How to Use AI Document Review to Boost New Employee Planning Skills and Motivation Simultaneously

Tools to Use

File Analysis Assistant

Objective Evaluation and Constructive Feedback: Changing the Face of Talent Development

In talent development, improving the quality of planning documents and proposals created by employees directly links to increased productivity for the entire organization. However, in the midst of a busy workday, it isn't easy to take the time to carefully coach every single document.

Subjective critiques often lack persuasion, leaving employees feeling unconvinced. Furthermore, a constant stream of purely negative corrections can lower motivation and dampen an employee's desire to grow.

In this article, we’ll share a practical example of how a manager can use AI to provide efficient and accurate feedback, using a presentation draft created by a new employee for a travel agency as our case study.

Specific Steps

Step 1: Upload the Proposal and Request Evaluation and Guidance

First, upload the file of the proposal created by the employee to the "File Analysis Assistant" and click "Analyze."

Next, provide the following instructions to the AI:

The AI will analyze the proposal from multiple angles and present an objective evaluation.

Step 2: Review the Evaluation and Generate Guidance Text

Based on the evaluation results, generate guidance text that includes specific areas for improvement along with positive feedback to boost the new employee's motivation.

Review the generated content to check if the AI's evaluation is reasonable and if the coaching text aligns with your company's educational policies and the realities of the workplace. You can refine the feedback by adjusting expressions or adding supplementary explanations as needed.

Step 3: Generate an Improved Version of the Proposal

Once you have confirmed that the AI's evaluation and guidance are appropriate, make the following request to the AI:

With this instruction, the AI will present specific suggestions for revision and examples of how the text should look after the improvements.

By providing concrete examples rather than just abstract critiques, it becomes clear how the employee should make corrections, leading to a practical upgrade of their skills.

Step 4: Deliver Feedback and Encourage Growth through Dialogue

After making final adjustments to the improved proposal and the coaching text to match your internal rules and training policies, share them with the employee.

At this stage, it is crucial not only to hand over the documents but also to create an opportunity for a direct conversation about the guidance. Based on the AI feedback, discuss how the staff member can improve and what kind of support they need, and share a specific action plan for their next proposal.

Through this dialogue, staff can accurately understand their strengths and weaknesses, allowing them to approach their growth with a positive mindset.

Examples of Results Achieved

• Rapid Improvement in Business Writing Skills
  By providing objective and specific evaluations and improvements via AI, staff can accurately grasp their weaknesses and level up efficiently. This data-driven, clear feedback—rather than subjective coaching—leads to a rapid improvement in the quality of business documents.
• Reduced Burden on Trainers, Allowing Focus on Strategic Development
  Because the AI supports the evaluation of documents and the creation of coaching text, the burden on those responsible for training is significantly reduced. The time saved can be spent on more strategic talent development initiatives or individual career support.
• Positive Feedback Stimulates Proactive Growth
  Positive feedback and specific improvement guidance from the AI stimulate an employee’s desire to grow. Additionally, providing guidance that includes positive reinforcement helps improve the management skills of the person in charge of training.

Target Metrics After One Month

• Average score of proposals and presentation materials (AI evaluation): 10% increase.
• Rate of feedback reflected in the next document: 70% or higher.
• Number of revision requests per document: 20% average reduction.

For more use cases and the latest information, visit the mitsumonoAI blog.

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