In Opkey’s 2026 State of Enterprise Application Lifecycle Management survey of over 200 IT leaders, expectations for the use of agentic AI in managing enterprise applications are nothing short of transformational: most plan to adopt it, and they’re already planning how to reinvest the time and money they expect to save.
Management of enterprise apps is already consuming IT
Before we get to AI, it is worth grounding the reality IT leaders are living in today.
- Managing enterprise apps consumes IT budgets: 64% of organizations allocate 21–50% of their total IT budget to implement and manage enterprise applications, with another 6% spending more than half their budget here.
- Investment in enterprise apps is increasing: 83% say their total enterprise application investment increased year‑over‑year. And the expectation of growth continues: 80% expect budgets to grow again over the next 12 months and 86% expect spend to grow over the next 3–5 years.
At the same time, cloud release velocity keeps climbing, and already today 73% of organizations manage three or more major app releases per year; and 36% manage 12 or more. The single most challenging task IT leaders report is the time and effort to configure new features for each cloud release, cited by 51% of respondents, followed closely by identifying config changes required for business needs (46%) and understanding current business processes (45%).
The result is a landscape where application operations are expensive, change is constant, and staff are heavily consumed by low‑leverage work.
The current model is straining
As we discussed in an earlier blog, the survey shows that IT leaders are acutely aware their current operating model is unsustainable. Production instability is common: over half report experiencing production issues from configuration or process changes sometimes, often, or almost always.
Strategically, the number one burden leaders identify is “difficulty assessing the impact of changes and updates,” which 34% rank as their top strategic issue—well ahead of cost and staffing constraints, which are ranked first by only 25% of respondents.
Constant change is causing issues to show up in production. In this context, agentic AI is not a nice‑to‑have, but is the first credible way to break the cycle.
Expectations for agentic AI are sky‑high
When presented with the idea of a secure, enterprise‑grade agentic AI system that can autonomously identify process inefficiencies, recommend and validate configuration changes, generate and maintain test scripts, update documentation, and smooth post‑release support, 83% of respondents say their organization is completely or very likely to adopt it. Only 1% are “hardly likely,” and none say “not likely at all.”
IT leaders do not view agentic AI as a lateral move from today’s chatbots and copilots. 64% believe agentic AI will deliver significantly or somewhat more value than the AI tools they’ve invested in over the last several years.
The perceived payoff is not abstract. When asked how much time their teams could realistically save if the enterprise app lifecycle were automated and optimized with agentic AI: 69% estimate savings of 5,000–30,000 hours per year. For IT leaders running a 20–30 person app team, this is the equivalent of reclaiming years of human effort every budget cycle to redeploy into higher‑value work.
In short, for IT leaders, agentic AI is now a fundamental part of their strategy for managing their applications.
Where IT leaders plan to reinvest the agentic AI dividend
Perhaps the most interesting aspect of this equation is not how much IT leaders think they will save, but how they plan to use those savings.
When asked how they would reallocate hours and costs freed by agentic AI‑driven automation of their application lifecycle, IT leaders prioritize four themes:
- Improving employee experience and adoption – 42%
- Innovating on new business capabilities – 42%
- Reskilling or redeploying staff to higher‑value work – 38%
- Reducing IT backlog – 38%
Cost reduction is present, but not dominant:
- 36% would specifically reduce external consulting spend, and 34% would reduce overall operational cost.
This is an important signal. IT leaders are not planning to use agentic AI only to shrink their budgets; they are planning to rebalance their portfolio of work and shift from manual remediation to proactive innovation.
What this means for IT leaders in 2026
For IT leaders, the message is clear: expectations for agentic AI are extremely high and are being set by peers who are looking at the same pressures you are.
Two implications stand out:
- Innovation and employee experience are as important as cost savings. The majority of leaders want to reinvest savings into better employee adoption, new business capabilities, and backlog reduction, not just budget cuts. Your roadmap for agentic AI should explicitly connect automation gains to these growth oriented outcomes.
- Agentic AI must be deeply embedded in the application lifecycle, not bolted on. The pain points IT leaders highlight—configuring new features per release, understanding process and change impact, maintaining coverage and continuity—sit at the heart of cloud application lifecycle management. Tools that operate at the surface (e.g., generic LLMs) will not close that gap; the expectations being set are for domain aware, workflow embedded agents that can act across the lifecycle with guardrails.
At Opkey, we see these findings as a mandate: agentic AI for enterprise applications has to be measured by how much complexity and risk it actually removes from your change pipeline, and by how much time, budget, and talent it frees to focus on strategic priorities rather than survival.
