This article is adapted from the first chapter of The AI Portfolio Explosion, a new benchmarking report published by Corinium and ModelOp exploring why growing AI activity does not always translate into measurable value. The full report is available to download.
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Many organizations are also getting models into production faster than they were a year ago, often by relying on third-party platforms, tools, and prebuilt capabilities.
However, this acceleration may be mistaken for progress. AI portfolios are growing in volume and complexity, while enterprise control is not keeping pace.
As agentic systems connect to more tools, APIs, and external services, gaps in visibility and accountability become harder to contain. This is one factor that makes it difficult to translate AI activity to value.
Our research shows that enterprise AI portfolios have expanded significantly over the past year. In 2025, most organizations (80%) reported fewer than 100 proposed AI and machine learning use cases across their portfolios. In 2026, 67% reported between 101 and 250 proposed AI and ML use cases, and a growing share of enterprises reported more than 250—compared to only 21% who reported fewer than 100 proposed use cases.
This explosion of activity is not confined to early-stage ideation. 86% of respondents reported 26 or more use cases in development, 50% report 26 or more in pilot, and 60% reported 6 to 25 AI systems in scaled production.
As a result, many organizations are managing hundreds of AI ideas, pilots, and production deployments at the same time, introducing levels of operational complexity that few organizations were designed to support.
Large AI portfolios, few use cases at production scale
Compared with last year’s research, AI initiatives are reaching initial production environments more quickly.
In 2025, more than half of enterprises reported GenAI initiatives taking between six and eighteen months to reach production. In 2026, respondents said that the majority of GenAI use cases now reach production within six months, with the largest share deploying in one to three months.
Similar acceleration appears across other AI categories. A significant portion of traditional machine learning models reach production within one to three months, while most complete deployment within six months. Agentic AI initiatives also move quickly, with the majority reaching production within three to six months and only a small minority extending beyond a year.
This acceleration is partly enabled by increased reliance on vendor-provided and third-party AI capabilities. These reduce development friction and infrastructure requirements, allowing organizations to move individual use cases into production environments more rapidly.
However, speed does not equal scale. Our research shows that faster deployment does not translate to scaled production adoption.
While enterprises can now move individual AI initiatives into production environments with greater speed, only a limited subset progress beyond initial deployment into sustained operational use. Furthermore, faster deployment may increase risk, cost opacity, and operational friction without an industrial-scale approach to delivering AI.
The constraint facing enterprises is no longer how quickly a single AI use case can be brought to production, but how many systems can be operated, governed, and sustained at once. More importantly, enterprises need to know which use cases are truly delivering value.
Fragmented AI environments mean complexity at scale
As AI portfolios expand, the environments supporting them are increasingly heterogeneous.
Rather than deploying AI within unified platforms most enterprises operate across a mix of cloud services, development frameworks, data platforms, and vendor tools. Different teams adopt different stacks, often optimized for speed and experimentation rather than long-term operational consistency and efficiency.
This expansion is accelerating as organizations adopt agentic AI.
Our research shows that most enterprises connect agentic systems to between six and twenty third-party tools and services. Each additional connection introduces new dependencies, ownership boundaries, and potential points of failure.
As Ramila Peiris, Chief Data Architect at Sanofi, explains, even minor changes can have outsized consequences once AI systems operate across multiple environments:
“Beyond app-level controls, the real governance challenge is coordination across data system owners. If someone changes a source system without communicating it or following standards, the downstream impact can break AI functionality.”
Emerging standards such as Model Context Protocol (MCP) are enabling this expansion by simplifying how agents access external systems. While this increases flexibility and speed, it also significantly broadens the operational surface area that enterprises must manage.
At portfolio scale, these interconnected environments become difficult to manage. Teams may understand individual models or tools but lack a clear view of how systems interact once deployed.
As a result, complexity compounds with each new use case instead of increasing linearly.
The illusion of value
The research points to an enterprise AI landscape that is larger, faster moving, and more complex than ever before. Organizations are generating more AI use cases, moving models into production more quickly, and experimenting across a growing mix of platforms, tools, and vendors.
On the surface, this momentum suggests rapid progress. In practice, however, activity and interdependencies are scaling faster than enterprise control.
As Mutual of Omaha VP of Financial Data and Analytics Strategy Jeannie Furlan explains, the challenge is not a lack of ambition, but the widening gap between technological acceleration and organizational adaptability:
“The pace at which these AI capabilities demand us to change is far faster than our ability to change. And that is both the people-side as well as the technology-side.”
The result is an illusion of value. More models reach production. More agents are deployed. More integrations are added. Yet visibility in how these systems behave together across the enterprise does not increase at the same rate.
Understanding why this illusion has emerged, and why speed alone cannot close it, is critical to generating sustained value going forward.
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This article is an excerpt from The AI Portfolio Explosion: When Activity Creates the Illusion of AI Value. Download the full report to explore the research and learn how enterprises can industrialize AI delivery and manage AI as an accountable portfolio.