Generative AI is increasingly being used across a wide range of business tasks, including document creation, summarization, inquiry handling, and search support. However, many companies begin implementation only to stall at the pilot stage.

This is because successful adoption depends not only on the tool’s performance, but also on how it is integrated into workflows, how information is managed, how results are evaluated, and what internal rules are in place. In corporate use, simply adopting a tool because it seems convenient rarely leads to lasting adoption; it is essential to define the purpose and design the operating model.

This article clearly outlines five common challenges companies often face when implementing generative AI and how to overcome them.

One of the most common mistakes is beginning implementation without a clear understanding of why generative AI is being used.

For example, if generative AI is introduced simply because it is a topic of discussion internally or because competitors have started using it, the connection to real business challenges will be weak. As a result, frontline teams are likely to feel unsure about what to use it for or unable to see clear results even when they do use it.

Common examples

Approach to Response

Before introducing generative AI, it is important to clearly define which business process and which specific task you want to improve, and by how much. By translating this into concrete objectives, such as reducing the time needed to draft proposals or speeding up the initial search for internal FAQ information, it also becomes easier to evaluate results.

Information security and data management are major reasons why companies take a cautious approach to adopting generative AI.

If it is unclear how entered information will be handled, whether confidential information can be submitted, or how far integration with external services is permitted, employees cannot use these tools with confidence. In workflows that handle customer information, contract details, design data, or HR information in particular, expanding use without clear rules is risky.

The OECD AI Principles also emphasize privacy, safety, and accountability alongside fairness and transparency. Similarly, the NIST AI Risk Management Framework states that the use of AI requires ongoing risk management.

Common examples

Approach to Response

In the early stages of implementation, it is safest to clearly define what information may be entered, what is prohibited, and which cases require approval, then begin with a limited scope of use. It is also essential to manage logs, set access permissions, and review the contractual terms of the services being used.

Generative AI is useful, but it does not always provide accurate answers. It may produce plausible but incorrect responses or explanations with weak supporting evidence. Therefore, companies need to determine not only whether it can be used, but whether its output is reliable enough for business use.

In practice, however, evaluation criteria are often not defined, so decisions tend to be made based on each person’s individual judgment.

Common examples

Approach to Response

It is important to define evaluation criteria for each use case in advance, such as accuracy, reproducibility, ease of updates, effort required for revisions, and user satisfaction. The required quality level will vary depending on whether a person will perform the final review or the goal is to increase the rate of automated processing.

Especially when using it for external documents or customer communications, it is more practical to include a human review process in the workflow.

Generative AI will not be adopted simply because it has been introduced. In many cases, only certain employees become proficient in using it, while adoption fails to spread across the wider organization.

Behind this are adoption challenges such as employees not knowing how to use the tools, the tools not being integrated into business workflows, and a lack of input examples or templates. Simply granting access to a tool does not guarantee that it will be naturally adopted in day-to-day operations.

Common examples

Approach to Response

To encourage adoption in day-to-day operations, it is effective to design usage scenarios for each department. For example, sales teams might use it to draft proposal outlines, general affairs teams to prepare internal announcements, and development teams to organize specifications. Showing concrete use cases makes it easier for employees to understand how it can be applied.

It is also helpful to create templates for commonly used prompt examples, prohibited use cases, and review criteria, making it easier to reduce reliance on individual staff members.

When using generative AI in business operations, companies need to define who is responsible for making which decisions; otherwise, operations can become inconsistent.

For example, if it remains unclear whether the information systems department will create usage rules, whether department heads will approve them, where the legal team will conduct reviews, or who will respond to system failures or incorrect outputs, confusion is likely to arise after implementation.

The NIST AI RMF also emphasizes that AI risk management is not something an organization can define once and consider complete; it must be reviewed continuously at an organizational level. Because generative AI evolves rapidly, relying on initial rules alone for long-term operation can be difficult in some situations.

Common examples

Approach to Response

Even when starting small, a basic operating structure is necessary. By briefly defining the usage policy, applicable tasks, approvers, review responsibilities, log handling, and review timing, it becomes easier to scale later.

To make generative AI adoption successful, it is more practical to start with a focused set of business processes and roll it out in stages rather than deploying it across the entire company all at once.

Example approach

This approach makes it easier to identify practical use cases while keeping excessive investment and unexpected risks under control.

Companies commonly face the following five key challenges when implementing generative AI.

These issues cannot be solved by the capabilities of generative AI alone. It is important to align business process design, operating rules, evaluation criteria, and internal structures.

SMILE supports phased implementation, from organizing business challenges and evaluating systemization to development and operation.

Companies considering DX or the use of AI should start with small operational improvements.