It’s no secret that the use of AI has surged exponentially in recent years. New research found 96% of global organizations have deployed AI models and the technology is transforming business initiatives unlike anything we have experienced before. Despite this, concerns and frustrations persist for many organizations trying to unlock its full potential.
The pressure to deliver faster, more secure and more efficient applications is intense. Yet complexity, legacy practices and misalignment are undermining AI’s full scope of abilities. To stay competitive, organizations must align strategies across security, automation and deployment or risk digital transformation efforts becoming stagnated.
Distinguished Engineer at F5.
Legacy operations blocking AI adoption
The industry is laser focused on how AI and automation will transform everything, yet many IT teams still rely on manual steps. Although AI promises automated tasks like traffic optimization, nearly 29% of teams are still mired in scriptwriting and 56% rely on human operators to kick off processes, which often require multiple manual approvals and ticketing.
Legacy methods and manual interventions choke the pipeline. Even the most advanced AI can’t deliver results if the infrastructure relies on time-consuming, error-prone manual steps. This not only delays deployments but also saps team morale.
Traditional deployment practices, such as reliance on human operators and cumbersome ticketing systems, cause significant delays, with 23% of IT leaders citing ticketing integration as a primary automation roadblock. Modern continuous integration and deployment pipelines are built for speed, but outdated processes slow them to a crawl. If companies aim for continuous delivery yet still depend on manual approvals, agile deployment becomes redundant.
AI fuels hybrid cloud adoption
As AI continues to gain traction, so too does the shift towards hybrid cloud architectures. Today, 94% of organizations deploy applications across multiple cloud environments, driven by the need for scalability, cost-efficiency, and regulatory compliance. The hybrid approach allows organizations to tailor their infrastructure to the specific needs of different workloads and business demands.
As 91% of IT decision-makers have identified adaptability to evolving business demands as a top advantage of hybrid cloud strategies. This adaptability is especially important for AI workloads, where data locality, latency, and cost control are key considerations.
Notably, more than half of organizations plan to run AI models across both cloud and on-premises infrastructure in the foreseeable future. Interestingly, a growing number of organizations are also reevaluating their public cloud strategies.
Approximately 79% have recently moved at least one application from the public cloud back to an on-premises or co-location setup. The reasons for this shift are varied, but commonly include the need for better cost management, enhanced security and increased predictability.
However, this hybrid model is not without its challenges. Fragmented security policies, inconsistent delivery policies, and operational silos can complicate AI deployment across environments. Misalignment across teams and platforms, whether due to differing security standards, outdated workflows or uncoordinated strategies, remains one of the biggest hurdles.
While AI can help optimize existing processes, it cannot compensate for fundamental strategic misalignment. To truly move forward, organizations must go beyond simply adopting new tools. They need to rethink whether their current workflows are still fit for purpose in the age of AI.
The path towards AI’s full potential
To fully harness AI’s potential, businesses need to commit to building modern, programmable IT environments. These environments should not only support automation but also standardize application delivery and security practices. By creating a more consistent and scalable foundation, organizations can eliminate many of the inefficiencies that currently limit AI’s impact.
By 2026, AI is expected to move beyond isolated task automation to orchestrating comprehensive, end-to-end IT operations. With platforms that feature natural language processing and programmable interfaces, traditional management consoles will give way to more intuitive, AI-driven control systems. This evolution will bring unprecedented accuracy, speed, and agility to IT operations.
In this context, flexibility and automation are no longer optional, but essential. Organizations that successfully modernize their infrastructure and align their strategies will be better positioned to unlock AI’s full capabilities. They will not only enhance operational efficiency but also deliver superior customer experiences and drive meaningful digital transformation, at scale.
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