[Understand in 3 Minutes] AI Industry 2025 Complete Guide

~The 'Next-Generation Intelligence Industry' Entering the Social Implementation Phase~

📎 Recommended for those who

• Want to grasp the overall picture of the AI industry from a business perspective

• Want to know how practical AI (other than generative AI) is used

• Want to analyze AI ventures based on corporate data and financial indicators

🔰 Introduction | What is the 'AI Industry'?

The AI industry is a 'super-cross-disciplinary growth industry' that handles not only the research and development of 'Artificial Intelligence' but also its implementation in society, business support, and productization.
It influences every field—manufacturing, finance, medicine, education, distribution, advertising, etc.—and has become an entity that can no longer be contained within just the 'IT industry'.

Especially since 2023, with the emergence of generative AI such as ChatGPT, Claude, and Gemini, AI that can 'think, speak, and draw like a human' has begun to enter practical business.
As of 2025, AI is shifting from a 'research subject' to the 'OS of society'.It is evolving from the stage of technical development to one of establishment and mastery.

🚩 Recent Industry Trends

• 'Social Infrastructure' of Generative AI
  → ChatGPT has permeated translation, proposal, and summarization tasks, becoming standard equipment for corporate activities.

• Development of Domestic LLMs and the Rise of 'Japanese AI'
  → NTT, ELYZA, rinna, and others are releasing LLMs specialized for Japanese one after another.

• Moving Beyond PoC and the Evaluation of 'Implementation-Type AI'
  → AI that is established in the field and directly linked to sales and efficiency improvements is now highly valued.

🏗 Industry Structure

The essence of the AI industry is to 'reproduce the mechanisms of intelligence through technology and incorporate them into the mechanisms of society'.
It has now become a social implementation-type industry that permeates not only engineers but also business roles, policy making, and educational settings.

The main structure is as follows:

• Upstream (Infrastructure Development): The layer of algorithm, LLM, and GPU development handled by NVIDIA, OpenAI, Google, etc.

• Midstream (AI Products & Implementation Support): SaaS and BtoB specialized companies such as PKSHA, ABEJA, and Laboro.AI.

• Downstream (Application Sites): Companies like VRAIN Solution that execute 'on-site implementation' in manufacturing, retail, and medicine.

The industry is now at a turning point from 'AI that can be used' to 'AI that can be mastered'.

📊 Market Size and Current Status

・The global AI market is expanding to approximately 50 trillion yen as of 2025 (IDC forecast)
・The domestic Japanese market is estimated to be around 3 trillion yen. Adoption by governments, local municipalities, and large corporations is accelerating.
・Growth drivers are in the following 3 areas:
① Generative AI (LLM/Automation-based)
② Business support AI (Chatbots, OCR, RPA, etc.)
③ AI for manufacturing and infrastructure (Image analysis, anomaly detection, etc.)

A structure of small teams and high profitability is also a feature, with companies with 40 employees and an ROE of over 60% existing. A model for AI ventures that "win through efficiency" is becoming established.

🏢 Characteristics and Positions of Major Players

🌍 Overseas Mega-Players

• NVIDIA: Dominating the infrastructure for generative AI through GPU supply

• OpenAI × Microsoft: The axis of collaboration between LLMs and cloud strategy

• Google/Meta: Competing for hegemony through the fusion of their own LLMs with advertising and search

🇯🇵 Notable Japanese AI Companies

• PKSHA Technology: Achieving both steady implementation and profitability through an algorithm + SaaS model

• User Local: One of the industry's most highly efficient companies with nearly 100 employees and a 44.2% profit margin

• VRAIN Solution: A leading company in AI implementation for manufacturing sites (ROE 60.8%)

🗺 Company Classification and Industry Map

• Infrastructure/Platform-based (NVIDIA/OpenAI)
  Responsible for foundational AI technology and large-scale model development

• Algorithm & SaaS-based (PKSHA/User Local, etc.)
  The core layer of domestic AI. Horizontal expansion in BtoB is key

• Implementation/Introduction Support-based (VRAIN/Laboro.AI/ABEJA, etc.)
  Strong in introducing custom AI for sites such as manufacturing, retail, and logistics

• Specialized AI Vendors (Advanced Media/Metareal, etc.)
  Handling niches for function-specific AI, such as speech recognition and translation

📈 Companies by the Numbers | Top 3 Companies in 5 Metrics

・Operating Profit Margin (Earning Efficiency)
User Local (44.2%) / VRAIN (27.7%) / Advanced Media (22.8%)
・ROE (Capital Efficiency)
VRAIN (60.8%) / AI inside (11.5%) / BrainPad (17.4%)
・Equity Ratio (Financial Stability)
Laboro.AI (92.3%) / ABEJA (91.9%) / Kudan (87.9%)
・Average Annual Salary
PKSHA (11.08 million yen) / Laboro.AI (10.46 million yen) / ABEJA (9.55 million yen)
・Small Elite Teams
Kudan (38 people) / Sapeet (41 people) / VRAIN (49 people)

🌟 Notable Company Pickups

① [PKSHA Technology]

■ Why the attention?
Widely adopted in government, manufacturing, insurance, and more, centered on natural language processing and image analysis. With an algorithm x SaaS-based stable revenue model, it is also attractive in terms of compensation, with an average annual salary of 11.08 million yen.
■ Summary of Features
・Growth Speed: O (Abundant track record, steady expansion)
・Profitability: O (Operating profit margin over 19%)
・Investor Perspective: A core presence listed on the Tokyo Stock Exchange Prime Market

② [User Local]

■ Why the attention?
Provides SNS and Web analysis SaaS, achieving a profit margin of over 44% with fewer than 100 employees. Truly a "small elite x ultra-high efficiency" model.
■ Summary of Features
・Growth Speed: O (Stable profit type)
・Profitability: ◎ (Excellent in both profit margin and ROE)
・Investor Perspective: Suitable for long-term investment due to high ROE and dividend payout ratio

③ [VRAIN Solution]

■ Why the attention?
Deploys AI inspection + DX support specialized for manufacturing sites with a small, elite team. Attracting attention for overwhelming figures such as 60.8% ROE and 27.7% profit margin.
■ Summary of Features
・Growth Speed: ◎ (Riding the wave of rising implementation needs)
・Profitability: ◎ (High-efficiency management despite small scale)
・Investor Perspective: A successful model of "Japanese-style AI" attracting attention in emerging markets

🔄 Industry Structural Changes and Future Predictions

• Shift from PoC to "On-site Integration" is progressing
  　→ Entering an era where AI that integrates into business flows and daily operations, rather than ending at proof-of-concept experiments, is valued.

• The "AI = SaaS Company" model has begun to take root
  　→ The trend is shifting toward ensuring scalability through recurring billing and data-linked SaaS models rather than one-off development.

• The "mastery gap" in generative AI is starting to dictate corporate competitiveness
  　→ Even when using the same tools, significant differences in results are emerging depending on business design and knowledge integration.

🔭 Future Points of Interest

• Will company-wide adoption of generative AI really progress?
  　→ Breaking away from use in limited departments or experimental use, the level of penetration where all employees "use it daily" will be the dividing line for competitiveness.

• Can AI companies from Japan succeed in productization?
  　→ The next battleground is whether they can move away from custom-made models and scale as reproducible SaaS-type AI.

• Entering an era where legal regulations and ethical guideline compliance become "competitiveness"
  　→ Legal compliance, such as the AI Act, personal information protection, and bias countermeasures, is directly linked to corporate reliability and adoption foundations.

👤 Recommended for people like this

• Those who want to bridge the gap between technology and business

• Those who want to grow in a fast-paced market

• Those who want to do work that makes an impact on society

✅ Summary | AI is entering an era of "mastery" rather than just "usability"

The AI industry is no longer an era where "having technology" makes a difference.
The dividing line is not just "introducing" generative AI or business support AI, but whether you can "master it and produce results."

Will you be someone who 'uses' AI, or someone who 'masters' it?──
I hope this article serves as a hint for that first step.

◆ Recommended Articles

・"A Book to Thoroughly Understand AI Industry Trends and Mechanisms" (Shuwa System)
・"Approaching the Essence of DX" BayCurrent (Toyo Keizai)
・[Understand in 3 Minutes] Consulting Industry 2025 | Maps, Power Dynamics, and Compensation Structures in the DX Era

[Supplementary Article] How to Engage as a Consultant

① Engage consistently from "implementation support" to "strategy design"

• Many companies are not at the stage of "wanting to introduce AI," but rather "not knowing what to use it for to get results."

• As a consultant, you are expected to act as a "bridge" between **management strategy × business reform × AI technology**.

• Example: Instead of ending with a PoC (Proof of Concept), design how to "link it to business KPIs" and "make it span across departments."

② Propose a shift from "AI introduction = cost" to "AI utilization = business value"

• Successful AI utilization is not just about automation, but is directly linked to a "structure that generates revenue."

• Consultants should not treat AI introduction as just "a means of business improvement," but rather as a key proposal linked tonew business, service enhancement, and human capital reform.

• Example: Supporting anomaly detection AI in manufacturing by expanding it to DX strategy and supply chain restructuring.

③ "AI governance design" including legal regulations, ethics, and organizational penetration

• From now on, it will be important not only to be able to "use AI," but also to know "how to control it" and "how to fulfill accountability."

• Consultants are required to provide "governance support" that includesAI ethics, bias countermeasures, and internal rule development.

• Example: Formulating internal usage policies for generative AI, designing for privacy, and designing and operating internal education.

[Conclusion]

The AI industry has entered an era where things cannot be left solely to engineers.
Consultants who can connect "vision × technology × organization"are in the position to contribute the most.

The keyword is,

• "Getting involved from the upstream of AI introduction"

• Outline utilization methods that lead to results and KPIs

• Design it as a company-wide transformation

--By keeping these three points in mind, you can elevate the AI industry from a 'support target' to a 'co-creation partner'.