top of page
Academic_640x160_en.png

Chapter 9: Automating Managerial Tasks—How “AI Advisors” and “AI Secretaries” Are Transforming the Decision-Making Process

  • 9 hours ago
  • 5 min read

9-1. Managers Overwhelmed by Documents, Meetings, and Emails

Not only in Japan but globally, many managers struggle with the same issue: their days are consumed by document creation, email responses, and meetings. According to the Stanford AI Index 2025, 78% of organizations worldwide were using AI in some form in 2024, with productivity improvement in knowledge work being a primary objective.

Microsoft’s Work Trend Index special report shows that among early Copilot users, 70% reported increased productivity and 68% reported improved work quality. Tasks such as search, writing, and summarization were completed 29% faster on average. In particular, “catching up on meetings” became approximately 3.8 times faster, demonstrating that AI is already becoming a practical support tool for managers overwhelmed by meetings and documentation.



9-2. The Potential of “AI Advisors” Through Copilot-Style Tools

Copilot-style tools (such as Microsoft Copilot, Notion AI, and Google Workspace AI assistants) support a wide range of managerial tasks. Microsoft’s experiments indicate that Copilot users achieved:

  • 29% faster performance in search, writing, and summarization tasks

  • Approximately 3.8× faster meeting recap processing

  • 26% faster task completion with 44% higher accuracy

Additional reports show that 69% of users completed tasks faster, and 61% improved output quality. Around 65% of managers observed improvements in both speed and quality at the team level.

Over an 11-week period, users saved an average of 11 minutes per day—equivalent to more than 10 hours total—with 75% reporting productivity gains, 57% improved work-life balance, and 37% reduced meeting time.

These results indicate that AI can take over peripheral managerial tasks—such as report summarization, document drafting, and email organization—allowing managers to reclaim time for strategic thinking and decision-making.



9-3. Automating Meeting Design, Documentation, and Follow-Up

Meetings are one of the biggest time drains for managers. Some analyses estimate that in organizations with over 100 employees, unproductive meetings can cost hundreds of thousands of dollars annually. Other studies highlight that many meetings lack clear agendas or actionable outcomes.

AI is now directly addressing these inefficiencies:

  • Scheduling AI automatically finds optimal meeting times across participants

  • Real-time transcription and summarization generate meeting notes instantly

  • Action items are automatically created and assigned after meetings

These capabilities significantly reduce manual follow-up work and accelerate decision-making. Zoom has also emphasized that while over half of employees want automated summaries and action items, far fewer consistently receive them—highlighting a clear gap AI can fill.

For Japanese companies, where meeting minutes and follow-ups are particularly labor-intensive, automating these processes enables managers to focus on decision quality rather than administrative overhead.



9-4. Automating Task and Project Management

A key responsibility of managers is defining “who does what by when” and monitoring progress. AI-integrated project management tools (such as Asana Intelligence, Motion AI, and Notion AI) now enable:

  • Automatic extraction of tasks from emails and meetings

  • Context-based assignment of priorities, deadlines, and owners

  • Workload analysis and dynamic schedule optimization

  • Automated progress reporting and risk alerts

Productivity studies show that employees using AI tools save approximately 2–3 hours per week, primarily from reduced documentation, scheduling, and follow-up tasks. Teams using AI task management also report improved accountability and fewer missed commitments.

For Japanese managers, this represents a shift from “overtime-driven management” to “system-supported management.”



9-5. Managing Information Overload with AI Filtering

Managers are constantly exposed to large volumes of emails, chat messages, and documents. Microsoft reports that 64% of Copilot users spend less time processing emails, while 85% reach strong first drafts faster and 87% find it easier to get started writing.

Copilot-style tools support:

  • Email thread summarization and reply generation

  • Context-aware summaries of current status and next steps

  • Extraction of key points from long documents and presentations

Combined with enterprise search AI, managers can instantly answer questions such as:

  • “What discussions led to this decision?”

  • “How did we handle a similar case in the past?”

This reduces the cognitive burden of processing information and enables faster, more informed decisions.



9-6. How Far Will AI Go in Decision-Making?

Currently, AI advisors primarily handle information organization and option generation, while final decisions remain with human managers. However, research by Anthropic suggests that AI can reduce task completion time by up to 80%, indicating its potential to play a deeper role in decision-making processes.

The Stanford AI Index also highlights growing evidence that AI enhances human productivity and helps bridge skill gaps. Meanwhile, Japanese labor research emphasizes that AI will not eliminate jobs outright but will transform their nature.

In practice, the emerging model is:

  • AI presents data-driven scenarios and risk factors

  • Managers make final decisions based on strategy, values, and context

  • AI documents the decision-making process for transparency and review

This represents a shift toward “AI-supported, explainable decision-making.”



9-7. The Changing Role of Managers

As AI adoption accelerates, the role of managers is evolving:

  • From “information aggregation and reporting” to “interpretation and prioritization of AI-generated insights”

  • From “task supervisors” to “managers of team energy and motivation”

  • From “instruction transmitters” to “orchestrators of human-AI collaboration”

Reports from organizations such as McKinsey emphasize that managers who can effectively collaborate with AI will be critical to organizational performance. AI literacy and change management are becoming essential leadership skills.



9-8. Japan-Specific Challenges: Consensus and Decision Processes

Japanese management culture includes unique processes such as nemawashi (informal consensus-building), ringi (formal approval workflows), and collective decision-making. These processes often rely on tacit knowledge and informal communication, which are difficult for AI to fully capture.

However, AI can support these processes by:

  • Generating draft approval documents with structured arguments

  • Visualizing past similar cases and approval pathways

  • Preparing briefing notes tailored to stakeholders’ concerns

For AI tools to succeed in Japan, they must go beyond workflow automation and support context-sharing and consensus-building.



9-9. Practical Considerations for Implementing AI Assistants

When introducing AI tools for managers, Japanese companies should focus on:

Start with small use cases

  • Meeting summaries and action extraction

  • Weekly report drafting

  • Email summarization and reply suggestions

Design data access and governance

  • Define which tools can access which data

  • Implement permission inheritance and audit logs

Establish KPIs

  • Measure meeting time, document creation time, email processing time, and decision lead time

  • Monitor employee satisfaction and burnout indicators

Align with the team

  • Clearly communicate that AI supports, not replaces, human work

  • Define accountability for AI-generated outputs

These steps help position AI assistants not as imposed tools, but as trusted partners embraced by the entire team.



9-10. Bridge to the Next Chapter — Autonomous Cross-Functional Management

As individual managers become more productive with AI, the next challenge is cross-functional optimization. AI agents coordinating across departments—sales, marketing, supply chain, and finance—are enabling a new paradigm: autonomous cross-functional operations.

The next chapter explores how AI agents are beginning to support “autonomous management” in areas such as demand forecasting, inventory optimization, pricing, and risk management, transforming enterprise operations at scale.


Refarence

Comments

Latest Articles
archive

© JASEC 2017

Japan E-Commerce Association

Japan Academic Society for E-Commerce

 

Shoji NISHIMURA Lab., Faculty of Human Sciences, Waseda Univ.
2-579-15 Mikajima, Tokorozawa, Saitama 359-1192, Japan

info@jasec.or.jp +81-4-2947-6717

  • meta-70x70
  • X
  • Youtube
  • JASEC 一般社団法人 日本イーコマース学会：LinkedIn
bottom of page