Many people likely think of AI solely as a tool for generating text. In reality, AI’s applications extend far beyond that. Beyond text generation, practical implementations are advancing across diverse fields including images, audio, video, data analysis, and business automation. Already adopted by numerous companies and workplaces, AI is fundamentally changing how tasks are performed.
Indeed, Japan’s Cabinet Office Basic Plan on Artificial Intelligence explicitly states the goal of “promoting ‘AI Innovation’ that actively advances AI utilization and R&D to drive economic and social transformation and create added value.” This reflects a nationwide push for AI adoption. Interest in AI is expected to continue growing.
That said, some may wonder, “What can it actually be used for?” or “Is it practical enough for real-world applications?” This article will organize currently utilized AI technologies and introduce them through actual implementation examples.
AI is not a universal tool that solves every problem. However, correctly understanding its characteristics and applying it appropriately can lead to improved operational efficiency and higher-quality results. Let’s first grasp the overall picture of AI utilization.
Not Just Text Generation! 7 Things AI Can Achieve
Here, we introduce seven representative AI domains. Let’s take a closer look at the specific value each one delivers.
①Image Generation
Image generation AI has gained significant attention in recent years. Simply provide text instructions, and it automatically creates images matching your vision.
Primary Use Cases
- Drafting advertising banners
- Creating visuals for websites and landing pages
- Visual assets for presentation materials
- Prototyping concept designs
The key point is not to “produce a finished product immediately,” but to quickly visualize ideas in a tangible form.
For example, with design proposals, it traditionally took several days from commissioning a designer to receiving the first draft. However, using image generation AI allows multiple candidates to be visualized in just minutes. This speed delivers significant value in the early planning stages.
Of course, challenges remain, such as copyright issues for commercial use and variations in generation quality depending on the model. Nevertheless, image generation AI is already being utilized in practical work for its role in “reducing the time spent thinking from scratch and creating a foundation to draw out human creativity.”
Image generation AI is increasingly becoming a tool that reduces the burden on designers and creators, supporting an environment where they can focus on more essential creative work.
②Image Recognition
As a distinct field from text generation, image recognition AI is also rapidly expanding. This technology involves AI analyzing images captured by cameras or sensors to automatically extract necessary information.
Primary Use Cases
- Defect detection in factories
- Analysis of store crowding levels and customer counts
- Product inventory checks
- Diagnostic assistance for medical imaging
AI supports these tasks that were previously done by human visual inspection. Crucially, AI does not make fully autonomous judgments; humans retain the final decision-making role.
By having AI “quickly identify potential anomalies,” humans can focus on “determining whether an anomaly exists.” This division of labor enables the workplace to achieve both high accuracy and speed in operations.
For example, on a factory inspection line, AI instantly checks thousands of products and flags those suspected of defects. Inspectors can then focus solely on verifying these flagged items, reducing oversights and significantly shortening overall inspection time.
③Speech Recognition and Transcription
One area where AI has reached the highest level of practical application is speech recognition and transcription. This technology converts spoken content into text in real time, enabling it to be recorded and preserved.
Primary Use Cases
- Creating meeting minutes
- Full-text recording of online meetings
- Real-time translation of multilingual conversations
- Transcribing customer service and phone call logs
Traditionally, speech was “consumed information,” but AI has transformed it into an “automatically accumulated, searchable, and shareable asset.”
In business settings especially, reducing the burden of note-taking allows participants to focus on the discussion itself. This creates significant benefits, such as later confirmation of key points and information sharing with other departments or stakeholders.
For example, manually transcribing a one-hour meeting often takes several hours. However, using speech recognition AI allows the text data to be completed the instant the meeting ends. Only the key points need to be organized afterward, drastically reducing the work time.
The major feature of speech recognition AI is its ability to simultaneously enhance productivity and the quality of information sharing. The ability to accumulate information also improves the quality of knowledge sharing and reflection across the entire organization.
④Voice Synthesis and Narration
Voice AI technology converts text into natural-sounding speech that can be used for narration.
Primary Use Cases
- Narration for video tutorials and e-learning
- Audio content for internal training
- In-store announcements and guidance
- Web article read-aloud functionality
Traditionally, this required arranging narrators and handling recording/editing tasks. However, leveraging voice synthesis AI enables rapid prototyping of multiple variations, promising improved customer experiences and greater production flexibility.
For example, it easily adapts voices—bright and gentle for children, calm and professional for business—allowing free switching of voice quality, speed, and tone to suit the situation. Revisions are also simple: just modify the text and regenerate the audio.
The significant value of AI voice synthesis lies in enabling content design that is more closely aligned with user needs compared to traditional methods.
⑤Data Analysis
AI also excels in the field of data analysis. It’s a technology that identifies trends and patterns within large datasets to support decision-making.
Primary Use Cases
- Sales and demand forecasting
- Churn rate analysis
- Customer segmentation extraction
- Automated generation of advertising effectiveness reports
It primarily fulfills three roles: “quickly grasping trends,” “automatically detecting anomalies,” and “presenting candidate hypotheses.”
For example, it can extract “trends of products selling well on specific days of the week” from tens of thousands of sales records in seconds. Tasks that previously took hours manually can now be processed quickly using AI.
AI handles the task of finding “insights” within vast amounts of data. Humans then make the final decision based on these insights, considering the purpose, context, and where responsibility lies. This division of roles enables highly accurate, data-driven decision-making.
⑥ Information Organization and Summarization
Information organization and summarization AI serves a different purpose than text generation. It consolidates multiple documents and scattered information, organizing it into a format that allows users to grasp key points quickly.
Primary Use Cases
- Automatic summarization of long texts
- Extracting key points from meeting minutes
- Classifying FAQs and inquiry histories
- Integrating and organizing knowledge bases
In today’s digitally evolving work environment, information remains scattered across various tools like chat platforms, emails, documents, and logs. AI can rapidly ingest this data, structure it, and extract the essentials.
For example, it can summarize a report spanning dozens of pages in just minutes or extract only the decisions made from a week’s worth of meeting records. This allows rapid access to key points without requiring humans to review all the information.
Here too, the final judgment remains with humans. However, AI delivers significant value by enabling quick access to critical points. By reducing the time spent searching for information, it frees up more time for humans to focus on essential tasks that only they can perform.
⑦Business Automation
AI technologies for automating business processes are also expanding. AI can replace and handle tasks that follow fixed, repetitive patterns.
Primary Use Cases
- Initial responses to inquiries
- Automatic generation of routine reports
- Data entry assistance
- Chatbot operation
In these cases, the mainstream approach is role-based automation where AI handles simple tasks while humans manage areas requiring judgment.
For example, AI provides immediate responses to standard inquiries, while humans handle cases requiring judgment or consideration. This division of labor not only reduces customer wait times but also allows staff to focus on more specialized tasks. Consequently, variability in response quality and human error decrease, leading to significant productivity gains across operations.
Tasks suited for AI are delegated to it, while humans focus their efforts on judgment and creative work that only humans can perform. This represents a realistic and effective form of operational automation. AI does not take away human jobs; rather, it creates the foundation for maximizing human value.
How is AI Actually Used in the Field? AI Implementation Case Studies
We’ve introduced practical examples of what AI can do, but how is it actually being utilized in companies?
Here, we’ll present case studies from two companies, focusing on how they leveraged AI to address their respective challenges and achieve solutions.
Case Study 1: Software Company
Company A, a software company, faced demands for increased productivity due to diversifying customer needs. While routine tasks were streamlined through automation and outsourcing, non-routine tasks remained a challenge. The company explored using generative AI to address this gap.
Company A introduced an “AI Assistant Service” for all employees, powered by generative AI software. This service features an interface optimized for workplace use and includes necessary customizations, such as specifications to prevent the leakage of internal information.
During implementation, only general use cases were demonstrated, leaving specific usage methods to employee discretion. This approach was taken because, due to the nature of generative AI, use cases vary significantly by department and job type. Fixing the applications risked limiting the scope of utilization.
Approximately six months after implementation, a seminar on effective prompt writing was held, but the choice of applications remains entrusted to employees.
The implementation yielded significant operational efficiencies, with AI utilized across diverse tasks including research, meeting summarization, document creation, survey analysis, and programming. Furthermore, a culture of fact-checking before use has become ingrained. A key benefit is that having all employees become AI users allows them to gain firsthand experience with the technology’s characteristics.
Case Study 2: Healthcare and Nursing Care Systems Company
Company B, a healthcare and nursing care systems company, faced challenges in securing personnel and improving operational efficiency within medical and nursing care settings as the aging society progressed. Creating staff schedules was particularly time-consuming, requiring consideration of numerous factors such as required personnel, job types, team structures, and vacation requests. Additionally, manually verifying compliance with staffing standards led to human error issues.
Company B therefore implemented an AI-powered automated shift scheduling system. A key feature of this system is its low implementation barrier, achieved through AI learning trends from historical shift data and automatically configuring necessary information.
Its actual functionality includes a mechanism for automatic integration of staff data, allowing the AI to create monthly shift schedules quickly while considering complex conditions. By generating optimal shifts based on each employee’s characteristics (such as cross-functional roles, qualifications, and skills) and operational requirements, it achieved a significant reduction in workload.
Furthermore, it automatically diagnoses the optimization of created shifts and visualizes improvement points, leading to continuous shift schedule refinement.
AI is not a tool that takes away jobs, but a tool that expands work.
Current AI is advancing practical-level applications across a wide range of fields, including text generation, image creation, speech processing, data analysis, and business automation.
The key is not to view AI as a panacea, but to utilize it as a tool that expands human judgment and creativity.
AI does not take away jobs. Rather, those who master AI become capable of performing higher-value work. Our way of working is precisely at this turning point.
A concrete example of AI application in actual medical settings is DIP Ceph, a cloud-based AI cephalometric analysis support system. This service utilizes AI to streamline cephalometric analysis for dentists, supporting professional tracing and the visualization and proposal of treatment plans.
It exemplifies how AI can be used as a tool to “expand work,” enhancing both the quality and efficiency of clinical practice through AI support.
For more details on DIP Ceph, please see here.