According to Gartner 30 % of the Genai project Before the end of 2025, it may be abandoned after the concept verification certificate. The early adoption of Genai showed that the data infrastructure and governance practice of most enterprises have not been prepared for effective AI deployment. The first wave produced by Genai is facing huge obstacles, and many organizations are trying to surpass the concept verification stage to achieve meaningful business value.
When we enter the second wave of AI productive forces, the company realizes that successful implementation of these technologies not only need to connect LLM to its data. The key to unlocking AI’s potential lies in the three core pillars: obtain data and ensure that it is ready to integrate with AI; large repair of data governance practices introduced by Genai; Users will not be forced to learn professional skills or accurate use models. These pillars have created a solid foundation for the safe and effective AI agent in the corporate environment.
Prepare data for AI appropriately
Although structured data may appear on the naked eye, neatly arranged in the tables and columns, LLMS is usually difficult to effectively understand and use the structured data. The reason why this happened is that in most companies, the data is not marked with semantic meaning. Data usually has a hidden label, such as “ID”, which does not clearly indicate that it is a customer, a product or a trading identifier. With the help of structured data, it is also difficult to capture the appropriate context and relationship between different interconnected data points, such as how the steps in the customer journey are interrelated. Just as we need to mark each image in a computer visual application to achieve meaningful interaction, the organization must perform complex tasks, that is, marking its data semantically and recording the relationship between all systems to achieve meaningful AI Interactive.
In addition, data is scattered in many different places-from traditional servers to various cloud services and different software applications. This system’s patchwork leads to key interoperability and integration problems, which becomes more problematic when implementing AI solutions.
Another basic challenge is that the business definition of different systems and departments is inconsistent. For example, the customer’s successful team may define “up set” in one way, while the sales team defines it in another way. When you connect the AI agent or chat robot to these systems and start asking questions, you will get different answers because the data definition is not aligned. Lack of consistency is not a small inconvenience, it is a key obstacle to implementing a reliable AI solution.
Poor data quality will create a classic “garbage, garbage elimination” solution. When AI tools are deployed in enterprises, the solution becomes more serious. Error or chaotic data will affect more than one analysis-it spreads incorrect information to everyone through their problems and interactions. In order to establish a trust in AI systems that actual business decisions, enterprises must ensure that its AI applications have data that is cleaned, accurate and understood in a proper business environment. This means that the organization must consider the basic change of data assets in the AI era. In this case, quality, consistency and semantic clarity become as important as the data itself.
Strengthen governance method
In recent years, data governance has been the main focus of the organization, mainly focusing on the data used in management and protection analysis. The company has been working hard to draw sensitive information, comply with access standards, complies with laws such as GDPR and CCPA, and detect personal data. These measures are essential for creating AI-Ready data. However, as the organization introduces the generated AI agent into its workflow, the scope of governance challenges will not only extend to the data itself, to cover the entire user interactive experience with AI.
Now, we must not only manage basic data, but also manage the process of interacting with users through the AI agent. Existing legislation, such as the EU AI Act, and more laws and regulations emphasize the necessity of the management question process itself. This means that the AI agent provides transparency, explained and traceable response. When the user receives the answer from the black box, for example, ask: “How many influenza patients yesterday?” Only “50” did not context-hard to believe this information for key decisions. If you do n’t know the source of data, how to calculate data or definitions such as “admission” and “yesterday”, the output of AI will lose reliability.
Unlike the interaction with documents, users can trace the answer back to a specific PDF or strategy to verify the accuracy. The interaction between AI proxies and structured data usually lacks this traceability and interpretation level. In order to solve these problems, the organization must take governance measures, not only protecting sensitive data, but also managing AI interactive experience to be managed and reliable. This includes establishing a strong access control to ensure that only authorized personnel can access specific information, define clear data ownership and management responsibilities, and ensure that the AI agent provides interpretation and reference for its output. Through the practice of large repair data governance, including these considerations, enterprises can safely use the power of AI agents while complying with the development of regulations and maintaining user trust.
Thinking beyond the rapid project
When the organization introduces the generated AI agent to improve data accessability, timely engineering has become a new technical barrier for enterprise users. Although it is touted as a promising career path, timely projects are basically reproducing the obstacles of our efforts in data analysis. It is not different to create a perfect prompt to write a special SQL query or build a dashboard filter-it transferred technical expertise from one format to another, and still requires most business users who do not and do not need professional skills that do not need unnecessary skills. Essence
For a long time, companies have been trying to solve data accessability by training users to better understand data systems, create documents and develop professional roles. But this method is backward-we require users to adapt to data, not to adapt data to users. The rapid engineering threat continues this model by creating another technical intermediary agency.
Real data democratization needs to understand the system of business language rather than understanding data language. When executives ask the customer’s retention rate, they do not need perfect terms or prompts. The system should understand the intention, identify the relevant data of different labels (whether “loss”, “reservation” or “customer life cycle”), and provide context answers. This makes corporate users focus on decision -making, rather than learning to raise technical perfect issues.
in conclusion
Artificial intelligence agents will make important changes in the operation of enterprises, but to propose their own unique challenges, these challenges must be resolved before deployment. When using AI, when non -technical users have self -service access permissions, each error will be magnified, which is essential for the correct foundation.
The basic challenges that successfully cope with the quality of data, semantic consistency and governance will move on the limitations of rapid engineering to securely make data access and decision -making democratization. The best method involves creating a collaborative environment to promote teamwork and keep the interaction between people and machines and machines consistent. This ensures that the AI driver’s opinion is accurate, safe and reliable, and encourages a culture within the scope of organization. The culture can make full use of its management, protect and maximize data.
