Five Generative AI Use Cases for Businesses: Implementation Tips and Key Considerations for Improving Operational Efficiency
This article outlines common ways companies use generative AI and the key implementation points for improving operational efficiency.
Why Companies Are Accelerating the Use of Generative AI Now
"Preparing meeting minutes takes too much time," "internal inquiries are concentrated on specific staff members," and "drafting proposals and emails takes time every time." These challenges are common to many companies across industries. As labor shortages persist, it is especially important to reduce routine and repetitive tasks and build a structure that allows employees to focus on core responsibilities such as decision-making and customer support.
According to the Ministry of Internal Affairs and Communications' 2025 Information and Communications White Paper, 49.7% of Japanese companies have established a policy to either actively use generative AI or use it only in specific areas. In addition, 55.2% said they use generative AI in some part of their operations. Meanwhile, many small and medium-sized enterprises have yet to establish policies, and not knowing how to use it effectively is cited as a major concern when introducing it.
In other words, the key issue is no longer whether to use generative AI, but whether the organization can clearly define which tasks to use it for and under what rules.
Five Business Use Cases to Consider When Using Generative AI
Here are five practical use cases that are easy for companies to implement and deliver visible results. These areas are not limited to a specific industry and can be readily applied across many companies.
Pattern 1: Document Creation and Summarization Support
One of the easiest areas in which to introduce generative AI is drafting and summarizing text. Examples include organizing key points from meeting minutes, creating first drafts of sales emails, developing headline ideas for proposals, and polishing internal reports.
By reducing the need to write from scratch, staff can spend more time evaluating the content rather than drafting it. This is especially effective for tasks that require communicating the same information to multiple stakeholders in different ways.
Pattern 2: Internal Inquiry Handling and Knowledge Search
General affairs, human resources, and information systems departments often have to answer the same types of questions repeatedly. In many cases, information such as work rules, expense reimbursement procedures, PC settings, and various application processes exists but is difficult to find.
In this area, a generative AI-based question-and-answer system built on internal FAQs and company policies can be highly effective. In the case of the Ezaki Glico Group, the company is reported to have improved the efficiency of back-office inquiry handling and reduced internal inquiries by around 31%. An important benefit is not only automating responses, but also making it easier to build a culture in which employees first search and check information before consulting the person in charge.
Pattern 3: Customer Support and Chatbot Use
For external-facing operations, generative AI can be used for initial responses to web inquiries, product explanations, FAQ guidance, and organizing received requests. Full automation is not necessary; a setup where AI handles the first response and triage, while people take over only important cases, can still deliver significant benefits.
This approach is well suited to handling inquiries outside business hours and smoothing out inquiry volumes. It is especially effective for companies where questions tend to follow predictable patterns, making it easier to improve response speed while reducing the workload on staff.
Pattern 4: Planning and Marketing Support
Generative AI is also well suited to creating initial drafts of plans and organizing messaging angles. Examples include blog outlines, advertising copy, product descriptions, draft comparison tables, and messaging points tailored to each persona.
Of course, final decisions still need to be made by people, but a major advantage is the ability to speed up the initial idea-generation process. Because information gathering, summarizing key points, and comparing multiple options can be done quickly, staff can reduce the time spent collecting the materials they need to think through an issue.
Pattern 5: Internal Workflow Improvement and Department-Specific Assistants
Recently, generative AI has increasingly been used not only for one-off text generation, but also as a business assistant for specific departments. Examples include supporting proposal preparation for sales teams, organizing contract review points for legal teams, searching procedure manuals for manufacturing teams, and assisting IT departments with incident triage.
By leveraging each department’s operational data and past insights, generative AI can become more than a convenient tool; it can function as a support platform that takes root in day-to-day operations. Rather than applying it uniformly across the entire company, starting with a focused set of business processes makes it easier to measure impact and drive continuous improvement.
Expected Benefits of Each Use Case
Simply rolling out a tool will not lead to adoption if employees are unclear about when and how to use it. What frontline teams want is not a new system for its own sake, but something that makes their work a little easier. It is important to define the target tasks clearly and narrow down the intended use cases.
Reduced Processing Time
By reducing the time spent on tasks such as drafting, summarizing, searching, and categorizing, generative AI can help cut small amounts of daily workload. Even incremental time savings can make a significant difference across an entire department.
Reduced Dependency on Individuals
By organizing information that only certain staff members understand and making it easier to ask questions, you can reduce knowledge gaps. This can also help lower the burden of handovers and training.
Standardizing Response Quality
By making it easier to prepare baseline email wording and response templates, you can reduce variation among individual staff members. Maintaining consistent quality is especially important in customer service and internal support.
Focusing on Core Work
By reducing routine tasks, employees can spend more time on work where people can create greater value, such as sales, planning, improvement proposals, and customer negotiations. A white paper by Japan’s Ministry of Internal Affairs and Communications also notes that Japanese companies have high expectations for generative AI to improve operational efficiency and help address workforce shortages.
Risks and Operational Challenges to Consider During Implementation
Generative AI is useful, but simply introducing it does not automatically produce results. In particular, the following points should be clarified in advance.
Risk of information leakage
Entering internal documents, customer information, contract details, and similar data as-is may create information management issues depending on the applicable handling rules. It is necessary to review the service specifications, define input rules, and manage access permissions.
Incorrect Answers and Hallucinations
Even if a statement sounds plausible, its content may not be accurate. Human review must not be removed, especially for figures, legal matters, contracts, security, and external communications.
Copyright and Expression Risks
Generated content may closely resemble existing wording or include inappropriate expressions. For public content and advertising copy, a proper proofreading and review process is essential.
Failure to achieve adoption in daily operations
Simply rolling out a tool will not lead to adoption if employees are unclear about when and how to use it. What frontline teams want is not a new system for its own sake, but something that makes their work a little easier. It is important to define the target tasks clearly and narrow down the intended use cases.
A Practical Approach That Helps Avoid Failure
To successfully introduce generative AI, it is better to start small and verify its impact rather than rolling it out across the entire company from the outset.
Step 1: Narrow the target process down to one task
Start by selecting one recurring, time-consuming task, such as handling inquiries, preparing meeting minutes, or drafting proposals.
Step 2: Define usage rules
Clearly define what information may be entered, what information is prohibited, who is responsible for reviewing outputs, and whether a pre-publication review is required.
Step 3: Measure the impact
Compare metrics before and after implementation, such as work time, number of inquiries, number of revisions, and usage rates. When the numbers are visible, it becomes easier to plan the next improvements and expand adoption across other areas.
Step 4: Improve based on feedback from the field
Gather feedback from the people who actually use it, such as what is helpful and what is difficult to use, then refine the prompts, FAQs, connected data, and screen flows.
Step 5: Expand adoption by department
Once results are visible in one department, expand the initiative to other departments facing similar challenges. This approach makes it easier to explain internally and helps keep the cost of failure under control.
How SMILE Can Help
When using generative AI, selecting the right tool is only part of the process. It is also important to decide which operations to start with, how to integrate AI into existing workflows, and how to establish rules for safe use.
SMILE provides phased support, from organizing business challenges and selecting target operations to implementation design, system integration, and operational improvement. Rather than making major changes all at once, we can propose an approach that starts small, verifies results, and minimizes impact on the workplace.
Summary
When using generative AI in business, it is important not to focus only on high-profile examples, but to identify the types of work where it can deliver results within your own organization. Five especially accessible starting points are document creation support, inquiry handling, customer service, planning support, and department-specific assistants.
What matters is not trying out a wide range of features that seem useful, but applying generative AI appropriately to real business challenges. With clear rules and review processes in place, starting small and refining the approach over time makes it easier for generative AI to become embedded in daily operations.
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.
FAQ
Q1. Which department should introduce generative AI first?
It is effective to start with departments that handle many repetitive tasks and where results are easy to measure. General affairs, human resources, information systems, sales support, and marketing are often good initial targets.
Q2. Can small and medium-sized enterprises also implement it?
Yes. In fact, companies with smaller teams may find it easier to see benefits from reducing the workload associated with document creation and inquiry handling. The key is to start by narrowing the scope to specific target tasks.
Q3. What is the most important point to keep in mind during implementation?
Information leakage prevention measures and a review framework are essential. You need to define rules for input data and decide from the outset who will review the generated output.
Designing a Small First Step for Using Generative AI
From organizing business challenges and designing the implementation to system integration and operational improvement, we work with you to design an approach that takes root on the front line.
- You can clarify the target operations and expected benefits.
- We can design the implementation to include information management and a review framework.
- You can define specific usage patterns for each department.
- You can start small and improve while measuring results.
