Survey Report | Over 70% of 200 AI/DX Promoters in Enterprise Companies Face Challenges with "AI Ready" Status

XAION DATA Inc. (Headquarters: Shibuya-ku, Tokyo; Representative Director: Yasuhide Sato; hereinafter referred to as "the Company"), a developer and provider of AI data platforms and AX solutions, conducted a "Survey on the Status of AX (AI Transformation) Promotion" targeting 200 individuals involved in AI and DX promotion at companies with 1,000 or more employees nationwide.

As investments in AI, including generative AI, accelerate, companies' focus is shifting from "whether to introduce AI" to "how to leverage invested AI to enhance business performance and decision-making."

The key to this is creating an environment where AI can understand and utilize necessary data for decision-making, in other words, an "AI Ready" environment. No matter how advanced the AI introduced, if internal data is fragmented, inconsistent in format and quality, and not connected with external data, the AI's basis for judgment will be limited, making it difficult to generate results.

This release explores from the survey results why traditional data infrastructure alone is insufficient for companies investing in AI, and why an "AI Ready data platform" is becoming an important next investment theme.

▍Survey Overview

This survey examines the progress of AI investment in enterprise companies and the challenges in data environments and AI readiness that hinder the generation of results.

79.0% of Enterprise Companies Implement AI in Operations; AI Investment Moves to Departmental/Company-Wide Levels

74.5% Have Introduced DWH/Data Lakes, but Face Challenges in "AI-First" Design

Over 70% Face the "AI Ready" Hurdle. Data Fragmentation, Quality, and External Data Integration are Key Issues

External Data Utilization is Advancing, but Challenges Remain in Connecting with Internal Data

76.0% Report "Improved AI Judgment Accuracy" Through External Data Integration

▍Survey Results

79.0% of Enterprise Companies Implement AI in Operations; AI Investment Moves to Departmental/Company-Wide Levels

When asked about the operational implementation phase of AI at their workplaces, the most common response was "Integrating AI into business processes across the entire company" at 39.0%. This was followed by "Integrating AI into parts of business operations at the departmental level" at 30.5%, "Some employees personally using generative AI, etc." at 17.5%, and "AI autonomously handling decision-making and business execution" at 9.5%.

Combining "Integrating AI into parts of business operations at the departmental level," "Integrating AI into business processes across the entire company," and "AI autonomously handling decision-making and business execution" totals 79.0%, indicating that AI investment is moving beyond individual use and consideration to operational implementation at the departmental and company-wide levels.

74.5% Have Introduced DWH/Data Lakes, but Face Challenges in "AI-First" Design

Regarding the introduction status of internal data integration platforms (DWH, data lakes, data fabrics, etc.) at their workplaces, 28.0% responded "Introduced and operating on a company-wide scale," and 46.5% responded "Introduced and operating in some departments/areas."

Combined, 74.5% are already introducing or operating data integration platforms in some form.

However, many of these data platforms were built with the goals of DX promotion, BI/reporting, and departmental operational efficiency. Therefore, introducing a DWH or data lake is not synonymous with having data prepared in a state that AI can utilize for business operations and decision-making.

The success of AI is not solely determined by the performance of models and algorithms; it is significantly influenced by the company's ability to prepare data in a format that AI can understand and utilize, and to make it available when needed. In other words, to translate AI investment into results, "AI Ready" status, which involves redesigning existing data platforms with an AI-first approach, is crucial.

There are primarily four challenges in traditional data platforms that tend to hinder AI investment:

Data Silos: When data is managed independently by business processes or departments, cross-departmental analysis and collaboration become difficult, leading to a lack of material for AI to make comprehensive judgments.

Increased Integration/Transformation Costs: If data formats, granularity, or units are inconsistent, significant preprocessing and adjustments are required to make the data usable by AI.

Lack of Real-time Capability: Traditional platforms often rely on periodic batch updates, making it difficult for AI and automation tools to access the latest information immediately.

Constraints on External Data Utilization: If the design is insufficient for incorporating Open Data or external APIs, it becomes difficult to include information such as market and industry trends, which cannot be captured by internal data alone, as input for AI judgments.

In essence, while traditional data platforms have played a role in DX promotion and operational efficiency, challenges remain in preparing data to a state where AI can use it for judgment and analysis. To achieve results from AI investment, it is necessary to review existing data platforms with an AI-first perspective and evolve them into an "AI Ready" state that enables cross-functional utilization of internal and external data.

Next, we will look at the data preparation challenges that companies are actually facing.

70% Face the "AI Ready" Hurdle. Data Fragmentation, Quality, and External Data Integration are Key Issues

When asked about the data preparation status for advancing AI investment, 76.5% responded "Internal data is fragmented by department/system and has not been integrated" (strongly agree or somewhat agree).

Furthermore, 72.5% responded that "Data formats, quality, and update frequencies are inconsistent, making it difficult for AI to utilize," 65.0% responded that "Data freshness is not maintained, failing to reflect the latest market and industry trends," and 73.5% responded that "Integration with external data (open data, market data, etc.) has not been achieved."

These results indicate that while AI investment and data platform development are progressing to some extent, many companies have not yet prepared their data to a state where AI can use it for practical judgment and analysis. Fragmented data by department, data with inconsistent formats or granularity, and data with insufficient update frequency limit the AI's basis for judgment and hinder the creation of business results.

In particular, when external data cannot be integrated with internal data, it becomes difficult to incorporate market trends, industry changes, and external environmental changes into the AI's decision-making process. To ensure continuous results from AI investment, it is important to prepare data not only internally but also externally so that AI can utilize it.

External Data Utilization is Advancing, but Challenges Remain in Connecting with Internal Data

When asked about the status of incorporating external data (market data, industry statistics, open data, etc.) into AI utilization at their workplaces, 25.0% responded "External data is integrated with internal data and used for AI judgment and analysis."

On the other hand, 44.0% responded "External data is used for AI, but not integrated with internal data." This suggests that while the utilization of external data itself is progressing, cases where it can be used cross-functionally with internal data as a basis for AI judgment are limited.

76.0% Report "Improved AI Judgment Accuracy" Through External Data Integration

When asked about the effects and value of integrating external data with internal data for AI judgment and analysis, the most common response was "Improved AI judgment accuracy" at 76.0%. This was followed by "Gained insights not visible with internal data alone" at 68.0%, "Expanded AI application areas and use cases" at 52.0%, and "Led to the discovery of new industry/market opportunities" at 50.0%.

These results indicate that external data can add judgment material that internal data alone cannot compensate for, thereby expanding AI's accuracy and scope of application.

However, only 25.0% of companies are able to integrate external data with internal data for AI judgment and analysis. To achieve greater results from AI investment, it is important not only to utilize internal and external data separately but also to connect them and create a state where AI can make cross-functional judgments.

The Success of AI Investment Hinges on Making Data Platforms "AI Ready"

This survey revealed that while AI investment and operational implementation of AI are progressing at the departmental and company-wide levels, many companies face challenges such as data fragmentation, inconsistent formats, quality, and update frequencies, and insufficient integration with external data.

In other words, the decisive factor for AI investment is shifting from "whether AI is introduced" to "whether data is prepared in a state that AI can use for judgment and analysis, and connected to business operations and decision-making."

What will become important from now on is not just preparing platforms to "store" data. It is about connecting internal Closed Data with external Open Data that captures the external environment, and evolving into an "AI Ready data platform" that AI can understand and utilize at the necessary timing. Companies are now entering a phase of connecting internal and external data for practical business results, not just introducing and verifying AI.

▍Survey Summary

▍Survey Overview

Survey Name: Survey on the Status of AX (AI Transformation) Promotion

Survey Period: May 11 (Mon) - May 14, 2026

Survey Method: Internet Survey

Target Respondents: Full-time employees working at companies with 1,000 or more employees and involved in AI/DX promotion.

Valid Responses: Screening Survey: 6,000 individuals, Main Survey: 200 individuals.

▍"No Data, No AI": The Success of AI Investment is Determined by AI Ready Data Platforms and AI Context Data

XAION DATA supports companies in preparing data to a state where AI can be utilized, aiming to realize an "AI Native" world.

Data is indispensable for AI, and building a data platform that AI can utilize is a crucial element for translating a company's AI investment into results. However, the success of AI investment is not solely determined by the presence of a platform for accumulating and integrating data. What is important is establishing an environment that can generate high-quality "AI Context Data" that AI can understand and use for business operations and decision-making, utilizing data that exists internally and externally.

AI Context refers to the domain of organizing data meaning, relationships, business context, permissions, and update status, creating a state where AI can utilize it for judgment, analysis, and proposals. For example, even if internal HR, sales, customer, and financial data are connected with external market data and industry trends, AI cannot deliver sufficient value if it is not organized to understand what each piece of data means and which business decisions it can be used for.

Many companies have introduced data platforms such as DWH and data lakes, but these were traditionally built for purposes such as DX promotion, BI/reporting, and departmental operational efficiency. Therefore, in many cases, they have not reached a state where AI can cross-reference data and make judgments and analyses in line with business context. In other words, for future AI investment, it is important to transform data from a state of "accumulation" to a state of "usability" by AI.

Under the philosophy of "No Data, No AI," XAION DATA advocates "Do for AI (Data Orchestration for AI)" as its data strategy for the AI era. Do for AI is not merely data integration but an approach to orchestrate internal (Closed Data) and external (Open Data) data appropriately, transforming them into a state where AI can be utilized for business operations and decision-making.

The "data fragmentation," "inconsistent formats, quality, and update frequencies," "lack of freshness," and "insufficient integration with external data" revealed by this survey are structural challenges faced by many companies advancing AI investment. To resolve these issues, it is essential not only to review existing data platforms with an AI-first perspective but also to establish AI context that enables AI to understand the meaning and relationships of data and utilize it for practical business judgments.

XAION DATA supports companies in promoting AX (AI Transformation) by leveraging its proprietary open data platform and patented technologies "WEB VISION / XD Foundry" for data collection and structuring, transforming distributed and unstructured data into a platform for AI investment. Furthermore, by connecting, structuring, and operating internal and external data, and establishing AI context that AI can utilize for business operations and decision-making, we support companies' AX (AI Transformation) promotion.

▍About XAION DATA

Company Name: XAION DATA Inc.

Representative: Yasuhide Sato

Headquarters Address: 5-27-5 Sendagaya, Shibuya-ku, Tokyo, Link Square Shinjuku 16F

Business Activities: Development of services utilizing open data based on patented technology for data collection and structuring (Patent No. 7116940), and provision of AI/DATA solutions utilizing data.

URL: https://xaiondata.co.jp/