2025 AI Web/App Business Roadmap: An Ultra-Gap Strategy to Dominate the Market with 100 Core Features

Introduction: The AI Business Inflection Point, September 2025

If 2024 was the year large-scale adoption of AI technology began, September 2025 marks the point where AI has reached an inflection point directly linked to clear and measurable business value creation for enterprises. Simple ‘me-too’ AI adoption is disappearing, and generating clear value (ROI) relative to investment has become the basic expectation for all AI projects. In particular, moving beyond excessive expectations for generative AI, a realistic approach that provides practical value to businesses by combining it with traditional AI/machine learning (ML) technologies is becoming important.

This report presents 100 core features for AI web/app businesses as of September 2025, ordered by importance. It goes beyond simply listing features, providing an in-depth analysis of each feature’s business impact, implementation difficulty, and ‘practical and foul play-inclusive’ strategic utilization methods that can overwhelm competitors. This report will serve as a concrete operational guide for business leaders and entrepreneurs who aim to lead the market and secure an ultra-gap.

Part 1: AI Business Core Value and ROAI Maximization Strategy

AI Business Top Priority: Beyond Superficial Adoption to Real Value Creation Despite increasing AI investment, research showing that the actual return on investment (ROI) achievement rate is only 24% suggests that many companies are focusing only on adopting AI technology and failing to measure and maximize its value. This situation highlights the importance of ROAI (Return on AI), a new framework for more precisely evaluating the value of AI investment.

ROAI does not simply measure direct financial benefits such as cost reduction or revenue increase, i.e., ‘hard returns.’ It comprehensively considers indirect benefits that are difficult to quantify in monetary terms, such as improved customer satisfaction, increased employee productivity, enhanced decision-making power, and reduced error rates, i.e., ‘soft returns.’ For example, an AI-based customer support solution simultaneously achieves reduced customer churn (soft return) and increased call handling rates and reduced average handling time (hard return). As such, the value of AI can only be fully understood when hard and soft returns are measured in a balanced way.

Success in AI business does not lie in the flashy AI model itself. It depends on a much more fundamental problem: the ‘quality of data.’ Currently, only 10% of most corporate data is accessible, and it is difficult to secure an integrated data view across the organization due to data silo issues. This poor data foundation hinders AI performance and, in the case of generative AI, causes ‘hallucinations,’ making it difficult to create practical business value. Ultimately, this becomes the decisive reason for discontinuing AI projects or falling behind in the competition. Therefore, the most important first feature of AI business in 2025 is to secure data quality and accessibility before flashy technology.

The following is a list of 100 core features that should be considered first for the success of AI web/app businesses in 2025. They are listed in order of importance, and each feature will be a strategic weapon that redefines business models and leads the market.

Table 1: 2025 AI Web/App Business 100 Core Features List and Importance Ranking

No.	Feature Name	Key Content
1	AIaaS-based Infrastructure Construction	Build a flexible and scalable business model using cloud-based AI services without high-cost in-house infrastructure investment.
2	Integrated Data Platform	Integrate online/offline, web/app data to secure 360-degree customer and business profiles and insights.
3	Unstructured Data Analysis and Structuring	Analyze unstructured data such as customer logs and VOCs with LLM to summarize, classify, and extract sentiment.
4	AI-based Dynamic Pricing	Maximize revenue by analyzing real-time demand, competitor prices, and customer behavior to suggest optimal prices.
5	Predictive Maintenance and Inventory Management	Predict machine failures and demand fluctuations to reduce operating costs and strengthen supply chain resilience.
6	Real-time Anomaly Detection	Detect and prevent financial fraud, unauthorized use, and cyber threats in real time.
7	Hyper-Personalized Subscription Model	Propose personalized subscription plans optimized for each customer through user behavior analysis and prevent customer churn.
8	Customer Experience Redefinition Multimodal AI	AI agents that communicate with customers in various ways such as voice, image, and text.
9	Intelligent Automation	Beyond simple repetitive tasks, autonomously process complex workflows and make decisions.
10	XAI (Explainable AI)-based Decision Support	Secure trust by explaining the reasons for AI decisions in a way that humans can understand.

1.2 AI Core Features Redefining Business Models (1-10)

Flexible AIaaS (AI as a Service)-based Infrastructure Construction: Companies no longer need to build complex and high-cost AI infrastructure in-house. By utilizing AIaaS provided by cloud service providers (CSPs) such as AWS, Google Cloud, and Azure, they can access advanced AI technology on a pay-as-you-go basis. This dramatically reduces initial investment costs, enabling startups to quickly launch AI-based businesses, and allowing large enterprises to focus resources on core tasks.
Integrated Data Platform: This feature integrates scattered online/offline sales data, web/app user behavior data, and external system data within the enterprise into a single platform to build a 360-degree customer profile. Integrating vast amounts of data through data warehouses like BigQuery increases business agility and quickly provides insights needed for decision-making.
Unstructured Data Analysis and Structuring: Unstructured data, such as customer chat logs, VOC (Voice of Customer), images, and videos, which account for most of today’s corporate data, are difficult to analyze on their own. AI analyzes this unstructured data to perform functions such as summarization, classification, and sentiment extraction, thereby uncovering hidden insights and realizing the value of the data.
AI-based Dynamic Pricing: This is one of the most powerful features for maximizing sales and profits in the market. AI analyzes real-time demand, competitor prices, and customer behavior data to immediately suggest optimal prices. It can increase conversion rates by understanding customer purchase intent and price sensitivity and providing customized discounts.
Predictive Maintenance and Inventory Management: This is a core feature that significantly reduces operating costs in manufacturing, logistics, and other industries. AI analyzes IoT sensor data attached to machines to predict failures before they occur, and accurately predicts demand to improve inventory overstocking or shortage issues, thereby increasing supply chain resilience.
Real-time Anomaly Detection: As in Financial Transaction Detection Systems (FDS) in the financial services sector, AI detects anomalies that differ from normal patterns in real time to prevent fraudulent transactions, financial crimes, and cyber threats. This plays a decisive role in minimizing financial losses for businesses and increasing customer trust.
Hyper-Personalized Subscription Model: AI analyzes customer behavior, preferences, and purchase history to propose product/service subscription plans optimized for each individual customer. This is essential for increasing customer satisfaction, reducing churn, and building long-term relationships.
Multimodal AI for Customer Experience Redefinition: AI agent systems that understand and interact with various modalities such as text, voice, and images revolutionize the customer journey in various fields such as customer support, shopping experience, and education. They autonomously handle complex and multi-stage tasks beyond simple chatbots.
Intelligent Automation: Beyond RPA (Robotic Process Automation) that automates simple repetitive tasks, this system uses AI to autonomously process complex workflows and make decisions. This dramatically improves overall productivity by allowing humans to focus on strategic tasks.
XAI (Explainable AI)-based Decision Support: This feature allows humans to understand the reasons for AI decisions. It is essential for securing the reliability of AI, especially in areas where accountability is crucial, such as financial loan screening or medical diagnosis. For example, XAI can provide customers with the reasons why AI recommends a particular financial product.

Part 2: Customer Experience Innovation and Hyper-Personalization Engine (11-40)

AI Redefining the Customer Journey: The End of Uniform Experiences Customers in 2025 no longer want uniform services. AI enables ‘mass personalization’ through chatbots, digital assistants, and recommendation engines. This technology not only increases customer satisfaction but also leads to direct business outcomes such as increased sales, customer retention, and improved marketing ROI. AI-based hyper-personalization is a sophisticated strategic tool that analyzes customer preferences, predicts the most likely products to be purchased based on them, and ultimately induces customer behavior to increase sales.

Killer Features Maximizing Customer Engagement and Satisfaction (11-40)

Conversational AI & AI Agents (11-20)

24/7 AI Chatbot: A feature that responds to customer inquiries in real time and automatically resolves frequently asked questions (FAQs) to improve customer support efficiency.
Emotion Analysis-based AI Consultation: Analyzes customer’s tone and demeanor to detect dissatisfaction or discomfort, and connects to a human consultant if necessary to provide smooth service.
AI Banker/Personal Assistant: Automates complex banking tasks such as explaining financial products, managing account deposits/withdrawals, and issuing certificates, and provides personalized financial advice.
Voice-based Automated Ordering System (AOT): A system that receives and processes orders by voice, simplifying the ordering experience in fast-food restaurants and other establishments.
AI Language Tutor: An educational AI that performs native-level pronunciation correction, advanced grammar learning, and real-time conversation partner roles.
Socratic AI Tutor: An educational feature that, instead of directly answering the learner’s questions, asks questions in return to induce them to think and find answers on their own.
AI-based Academic Dropout Prevention System: Analyzes students’ learning data to predict the likelihood of academic dropout, and an AI chatbot proactively sends support messages.
Multimodal AI Customer Support: An integrated consultation solution that processes customer inquiries received through various modalities such as voice, text, and images in a single system.
Lead Generation through AI Chatbot: A feature that interacts with website visitors, collects potential customer information, and forwards it to sales representatives.
AI Character Chatbot: A character-based conversational AI that enhances entertainment elements, a business model that maximizes the participation and loyalty of Gen Z customers.

Hyper-Personalized Content and Product Recommendation Engine (21-40)

Real-time Behavior Analysis-based Recommendation: Analyzes user’s real-time clicks, scrolls, and dwell time to immediately recommend personalized products/content.
Collaborative Filtering and Content Curation: Like Netflix and Spotify, it groups users with similar tastes and suggests customized content based on viewing/listening/browsing history.
Personalized Shopping Journey: Identifies customer’s purchase intent and price sensitivity to suggest customized products or discounts to induce purchase.
AI for Search/Discovery Optimization: Contextually understands user intent to reduce information overload and provide highly relevant search results.
AI-based Dynamic Content Generation: Automatically generates and displays customized images, videos, and text ads for each customer, maximizing marketing efficiency.
Large Language Model (LLM)-based ‘Zero-Click’ Search: A feature where AI itself finds and provides information tailored to the user’s needs without the user having to perform a separate search action.
Custom Learning Path Design: AI analyzes the learner’s level, speed, and error frequency to automatically generate an optimized personalized curriculum.
Multimodal Learning Content Generation: Generates educational content combining text, images, audio, and video to provide an optimized experience for both visual and auditory learners.
Email Marketing Automation and Personalization: Optimizes lead nurturing by automatically sending segmented targeting and personalized messages based on customer behavior data.
AI-based Customer Data Platform (CDP): Integrates customer data scattered across multiple channels and builds a 360-degree customer profile to provide real-time analysis needed for marketing strategy formulation.
AI-based CRM Solution: Improves the work efficiency of sales representatives by managing customer meetings and suggesting sales actions, thereby enhancing the sales performance of B2B companies.
AI-based Predictive Personalization: Predicts what customers will need or want next and proactively provides customized recommendations.
Upselling/Cross-selling through Recommendation Engine: AI recommends additional products that match the customer’s interests during the payment process or product browsing stage to increase the average order value.
Personalized Shopping Journey: Identifies customer’s purchase intent and price sensitivity to suggest customized products or discounts to induce purchase.
AI-based A/B Test Automation: Analyzes user behavior data to automatically find and apply landing pages, ad copy, and UX designs that show the highest conversion rates.
AI-based Customer Re-engagement Campaign: AI automatically sends personalized notifications, emails, and coupons to users who are expected to churn from the app.
AI-based Chatbot Marketing: Interacts with customers through chatbots to deliver promotions, new product information, etc., and increase conversion rates.
AI-based Customer Review/Feedback Analysis: AI analyzes customer reviews, comments, and inquiries to utilize them for product improvement and marketing strategy formulation.
AI-based Ad Budget Optimization: Analyzes real-time campaign performance data to allocate optimal ad budgets to each channel and maximize ROI.
AI-based Social Media Management: AI generates social media post drafts, images, and hashtags, and analyzes performance to reflect it in the next content creation.

Part 3: Operational Efficiency and Intelligent Automation (41-70)

‘Invisible Hand’ AI: Reduce Costs and Increase Productivity The value of AI is not limited to customer experience innovation. Improving internal operational efficiency provides direct ‘hard returns’ such as labor cost, operating cost, and time savings, and is becoming a core element of corporate competitiveness in 2025. In particular, as the basic functions of large language models (LLMs) become universal, the core of competitiveness is shifting from ‘what LLM to use’ to ‘how to solve business-specific problems.’ Companies responding to this change are securing an ultra-gap with a ‘vertical SaaS’ model that combines domain-specific knowledge and data while leveraging the advantages of general-purpose LLMs.

AI Solutions Revolutionizing Internal Workflows (41-70)

Intelligent Automation and RPA (41-50)

AI-based Document Processing Automation: A feature that digitizes numerous documents such as contracts and invoices, and AI analyzes the content to extract, classify, and store necessary information.
Sales Process Automation: Analyzes sales data to predict potential customer behavior, executes automated marketing scenarios, and suggests optimal actions to sales representatives.
Manufacturing Process Optimization: AI analyzes IoT sensor data in the factory to optimize production plans and analyzes worker behavior to predict dangerous situations.
Logistics Route Optimization: Derives optimal delivery routes considering past delivery data and real-time traffic and weather to minimize time and cost.
Autonomous Robot Management System: A feature where AI controls robots such as AMR/AGV responsible for sorting and transporting goods in a logistics warehouse and manages optimal movement paths.
Ambient Listening: A feature where AI automatically records and summarizes conversations between doctors and patients, dramatically reducing the time spent on writing medical records.
AI-based Code Generation and Automation: AI automatically generates repetitive coding tasks, schema markups, and regular expressions to increase developer productivity.
Human Resources (HR) Management Automation: AI predicts employee satisfaction and turnover rates, and supports training programs and performance measurement.
Financial/Accounting Automation: A feature that automatically classifies invoices, payments, income/expenses, and automatically generates accounting ledgers.
Security Certification Management Automation: A feature where AI automatically manages and monitors corporate security certifications (SOC2, etc.).

Productivity and Collaboration Tools (51-70)

AI-based Document Summarization and Generation: AI automatically summarizes large amounts of documents and generates drafts to improve work efficiency.
Automated Meeting Minutes Recording and Summarization: AI converts video conference content into text and automatically summarizes key content and action items.
Real-time Translation and Multilingual Support: Provides real-time translation to improve the efficiency of collaboration in multinational corporations.
AI-based Collaboration Platform: Analyzes team members’ data to predict work progress, bottlenecks, etc., and suggests optimal collaboration methods.
AI-based Custom Internal Tool Building: A platform that quickly builds tools needed for internal work through APIs.
Automated Workflow Generation: A feature that connects hundreds of apps to automate complex workflows.
AI-based Sales Document Analysis: Analyzes how potential customers view documents to provide insights and formulate customized sales strategies.
AI-based Customer Support Center Workload Reduction: Dramatically reduces customer response time by automatically generating inquiry responses and summarizing conversations.
AI-based Medical Record Management: A feature where AI manages electronic medical records (EMR) and helps doctors focus on patient care.
AI-based Content Management System: Automates the creation, classification, and optimization of content such as product descriptions, images, and videos to maintain consistency.
AI-based Logistics Inventory Management: Prevents inventory overstocking and shortages through demand forecasting and effectively responds to supply chain risks.
AI-based Quality Control and Inspection: Analyzes camera footage at manufacturing sites to detect defective products or confirm whether workers are wearing safety helmets in real time.
AI-based Customer Service Agent Training: Analyzes conversation data with AI chatbots to provide necessary training content to new agents and improve customer service quality.
AI-based Contract Review and Analysis: AI reviews and summarizes complex documents such as legal documents and contracts to improve the work efficiency of the legal team.
AI-based Market/Competitor Analysis: AI collects and analyzes competitor website, news, and social media data to provide insights into market trends and competitor strategies.
AI-based Research Assistant: AI summarizes vast amounts of materials such as academic papers and industry reports, and organizes relevant content to reduce research time.
AI-based Chatbot Lab: Provides an environment to retrain language models at least once a week and test various scenarios to improve AI chatbot performance.
AI-based Sales Document Analysis: Analyzes documents sent by potential customers to identify interest and purchase intent, and formulate sales strategies.
AI-based Drug Discovery and Research Acceleration: Combines quantum computing and AI to dramatically reduce the time required for data analysis and simulation in drug discovery.
AI-based Educational Content Automatic Generation: A teacher assistant AI system automatically generates problems, quizzes, and simulation content tailored to students’ levels.

Part 4: Data-Driven Aggressive Marketing and Growth Hacking (71-90)

Growth Hacking Armed with AI: Exploiting Competitors’ Gaps Growth hacking goes beyond traditional marketing that simply runs ads; it’s a strategy that analyzes data and participates in decision-making throughout the customer journey of app users. AI elevates this growth hacking to a more sophisticated and automated level. Growth hacking using AI becomes a powerful weapon that generates direct ‘hard returns’ such as increased sales and cost reduction.

Table 2: ROAI (Return on AI) Analysis by AI Feature

Feature Name	Hard Return Example	Soft Return Example
AI Ad Content Automatic Generation	20% reduction in ad production costs, 44% increase in conversion rate	Brand message consistency, reduced marketer workload
AI-based Targeting	Up to 50% reduction in customer acquisition costs, 5-15% increase in sales	Increased customer engagement, improved marketing ROI
AI Chatbot	80% reduction in customer consultation workload, reduced operating costs	Improved customer satisfaction, reduced counselor turnover rate
Predictive Maintenance	30% reduction in unexpected downtime, cost savings	Strengthened supply chain resilience, stable production schedule

AI-based Growth Engine and Marketing Automation (71-90)

Content Generation and Ad Optimization (71-80)

AI-based Ad Content Automatic Generation: Quickly creates various ad content tailored to seasons or trends based on basic product images and target audience profiles.
AI-based SEO (Search Engine Optimization) Automation: Analyzes website content to identify keywords that increase search rankings and automates time-consuming SEO tasks such as link building.
Video Script and Storyboard Automatic Generation: AI writes narrative video scripts and creates storyboards with AI-based image generation models to reduce content production time.
AI-based Ad Budget Optimization: Analyzes real-time campaign performance data to allocate optimal ad budgets to each channel and maximize ROI.
AI-based Viral Campaign Prediction: Analyzes social media trends and user conversations to predict content with high viral potential and utilizes it in campaigns.
AI-based A/B Test Automation: Analyzes user behavior data to automatically find and apply landing pages, ad copy, and UX designs that show the highest conversion rates.
Predictive Analysis-based Targeting: Analyzes customer behavior data to predict future purchase intent, and AI agents send personalized messages tailored to the customer journey.
Funnel Analysis and Conversion Rate Optimization: Uses AI-based analysis tools to track user behavior within the app/website, identify exit points at each stage, and increase conversion rates.
Upselling/Cross-selling through Recommendation Engine: AI recommends additional products that match the customer’s interests during the payment process or product browsing stage to increase the average order value.
Zero-Click AI-based Marketing Strategy: A method where AI itself identifies customer needs and provides relevant information without the customer having to click on marketing messages.

Lead Acquisition and Customer Re-engagement (81-90)

AI-based Lead Generation: Analyzes website visit history, social media activity, and CRM data to identify and prioritize potential customers.
AI-based Email Marketing: Optimizes lead nurturing by automatically sending segmented targeting and personalized messages based on customer behavior.
AI-based Customer Re-engagement Campaign: AI automatically sends personalized notifications, emails, and coupons to users who are expected to churn from the app.
AI-based CRM Solution: Improves the work efficiency of sales representatives by managing customer meetings, quick responses, and suggesting sales actions.
AI-based Chatbot Marketing: Interacts with customers through chatbots to deliver promotions, new product information, etc., and increase conversion rates.
AI-based Predictive Personalization: Predicts what customers will need or want next and proactively provides customized recommendations.
AI-based Ad Optimization: A feature that identifies and targets the most valuable customers to maximize ROAS (Return on Ad Spend).
AI-based Customer Support Solution: A feature that improves customer satisfaction and counselor productivity through collaboration between AI chatbots and human counselors.
AI-based Social Media Management: AI generates social media post drafts, images, and hashtags, and analyzes performance to reflect it in the next content creation.
AI-based Customer Review/Feedback Analysis: AI analyzes customer reviews, comments, and inquiries to utilize them for product improvement and marketing strategy formulation.

Part 5: ‘Foul Play’ Features and Defense Strategies to Overturn the 2025 Competitive Landscape (91-100)

AI Technology’s Double-Edged Sword: Tools for Both Offense and Defense AI technology is used not only for positive values such as hyper-personalization and efficiency improvement but also as a tool for threats that cross ethical boundaries, such as market manipulation, public opinion manipulation, and fraud. Top experts must understand the reality of such ‘foul play’ and be able to defend against or strategically utilize it. The AI business ecosystem in 2025 shows a simultaneous evolution of offense and defense, and understanding the dual nature of this technology is essential.

Bold Strategies Crossing Ethical Boundaries (91-100)

AI-based Aggressive Marketing and Public Opinion Manipulation (91-95)

AI Deepfake-based Viral Marketing: Generates deepfake images of celebrities to execute unconventional advertisements or creates viral content that blurs the line between reality and virtuality. This can immediately attract people’s attention and generate high viral effects.
AI-based Public Opinion Manipulation and Spam Campaigns: Mass distributes ‘plausible’ phishing emails or spam messages generated by AI to create negative/positive public opinion about specific products/services. AI translation technology can create sophisticated phishing messages across language barriers.
AI-based Market Manipulation: A feature where AI analyzes subtle movements in the stock market and automates price manipulation using multiple anonymous accounts. This is already a materialized threat to the extent that financial authorities are introducing AI-based market surveillance systems.
Competitor AI Model Analysis: Analyzes competitor app/web AI models (e.g., chatbots, recommendation engines) to find their weaknesses (hallucinations, inaccurate answers) and uses this to emphasize the superiority of its own service in marketing.
AI-based Identity Forgery and Fraud: Uses deepfake images/audio to bypass identity verification systems or imitates the voice of a specific person (e.g., company CFO) to commit fraud.

Defense Strategies and Security Enhancement Against ‘Foul Play’ (96-100)

AI-based Security Threat Detection: A feature that uses AI to detect and defend against cyber threats such as phishing and ransomware in real time. In the era of AI-based attacks, AI-based defense is essential.
AI-based Market Surveillance System: A system where financial authorities use AI to detect and monitor unfair transactions and stock price manipulation in advance.
AI Model Watermarking and Authenticity Verification: An AI model that embeds watermarks in generative AI content or verifies the authenticity of deepfake images/audio.
AI Model ‘Jailbreak’ Defense: A defense system that prevents AI models from being hacked and leaking harmful information such as unethical answers or malware generation.
AI Ethics Framework Construction: A feature that establishes clear principles, policies, and control measures for AI utilization to ensure the ethical use of AI models.

Part 6: Prerequisites for Sustainable AI Business

AI business cannot succeed with only technological superiority. It is sustainable only on two axes: a solid data foundation and ethical risk management. Top experts should not neglect investment in these two cornerstones.

Data Quality Management and Governance: The success of all AI projects depends on high-quality data. Building an integrated data governance system, including data lineage, quality, and profiling, is not an option but a necessity. This will be the core foundation for fine-tuning LLMs using the company’s unique data and creating differentiated value compared to competitors.
AI Ethics and Legal Risk Management: As cases of AI misuse such as deepfakes, misinformation, and personal information infringement increase, companies must establish and adhere to strong AI principles. They must recognize and manage legal responsibilities (civil damages) for portrait rights infringement, copyright, and misinformation dissemination.
Cloud Infrastructure Optimization and Cost Management: AI model training and operation consume vast computing resources and costs. Efforts to reduce cloud consumption must be combined with transparent usage-based pricing policies, cost management tools, and energy-efficient model development. This is an important factor that guarantees long-term business sustainability.

Table 3: Key Adoption Cases by AI Technology Type

Industry	AI Technology Type	Key Cases
Financial Services	AI Banker, FDS (Financial Transaction Detection System), XAI	Shinhan Bank’s unmanned branch ‘AI Branch’, Kakao Bank’s FDS applying XAI model
Education	AI Chatbot, Adaptive Learning Platform	Georgia State University’s AI chatbot ‘Pounce’, University of Sydney’s adaptive learning platform
E-commerce	Predictive Analytics, Dynamic Content, Recommendation Engine	McDonald’s IBM watsonx-based automated ordering system, Netflix/Spotify’s recommendation engine
Manufacturing	Predictive Maintenance, Digital Twin, AI-based Quality Inspection	Tokyo Electron’s industrial accident prevention AI system, Toyota’s in-house AI platform construction
Logistics	Route Optimization, Demand Forecasting, Robot Adoption	DHL’s ‘Resilience360’ supply chain management platform, AI-based small package sorting robots

Conclusion and Recommendations

As of September 2025, the success of AI web/app businesses depends not merely on ‘applying’ AI technology, but on perfectly aligning AI technology with business value creation.

The most important strategic recommendations are as follows:

Prioritize Data: Before flashy AI features, invest in data governance and integration to secure high-quality fuel for AI models to learn from.
Go Beyond ‘General Purpose’ to ‘Specialization’: Seize the opportunity of general-purpose LLM commoditization to build unique competitiveness through ‘vertical SaaS’ or ‘micro SaaS’ models that solve specific industry or customer problems.
Make ROAI a Key Business Metric: Clearly define expectations for AI investment as ‘hard returns’ and ‘soft returns,’ and continuously measure them to prove the value of AI projects.
Prepare for Both ‘Offense’ and ‘Defense’: Understand the dual nature of AI, pursue aggressive growth strategies, and at the same time build defense systems against AI-based security threats.

AI is no longer just a technological tool. It is the most powerful strategic asset that can redefine the business ecosystem and overturn the market landscape. We hope you will use the 100 core features presented in this report as a compass to create an ultra-gap in the turbulent market of 2025.

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