Artificial intelligence (AI) is used by nearly every organization today, but very few have implemented it at scale. According to F5’s new “2025 State of AI Application Strategy Report,” only a small percentage of organizations are completely prepared to deploy AI across their operations. Most are stuck in early phases, unable to move beyond isolated use cases or small-scale pilots.
F5 surveyed 650 global IT leaders and 150 AI strategists at companies with annual revenues of at least $200 million, uncovering a clear divide in the level of readiness. The majority of organizations (77%) fell into the “moderate readiness” category. These companies have some AI in place, but still have gaps in governance, security and infrastructure. Meanwhile, 21% fell into the “low readiness” category, where AI is limited to isolated teams or experimental projects.
This data point is consistent with my research, which has found that while interest in AI is high and companies have a handful of individuals well-schooled in AI, more skilled people are required. Businesses are trying to beef up their teams, but there is currently a lack of AI talent, creating an excellent opportunity for IT pros to reskill for the next wave of their career.
At the other end of the spectrum, just 2% of organizations reached what F5 calls “high AI readiness.” These companies stand out for adopting AI at scale. They embed AI across most of their applications, backed by strong governance, standardized processes and dedicated infrastructure. For everyone else, the gap is growing, and catching up will require more than adding another AI model to the stack.
AI Governance and Security
Even among organizations with moderate AI readiness, governance remains a challenge. According to the report, many companies lack comprehensive security measures, such as AI firewalls or formal data labeling practices, particularly in hybrid cloud environments. Companies are deploying AI across a wide range of tools and models. Nearly two-thirds of organizations now use a mix of paid models like GPT-4 with open source tools such as Meta’s Llama, Mistral and Google’s Gemma — often across multiple environments. This can lead to inconsistent security policies and increased risk.
The other challenges are security and operational maturity. While 71% of organizations already use AI for cybersecurity, only 18% of those with moderate readiness have implemented AI firewalls. Only 24% of organizations consistently label their data, which is important for catching potential threats and maintaining accuracy. Not having those protections in place makes organizations more vulnerable as they transition to open source models and hybrid cloud environments.
More Complexity
Hybrid complexity has become the norm. Nearly all organizations (94%) have deployed applications across multiple environments, including public cloud, on premises, software as a service (SaaS) and edge. Organizations typically use a median of four public cloud providers, which further complicates IT environments. More than half of the organizations surveyed (53%) said they struggled with inconsistent application security policies, while 47% reported the same for delivery policies.
This pivot from public clouds to a hybrid model has been interesting to watch. Many organizations that once planned to be 100% in the public cloud have walked that back and are building hybrid environments. Data is the fuel that powers AI, and more organizations want greater control over it, making hybrid ideally suited for these organizations.
Application programming interface (API) sprawl is another growing issue, according to 58% of the respondents. Organizations rely on APIs to manage communication between services, clouds and vendors, but this has become a significant pain point. Nearly a third (31%) reported that managing vendor APIs is the most time-consuming task in their automation workflows. Respondents cited writing custom scripts (29%) and integrating with legacy ticketing systems (23%) as other time-consuming tasks.
AI to Optimize
These day-to-day inefficiencies are slowing progress. The findings show that 73% of organizations want to use AI to optimize app performance. However, 60% are still carrying out tasks manually. Many organizations are juggling APIs, vendor tools and traditional ticketing systems — workflows that the report identified as major roadblocks to automation. Scaling AI across the business remains a challenge for organizations.
Still, things are improving, thanks in part to wider use of observability tools. In 2024, 72% of organizations cited data maturity and lack of scale as a top barrier to AI adoption. Today, more than nine in 10 organizations have a strategy for managing observability data, pointing to growing data maturity.
Tools like OpenTelemetry are playing a key role, with 95% of organizations standardizing around them. At the same time, 38% have consolidated their data into a single data lake to streamline analysis and operations. Additionally, two-thirds of organizations now use telemetry primarily to drive automation. This is a major change from 2024, when just 47% of organizations used it mainly for alerts and reporting.
Nearly all (99% ) of the respondents said they’re now comfortable using AI to automate at least one IT function. Nevertheless, most organizations aren’t there yet when it comes to fully using AI in IT operations (AIOps). Many are either spending too much time on manual tasks or don’t have the skills necessary for implementing AIOps.
The report made it clear that organizations need to use AI more effectively in IT before they can deploy it widely across the business. That starts with reducing complexity by streamlining tools, APIs and processes, which slow teams down.
AI Readiness Index
To help organizations measure operational maturity, F5 introduced a framework called the AI Readiness Index. Using the framework, organizations can take specific steps toward deploying AI at scale. For example, they can use a mix of commercial and open source AI models to improve governance and expand AI use across workflows by embedding AI in operations, analytics and security.
The use of the AI Readiness Index can be highly beneficial in helping organizations understand where they are with AI. In my experience, if you ask an IT leader to estimate how ready they are with new technology, the initial approximation is over the reality of where the organization is. A tool like F5’s index can quantify the actual maturity of a company and enable it to put a roadmap in place to get from vision to reality.
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