safely absorb, creating a growing execution gap between business expectations and operational reality. In this blog, we dig into why and what IT leaders are doing about it.
Cloud Spend and Release Velocity are Rising
Today, 73% of organizations undergo 3+ major releases or updates annually, and 36% experience 7 or more.
And, investment in cloud applications is continuing to rise: 85% report that their investment in cloud-based enterprise applications has increased year-over-year. One of the major drivers of this increased spend is the pace of innovation that comes with cloud applications and specifically adoption of automation and AI (54%).
While hopes are high, so is the cost of managing all these cloud apps. Teams must repeatedly configure, test, document and communicate change while keeping the lights on and delivering strategic initiatives. With all these demands, it’s no wonder that, over the next 3–5 years, 83% of respondents expect spend and resource requirements for enterprise applications to increase.
Which Tasks Overwhelm the Update Lifecycle?
As cloud change velocity rises, the same lifecycle activities keep surfacing as both difficult and strategically important.
When asked about the top challenges of keeping up with cloud updates, “IT staff time and resources” ranked #1.
| Allocating IT staff time and resources | 42% |
| Configuration management – enable, document, validate | 36% |
| Ensuring test coverage across all changes | 34% |
| Maintaining business continuity during updates | 34% |
| Understanding impact of changes to environment | 33% |
When probed further on which manual tasks create challenges, configuration dominated with the top two responses. With that said, it is clear that as opposed to a single pressure point, the challenge is observability and efficiency across the entire lifecycle of app management. With cloud velocity increasing, teams are expected to carry out the process of evaluating, planning, testing, and executing on changes far more often.
| Time and effort to configure new features | 51% |
| Identify config changes required for business needs | 46% |
| Understanding current business processes/discovery | 45% |
| Test application changes across modules and integrations | 44% |
| Ensuring continuous governance, controls and audit readiness | 40% |
Confidence vs. Reality: Delivery Risk
Despite the challenges they face, IT leaders express strong confidence in their ability to deliver change quickly without introducing risk. 79% say they are “extremely” or “very” confident in their ability to deliver changes quickly without risk.
But, that confidence seems to be misplaced in the face of the real occurrence of production issues. When asked about the frequency of production issues, only 19% say they “almost never” experience production issues from configuration or process changes, while 27% say “rarely,” 35% “sometimes,” 8% “often,” and 11% “almost always.”
Why Automation and AI are now Central, not Optional
Against this backdrop of increasing release cadence, and constrained human capacity, it is not surprising that automation and AI show up as primary levers to manage this conundrum.
When presented with a scenario where the enterprise application lifecycle is automated and optimized with agentic AI, leaders show strong intent to adopt:
- 83% say they would be “completely likely” or “very likely” to adopt such technology.
They also see substantial potential time savings from lifecycle automation:
- 22% expect to save 5,000 to 15,000 hours per year.
- 29% expect 15,000 to 30,000 hours per year.
- 3% expect 30,000 hours or more per year.
- Only 5% expect savings under 1,000 hours.
It is clear that leaders view automation and agentic AI not as marginal efficiency tools, but as mechanisms to reclaim tens of thousands of hours of high-skill IT work. They also believe that it will reduce risk to the tune of multi-millions from failed or fragile changes.
What This Means for Your Roadmap
Taken together, the data paints a clear picture:
- Cloud and enterprise app investment are rising and are expected to keep increasing over both 12-month and 3–5 year horizons.
- Cloud release velocity and continuous update models are now structural drivers of operating complexity and future spending.
- IT capacity—defined by people, partners, and budget—is not scaling at the same rate, and cost/staffing constraints are among the top strategic burdens.
- The most strained activities are those at the heart of safe change: configuring new features per release, understanding process impact, testing across complex integrations, and maintaining controls and documentation.
- Leaders overwhelmingly believe automation and agentic AI are the practical way to close this gap, with most expecting thousands to tens of thousands of staff hours saved per year and strong willingness to adopt.
If you are planning your next 1–3 years of enterprise app strategy, this suggests prioritizing projects that directly reduce the burden of change with automation and AI, rather than simply adding more manual checkpoints or outsourcing more work. Top tasks to automate with technology available today include impact analysis, testing, governance, and documentation.
If you’d like to discuss how Opkey can help you automate the management of your enterprise applications, schedule a consultation:
