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Japanese Enterprise Reactions to New Generative AI Features from Ignite 2024 & 12 Days of OpenAI

内山 咲 - Microsoft Top Partner Engineer Award 2024

Introduction:

Hello everyone! My name is Saki Uchiyama, and I'm with Nomura Research Institute(NRI). 
I'm responsible for business planning, relationship management (RM) for the T-STAR Service provided by NRI, as well as technical research on GenAI and supporting its utilization by enterprise clients. 
Regarding my activities in GenAI, I was selected for the 2024 Microsoft Top Partner Engineer Award in the AI category.

Furthermore, I attended Microsoft Ignite 2024 (hereafter, "Ignite 2024"), held in Chicago, USA, in November 2024. 
After returning to Japan, I've conducted seminars and study sessions, introducing new GenAI features and use cases released by OpenAI and Google during and around the Ignite 2024 event (from December 2024 to February 2025) to individuals at Japanese companies in the financial and pharmaceutical industries.

Through these seminars, I've gratefully received a variety of Feedback, Opinions, and Comments on the features from various companies.

In this article, I will introduce the outline of the seminars I have conducted, summarize the feedback received, and discuss the potential impact of the aforementioned new features on organizations across a range of industries, including finance and pharmaceuticals. I will also share my personal perspectives on the feedback received.

*NoteⅠ: The content of this article is based on information publicly available up to February 3, 2025 (the release date of GPT-DeepResearch).
NoteⅡ: The views expressed in this article are my own and do not represent the official opinions or views of my affiliated organization.

 

Chapter 1: How Will New Services Transform Organizational Activities?

From November 2024 to February 2025, the AI industry saw a flurry of new announcements regarding models and services.
For example, OpenAI announced new large language models, GPT-o3mini and o1-promode (both next-generation AI models improving upon current LLMs). Microsoft released Azure AI Foundry (an AI development platform) and enhancements to the Copilot features in Microsoft 365 (expansion of the AI assistant features integrated into Microsoft 365 apps).
Furthermore, new features capable of handling specialized and advanced tasks have emerged, such as GPT-DeepResearch (ChatGPT's advanced research and task support function) and GPT-Operator (OpenAI's new AI agent*1/operation linkage tool). Many Japanese companies, including those in the financial and pharmaceutical industries, are paying close attention to these technological trends.

This chapter aims to help you catch up on these technological trends, focusing on the key releases and their potential applications in business operations.

 

Current Status of Each Company (GenAI Utilization Status Before Feature Releases)

First, as a baseline before the new releases (Before Ignite 2024), I'll introduce the framework that companies are using for organizational adoption of GenAI.

I believe that, for any technology, the goal is not simply adoption, but rather to realize tangible benefits (such as increased efficiency and cost savings) for the organization.
I believe the activities (framework) necessary for this return of effects are steps 1 to 4 in Figure 1 below.

Figure 1

With this framework as a common understanding, I have been working with my client companies to establish GenAI within their organizations.

Figure 1 Supplement

  • The steps (1-4) shown in Figure 1 assume a top-down approach.
  • However, a bottom-up approach is also entirely possible, where the frontline employees estimate the effectiveness of tool introduction, gain understanding from management, monitor the effects, and return them to the organization.

The actual approach to choose should be decided on a case-by-case basis, taking the situation of each company into account.

Based on this framework, it is necessary to review and re-execute each step as needed, in accordance with technological and industry trends.

And with the release of new features at Ignite 2024 and OpenAI's "12 Days of OpenAI" event in December 2024, I believe that technology trends have changed significantly, and that it is necessary to review and re-execute the steps within each organization.

At this time, I believe that "management understanding and support" and "implementation by frontline teams" should be the driving forces, and I conducted seminars to introduce new features to the management and frontline departments (including IT departments) of each company.

 

How Will New Services Transform Organizational Activities?

The impact of the new releases on organizational activities is as described above. Next, I would like to introduce which operations within the organization will be affected, and I hope this will help you visualize the potential applications of these features.

Figure 2 below is a bottom-up organization of release features and their target business operations.

Figure 2

Figure 2 Supplement

  • Horizontal axis: Represents the characteristics of operations A to D (business improvement or value creation) and the degree of transformation to the business through tool use (incremental or revolutionary).
    Vertical axis: Represents the degree of impact on the organization (company-wide, departmental, or individual) when the tool is used for operations A to D.
  • A to D are the target business operations for the release features announced at the events covered in this article (not the target business operations for GenAI services in general).
  • This article only covers general-purpose operations, organized into A to D. For each company, we also provide a focused organization for their specific operations.
  • The "ToBe" descriptions are the new features released by Ignite 2024, OpenAI, and Google.

Note that the above figure is not a MECE organization of operations, so overlap between A to D operations may occur.

 

Chapter 2: Introduction of New Features (Feature, Live Demo Introduction)

Of the items in Figure 2 in the previous chapter, the features listed under "ToBe" are the new features released by Ignite 2024, OpenAI, and Google.
I will excerpt and introduce the content of the introductions, live demonstrations, and hands-on sessions conducted for each company, based on business use cases. (Presented below in the format "Feature Name: Summary of Feedback")

 

M365Apps in Copilot:Much of positive feedback was about the enhanced Excel integration, particularly the ability to generate complex formulas and analyze data using natural language queries.
However, for PowerPoint in Copilot, users requested more firm editing and translation capabilities within Copilot after the first draft generation.

 

AI Agent in Copilot Studio:This is the much-anticipated AI agent feature. The strengths of Microsoft Copilot Studio's AI agent are clearly its ability to create original task flows and to share those flows within the company. However, some feedback indicated that it might be difficult for non-engineer users to create AI agents.

 

Azure AI Foundry:This platform is equipped with numerous LLMs!  Since each model has its strengths, it's very appealing to be able to select an LLM based on the specific purpose.

 

Gemini1.5DeepResearch:Attractive points are the ability to break down tasks and design workflows based on instructions (automatic AI agent creation), the ability to arrange task flows, and the ability to create reports based on the latest information on websites. The cost is also relatively low (2,900 yen/month).

GPT-DeepResearch:This service, featuring an AI agent and the latest model, generated the most excitement at the seminars! It can automatically create AI agents, similar to Google Gemini. However, it is based on the latest inference-specialized model, GPT-o3, so it can execute complex tasks with high accuracy, which sets it apart from other services. The cost is relatively high ($200/month), but it provides a commensurate level of effectiveness.

 

Chapter 3: Feedback from Japanese Companies on the New Features Announced at Ignite 2024 and "12 Days of OpenAI"

Finally, I will categorize and present the feedback received from various companies during the seminars, as well as their respective stances on the new features and services. (Figure 3)

Figure 3

Figure 3 Supplement①Maintaining the Status Quo: Prioritizing Existing Investments

This is a mindset that prioritizes maximizing the return on previously invested resources and costs, placing the highest emphasis on the ROI (Return on Investment) from past investments. Approximately 30% of my client companies fall into this category.

Many companies have already made significant investments in building and operating in-house GenAI systems, RPA, and EUC within their DX (Digital Transformation) budgets.
RPA (Robotic Process Automation) and EUC (End-User Computing; typically tools created by users themselves, such as Excel macros) involve non-negligible costs for their implementation, maintenance, and operation.
These existing systems are already operational in their businesses, and switching to a different new service raises concerns about increased workload and costs due to the transition.
Therefore, unless there is a major problem with the currently used system, they will not switch to the newly announced service.

 

Figure 3 Supplement②Considering New Services Based on ROI and Strategic Fit

The basic idea is to prioritize ROI, but if the conditions are right, they will also consider replacing with a new service. Approximately 20% of my client companies fall into this category.

They compare new and old services, comprehensively evaluating financial indicators such as the expected payback period and return, as well as NPV (Net Present Value) and IRR (Internal Rate of Return). If these indicators show that the new service outperforms the existing service, they will consider replacing it.
In addition to numerical evaluation, there is also the possibility of replacement if there are issues within the company that cannot be solved by the currently used service. If a new service can solve those issues, they are likely to switch to the new service, despite their existing investments despite their investments in the existing one.

 

Figure 3 Supplement③Phased Adoption of New Technologies

They have high expectation for the new service and a desire to "definitely try it," but there is also a cautious stance toward rapidly transitioning the entire system, which they are currently accustomed to using, to the new technology.
Approximately 40% of my client companies fall into this category.

This is because they are concerned about the risk that the new technology will not be well adopted within the company or that there will be resistance from the frontline employees who are accustomed to the traditional methods.
Companies of this type will first adopt a "coexistence" approach, using the new service and the traditionally used service in parallel. They will then consider a gradual transition to the new service while observing feedback (usability, effectiveness, etc.) from the employees who actually use it.

Example: In areas where business automation is currently performed using RPA and EUC, GenAI might first be used for a limited purpose, such as reducing development costs. The actual daily operations would continue to be handled by the existing RPA, and it is anticipated that it will be several years before GenAI completely covers the role of RPA. Companies exhibiting this perspective are categorized as category ③.

 

Figure 3 Supplement④Aggressive Adoption for First-Mover Advantage

They have high motivation to introduce new services, and a stance of executing a bold, complete replacement. Approximately 10% of my client companies fall into this category.

④ is the feedback from companies that seek to gain a first-mover advantage by being the first to adopt the latest service. By being the first in the industry to adopt new technology, they aim to gain an advantage over their competitors.
Among the companies in this category, several companies with a particularly high awareness of technology trends raised opinions based on a future vision of the business application of GenAI, such as "AI agents and GenAI will eventually become the interface for all business operations across the company." They believe that it is only a matter of time before GenAI, centered on AI agents, becomes the core of business tools, and therefore, "it is better to adopt it quickly."

 

Chapter 4: Conclusion

Finally, I would like to share the reasons why I believe GenAI should be promoted and established within organizations. There are two main reasons.

Reasons to Adopt GenAI Organizationally

  • Profit expansion through operational efficiency (cost reduction).
  • Profit expansion by achieving "what was previously impossible.”

 

Profit Expansion Through Operational Efficiency

By actively utilizing GenAI, operational efficiency can be significantly improved through the automation of routine tasks.  By leveraging the extra capacity created by this efficiency to handle more cases with the same resources, sales opportunities will increase, leading to corporate profit expansion.  Including the effects of resolving labor shortages and reducing human error, the benefits of cost reduction and productivity improvement are extremely significant.

 

Profit expansion by achieving "what was previously impossible."

On the other hand, it is notable that GenAI makes it possible to do things that were previously impossible. As shown in the features and use cases introduced in Chapters 1 and 2, GenAI accelerates idea generation and new service creation. This means that companies can challenge new businesses beyond the boundaries of their respective industries, and at the same time, companies in other industries that were not previously considered competitors can enter the market. In fact, entry barriers between industries are already beginning to crumble.

For example, in the pharmaceutical industry, Amazon's entry into the market is attracting attention. Amazon has launched "Amazon Pharmacy," an online platform service for medication guidance and prescription drug provision, and pharmaceutical companies are forced to recognize this service as a threat and competitor.
So, what kind of companies can achieve entry into and expansion into other industries? I believe the key points are the following three:

Companies That Can Successfully Expand into Different Industries: 

  1. Possess Abundant User Data
  2. Have Sufficient Financial Resources to Support New Ventures
  3. Possess High Technical Capabilities, including Data Analysis, Conceptualization, Implementation, and Design

For companies with the above strengths, the use of GenAI is an excellent opportunity to expand their reach into other industries. Conversely, even for companies that do not sufficiently possess these strengths, it is now essential to work on cost reduction through GenAI, and at the same time, it is necessary to further solidify their own unique "strengths" and "advantages." If companies do not strengthen their own strengths and adopt new technologies, they may be at a disadvantage in competition with players entering from other industries.

 

Conclusion: To Seize Opportunities and Secure Advantages

Under these circumstances, is it wise to take a wait-and-see approach to the introduction of GenAI" (the " Focus on existing investment ROI " stance in Figure 3)? I believe that companies should instead view GenAI as an opportunity for growth and actively utilize it.
I hope that those who have read this article will recognize the utilization of GenAI as a good opportunity for their organization. I want you to leverage GenAI to both expand into new areas and secure your company's advantages.

If you agree with the message of this article and would like to join us in driving change in the industry and organizations, please feel free to contact me at the following address.

LinkedIn of Saki Uchiyama:
https://www.linkedin.com/in/saki-uchiyama%EF%BC%88sato%EF%BC%89-4832b12b5?lipi=urn%3Ali%3Apage%3Ad_flagship3_profile_view_base_contact_details%3BH%2Bcfq88kS9K6fW4bUDejjQ%3D%3D

 

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*1:AI Agent:A GenAI service that autonomously breaks down and executes tasks based on human instructions.