Only 2% of Global Organizations Ready to Scale AI Securely, New Report Reveals

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A new global report warns that the race to adopt artificial intelligence (AI) may be outpacing enterprise readiness. While AI pilots and enthusiasm are on the rise, few organizations have the infrastructure, talent, or governance models needed to scale these technologies securely and effectively.

Only 2% of organizations worldwide are fully prepared to scale AI securely across their operations, according to F5’s newly released 2025 State of AI Application Strategy Report. The study, based on insights from 650 global IT leaders and 150 AI strategists, reveals that while AI adoption is accelerating, foundational gaps in governance, infrastructure, and security are putting enterprises at risk.

The report, which draws on responses from enterprises with annual revenues exceeding $200 million, paints a complex picture of AI maturity across the globe. While 77% of organizations exhibit moderate readiness, many are still grappling with key architectural and security challenges. Another 21% fall into the low-readiness bracket, potentially losing competitive advantage as AI reshapes entire sectors.

“As AI becomes core to business strategy, readiness requires more than experimentation—it demands security, scalability, and alignment,” said John Maddison, Chief Product and Corporate Marketing Officer at F5. “This report highlights actionable steps for organizations to operationalize AI with confidence. AI is already transforming security operations, but without mature governance and purpose-built protections, enterprises risk amplifying threats.”

Widespread Use, Fragmented Implementation

The study highlights the rapid pace of AI adoption—even among organizations not yet fully equipped to manage its complexity. Approximately 70% of moderately ready companies already have generative AI in active use, with most others planning deployments. On average, one in four enterprise applications currently incorporates AI, though that figure rises sharply among highly ready organizations.

Moderately ready enterprises typically embed AI into one-third of applications, while those with low readiness remain limited to siloed or experimental use cases. As AI adoption expands, infrastructure strain and cross-cloud inconsistencies continue to be key concerns.

Notably, 65% of respondents report using at least two paid AI models along with one or more open-source alternatives; an indicator of growing complexity. The average enterprise utilizes three models, often across multiple environments. The most cited open-source models include Meta’s Llama family, Mistral AI variants, and Google’s Gemma.

Security Lags Behind AI Innovation

As AI becomes more integral to enterprise workflows, the risks associated with its implementation grow. F5’s report identifies significant gaps in how organizations are securing AI workloads.

While 71% of surveyed organizations already leverage AI to bolster cybersecurity, only 18% of moderately ready enterprises have implemented dedicated AI firewalls. Nearly half (47%) plan to do so within the next year. Meanwhile, just 24% of organizations practice continuous data labeling—a critical process for maintaining transparency and defending against adversarial attacks.

Hybrid and multi-cloud environments also contribute to fragmented governance, exposing organizations to inconsistencies in policy enforcement and data protection. The report underscores how the increasing diversity of AI models in use expands the attack surface—especially when open-source tools lack centralized control frameworks.

AI Readiness Index: A Strategic Benchmark

To help organizations navigate these challenges, F5 introduces the AI Readiness Index, a framework that evaluates six dimensions of operational maturity, including infrastructure alignment, governance, and security controls.

Among its key recommendations:

• Diversify AI models while improving oversight and governance.
• Scale AI adoption across operations, analytics, and cybersecurity functions to drive enterprise-wide transformation.
• Deploy AI-specific protections, including firewalls and formalized data governance practices such as continuous data labeling.

According to the report, organizations that achieve high AI readiness can not only scale AI effectively but also manage risks and seize innovation opportunities. Those that fall behind may encounter compliance barriers, operational inefficiencies, and missed growth potential.