AI Techniques to Dramatically Speed Up Your Work:
The Essence of Operational Efficiency and Patterns for Success
When people think of “AI utilization,” the impression of “automating tasks with convenient tools” often takes precedence. However, those who achieve real results in business view AI as a “mindset” or “design philosophy” even before considering it a “tool.”
This is because AI does not produce results simply by being introduced. It generates value only when used after understanding where time is spent and where judgment occurs within a workflow. This article explains the universal structure of AI utilization—applicable to any job or task—without depending on specific tools.
The Essence: How AI Speeds Up Work
The first thing to understand to maximize AI’s effect is that AI excels not just at the “task” itself, but at the “preparation, organization, and verbalization” surrounding the task. Most operations can be divided into three layers:
- Input/Collection: Gathering data and information.
- Organization/Transformation: Aggregating, shaping, comparing, summarizing, and drafting explanations.
- Judgment/Decision-Making: Deciding actions, setting priorities, and granting approval.
AI is strongest in Layer 2: Organization and Transformation. This layer takes significant time, yields low visible results, and is prone to errors. When AI supports this area, time is freed up for Layer 3 (Judgment), resulting in a state where “work moves faster.”
3 Misconceptions That Lead to Failure
Failures in AI implementation are rarely due to tool performance; they usually stem from “misplaced expectations.”
- Expecting AI to provide the “Right Answer”: AI generates plausible responses at high speed, but the “correct” business answer depends on company policy and specific field conditions. Therefore, it is realistic to use AI to prepare the materials for judgment rather than the “answer” itself.
- Attempting to delegate everything: Over-delegating leads to misunderstood premises and missed exceptions. The standard approach is a partial implementation focusing on the “Organization/Transformation” domain.
- Refusing to change existing workflows: The most common failure is “adding” AI without changing the flow, which ironically increases the workload. It is crucial to think of “replacing” or “shortening” steps rather than just “adding.”
Criteria: Which Tasks Benefit Most from AI?
Regardless of the tool name, effective tasks for AI integration share common traits. Check these five criteria:
- Repetition (Weekly/Monthly/Routine): Tasks that follow the same format are easy to turn into a “system” with AI.
- High Volume of Shaping and Summarizing: The more transformation work involved (shaping tables, extracting key points), the greater the effect.
- Quality Varies by Person: AI helps standardize and raise the floor of quality for tasks that have become “person-dependent.”
- 80% is Routine (Even if exceptions exist): AI struggles with exceptions, but moving the 80% that is routine to AI still yields massive results.
- Preparation for Judgment takes time: The real bottleneck is often the pre-processing before a decision is made. Applying AI here is highly effective.
Division of Labor: Humans vs. AI
Organizations where AI utilization has taken root have a clear division of roles:
Entrust to AI (High Reproducibility)
- Data shaping and fixing notation inconsistencies.
- Aggregation and comparison (Year-over-year, month-over-month).
- Key point extraction, summarizing, and bullet-pointing.
- Drafting reports and outlining documents.
- Suggesting potential anomalies (as “possibilities”).
Retained by Humans (Responsibility and Accountability)
- Setting objectives and prerequisites.
- Defining judgment criteria (rules and definitions).
- Handling exceptions and final quality checks.
- Decision-making and accountability (approval and consensus building).
Conclusion: How Work Ultimately Changes
The effect of AI is not just “saving time.” Fundamentally, three changes occur:
- Shift from “Tasks” to “Judgment”: Time returns to the work humans should do, raising the quality of decisions.
- Reduction of Dependency: Knowledge and procedures are verbalized and become reusable assets.
- Data Becomes a “Usable Asset”: Work doesn’t end at aggregation; it connects to analysis and visualization, speeding up decision-making.
Service Introduction: Implementing the Essence of AI into Practice
We offer a service that brings the “Essence of AI (Support for organization, summarization, and analysis)” into a form easy to use in daily operations.
By linking Excel and ChatGPT, we expand the scope of automation—previously limited to Macros/VBA—to include statistical processing, analysis, and report generation. While traditional Excel automation focused on repeating fixed rules, our service uses natural language instructions to automatically generate analysis comments and detect anomalies. We realize both efficiency and quality improvement simultaneously for daily/weekly reports and cross-departmental data integration.
This service is for you if:
- The burden of Excel aggregation and report creation is too high.
- You spend too much time writing explanations for figures.
- Work is “person-dependent,” making handovers difficult.
- Data is scattered across departments and cannot be integrated.