The enterprise AI landscape is entering a new phase of implementation following the initial wave of agent technology.
Over the past two years, the application of large language models in corporate settings has primarily focused on scenarios like knowledge Q&A, intelligent customer service, and content generation, essentially serving to enhance individual employee productivity.
From bank knowledge bases and securities research assistants to enterprise collaboration tools, a growing number of institutions have completed their initial deployment of AI capabilities.
However, as model capabilities become more widespread, the market's focus is shifting.
Rather than simply having employees use AI for specific tasks, companies are increasingly concerned with a different question: can AI truly integrate into business processes and create quantifiable operational value?
At the "Digital Cloud Primordial Force 2026" forum held in Beijing on June 9th, multiple industry figures engaged in discussions centered on "AI for Process."
Unlike previous focuses on tool forms like Agents and Copilots, this approach emphasizes embedding AI into enterprise operational workflows, enabling it to participate in decision-making, execution, and collaboration, rather than remaining confined to information lookup and content generation.
This shift is not accidental.
The past year has seen rapid expansion in enterprise AI.
Numerous organizations have completed the integration of large models, the construction of knowledge bases, and the deployment of intelligent assistants.
Yet, after initial exploration, a growing number of companies are realizing that an AI capable of answering questions does not necessarily create business value.
In many scenarios, AI can assist employees with retrieving information, drafting documents, and generating reports, but it often struggles to genuinely drive business processes forward.
This is particularly true in areas involving multi-departmental coordination, complex rule systems, and accumulated professional expertise, where purely Q&A-based agents frequently fail to cover the entire business chain.
This represents a common challenge facing enterprise AI today: moving from managing productivity to enabling operational productivity.
At the forum, Li Ying, CEO of Digital China Group Co.,Ltd., stated that compared to improving individual work efficiency, the enterprise context requires AI to directly enter business processes and become an integral part of the operational system.
Only when AI can understand business logic, participate in process execution, and continuously accumulate experience can enterprise AI deliver lasting value.
This trend is particularly evident in the financial industry.
Over the past year, banks, insurance companies, and securities firms have all initiated large model projects.
Applications have expanded from intelligent customer service and marketing assistants to investment research Q&A.
However, compared to these standardized scenarios, core business processes like credit approval, risk management, compliance review, and operations management involve extensive institutional rules, historical experience, and cross-departmental collaboration, making them far more complex than single-point applications.
As the first wave of technical deployments is completed, more financial institutions are shifting their exploration from "tool application" to "process application."
The new focus is on how to enable AI to participate in business decisions, interact with internal systems, understand corporate rules, and develop continuous iteration capabilities during execution.
From this perspective, the nature of competition in enterprise AI is changing.
The past was about competing on model capabilities, knowledge base size, and the number of agents; the future will likely hinge more on a company's understanding of its business processes and its ability to embed AI within them.
Digital China Group Co.,Ltd. believes the next phase of enterprise AI development is not about adding more agents, but about driving AI into operational workflows.
The Digital China Ask 2.0 platform, launched that day, was designed around this concept, aiming to integrate employees, business systems, corporate knowledge, and AI agents into a unified workspace to achieve capability accumulation and experience reuse through process collaboration.
Simultaneously, Digital China Holdings showcased a supply chain AI control tower solution, attempting to apply AI to demand forecasting, procurement planning, warehouse network allocation, and fulfillment management.
Digital China Information also shared practical cases of AI in finance, covering multiple business scenarios across front, middle, and back offices in banking.
Whether in supply chain management or financial operations, the common direction is moving AI from an auxiliary tool to an active participant in processes.
According to prior research from IDC, enterprise AI development is gradually transitioning from a technology validation phase to a value realization phase.
As foundational model capabilities mature, corporate evaluation criteria for AI are also changing, with increasing importance placed on return on investment, business conversion efficiency, and organizational synergy.
From an industry development timeline perspective, 2023 to 2025 addressed the question of "having AI."
Entering 2026, the market is beginning to discuss more extensively "how AI creates value."
As model capabilities become more of a commodity, differentiation between companies may no longer lie in how many models they have integrated, but in their ability to truly embed AI into business processes and form a continuously optimized operational system.
This also signifies that enterprise AI is shifting from a competition of tools to a competition of processes.
For financial institutions currently advancing digital transformation, this change may just be beginning.
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