2026: Only by Making It the 'Year of Unlocking Data Factor Value' Can It Avoid Being the 'Year of the AI Bubble Burst'

[Ebrun Original] In the business world of 2025, Artificial Intelligence (AI) was undoubtedly the hottest buzzword. Entering 2026, its momentum remains undiminished. From global tech giants to startups, all view AI as a core strategy crucial for future survival and development. However, after years of fervent pursuit and massive investment, the market is characterized by a mix of high expectations, high valuations, and high uncertainty. A more sober and pragmatic consensus is emerging: for the vast majority of enterprises, the real competitive barrier does not come from independently developing a general large model or cutting-edge algorithm, but rather from the enterprise's own unique business data, and the ability to use AI technology to transform this data into measurable economic returns.

I. Farewell to the Hype: The AI Value Illusion and the Core 'Data Competitiveness' of Enterprises

In recent years, AI, particularly Generative AI, has sparked a global technological race. Many enterprises fell into a 'capability anxiety,' believing they must possess their own large model to secure a future foothold. However, practice has proven this to be a costly and uncertain path. High computing power costs, massive data requirements, and the scarcity of top-tier talent make developing proprietary large models a game for only a few giants. More importantly, while powerful, general large models lack industry depth (Know-How) and understanding of specific business scenarios, making it difficult for them to directly address core enterprise pain points.

The reality is that AI project failure rates remain high. Multiple studies indicate that enterprise AI project failure rates commonly range between 60% and 95%, with many projects failing to deliver measurable Return on Investment (ROI). Numerous prominent investors and professional institutions are warning of an impending AI bubble burst. 2026 is even being speculated as a potential 'year of the AI bubble burst,' where enterprises must prove that AI investments can translate into quantifiable business value, or face risks of budget cuts and even project termination.

Amid this AI frenzy, enterprises need to re-examine their most core and unique assets. For a manufacturing company, this is its decades of production process data, equipment operation data, and supply chain collaboration data. For a retail company, it's its vast amounts of user behavior data, transaction data, and inventory flow data. These private domain data, tightly bound to core business processes, are strategic resources that no external general large model can replace—they are the true 'moats' for building core competitiveness.

The true value of AI lies in being the 'key' to unlocking these valuable assets. AI technologies, especially machine learning and deep learning, can uncover patterns, predict trends, and optimize decisions from dormant enterprise data, thereby achieving cost reduction, efficiency gains, enhanced customer experience, and the creation of new revenue streams. Therefore, the strategic goal for enterprises should shift from 'owning AI technology' to 'achieving enterprise AI-ization.' 'AI-ization' is not simply about procuring a few AI software packages or setting up an algorithm team; it involves deeply embedding AI into every business process and decision-making link, making it a fundamental infrastructure like water and electricity. This is also the fundamental reason why AI Agents are currently gaining widespread traction.

II. From Strategy to Execution: A Technical Implementation Blueprint for Building Enterprise Data Factor Competitiveness

The core prerequisite for achieving 'enterprise AI-ization' is building strong data factor competitiveness. This means enterprises must possess the capabilities for high-quality data collection, governance, management, and application. Without clean, standardized, and trustworthy data, even the most powerful AI model is like 'a clever woman cannot cook without rice'—unable to deliver real effectiveness. This is the underlying logic behind the national designation of 2026 as the 'Year of Unlocking Data Factor Value'—to guide enterprises in consolidating their data foundations, paving the way for the genuine value realization of AI.

For enterprises hoping to seize the opportunity in 2026, building data factor competitiveness is not an overnight task but a systematic project. Below is a clear, actionable technical implementation blueprint.

First, AI implementation must start with the business and end with value. Before initiating an AI project, enterprises should avoid 'AI for AI's sake' and instead comprehensively review their business processes to identify the most urgent pain points and the most valuable optimization opportunities. These scenarios typically have the following characteristics: good data foundation, high business value, high process repetitiveness, and significant room for improvement. Examples include predictive maintenance for equipment, intelligent quality inspection of products, energy consumption optimization, and supply chain demand forecasting in manufacturing; personalized user recommendations, intelligent product selection and pricing, automatic inventory replenishment, and automated customer service in retail; and intelligent risk control, anti-fraud, quantitative trading, and robo-advisors in finance.

Second, building an enterprise data asset management system is crucial. This is the most critical and yet often overlooked step in the entire blueprint. Enterprises must invest resources in data governance, breaking down data silos scattered across various business systems (ERP, CRM, MES, etc.) to build a unified data foundation. Common specific measures include establishing data standards and specifications, data cleaning and integration, and building data middle platforms or data lakehouses.

Then, on top of a solid data foundation, enterprises can flexibly select and integrate AI toolchains without necessarily developing underlying technologies themselves. This includes leveraging cloud providers' AI platforms, introducing mature AI applications, and building MLOps systems to improve the efficiency of AI application development.

Finally, in the last step of value realization, AI models and applications are deeply embedded into actual business processes to drive automation and intelligence, ultimately forming a virtuous cycle where 'data drives business decisions, and business generates new data.' Such a complete implementation process encompasses the entire journey from business case definition, data preparation, and model building, to operational services, delivery planning, and finally governance and operations, forming a continuously iterative and optimized closed loop for sustainable success.

III. Industrial Internet: The 'Super Connector' and Value Incubator for Data Factor Circulation

To unlock the value of data factors, 'data silos' and 'data chimneys' are persistent challenges plaguing various industries. The main reason is that a single enterprise's data is often partial and limited. Only when data across the entire industrial chain—upstream and downstream—is interconnected can a significant multiplier effect be generated. In this context, the value and role of industrial internet platforms become prominent.

Unlike general-purpose internet platforms, industrial internet platforms are deeply rooted in specific vertical industries such as steel, chemicals, construction, and agriculture, possessing unparalleled advantages. For instance, industrial internet platforms typically serve as hubs for high-value data aggregation. In the platform, they gather large amounts of high-value, high-density, high-timeliness structured 'hot data' from enterprises across the upper, middle, and lower streams of the industrial chain. This data directly reflects the true pulse of industrial operations. Furthermore, industrial internet platforms not only possess data but, more importantly, understand the industry logic and business scenarios behind this data, enabling deep integration of data with industry knowledge. Additionally, through methods like SaaS services, supply chain collaboration, and IoT integration, industrial internet platforms naturally connect the data links of various segments in the industrial chain, providing the infrastructure for the secure and compliant circulation of data factors.

Currently, the profit models of industrial internet platforms have also evolved beyond traditional transaction commissions or membership fees into more diverse and advanced forms. Value-added data services, such as providing data cleaning, analysis, modeling, and visual reporting services, help enterprises extract business insights from raw data. For example, established industrial data service providers like Shanghai SteelHome and SCI99 have developed mature profit models through data subscriptions and consulting services. Another model involves packaging processed and modeled data into standardized data products, such as industry prosperity indices, price prediction models, and supply chain risk alerts, for direct sale.

Through these models, industrial internet platforms act not just as 'plumbers' for data but also as 'incubators' and 'amplifiers' of data value, providing fertile ground for the AI transformation of entire industries. Enterprises, in turn, can combine the data factors provided by industrial internet platforms with their own data resources, leveraging multi-dimensional data integration to delve deeper into its value. Thus, the industrial internet becomes an indispensable aid in the process of enhancing an enterprise's data factor competitiveness.

Only by successfully making 2026 the 'Year of Unlocking Data Factor Value' can we prevent it from becoming the 'Year of the AI Bubble Burst.' Standing before the Spring Festival of 2026 and looking back at the surging wave of AI development, we increasingly clearly recognize that the value of technology must ultimately return to the essence of business—creating customer value and achieving economic returns. The National Data Administration's designation of 2026 as the 'Year of Unlocking Data Factor Value' is not merely a policy deployment but also a strategic guide for all market participants.

The hype will eventually fade; only value endures. Enterprises, regardless of size, that can resist temptation, maintain strategic focus, return to business fundamentals, and view AI as a powerful tool for unlocking the value of their own data factors will stand out in the new round of competition and build truly sustainable core competitiveness. Ebrun Think Tank will continue to focus on the development of enterprise data factor competitiveness, reporting on innovative cases from leading enterprises and new achievements in the development of related industrial chains.

Contact email: huangbin@ebrun.com