Why most AI transformations fail and what agentic AI changes

The study introduces the concept of algorithmization to describe how organizations can be transformed into federated algorithmic ecosystems. In an algorithmized institution, intelligence flows through standardized decision channels rather than isolated applications. Models, rules, and human judgment coexist within a shared governance framework that ensures consistency and resilience.

AI investment is booming, yet many projects fail before reaching production. A new research paper on arXiv argues the root cause is not weak technology, but poor governance and institutional design around how intelligence is deployed.

The study, titled Advances in Agentic AI: Back to the Future, challenges the dominant narrative that large language models represent the future of autonomous AI. Instead, it presents a structural theory of Agentic AI that reframes artificial intelligence as an architectural and organizational discipline rather than a model-centric breakthrough.

Why large language models are not agentic intelligence

While LLMs demonstrate impressive generative abilities, the study points out that they are fundamentally statistical systems optimized for language prediction rather than decision-making in constrained environments.

The authors introduce a clear distinction between two layers of artificial intelligence. The first, referred to as M1, includes machine learning models, training pipelines, inference engines, and the heavy engineering required to operate them. This layer is expensive, resource-intensive, and increasingly commoditized as model performance converges across vendors.

What is missing, according to the study, is the second layer: M2. This layer represents the actual machinery of Agentic AI. M2 is not a model, but a strategies-based, federated architecture that coordinates models, heuristics, rules, constraints, cybersecurity controls, and human oversight into a coherent system. Without M2, AI systems remain brittle, opaque, and unsafe for real-world deployment.

The paper shows that LLM-based agents fail systematically when placed under real operational constraints. They hallucinate under uncertainty, struggle with compliance requirements, consume unpredictable resources, and lack the ability to reason across long-term objectives. These weaknesses are not bugs to be fixed with better training, but structural limitations of probabilistic language systems.

As a result, organizations attempting to scale AI through LLM agents often encounter cascading failures. Pilot projects succeed in controlled demos but collapse when exposed to regulatory audits, adversarial behavior, or cost controls. The study argues that no amount of prompt engineering or model fine-tuning can solve these issues without a governing architecture that sits above the models themselves.

The Machine Theory of Agentic AI and the rise of M2 systems

To address these failures, the authors propose the Machine Theory of Agentic AI, a framework that redefines intelligence as an emergent property of systems rather than individual models. In this theory, models are components, not agents. True agency arises from the orchestration of multiple decision mechanisms under explicit strategies.

M2 systems operate by decomposing intelligence into coordinated layers. At the core are strategies that define goals, risk tolerance, compliance boundaries, and economic constraints. These strategies govern how and when models are used, how outputs are validated, and how decisions are escalated or overridden. Models become tools that serve strategies, not autonomous actors.

This approach allows AI systems to be auditable by design. Every decision can be traced back to a strategy, a rule, or a validated input. This traceability is critical for regulated environments such as finance, healthcare, defense, and public administration, where accountability is non-negotiable.

The study introduces the concept of algorithmization to describe how organizations can be transformed into federated algorithmic ecosystems. In an algorithmized institution, intelligence flows through standardized decision channels rather than isolated applications. Models, rules, and human judgment coexist within a shared governance framework that ensures consistency and resilience.

Importantly, the authors emphasize that M2 systems are model-agnostic. They can integrate LLMs, traditional machine learning models, optimization algorithms, and even non-AI heuristics. This flexibility reduces dependency on any single technology and protects organizations from rapid shifts in the AI vendor landscape.

The paper documents the implementation of a full M2 system developed over more than a decade of applied work. This system has been deployed in high-stakes environments where failure is costly, including financial markets and cybersecurity operations. According to the study, these deployments demonstrate that Agentic AI is not a future aspiration but a present capability when built on the correct foundations.

Why AI success depends on consumption, not creation

According to the study, the future of AI competitiveness will be determined by consumption capacity rather than model creation. As models become cheaper and more widely available, competitive advantage shifts away from who builds the best model to who can deploy intelligence most effectively.

The authors warn that nations and organizations are misallocating resources by focusing almost exclusively on AI production. Massive investments in compute, data centers, and foundation models are yielding diminishing returns if institutions lack the structures needed to absorb intelligence into decision-making processes.

M2 systems redefine AI adoption as an organizational transformation challenge. Success depends on governance, process redesign, and cultural readiness as much as technical capability. Institutions must decide how intelligence is authorized, constrained, and evaluated before models are ever introduced.

The study extends this argument to a macroeconomic level. Countries that fail to develop AI consumption infrastructure risk falling behind even if they host cutting-edge research labs. Conversely, regions that build robust M2 architectures can leverage global AI advances regardless of where models are developed.

This perspective reframes AI policy debates. Regulation, standards, and institutional design become as important as innovation incentives. The authors argue that without a clear separation between intelligence generation and intelligence governance, societies will struggle to deploy AI safely at scale.

The paper also challenges the narrative of autonomous AI replacing human decision-makers. In M2 systems, humans remain integral, but their role shifts. Human judgment is elevated to strategic oversight rather than reactive intervention. This hybrid model allows organizations to combine machine efficiency with human accountability.